Artificial_Intelligence | Theodore C. Tanner Jr.

Pre-Computing War

Sora’s idea of a Neon Drone War With Audio Track

Sometimes it is the people no one can imagine anything of who do the things no one can imagine.

~ Alan Turing

First, i trust everyone is safe. Second, i had a creative spurt of late and wrote the following blog in one sitting after waking up thinking about the subject.

NOTE: This post is in no way indicative of any stance or provides any classified information whatsoever. It is only a thought piece concerning current technology-driven areas of concern.

Preamble

There is a paradigm shift happening that will affect future generations and possibly the very essence of what it means to be human, and this comes from how technology is transforming war. We stand at a precipice, gazing into a future where the tools of war no longer resemble the clashing steel and human courage of centuries past. How important is conflict to humanity? What is the essence and desire for this conflict? The current uptick in drone usage of the past years has created an inflection point for what I am terming “abstraction levels for engagement.”

We continue to underestimate how important drones are going to be in warfare—a miscalculation that echoes through history’s long ledger of missed signals. I’m going to go out on a long limb here and say the future of most warfare will be enabled by, driven by, and spearheaded by drones. In fact, other than some other types of autonomous vehicles, there might not be anything else on the battlefield.

Here is a definition (of course, AI bot generated because really who reads Webster’s Dictionary nowadays? For the record, i have read Webster’s 3 times front to back):

“A military drone, also known as an unmanned aerial vehicle (UAV) or unmanned aircraft system (UAS), is an aircraft flown without a human pilot on board, controlled remotely or autonomously, and used for military missions like surveillance, reconnaissance, and potentially combat operations. “

This isn’t hyperbole; it’s the logical endpoint of a trajectory we’ve been on since the first unmanned systems took flight. And at the end of that trajectory lies something even more radical: autonomous bullets—not merely guided, but self-directed, a fusion of machine intelligence and lethal intent that could redefine conflict itself.

Note: I edited this part as the kind folk on The LazyWeb(tm) were laser-focused on the how of “smart bullets.: Great feedback and thank you. git push -u -f origin main.

Guided autonomous bullets, often referred to as “smart bullets,” represent an advanced leap in projectile technology, blending precision guidance systems with small-caliber ammunition. These bullets are designed to adjust their flight path mid-air to hit a target with exceptional accuracy, even if the target is moving or environmental factors like wind interfere. The concept builds on precision guided munitions used in larger systems like missiles, but shrinks the technology to fit within the constraints of a bullet fired from a firearm.

Autonomous bullets, often referred to as “smart bullets,” are advanced projectiles designed to adjust their trajectory mid-flight to hit a target with high precision, even under challenging conditions like wind or a moving target. While the concept might sound like science fiction, significant research has been conducted, particularly by organizations like DARPA (Defense Advanced Research Projects Agency) and Sandia National Laboratories, to make this technology a reality.

The core idea behind autonomous bullets is to integrate guidance systems into small-caliber projectiles, allowing them to self-correct their path after being fired. One of the earliest designs, as described in “historical research”, involved a bullet with three fiber-optic sensors (or “eyes”) positioned around its circumference to provide three-dimensional awareness. A laser is used to designate the target, and as the bullet travels, these sensors detect the laser’s light. The bullet adjusts its flight path in real time to ensure an equal amount of laser light enters each sensor, effectively steering itself toward the laser-illuminated target. This method prevents the bullet from making drastic turns like a missile. Still, it enables small, precise adjustments to hit exactly where the laser is pointed, even if the target is beyond visual range or the laser source is separate from the shooter. It hath been said that if you can think it DARPA has probably built it, maybe.

Given the advancements in drones, unmanned autonomous vehicles, land and air vehicles, and supposedly smart bullets (only the dark web knows how they work), imagine a battlefield stripped of human presence, not out of cowardice but necessity. The skies a deafening hum with swarms of drones (if a drone makes a sound and no one is there to hear it, does it make a sound?), each a “node” orchestrated via particle swarm AI models in a vast, decentralized network of artificial minds.

No generals barking orders, no soldiers trudging through mud, just silicon and steel executing a dance of destruction with precision beyond human capacity. The end state of drones isn’t just remote control or pre-programmed strikes; it’s autonomy so complete that the machines themselves decide who lives and who dies – no human in the loop. Self-directed projectiles, bullets with brains roaming the theater of war, seeking targets based on algorithms fed by real-time data streams. The vision feels like science fiction, yet the pieces already fall into place.

Generals gathered in their masses
just like witches at black masses
evil minds that plot destruction
sorcerers of death’s construction
in the fields the bodies burning
as the war machine keeps turning
death and hatred to mankind
poisoning their brainwashed minds, oh lord yeah!

~ War Pigs, Black Sabbath 1970

This shift isn’t merely tactical; it’s existential. Warfare has always been a contest of wills, a brutal arithmetic of resources and resolve. But what happens when we can compute the outcome before the first shot is fired? Drones, paired with advanced AI, offer the tantalizing possibility of simulating conflicts down to the last variable terrain, weather, enemy morale, and supply lines, all processed in milliseconds by systems that learn as they go. The autonomous bullet isn’t just a weapon; it’s a data point in a larger Markovian equation, one that could predict victory or defeat with chilling accuracy.

We’re not far from a world where wars are fought first in the cloud, their outcomes modeled and refined, before a single drone lifts off.If the future of warfare is of drone swarms of autonomous systems culminating in self-directed bullets, then pre-computing its outcomes becomes not just feasible but imperative. The battlefield of tomorrow isn’t a chaotic melee; it’s a high-stakes game, a multidimensional orchestrated chessboard where game theory, geopolitics, and macroeconomics converge to predict the endgame before the first move. To compute warfare in this way requires us to distill its essence into variables, probabilities, and incentives a task as daunting as it is inevitable. Yet again, there exists a terminology for this orchestrated chess game. Autonomous asymmetric mosaic warfighting, a concept explored by DARPA, envisions turning complexity into an asymmetric advantage by using networked, smaller, and less complex systems to overwhelm an adversary with a multitude of capabilitiesnvisions turning complexity into an asymmetric advantage by using networked, smaller, and less complex systems to overwhelm an adversary with a multitude of capabilities.

“A Nash equilibrium is a set of strategies that players act out, with the property that no player benefits from changing their strategy. ~ Dr John Nash.”

Computational Game Theory: The Logic of Lethality

At its core, warfare is a strategic interaction, a contest where players, nations, factions, or even rogue actors vie for dominance under the constraints of resources and information. Game theory offers the scaffolding to model this. Imagine a scenario where drones dominate: each side deploys autonomous swarms, programmed with decision trees that weigh attack, retreat, or feint based on real-time data. The payoff matrix isn’t just about territory or casualties, it’s about disruption, deterrence, and psychological impact. A swarm’s choice to strike a supply line rather than a command center could shift an enemy’s strategy, forcing a cascade of recalculations.

Now, introduce autonomous bullets, self-directed agents within the swarm. Each bullet becomes a player in a sub-game, optimizing its path to maximize damage while minimizing exposure. The challenge lies in anticipating the opponent’s moves: if both sides rely on AI-driven systems, the game becomes a duel of algorithms, each trying to out-predict the other. Zero-sum models give way to dynamic equilibria, where outcomes hinge on how well each side’s AI can bluff, adapt, or exploit flaws in the other’s logic. Pre-computing this requires vast datasets, historical conflicts, behavioral patterns, and even cultural tendencies fed into simulations that run millions of iterations, spitting out probabilities of victory, stalemate, or collapse.

Geopolitics: The Board Beyond the Battlefield

Warfare doesn’t exist in a vacuum; the shifting tectonic plates of geopolitics shape it. To pre-compute outcomes, we must map the global chessboard—alliances, rivalries, and spheres of influence. Drones level the playing field, but their deployment reflects deeper asymmetries. A superpower with advanced AI and manufacturing might flood the skies with swarms, while a smaller state leans on guerrilla tactics, using cheap, hacked drones to harass and destabilize. The game-theoretic model expands: players aren’t just combatants but also suppliers, proxies, and neutral powers with their own agendas.

Take energy as a (the main) variable: drones require batteries, rare earths, and infrastructure. A nation controlling lithium mines or chip fabs holds leverage, tipping the simulation’s odds. Sanctions, trade routes, and cyber vulnerabilities—like a rival hacking your drone fleet’s firmware—become inputs in the equation. Geopolitical stability itself becomes a factor: if a war’s outcome hinges on a fragile ally, the model must account for the likelihood of defection or collapse. Pre-computing warfare here means forecasting not just the battle, but the ripple effects—will a decisive drone strike trigger a refugee crisis, a shift in NATO’s posture, or a scramble for Arctic resources? The algorithm must think in networks, not lines.

I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.

~ Claude Shannon

Macroeconomics: The Sinews of Silicon War

No war is won without money, and drones don’t change that; they just rewrite the budget. Pre-computing conflict demands a macroeconomic lens: how much does it cost to field a swarm versus defend against one? The economics of autonomous warfare favor scale mass-produced drones and bullets could outpace legacy systems like jets or tanks in cost-efficiency. A simulation might pit a 10 billion dollar defense budget against a 1 billion dollar insurgent force, factoring in production rates, maintenance, and the price of countermeasures like EMPs or jamming tech.

But it’s not just about direct costs. Markets react to war’s shadow oil spikes, currencies wobble, tech stocks soar or crash based on who controls the drone supply chain (it is all about that theta/beta folks). A protracted conflict could drain a nation’s reserves, while a swift, computed victory might bolster its credit rating. The model must integrate these feedback loops: if a drone war craters a rival’s economy, their ability to replenish dwindles, tilting the odds. And what of the peacetime economy? States that mastering autonomous tech could dominate postwar reconstruction, turning military R&D into a geopolitical multiplier. Pre-computing this requires economic forecasts layered atop the game-theoretic core—GDP growth, inflation, and consumer confidence as resilience proxies.

The Supreme Lord said: I am mighty Time, the source of destruction that comes forth to annihilate the worlds. Even without your participation, the warriors arrayed in the opposing army shall cease to exist.~ Bhagavad Gita 11:32

The Synthesis: Simulating the Unthinkable

To tie it all together, picture a supercomputer or a distributed AI network running a grand simulation. It ingests game-theoretic strategies (strike patterns, bluffing probabilities), geopolitical alignments (alliances, resource choke points), and macroeconomic trends (war budgets, trade disruptions). Drones and their autonomous bullets are the pawns, but the players are human decision-makers, constrained by politics and profit. The system runs countless scenarios: a drone swarm cripples a port, triggering a naval response, spiking oil prices, and collapsing a coalition. Another sees a small state’s cheap drones hold off a giant, forcing a negotiated peace.

The output isn’t a single prediction, but a spectrum 75% chance of victory if X holds, 40% if Y defects, 10% if the economy tanks. Commanders could tweak inputs more drones, better AI, a preemptive cyberstrike and watch the probabilities shift. It’s not infallible; black swans like a rogue AI bug or a sudden uprising defy the math. But it’s close enough to turn war into a science, reducing the fog Clausewitz warned of to a manageable haze [1].

The thing that hath been, it is that which shall be; and that which is done is that which shall be done: and there is no new thing under the sun.

~ Ecclesiastes 1:9, KJV

Yet, this raises a haunting question: If we can compute warfare’s endgame, do we lose something essential in the process?

The chaos of flawed, emotional, unpredictable human decision-making has long been the wildcard that defies calculation. Napoleon’s audacity, the Blitz’s resilience, and the guerrilla fighters’ improvisation are not easily reduced to code. Drones and their self-directed progeny promise efficiency, but they also threaten to strip war of its human texture, turning it into a sterile exercise in optimization. And what of accountability? When a bullet chooses its target, who bears the moral weight—the coder, the commander, or the machine itself?

The implications stretch beyond the battlefield. If drones dominate warfare, the barriers to entry collapse. No longer will nations need vast armies or industrial might; a few clever engineers and a swarm of cheap, autonomous systems could level the playing field. We’ve seen glimpses of this in Ukraine, where off-the-shelf drones have humbled tanks and disrupted supply lines. Scale that up, and the future isn’t just drones it’s a proliferation of power, a democratization of destruction. Autonomous bullets could become the ultimate equalizer or the ultimate chaos agent, depending on who wields them.

Fighting for peace is like screwing for virginity.

~ George Carlin

A Moment of Clarity

i wonder: are we ready to surrender the reins? The dream of computing warfare’s outcome is seductive and humans are carnal creatures we lust for other humans and things, thus it promises to minimize loss, to replace guesswork with certainty, but it also risks turning us into spectators of our own fate, watching as machines play out scenarios we’ve set in motion (that which we lust after).

The end state of drones may indeed be a battlefield of self-directed systems, but the end state of humanity in that equation remains unclear. Perhaps the true revolution isn’t in the technology but in how we grapple with a world where war becomes a problem to be solved rather than a story to be lived.

We underestimate drones at our peril. They’re not just tools; they’re harbingers of a paradigm shift. The future is coming, and it’s buzzing overhead—relentless, autonomous, and utterly indifferent to our nostalgia for the wars of old.

Pre-computing warfare might make us too confident. Leaders who trust the model might rush to conflict, assuming the odds are locked. But humans aren’t algorithms; we rebel, err, and surprise. And what of ethics? A simulation that optimizes for victory might greenlight drone strikes on civilians to break morale, justified by a percentage point. The autonomous bullet doesn’t care; it’s our job to decide if the computation is worth the soul it costs.

In this drone-driven future, pre-computing warfare isn’t just possible—it’s already beginning. Ukraine’s drone labs, China’s swarm tests, the Pentagon’s AI budgets—they’re all steps toward a world where conflict is a solvable problem. It has been said that fighting and sex are the two book ends but one in the same. But as we build the machine to predict the fight, we must ask: are we mastering war, or merely handing it a new master for something else entirely?

Music To Blog By: Project-X “Closing Down The Systems. Actually, I wouldn’t listen to this if i were you, unless you want to have nightmares. Fearless (MZ412 Remix) does sound like computational warfare.

Until then,

#iwishyouwater <- recent raw Pipe footage of folks that got the memo.

Ted ℂ. Tanner Jr. (@tctjr) / X

References:

[1] “On War” by Carl von Clausewitz. He called it “The Fog of War”: Clausewitz stressed the importance of understanding the unpredictable nature of war, noting that the “fog of war” (i.e., incomplete, dubious, and often erroneous information and great fear, doubt, and excitement) can lead to rapid decisions by alert commanders.

[2] Thanks to Jay Sales for being the catalyst for this blog. If you do not know who he is look him up here. Jay Sales. One of the best engineering executives and dear friend.

NVIDIA GTC 2025: The Time Has Come The Valley Said

OpenAI’s idea of The Valley – Its Been A Minute

Embrace the unknown and embrace change. That’s where true breakthroughs happen.

~Jensen Huang

First i trust everyone is safe. Second i usually do not write about discrete events or “work” related items but this is an exception. March 17-21, 2025 i and some others attended NVIDIA GTC2025. It warranted a long writeup. Be Forewarned: tl;dr. Read on Dear Reader. Hope you enjoy this one as it is a sea change in computing and a tectonic ocean shift in technology.

NVIDIA GTC 2025: AI’s Raw Hot Buttered Future

March 17-21, 2025, San Jose became geek central for NVIDIA’s GTC—aka the “Super Bowl of AI.” Hybrid setup, in-person or virtual, didn’t matter; thousands of devs, researchers, and suits swarmed to see what’s cooking in AI, GPUs, and robotics. Jensen Huang dropped bombs in his keynote, 1,000+ sessions drilled into the guts of it, and big players flexed their wares. Here’s the raw dog buttered scoop—and why you should care if you sling code or ship product.

The time has come,’ the Walrus said,

      To talk of many things:

Of shoes — and ships — and sealing-wax —

      Of cabbages — and kings —

And why the sea is boiling hot —

      And whether pigs have wings.’

~ The Walrus and The Carpenter

All The Libraries

Jensen’s Keynote: AI’s Next Gear, No Hype

March 18, 2025 SAP Center and the MCenery Civic Center, over 28,000 geeks packed in both halls and out in the streets . Jensen Huang, NVIDIA’s leather-jacketed maestro, hit the stage and didn’t waste breath. 2.5 hours no notes and started with the top of the stack with all the libraries NVIDIA has “CUDA-ized” and went all the way down to the photonic ethernet cables. No corporate fluff, just tech meat for the developer carnivore. His pitch: AI’s not just chatbots anymore; it’s “agentic,” thinking and moving in the real world forward at the speed of thought. Backed up with specifications, cycles, cost and even calling out library function calls.

Here’s what he unleashed:

Blackwell Ultra (B300): Mid-cycle beast, 288GB memory, out H2 2025. Training LLMs that’d choke lesser rigs—AMD’s sniffing, but NVIDIA’s still king.
Rubin + Vera Rubin: GPU + CPU superchip combo, late 2026. Named for the galaxy guru, it’s Grace Blackwell’s heir. Full-stack domination vibes.
Physical AI & GR00T N1: Robots that do real things. GR00T’s a humanoid platform tying training together, synced with Omniverse and Cosmos for digital twin sims. Robotics just got real even surreal.
NVIDIA Dynamo: “AI Factory OS.” Data centers as reasoning engines, not just compute mules. Deploy AI without the usual ops nightmare. <This> will change it all.
Quantum Day: IonQ, D-Wave, Rigetti execs talking quantum. It’s distant, but NVIDIA’s planting CUDA flags for the long game.

Jensen’s big claim: AI needs 100 more computing than we thought. That’s not a flex it’s a warning. NVIDIA’s rigging the pipes to pump it.

He said thank you to the developer more than 5 times, mentioned open source at least 4 times and said ecosystem at least 5 times. It was possibly the best keynote i have ever seen and i have been to and seen some of the best. Zuckerburg was right – if you do not have a technical CEO and a technical board, you are not a technical company at heart.

Jensen with Disney Friend

What It Means: Unfiltered and Untrained Takeaways

As i said GTC 2025 wasn’t a bloviated sales conference taking over a city; it was the tech roadmap, raw and real:

AI’s Next Frontier: The shift to agentic AI and physical AI (e.g., robotics) suggests that AI is moving beyond chatbots and image generation into real-world problem-solving. NVIDIA’s hardware and software innovations—like Blackwell Ultra and Dynamo—position it as the enabler of this transition.
Compute Power Race: Huang’s claim of a 100x compute demand surge underscores the urgency for scalable, energy-efficient solutions. NVIDIA’s full-stack approach (hardware, software, networking) gives it an edge, though competition from AMD and custom chipmakers looms.
Robotics Revolution: With GR00T and related platforms, NVIDIA is betting big on robotics as a 50 trillion dollar opportunity. This could transform industries like manufacturing and healthcare, making 2025 a pivotal year for robotic adoption.
Ecosystem Dominance: NVIDIA’s partnerships with tech giants and startups alike reinforce its role as the linchpin of the AI ecosystem. Its 82% GPU market share may face pressure, but its software (e.g., CUDA, NIM) and services (e.g., DGX Cloud) create a formidable moat.
Long-Term Vision: The focus on quantum computing and the next-next-gen architectures (like Feynman, slated for 2028) shows NVIDIA isn’t resting on its laurels. It’s preparing for a future where AI and quantum tech converge.

Sessions: Ship Code, Not Slides

Over 1,000 sessions at the McEnery Convention Center. No hand-holding pure tech fuel for devs and decision-makers. Standouts:

Generative AI & MLOps: Scaling LLMs without losing your mind (or someone else’s). NVIDIA’s inference runtime and open models cut the fat—production-ready, not science-fair thoughting.
Robotics: Isaac and Cosmos hands-on. Simulate, deploy, done. Manufacturing and healthcare devs, this is your cue.
Data Centers: DGX Station’s 20 petaflops in a box. Next-gen networking talks had the ops crowd drooling.
Graphics: RTX for 2D/3D and AR/VR. Filmmakers and game devs got a speed boost—less render hell.
Quantum: Day-long deep dive. CUDA’s quantum bridge is speculative, but the math’s stacking up.
Digital Twins and Simulation: Omniverse™ provides advanced simulation capabilities for adding true-to-reality physics to scene compositions. Build on models from basic rigid-body simulation to destruction, fluid-dynamics-based fire simulation, and physics-based scene authoring.

Near Real-Time Digital Twin Rendering Of A Ship

The DGX Spark Computer

i personally thought this deserved its own call-out. The announcement of the DGX Spark Computer. It is a compact AI supercomputer. Let us unpack its specs and capabilities for training large language models (LLMs). This little beast is designed to bring serious AI firepower to your desk, so here’s the rundown based on what NVIDIA has shared at the conference.

The DGX Spark is powered by the NVIDIA GB10 Grace Blackwell Superchip, a tightly integrated combo of CPU and GPU muscle. Here’s what it’s packing:

GPU: Blackwell GPU with 5th-generation Tensor Cores, supporting FP4 precision (4-bit floating-point). NVIDIA claims it delivers up to 1,000 AI TOPS (trillions of operations per second) at FP4—insane compute for a desktop box.
CPU: 20 Armv9 cores (10 Cortex-X925 + 10 Cortex-A725), connected to the GPU via NVIDIA’s NVLink-C2C interconnect. This gives you 5x the bandwidth of PCIe Gen 5, keeping data flowing fast between CPU and GPU.
Memory: 128 GB of unified LPDDR5x with a 256-bit bus, clocking in at 273 GB/s bandwidth. This unified memory pool is shared between CPU and GPU, critical for handling big AI workloads without choking on data transfers.
Storage: Options for 1 TB or 4 TB NVMe SSD—plenty of room for datasets, models, and checkpoints.
Networking: NVIDIA ConnectX-7 with 200 Gb/s RDMA (scalable to 400 Gb/s when pairing two units), plus Wi-Fi 7 and 10GbE for wired connections. You can cluster two Sparks to double the power.
I/O: Four USB4 ports (40 Gbps), HDMI 2.1a, Bluetooth 5.3—modern connectivity for hooking up peripherals or displays.
OS: Runs NVIDIA DGX OS, a custom Ubuntu Linux build loaded with NVIDIA’s AI software stack (CUDA, NIM microservices, frameworks, and pre-trained models).
Power: Sips just 170W from a standard wall socket—efficient for its punch.
Size: Tiny at 150 mm x 150 mm x 50.5 mm (about 1.1 liters) and 1.2 kg—it’s palm-sized but packs a wallop.

The DGX Spark Computer

This thing’s a sleek, power-efficient monster styled like a mini NVIDIA DGX-1, aimed at developers, researchers, and data scientists who want data-center-grade AI on their desks – in gold metal flake!

Now, the big question: how beefy an LLM can the DGX Spark train? NVIDIA’s marketing pegs it at up to 200 billion parameters for local prototyping, fine-tuning, and inference on a single unit. Pair two Sparks via ConnectX-7, and you can push that to 405 billion parameters. But let’s break this down practically—training capacity depends on what you’re doing (training from scratch vs. fine-tuning) and how you manage memory.

Fine-Tuning: NVIDIA highlights fine-tuning models up to 70 billion parameters as a sweet spot for a single Spark. With 128 GB of unified memory, you’re looking at enough space to load a 70B model in FP16 (16-bit floating-point), which takes about 140 GB uncompressed. Techniques like quantization (e.g., 8-bit or 4-bit) or offloading to SSD can stretch this further, but 70B is the comfy limit for active fine-tuning without heroic optimization.
Training from Scratch: Full training (not just fine-tuning) is trickier. A 200B-parameter model in FP16 needs around 400 GB of memory just for weights, ignoring gradients and optimizer states, which can triple that to 1.2 TB. The Spark’s 128 GB can’t handle that alone without heavy sharding or clustering. NVIDIA’s 200B claim likely assumes inference or light fine-tuning with aggressive quantization (e.g., FP4 via Tensor Cores), not full training. For two units (256 GB total), you might train a 200B model with extreme optimization—think model parallelism and offloading—but it’s not practical for most users.
Real-World Limit: For full training on one Spark, you’re realistically capped at 20-30 billion parameters in FP16 with standard methods (weights + gradients + Adam optimizer fit in 128 GB). Push to 70B with quantization or two-unit clustering. Beyond that, 200B+ is more about inference or fine-tuning pre-trained models, not training from zero.

Not bad for 4000.00. Think of all the things you could do… All of the companies you could build… Now onto the sessions.

Speakings and Sessions

There were 2,000+ speakers, some Nobel-tier, delivered. Straight no chaser – code, tools, and war stories. Hardcore programming sessions on CUDA, NVIDIA’s parallel computing platform, and tools like Dynamo (the new AI Factory OS). Think line-by-line breakdowns of optimizing AI models or squeezing performance from Blackwell Ultra GPUs. Once again, slideware jockeys need not apply.

The speaker list was a who’s-who of brainpower and hustle. Nobel laureates like Frances Arnold brought scientific heft—imagine her linking GPU-accelerated protein folding to drug discovery. Meanwhile, Yann LeCun and Noam Brown (OpenAI) tackled AI’s bleeding edge, like agentic reasoning or game theory hacks. Then you had practitioners Joe Park (Yum! Brands) on AI for fast food RJ Scaringe (Rivian) on autonomous driving, grounding it in real-world stakes.

Literally, a who-who of the AI developer world baring souls (if they have one) and scars from the war stories, and they do have them.

There was one talk in particular that was probably one of the best discussions i have seen in the past decade. SoFar Ocean Technologies is partnering with MITRE and NVIDIA to power the future of ocean AI!

MITRE announced a joint effort to build an AI-powered ocean digital twin fueled by real-time data from the global Spotter network. Researchers, government, and industry will use the digital twin to simulate and better understand the marine environments in which they operate.

As AI supercharges weather prediction, even the most advanced models will need more ocean data to be effective. Sofar provides these essential observations at scale. To power the digital twin, SoFar will deliver data from their global network of real-time ocean sensors and collaborate with MITRE to rapidly expand the adoption of the Bristlemouth open connectivity standard. Live data will feed into the NVIDIA Omniverse and open up new pathways for AI-powered ocean understanding.

BristleMouth Open Source Orchestration UxV Platform

The systems of systems and ecosystem reach are spectacular. The effort is monumental, and only through software can this scale be achievable. Of primary interest to this ecosystem effort they have partnered with Ocean Exploration Trust and the Nautilus Exploration Program to seek out new discoveries in geology, biology, and archaeology while conducting scientific exploration of the seafloor. The expeditions launch aboard Exploration Vessel Nautilus — a 68-meter research ship equipped with live-streaming underwater vehicles for scientists, students, and the public to explore the deep sea from anywhere in the world. We embed educators and interns in our expeditions who share their hands-on experiences via ship-to-shore connections with the next generation. Even while they are not at sea, explorers can dive into Nautilus Live to learn more about our expeditions, find educational resources, and marvel at new encounters.

“The most powerful technologies are the ones that empower others.”

~Jensen Huang

The Nautilus Live Mapping Software

At the end of the talk, I asked a question on the implementation of AI Orchestration for sensors underwater as well as personally thanked Dr Robert Ballard, who was in the audience, for his amazing work. Best known for his 1985 discovery of the RMS Titanic, Dr. Robert Ballard has succeeded in tracking down numerous other significant shipwrecks, including the German battleship Bismarck, the lost fleet of Guadalcanal, the U.S. aircraft carrier Yorktown (sunk in the World War II Battle of Midway), and John F. Kennedy’s boat, PT-109.

Again Just amazing. Check out the work here: SoFar Ocean.

What Was What: Big Dogs and Upstarts

The Exhibit hall was a technology zoo and smorgasbord —400+ OGs and players showing NVIDIA’s reach. (An Introvert’s Worst Nightmare.) Who showed up:

Tech Giants: Adobe, Amazon, Microsoft, Google, Oracle. AWS and Azure lean hard on NVIDIA GPUs—cloud AI’s backbone.
AI Hotshots: OpenAI and DeepSeek. ChatGPT’s parents still ride NVIDIA silicon; efficiency debates be damned.
Robots & Cars: Tesla hinting at autonomy juice, Delta poking at aviation AI. NVIDIA’s tentacles stretch wide.
Quantum Crew: Alice & Bob, D-Wave, IonQ, Rigetti. Quantum’s sci-fi, but they’re here.
Hardware: Dell, Supermicro, Cisco with GPU-stuffed rigs. Ecosystem’s locked in.
AI Platforms: Edge Impulse, Clear ML, Haystack – you need training and ML deployment they had it.

Inception Program: Fueling the Next Wave

Now, the Inception program—NVIDIA’s startup accelerator—is the unsung hero of GTC. With over 22,000 members worldwide, it’s a breeding ground for AI innovation, and GTC 2025 was their stage. Nearly 250 Inception startups showed up, from healthcare disruptors to robotics trailblazers like Stelia (shoutout to their “petabit-scale data mobility” talk). These aren’t pie-in-the-sky outfits—100+ had speaking slots, and their demos at the Inception Pavilion were hands-on proof of GPU-powered breakthroughs.

The program’s a sweet deal: free to join, no equity grab, just pure support—100K in DGX Cloud credits, Deep Learning Institute training, VC intros via the VC Alliance. They even had a talk on REVERSE VC pitches. What the VCs in Silicon Valley are looking for at the moment, and they were funding companies at the conference! It’s NVIDIA saying, “We’ll juice your tech, you change the game.” At GTC, you saw the payoff—startups like DeepSeek and Baseten flexing optimized models or enterprise tools, all built on NVIDIA’s stack. Critics might say it locks startups into NVIDIA’s ecosystem, but with nearly 300K in credits and discounts on tap, it’s hard to argue against the boost. The war stories from these founders—like scaling AI infra without frying a data center—were gold for any dev in the trenches.

GTC 2025 and Inception are two sides of the same coin. GTC’s the megaphone—blasting NVIDIA’s vision (and hardware) to the world—while Inception’s the incubator, quietly powering the startups that’ll flesh out that vision. Huang’s keynote hyped a token-driven AI economy, and Inception’s crew is already living it, churning out reasoning models and robotics on NVIDIA’s gear. It’s a symbiotic flex: GTC shows the “what,” Inception delivers the “how.”

We’re here to put a dent in the universe. Otherwise, why else even be here?

~ Steve Jobs

Micheal Dell and Your Humble Narrator at the Dell Booth

I did want to call out one announcement that I think has been a long time in the works in the industry, and I have been a very strong evangelist for, and that is a distributed inference OS.

Dynamo: The AI Factory OS That’s Too Cool to Gatekeep

NVIDIA unleashed Dynamo—think of it as the operating system for tomorrow’s AI factories. Huang’s pitch? Data centers aren’t just server farms anymore; they’re churning out intelligence like Willy Wonka’s chocolate factory but with fewer Oompa Loompas (queue the imagination song). Dynamo’s got a slick trick: it’s built from the ground up to manage the insane compute loads of modern AI, whether you’re reasoning, inferring, or just flexing your GPU muscle. And here’s the kicker—NVIDIA’s tossing the core stack into the open-source wild via GitHub. Yep, you heard that right: free for non-commercial use under an Apache 2.0 license. It’s like they’re saying, “Go build your own AI empire—just don’t sue us!” For the enterprise crowd, there’s a beefier paid version with extra bells and whistles (of course). Open-source plus premium? Whoever heard of such a thing! That’s a play straight out of the Silicon Valley handbook.

Dynamo High-Level Architecture

Dynamo is high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities such as

Disaggregated prefill & decode inference – Maximizes GPU throughput and facilitates trade off between throughput and latency.
Dynamic GPU scheduling – Optimizes performance based on fluctuating demand
LLM-aware request routing – Eliminates unnecessary KV cache re-computation
Accelerated data transfer – Reduces inference response time using NIXL.
KV cache offloading – Leverages multiple memory hierarchies for higher system throughput

Dynamo enables dynamic worker scaling, responding to real-time deployment signals. These signals, captured and communicated through an event plane, empower the Planner to make intelligent, zero-downtime adjustments. For instance, if an increase in requests with long input sequences is detected, the Planner automatically scales up prefill workers to meet the heightened demand.

Beyond efficient event communication, data transfer across multi-node deployments is crucial at scale. To address this, Dynamo utilizes NIXL, a technology designed to expedite transfers through reduced synchronization and intelligent batching. This acceleration is particularly vital for disaggregated serving, ensuring minimal latency when prefill workers pass KV cache data to decode workers.

Dynamo prioritizes seamless integration. Its modular design allows it to work harmoniously with your existing infrastructure and preferred open-source components. To achieve optimal performance and extensibility, Dynamo leverages the strengths of both Rust and Python. Critical performance-sensitive modules are built with Rust for speed, memory safety, and robust concurrency. Meanwhile, Python is employed for its flexibility, enabling rapid prototyping and effortless customization.

Oh yeah, and for all the naysayers over the years, it uses Nats.io as the messaging bus. Here is the Github. Get your fork on, but please contribute back – ya hear?

Tokenized Reasoning Economy

Along with this Dynamo announcement, NVidia has created an economy around tokenized reasoning models, in a monetary sense. This is huge. Let me break this down.

Now, why call this an economy? In a monetary sense, NVIDIA’s creating a system where compute power (delivered via its GPUs) and tokens (the output of reasoning models) act like resources and currency in a marketplace. Here’s how it works:

Compute as the Factory: NVIDIA’s GPUs—think Blackwell Ultra or Hopper—are the engines that power these reasoning models. The more compute you throw at a problem (more GPUs, more time), the more tokens you can generate, and the smarter the AI’s answers get. It’s like a factory producing goods, but the goods here are tokens representing intelligence.
Tokens as Currency: In the AI world, tokens aren’t just data—they’re value. Companies running AI services (like chatbots or analytics tools) often charge based on tokens processed—say, (X) dollars per million tokens. NVIDIA’s optimizing this with tools like Dynamo, which boosts token output while cutting costs, essentially making the “token economy” more efficient. More tokens per dollar = more profit for businesses using NVIDIA’s tech. Tokens Per Second will be the new metric.
Supply and Demand: Demand for reasoning AI is skyrocketing—enterprises, developers, and even robotics firms want smarter systems. NVIDIA supplies the hardware (GPUs) and software (like Dynamo and NIM microservices) to meet that demand. The more efficient their tech, the more customers flock to them, driving sales of GPUs and services like DGX Cloud.
Revenue Flywheel: Here’s the monetary kicker—NVIDIA’s raking in billions ($39.3B in a single quarter, per GTC 2025 buzz) because every industry needs this tech. They sell GPUs to data centers, cloud providers, and enterprises, who then use them to generate tokens and charge end users. NVIDIA reinvests that cash into better chips and software, keeping the cycle spinning.

NVIDIA’s “tokenized reasoning model economy” is about turning AI intelligence into a scalable, profitable commodity—where tokens are the product, GPUs are the means of production, and the tech industry is the market. The Developers power the Flywheel. Makes the mid-90s look like Bush League sports ball.

Tori MCcaffrey Technical Product Manager Extraordinaire and Your Humble Narrator

All that is really missing is a good artificial intelligence to control the whole process. And that is the trick, isnt it? These types of blue-sky discussions always assume certain advances for a sucessful implmentation. Unfortunately, A.I. is the bottleneck in this case. We’re close with replication and manufacturing processes and we could probably build sufficiently effective ion drives if we had the budget. But we lack a way to provide enought intelligence for the probe to handle all the situations it could face.

~ Eduard Guijpers from the Convention Panel -Designing a Von Nueman Probe

Daily and Lecun – Fireside

Lecun FireSide Chat

Yann LeCun, Turing Award badass and Meta’s AI Chief Scientist brain, sat down for a fireside chat with Bill Daily, Chief Scientist at NVIDIA that cut through the AI hype. No fluffy TED Talk (or me talking) vibes here just hot takes from a guy who’s been torching (get it?) neural net limits since the ‘80s. With Jensen Huang’s “agentic AI” bomb still echoing from the keynote, LeCun brought the dev crowd at the McEnery Civic Center a dose of real talk on where deep learning’s headed.

LeCun didn’t mince words: generative AI’s cool, but it’s a stepping stone. The future’s in systems that reason, not just parrot think less ChatGPT, and more “machines that actually get real work done.” He riffed on NVIDIA’s Blackwell Ultra and GR00T robotics push, nodding to the computing muscle needed for his vision. “You want AI that plans and acts? You’re burning 100x more flops than today,” he said, echoing Jensen’s compute hunger warning. No surprise—he’s been preaching energy-efficient architectures forever.

The discussion further dug into LeCun’s latest obsession: self-supervised learning on steroids. He’s betting it’ll crack real-world perception for robots and autonomous rigs stuff NVIDIA’s Cosmos and Isaac platforms are already juicing. “Supervised learning’s dead-end for scale,” he jabbed. “Data’s the bottleneck, not flops.” There were several nods from the devs in the Civic Center. He also said we would be managing hundreds of agents in the future, vertically trained – horizontally chained so to speak.

No slides once again, just LeCun riffing extempore, per NVIDIA’s style. He dodged the Meta AI roadmap but teased “open science” wins—likely a jab at closed-shop rivals. For devs, it was a call to arms: ditch the hype, build smarter, lean on NVIDIA’s stack. With Quantum Day buzzing next door, he left us with a zinger: “Quantum’s cute, but deep nets will out-think it first.”

GTC’s “Super Bowl of AI” rep held. LeCun proved why he’s still the godfather—unfiltered, technical, and ready to break the next ceiling and pragmatic.

Jay Sales, Engineering Executive Rockstar and Your Humble Narrator

Bottom Line

GTC2025 wasn’t just a conference. GTC 2025 was NVIDIA flipping the table: AI’s industrial now, not academic. Jensen’s vision, the sessions’ grit, and the hall’s buzz screamed one thing—build or get buried. For devs, it’s a CUDA goldmine. For suits, it’s strategy. For the industry, it’s NVIDIA steering the ship—full speed into an AI agentic and robotic future. With San Jose’s dust settling, the code’s just starting to run. Big fish and small fry are all feeding on bright green chips. 5 devs can now do the output of 50. Building stuff so others can build is Our developer mantra. Always has been, always will be – Gabba Gabba Hey One Of Us, One of Us!

Huang’s overarching message was clear: AI is evolving beyond generative models into “agentic AI”—systems that can reason, plan, and act autonomously. This shift demands exponentially more compute power (100x more than previously predicted, he noted), cementing NVIDIA’s role as the backbone of this transformation.

Despite challenges—early Blackwell overheating issues, U.S. export controls, and a 13% stock dip in 2025. Whatevs. NVIDIA’s record-breaking 39.3 billion dollar revenue quarter in February proves its resilience. GTC 2025 reaffirmed that NVIDIA isn’t just riding the AI wave; it’s creating it.

One last thought: a colleague was walking with me around the conference and inquired to me how did this feel and what i thought. Context: i was in The Valley from 1992-2001 and then had a company headquartered out there from 2011-2018. i thought for a moment, looked around, and said, “This feels like 90’s on steroids, which was the heyday of embedded programming and what i think was then the height of some of the most performant code in the valley.” i still remember when at Apple the Nvidia chip was chosen over ATI’s graphics chip. NVIDIA’s stock was something like 2.65 / share. i still remember when at Microsoft the NVIDIA chip was chosen for the XBox. NVIDIA the 33 year old start-up that analyst are talking of the demise. Just like music critics – right? As i drove up and down 101 and 280 i saw all of the new buildings and names – i realized – The Valley Is Back.

until then,

#iwishyouwater <- Mark Healy Solo Outer Reef Memo

@tctjr

Muzak To Blog By: Grotus, stylized as G̈r̈oẗus̈, was an industrial rock band from San Francisco, active from 1989 to 1996. Their unique sound incorporated sampled ethnic instruments, two drummers, and two bassists, and featured angry but humorous lyrics. NIN, Mr Bungle, Faith No More and Jello Biafra championed the band. Not for the faint of heart. Nevertheless great stuff.

Note: Rumor has it the Rivian SUV does in fact, go 0-60 in 2.6 seconds with really nice seats. Also thanks to Karen and Paul for the tea and sympathy steak supper in Palo Alto, Miss ya’ll!

Only In The Valley

SnakeByte[19] – Software As A Religion

Religion is regarded by the common people as true, by the wise as false, and by the rulers as useful.
Lucius Annaeus Seneca

Dalle’s Idea of Religion

First as always i hope everyone is safe oh Dear Readers. Secondly, i am going to write about something that i have been pondering for quite some time, probably close to two decades.

What i call Religion Of Warez (ROWZ).

This involves someone who i hold in the highest regard YOU the esteemed developer.

Marc Andreeson famously said “Software Is Eating The Word”. Here is the original blog:

“Why Software Is Eating The World” by Marc Andreeson

There is a war going on for your attention. There is a war going on for your thumb typing. There is a war going on for your viewership. There is a war going on for your selfies. There is a war going on for your emoticons. There is a war going on for github pull requests.

There is a war going on for the output of your prompting.

We have entered into The Great Cognitive Artificial Intelligence Arms Race (TGCAIAR) via camps of Large Languge Model foundational model creators.

The ability to deploy the warez needed to wage war on YOU Oh Dear Reader is much more complex from an ideological perspective. i speculate that Software if i may use that term as an entity is a non-theistic religion. Even within the Main Tabernacle of Software (MTOS) there are various fissures of said religions whether it be languages, architectures or processes.

A great blog can be found here -> Software Development: It’s a Religion.

Let us head over to the LazyWeb^TMand do a cursory search and see what we find^[1] concerning some comparison numbers for religions and software languages.

In going to wikipedia we find:

According to some estimates, there are roughly 4,200 religions, churches, denominations, religious bodies, faith groups, tribes, cultures, movements, ultimate concerns, which at some point in the future will be countless.
Wikipedia

Worldwide, more than eight-in-ten people identify with a religious group. i suppose even though we don’t like to be categorized, we like to be categorized as belonging to a particular sect. Here is a telling graphic:

Let us map this to just computer languages. Just how many computer languages are there? i guessed 6000 in aggregate. There are about 700 main programming languages, including esoteric coding languages. From what i can ascertain some only list notable languages add up to 245 languages. Another list called HOPL, which claims to include every programming language ever to exist, puts the total number of programming languages at 8,945.

So i wasn’t that far off.

Why so much kerfuffle on languages? For those that have ever had a language discussion, did it feel like you were discussing religion? Hmmmm?

Hey, my language does automatic heap management. Why are you messing with memory allocation via this dumb thing called pointers?

The Art of Computer Programming is mapping an idea into a binary computational translation (classical computing rates apply). This process is highly inefficient compared to having binary-to-binary discussions^[2]. Note we are not even considering architectures or methods in this mapping. Let us keep it at English to binary representation. What is the dimensionality reduction for that mapping? What is lost in translation?

For reference, i found a very precise and well-written blog here -> How Much Code Has Ever Been Written?

The calculation involves the number of lines of code ever written up to that point sans the exponential rate from the past two years:

2,781,000,000,000

Roughly 2.8 Trillion Lines of Code have been written in the past 20 years.
Sage McEnery 2020

As i always like to do i refer to Miriam Webster Dictionary. It holds a soft spot in my heart strings as i used to read it in grade school. (Yes i read the dictionary…)

Religion: Noun

re·li·gion (ruh·li·jen)

: a cause, principle, or system of beliefs held to with ardor and faith

Well, now, Dear Reader, the proverbial plot thickens. A System of Beliefs held to faith. Nowadays, religion is utilized as a concept today applied for a genus of social formations that includes several members, a type of which there are many tokens or facets.

If this is, in fact, the case, I will venture to say that Software could be considered a Religion.

One must then ask? Is there “a model” to the madness? Do we go the route of the core religions? Would we dare say the Belief System Of The Warez^[3] be included as a prominent religion?

I have said several times and will continue to say that Software is one of the greatest human endeavors of all time. It is at the essence of ideas incarnate.

It has been said that if you adopt the science, you adopt the ideology. Such hatred or fear of science has always been justified in the name of some ideology or other.

If we take this as the undertone for many new aspects of software, we see that the continuum of mind varies within the perception of the universe by which we are affected by said software. It is extremely canonical and first order.

Most often, we anthropomorphize most things and our software is no exception. It is as though it were an entity or even a thing in the most straightforward cases. It is, in fact, neither. It is just information imputed upon our minds via probabilistic models via non convex optimization methods. It is as if it was a Rorschach test that allowed many people to project their own meaning onto it (sound familiar?).

Let me say this a different way. With the advent of ChatGPT we seem to desire IT to be alive or reason somehow someway yet we don’t want it to turn into the terminator.

Stock market predictions – YES

Terminator – NO.

The Thou Shalts Will Kill You
~ Joseph Campbell

Now we are entering a time very quickly where we have “agentic” based large language models that can be scripted for specific tasks and then chained together to perform multiple tasks.

Now we have large language models distilling information gleaned from other LLMs. Who’s peanut butter is in the chocolate? Is there a limit of growth here for information? Asymptotic token computation if you will?

We are nowhere near the end of writing the Religion Of Warez (ROWZ) sacred texts compared to the Bible, Sutras, Vedas, the Upanishads, and the Bhagavad Gita, Quran, Agamas, Torah, Tao Te Ching or Avesta, even the Satanic Bible. My apologies if i left your special tome out it wasn’t on purpose. i could have listed thousands. BTW for reference there is even a religion called the Partridge Family Temple. The cult’s members believe the characters are archetypal gods and goddesses.

In fact we have just begun to author the Religion Of Warez (ROWZ) sacred text. The next chapters are going be accelerated and written via generative adversarial networks, stable fusion and reinforcement learning transformer technologies.

Which, then, one must ask which Diety are YOU going to choose?

i wrote a little stupid python script to show relationships of coding languages based on dates for the main ones. Simple key value stuff. All hail the gods K&R for creating C.

import networkx as nx
import matplotlib.pyplot as plt

def create_language_graph():
    G = nx.DiGraph()
    
    # Nodes (Programming languages with their release years)
    languages = {
        "Fortran": 1957, "Lisp": 1958, "COBOL": 1959, "ALGOL": 1960,
        "C": 1972, "Smalltalk": 1972, "Prolog": 1972, "ML": 1973,
        "Pascal": 1970, "Scheme": 1975, "Ada": 1980, "C++": 1983,
        "Objective-C": 1984, "Perl": 1987, "Haskell": 1990, "Python": 1991,
        "Ruby": 1995, "Java": 1995, "JavaScript": 1995, "PHP": 1995,
        "C#": 2000, "Scala": 2003, "Go": 2009, "Rust": 2010,
        "Common Lisp": 1984
    }
    
    # Adding nodes
    for lang, year in languages.items():
        G.add_node(lang, year=year)
    
    # Directed edges (influences between languages)
    edges = [
        ("Fortran", "C"), ("Lisp", "Scheme"), ("Lisp", "Common Lisp"),
        ("ALGOL", "Pascal"), ("ALGOL", "C"), ("C", "C++"), ("C", "Objective-C"),
        ("C", "Go"), ("C", "Rust"), ("Smalltalk", "Objective-C"),
        ("C++", "Java"), ("C++", "C#"), ("ML", "Haskell"), ("ML", "Scala"),
        ("Scheme", "JavaScript"), ("Perl", "PHP"), ("Python", "Ruby"),
        ("Python", "Go"), ("Java", "Scala"), ("Java", "C#"), ("JavaScript", "Rust")
    ]
    
    # Adding edges
    G.add_edges_from(edges)
    
    return G

def visualize_graph(G):
    plt.figure(figsize=(12, 8))
    pos = nx.spring_layout(G, seed=42)
    years = nx.get_node_attributes(G, 'year')
    
    # Color nodes based on their release year
    node_colors = [plt.cm.viridis((years[node] - 1950) / 70) for node in G.nodes]
    
    nx.draw(G, pos, with_labels=True, node_color=node_colors, edge_color='gray', 
            node_size=3000, font_size=10, font_weight='bold', arrows=True)
    
    plt.title("Programming Language Influence Graph")
    plt.show()

if __name__ == "__main__":
    G = create_language_graph()
    visualize_graph(G)

Programming Relationship Diagram

So, folks, let me know what you think. I am considering authoring a much longer paper comparing behaviors, taxonomies and the relationship between religions and software.

i would like to know if you think this would be a worthwhile piece?

Until Then,

#iwishyouwater <- Banzai Pipeline January 2023. Amazing.

@tctjr

MUZAK TO BLOG BY: Baroque Ensemble Of Vienna – “Classical Legends of Baroque”. i truly believe i was born in the wrong century when i listen to this level of music. Candidly J.S. Bach is by far my favorite composer going back to when i was in 3rd grade. BRAVO! Stupdendum Perficientur!

[1] Ever notice that searching is not finding? i prefer finding. Someone needs to trademark “Finding Not Searching” The same vein as catching ain’t fishing.

[2] Great paper from OpenAI on just this subject: two agents having a discussion (via reinforcement learning) : https://openai.com/blog/learning-to-communicate/ (more technical paper click HERE)

[3] For a great read i refer you to the The Ware Tetralogy by Rudy Rucker: Software (1982), Wetware (1988), Freeware (1997), Realware (2000)

[4] When the words “software” and “engineering” were first put together [Naur and Randell 1968] it was not clear exactly what the marriage of the two into the newly minted term really meant. Some people understood that the term would probably come to be defined by what our community did and what the world made of it. Since those days in the late 1960’s a spectrum of research and practice has been collected under the term.

SnakeByte[18] Function Optimization with OpenMDAO

DALLE’s Rendering of Non-Convex Optimization

In Life We Are Always Optimizing.

~ Professor Benard Widrow (inventor of the LMS algorithm)

Hello Folks! As always, i hope everyone is safe. i also hope everyone had a wonderful holiday break with food, family, and friends.

The first SnakeByte of the new year involves a subject near and dear to my heart: Optimization.

The quote above was from a class in adaptive signal processing that i took at Stanford from Professor Benard Widrow where he talked about how almost everything is a gradient type of optimization and “In Life We Are Always Optimizing.”. Incredibly profound if One ponders the underlying meaning thereof.

So why optimization?

Well glad you asked Dear Reader. There are essentially two large buckets of optimization: Convex and Non Convex optimization.

Convex optimization is an optimization problem has a single optimal solution that is also the global optimal solution. Convex optimization problems are efficient and can be solved for huge issues. Examples of convex optimization include maximizing stock market portfolio returns, estimating machine learning model parameters, and minimizing power consumption in electronic circuits.

Non-convex optimization is an optimization problem can have multiple locally optimal points, and it can be challenging to determine if the problem has no solution or if the solution is global. Non-convex optimization problems can be more difficult to deal with than convex problems and can take a long time to solve. Optimization algorithms like gradient descent with random initialization and annealing can help find reasonable solutions for non-convex optimization problems.

You can determine if a function is convex by taking its second derivative. If the second derivative is greater than or equal to zero for all values of x in an interval, then the function is convex. Ah calculus 101 to the rescue.

Caveat Emptor, these are very broad mathematically defined brush strokes.

So why do you care?

Once again, Oh Dear Reader, glad you asked.

Non-convex optimization is fundamentally linked to how neural networks work, particularly in the training process, where the network learns from data by minimizing a loss function. Here’s how non-convex optimization connects to neural networks:

A loss function is a global function for convex optimization. A “loss landscape” in a neural network refers to representation across the entire parameter space or landscape, essentially depicting how the loss value changes as the network’s weights are adjusted, creating a multidimensional surface where low points represent areas with minimal loss and high points represent areas with high loss; it allows researchers to analyze the geometry of the loss function to understand the training process and potential challenges like local minima. To note the weights can be millions, billions or trillions. It’s the basis for the cognitive AI arms race, if you will.

The loss function in neural networks, measures the difference between predicted and true outputs, is often a highly complex, non-convex function. This is due to:

The multi-layered structure of neural networks, where each layer introduces non-linear transformations and the high dimensionality of the parameter space, as networks can have millions, billions or trillions of parameters (weights and biases vectors).

As a result, the optimization process involves navigating a rugged loss landscape with multiple local minima, saddle points, and plateaus.

Optimization Algorithms in Non-Convex Settings

Training a neural network involves finding a set of parameters that minimize the loss function. This is typically done using optimization algorithms like gradient descent and its variants. While these algorithms are not guaranteed to find the global minimum in a non-convex landscape, they aim to reach a point where the loss is sufficiently low for practical purposes.

This leads to the latest SnakeBtye[18]. The process of optimizing these parameters is often called hyperparameter optimization. Also, relative to this process, designing things like aircraft wings, warehouses, and the like is called Multi-Objective Optimization, where you have multiple optimization points.

As always, there are test cases. In this case, you can test your optimization algorithm on a function called The Himmelblau’s function. The Himmelblau Function was introduced by David Himmelblau in 1972 and is a mathematical benchmark function used to test the performance and robustness of optimization algorithms. It is defined as:

$f(x, y) = (x^2 + y - 11)^2 + (x + y^2 - 7)^2$

Using Wolfram Mathematica to visualize this function (as i didn’t know what it looked like…) relative to solving for $f(x,y)$ :

Wolfram Plot Of The Himmelblau Function

This function is particularly significant in optimization and machine learning due to its unique landscape, which includes four global minima located at distinct points. These minima create a challenging environment for optimization algorithms, especially when dealing with non-linear, non-convex search spaces. Get the connection to large-scale neural networks? (aka Deep Learnin…)

The Himmelblau’s function is continuous and differentiable, making it suitable for gradient-based methods while still being complex enough to test heuristic approaches like genetic algorithms, particle swarm optimization, and simulated annealing. The function’s four minima demand algorithms to effectively explore and exploit the gradient search space, ensuring that solutions are not prematurely trapped in local optima.

Researchers use it to evaluate how well an algorithm navigates a multi-modal surface, balancing exploration (global search) with exploitation (local refinement). Its widespread adoption has made it a standard in algorithm development and performance assessment.

Several types of libraries exist to perform Multi-Objective or Parameter Optimization. This blog concerns one that is extremely flexible, called OpenMDAO.

What Does OpenMDAO Accomplish, and Why Is It Important?

OpenMDAO (Open-source Multidisciplinary Design Analysis and Optimization) is an open-source framework developed by NASA to facilitate multidisciplinary design, analysis, and optimization (MDAO). It provides tools for integrating various disciplines into a cohesive computational framework, enabling the design and optimization of complex engineering systems.

Key Features of OpenMDAO Integration:

OpenMDAO allows engineers and researchers to couple different models into a unified computational graph, such as aerodynamics, structures, propulsion, thermal systems, and hyperparameter machine learning. This integration is crucial for studying interactions and trade-offs between disciplines.

Automatic Differentiation:

A standout feature of OpenMDAO is its support for automatic differentiation, which provides accurate gradients for optimization. These gradients are essential for efficient gradient-based optimization techniques, particularly in high-dimensional design spaces. Ah that calculus 101 stuff again.

It supports various optimization methods, including gradient-based and heuristic approaches, allowing it to handle linear and non-linear problems effectively.

By making advanced optimization techniques accessible, OpenMDAO facilitates cutting-edge research in system design and pushes the boundaries of what is achievable in engineering.

Lo and Behold! OpenMDAO itself is a Python library! It is written in Python and designed for use within the Python programming environment. This allows users to leverage Python’s extensive ecosystem of libraries while building and solving multidisciplinary optimization problems.

So i had the idea to use and test OpenMDAO on The Himmelblau function. You might as well test an industry-standard library on an industry-standard function!

First things first, pip install or anaconda:

>> pip install 'openmdao[all]'

Next, being We are going to be plotting stuff within JupyterLab i always forget to enable it with the majik command:

## main code
%matplotlib inline

Ok lets get to the good stuff the code.

# add your imports here:
import numpy as np
import matplotlib.pyplot as plt
from openmdao.api import Problem, IndepVarComp, ExecComp, ScipyOptimizeDriver
# NOTE: the scipy import 

# Define the OpenMDAO optimization problem - almost like self.self
prob = Problem()

# Add independent variables x and y and make a guess of X and Y:
indeps = prob.model.add_subsystem('indeps', IndepVarComp(), promotes_outputs=['*'])
indeps.add_output('x', val=0.0)  # Initial guess for x
indeps.add_output('y', val=0.0)  # Initial guess for y

# Add the Himmelblau objective function. See the equation from the Wolfram Plot?
prob.model.add_subsystem('obj_comp', ExecComp('f = (x**2 + y - 11)**2 + (x + y**2 - 7)**2'), promotes_inputs=['x', 'y'], promotes_outputs=['f'])

# Specify the optimization driver and eplison error bounbs.  ScipyOptimizeDriver wraps the optimizers in *scipy.optimize.minimize*. In this example, we use the SLSQP optimizer to find the minimum of the "Paraboloid" type optimization:
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-6

# Set design variables and bounds
prob.model.add_design_var('x', lower=-10, upper=10)
prob.model.add_design_var('y', lower=-10, upper=10)

# Add the objective function Himmelblau via promotes.output['f']:
prob.model.add_objective('f')

# Setup and run the problem and cross your fingers:
prob.setup()
prob.run_driver()

Dear Reader, You should see something like this:

Optimization terminated successfully (Exit mode 0)
Current function value: 9.495162792777827e-11
Iterations: 10
Function evaluations: 14
Gradient evaluations: 10
Optimization Complete
———————————–
Optimal x: [3.0000008]
Optimal y: [1.99999743]
Optimal f(x, y): [9.49516279e-11]

So this optimized the minima of the function relative to the bounds of $x$ and $y$ and $\epsilon$ .

Now, lets look at the cool eye candy in several ways:

# Retrieve the optimized values
x_opt = prob['x']
y_opt = prob['y']
f_opt = prob['f']

print(f"Optimal x: {x_opt}")
print(f"Optimal y: {y_opt}")
print(f"Optimal f(x, y): {f_opt}")

# Plot the function and optimal point
x = np.linspace(-6, 6, 400)
y = np.linspace(-6, 6, 400)
X, Y = np.meshgrid(x, y)
Z = (X**2 + Y - 11)**2 + (X + Y**2 - 7)**2

plt.figure(figsize=(8, 6))
contour = plt.contour(X, Y, Z, levels=50, cmap='viridis')
plt.clabel(contour, inline=True, fontsize=8)
plt.scatter(x_opt, y_opt, color='red', label='Optimal Point')
plt.title("Contour Plot of f(x, y) with Optimal Point")
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.colorbar(contour)
plt.show()

Now, lets try something that looks a little more exciting:

import numpy as np
import matplotlib.pyplot as plt

# Define the function
def f(x, y):
    return (x**2 + y - 11)**2 + (x + y**2 - 7)**2

# Generate a grid of x and y values
x = np.linspace(-6, 6, 500)
y = np.linspace(-6, 6, 500)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)

# Plot the function
plt.figure(figsize=(8, 6))
plt.contourf(X, Y, Z, levels=100, cmap='magma')  # Gradient color
plt.colorbar(label='f(x, y)')
plt.title("Plot of f(x, y) = (x² + y - 11)² + (x + y² - 7)²")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

That is cool looking.

Ok, lets take this even further:

We can compare it to the Wolfram Function 3D plot:

from mpl_toolkits.mplot3d import Axes3D

# Create a 3D plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# Plot the surface
ax.plot_surface(X, Y, Z, cmap='magma', edgecolor='none', alpha=0.9)

# Labels and title
ax.set_title("3D Plot of f(x, y) = (x² + y - 11)² + (x + y² - 7)²")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("f(x, y)")

plt.show()

Which gives you a 3D plot of the function:

3D Plot of f(x, y) = (x² + y – 11)² + (x + y² – 7)²

While this was a toy example for OpenMDAO, it is also a critical tool for advancing multidisciplinary optimization in engineering. Its robust capabilities, open-source nature, and focus on efficient computation of derivatives make it invaluable for researchers and practitioners seeking to tackle the complexities of modern system design.

i hope you find it useful.

Until Then,

#iwishyouwater <- The EDDIE – the most famous big wave contest ran this year. i saw it on the beach in 2004 and got washed across e rivermouth on a 60ft clean up set that washed out the river.

@tctjr

Music To Blog By: GodSpeedYouBlackEmperor “No Title As of 13 February 2024” – great band if you enjoy atmospheric compositional music.

SnakeByte[17] The Metropolis Algorithm

Frame Grab From the movie Metropolis 1927

Who told you to attack the machines, you fools? Without them you’ll all die!!

~ Grot, the Guardian of the Heart Machine

First, as always, Oh Dear Reader, i hope you are safe. There are many unsafe places in and around the world in this current time. Second, this blog is a SnakeByte[] based on something that i knew about but had no idea it was called this by this name.

Third, relative to this, i must confess, Oh, Dear Reader, i have a disease of the bibliomaniac kind. i have an obsession with books and reading. “They” say that belief comes first, followed by admission. There is a Japanese word that translates to having so many books you cannot possibly read them all. This word is tsundoku. From the website (if you click on the word):

“Tsundoku dates from the Meiji era, and derives from a combination of tsunde-oku (to let things pile up) and dokusho (to read books). It can also refer to the stacks themselves. Crucially, it doesn’t carry a pejorative connotation, being more akin to bookworm than an irredeemable slob.”

Thus, while perusing a math-related book site, i came across a monograph entitled “The Metropolis Algorithm: Theory and Examples” by C Douglas Howard [1].

i was intrigued, and because it was 5 bucks (Side note: i always try to buy used and loved books), i decided to throw it into the virtual shopping buggy.

Upon receiving said monograph, i sat down to read it, and i was amazed to find it was closely related to something I was very familiar with from decades ago. This finally brings us to the current SnakeByte[].

The Metropolis Algorithm is a method in computational statistics used to sample from complex probability distributions. It is a type of Markov Chain Monte Carlo (MCMC) algorithm (i had no idea), which relies on Markov Chains to generate a sequence of samples that can approximate a desired distribution, even when direct sampling is complex. Yes, let me say that again – i had no idea. Go ahead LazyWeb^TM laugh!

So let us start with how the Metropolis Algorithm and how it relates to Markov Chains. (Caveat Emptor: You will need to dig out those statistics books and a little linear algebra.)

Markov Chains Basics

A Markov Chain is a mathematical system that transitions from one state to another in a state space. It has the property that the next state depends only on the current state, not the sequence of states preceding it. This is called the Markov property. The algorithm was introduced by Metropolis et al. (1953) in a Statistical Physics context and was generalized by Hastings (1970). It was considered in the context of image analysis (Geman and Geman, 1984) and data augmentation (Tanner (I’m not related that i know of…) and Wong, 1987). However, its routine use in statistics (especially for Bayesian inference) did not take place until Gelfand and Smith (1990) popularised it. For modern discussions of MCMC, see e.g. Tierney (1994), Smith and Roberts (1993), Gilks et al. (1996), and Roberts and Rosenthal (1998b).

Ergo, the name Metropolis-Hastings algorithm. Once again, i had no idea.

Anyhow,

A Markov Chain can be described by a set of states $S$ and a transition matrix $P$ , where each element $P_{ij}$ represents the probability of transitioning from state $i$ to state $j$ .

Provide The Goal: Sampling from a Probability Distribution $\pi(x)$

In many applications (e.g., statistical mechanics, Bayesian inference, as mentioned), we are interested in sampling from a complex probability distribution $\pi(x)$ . This distribution might be difficult to sample from directly, but we can use a Markov Chain to create a sequence of samples that, after a certain period (called the burn-in period), will approximate $\pi(x)$ .

Ok Now: The Metropolis Algorithm

The Metropolis Algorithm is one of the simplest MCMC algorithms to generate samples from $\pi(x)$ . It works by constructing a Markov Chain whose stationary distribution is the desired probability distribution $\pi(x)$ . A stationary distribution is a probability distribution that remains the same over time in a Markov chain. Thus it can describe the long-term behavior of a chain, where the probabilities of being in each state do not change as time passes. (Whatever time is, i digress.)

The key steps of the algorithm are:

Initialization

Start with an initial guess $x_0$ , a point in the state space. This point can be chosen randomly or based on prior knowledge.

Proposal Step

From the current state $x_t$ , propose a new state $x^*$ using a proposal distribution $q(x^*|x_t)$ , which suggests a candidate for the next state. This proposal distribution can be symmetric (e.g., a normal distribution centered at $x_t$ ) or asymmetric.

Acceptance Probability

Calculate the acceptance probability $\alpha$ for moving from the current state $x_t$ to the proposed state $x^*$ :

$\alpha = \min \left(1, \frac{\pi(x^) q(x_t | x^)}{\pi(x_t) q(x^* | x_t)} \right)$

In the case where the proposal distribution is symmetric (i.e., $q(x^|x_t) = q(x_t|x^))$ , the formula simplifies to:

$\alpha = \min \left(1, \frac{\pi(x^*)}{\pi(x_t)} \right)$

Acceptance or Rejection

Generate a random number $u$ from a uniform distribution $U(0, 1)$
If $u \leq \alpha$ , accept the proposed state $x^*$ , i.e., $set x_{t+1} = x^*$ .
If $u > \alpha$ , reject the proposed state and remain at the current state, i.e., $set x_{t+1} = x_t$ .

Repeat

Repeat the proposal, acceptance, and rejection steps to generate a Markov Chain of samples.

Convergence and Stationary Distribution:

Over time, as more samples are generated, the Markov Chain converges to a stationary distribution. The stationary distribution is the target distribution $\pi(x)$ , meaning the samples generated by the algorithm will approximate $\pi(x)$ more closely as the number of iterations increases.

Applications:

The Metropolis Algorithm is widely used in various fields such as Bayesian statistics, physics (e.g., in the simulation of physical systems), machine learning, and finance. It is especially useful for high-dimensional problems where direct sampling is computationally expensive or impossible.

Key Features of the Metropolis Algorithm:

Simplicity: It’s easy to implement and doesn’t require knowledge of the normalization constant of $\pi(x)$ , which can be difficult to compute.
Flexibility: It works with a wide range of proposal distributions, allowing the algorithm to be adapted to different problem contexts.
Efficiency: While it can be computationally demanding, the algorithm can provide high-quality approximations to complex distributions with well-chosen proposals and sufficient iterations.

The Metropolis-Hastings Algorithm is a more general version that allows for non-symmetric proposal distributions, expanding the range of problems the algorithm can handle.

Now let us code it up:

i am going to assume the underlying distribution is Gaussian with a time-dependent mean $\mu_t$ , which changes slowly over time. We’ll use a simple time-series analytics setup to sample this distribution using the Metropolis Algorithm and plot the results. Note: When the target distribution is Gaussian (or close to Gaussian), the algorithm can converge more quickly to the true distribution because of the symmetric smooth nature of the normal distribution.

import numpy as np
import matplotlib.pyplot as plt

# Time-dependent mean function (example: sinusoidal pattern)
def mu_t(t):
    return 10 * np.sin(0.1 * t)

# Target distribution: Gaussian with time-varying mean mu_t and fixed variance
def target_distribution(x, t):
    mu = mu_t(t)
    sigma = 1.0  # Assume fixed variance for simplicity
    return np.exp(-0.5 * ((x - mu) / sigma) ** 2)

# Metropolis Algorithm for time-series sampling
def metropolis_sampling(num_samples, initial_x, proposal_std, time_steps):
    samples = np.zeros(num_samples)
    samples[0] = initial_x

    # Iterate over the time steps
    for t in range(1, num_samples):
        # Propose a new state based on the current state
        x_current = samples[t - 1]
        x_proposed = np.random.normal(x_current, proposal_std)

        # Acceptance probability (Metropolis-Hastings step)
        acceptance_ratio = target_distribution(x_proposed, time_steps[t]) / target_distribution(x_current, time_steps[t])
        acceptance_probability = min(1, acceptance_ratio)

        # Accept or reject the proposed sample
        if np.random.rand() < acceptance_probability:
            samples[t] = x_proposed
        else:
            samples[t] = x_current

    return samples

# Parameters
num_samples = 10000  # Total number of samples to generate
initial_x = 0.0      # Initial state
proposal_std = 0.5   # Standard deviation for proposal distribution
time_steps = np.linspace(0, 1000, num_samples)  # Time steps for temporal evolution

# Run the Metropolis Algorithm
samples = metropolis_sampling(num_samples, initial_x, proposal_std, time_steps)

# Plot the time series of samples and the underlying mean function
plt.figure(figsize=(12, 6))

# Plot the samples over time
plt.plot(time_steps, samples, label='Metropolis Samples', alpha=0.7)

# Plot the underlying time-varying mean (true function)
plt.plot(time_steps, mu_t(time_steps), label='True Mean ', color='red', linewidth=2)

plt.title("Metropolis Algorithm Sampling with Time-Varying Gaussian Distribution")
plt.xlabel("Time")
plt.ylabel("Sample Value")
plt.legend()
plt.grid(True)
plt.show()

Output of Python Script Figure 1.0

Ok, What’s going on here?

For the Target Distribution:

The function $mu_t(t)$ defines a time-varying mean for the distribution. In this example, it follows a sinusoidal pattern.
The function target_distribution(x, t) models a Gaussian distribution with mean $\mu_t$ and a fixed variance (set to 1.0).

Metropolis Algorithm:

The metropolis_sampling function implements the Metropolis algorithm. It iterates over time, generating samples from the time-varying distribution. The acceptance probability is calculated using the target distribution at each time step.

Proposal Distribution:

A normal distribution centered around the current state with standard deviation proposal_std is used to propose new states.

Temporal Evolution:

The time steps are generated using np.linspace to simulate temporal evolution, which can be used in time-series analytics.

Plot The Results:

The results are plotted, showing the samples generated by the Metropolis algorithm as well as the true underlying mean function $\mu_t$ (in red).

The plot shows the Metropolis samples over time, which should cluster around the time-varying mean $\mu_t$ of the distribution. As time progresses, the samples follow the red curve (the true mean) as time moves on like and arrow in this case.

Now you are probably asking “Hey is there a more pythonic library way to to this?”. Oh Dear Reader i am glad you asked! Yes There Is A Python Library! AFAIC PyMC started it all. Most probably know it as PyMc3 (formerly known as…). There is a great writeup here: History of PyMc.

We are golden age of probabilistic programming.

~ Chris Fonnesbeck (creator of PyMC)

Lets convert it using PyMC. Steps to Conversion:

Define the probabilistic model using PyMC’s modeling syntax.
Specify the Gaussian likelihood with the time-varying mean $\mu_t$ .
Use PyMC’s built-in Metropolis sampler.
Visualize the results similarly to how we did earlier.

import pymc as pm
import numpy as np
import matplotlib.pyplot as plt

# Time-dependent mean function (example: sinusoidal pattern)
def mu_t(t):
    return 10 * np.sin(0.1 * t)

# Set random seed for reproducibility
np.random.seed(42)

# Number of time points and samples
num_samples = 10000
time_steps = np.linspace(0, 1000, num_samples)

# PyMC model definition
with pm.Model() as model:
    # Prior for the time-varying parameter (mean of Gaussian)
    mu_t_values = mu_t(time_steps)

    # Observational model: Normally distributed samples with time-varying mean and fixed variance
    sigma = 1.0  # Fixed variance
    x = pm.Normal('x', mu=mu_t_values, sigma=sigma, shape=num_samples)

    # Use the Metropolis sampler explicitly
    step = pm.Metropolis()

    # Run MCMC sampling with the Metropolis step
    samples_all = pm.sample(num_samples, tune=1000, step=step, chains=5, return_inferencedata=False)

# Extract one chain's worth of samples for plotting
samples = samples_all['x'][0]  # Taking only the first chain

# Plot the time series of samples and the underlying mean function
plt.figure(figsize=(12, 6))

# Plot the samples over time
plt.plot(time_steps, samples, label='PyMC Metropolis Samples', alpha=0.7)

# Plot the underlying time-varying mean (true function)
plt.plot(time_steps, mu_t(time_steps), label='True Mean ', color='red', linewidth=2)

plt.title("PyMC Metropolis Sampling with Time-Varying Gaussian Distribution")
plt.xlabel("Time")
plt.ylabel("Sample Value")
plt.legend()
plt.grid(True)
plt.show()

When you execute this code you will see the following status bar:

It will be a while. Go grab your favorite beverage and take a walk…..

Output of Python Script Figure 1.1

Key Differences from the Previous Code:

PyMC Model Usage Definition:
In PyMC, the model is defined using the pm.Model() context. The x variable is defined as a Normal distribution with the time-varying mean $\mu_t$ . Instead of manually implementing the acceptance probability, PyMC handles this automatically with the specified sampler.

Metropolis Sampler:
PyMC allows us to specify the sampling method. Here, we explicitly use the Metropolis algorithm with pm.Metropolis().

Samples Parameter:
We specify shape=num_samples in the pm.Normal() distribution to indicate that we want a series of samples for each time step.

Plotting:
The resulting plot will show the sampled values using the PyMC Metropolis algorithm compared with the true underlying mean, similar to the earlier approach. Now, samples has the same shape as time_steps (in this case, both with 10,000 elements), allowing you to plot the sample values correctly against the time points; otherwise, the x and y axes would not align.

NOTE: We used this library at one of our previous health startups with great success.

Optimizations herewith include several. There is a default setting in PyMC which is called NUTS.
No need to manually set the number of leapfrog steps. NUTS automatically determines the optimal number of steps for each iteration, preventing inefficient or divergent sampling. NUTS automatically stops the trajectory when it detects that the particle is about to turn back on itself (i.e., when the trajectory “U-turns”). A U-turn means that continuing to move in the same direction would result in redundant exploration of the space and inefficient sampling. When NUTS detects this, it terminates the trajectory early, preventing unnecessary steps. Also the acceptance rates on convergence are higher.

There are several references to this set of algorithms. It truly a case of both mathematical and computational elegance.

Of course you have to know what the name means. They say words have meanings. Then again one cannot know everything.

Until Then,

#iwishyouwater <- Of all places Alabama getting the memo From Helene 2024

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

Music To Blog By: View From The Magicians Window, The Psychic Circle

References:

[1] The Metropolis Algorithm: Theory and Examples by C Douglas Howard

[2] The Metropolis-Hastings Algorithm: A note by Danielle Navarro

[3] Github code for Sample Based Inference by bashhwu

Entire Metropolis Movie For Your Viewing Pleasure. (AFAIC The most amazing Sci-Fi movie besides BladeRunner)

What Would Nash,Shannon,Turing, Wiener and von Neumann Think?

An image of the folks as mentioned above via the GAN de jour

First, as usual, i trust everyone is safe. Second, I’ve been “thoughting” a good deal about how the world is being eaten by software and, recently, machine learning. i personally have a tough time with using the words artificial intelligence.

What Would Nash, Shannon, Turing, Wiener, and von Neumann Think of Today’s World?

The modern world is a product of the mathematical and scientific brilliance of a handful of intellectual pioneers who happen to be whom i call the Horsemen of The Digital Future. i consider these humans to be my heroes and persons that i aspire to be whereas most have not accomplished one-quarter of the work product the humans have created for humanity. Among these giants are Dr. John Nash, Dr. Claude Shannon, Dr. Alan Turing, Dr. Norbert Wiener, and Dr. John von Neumann. Each of them, in their own way, laid the groundwork for concepts that now define our digital and technological age: game theory, information theory, artificial intelligence, cybernetics, and computing. But what would they think if they could see how their ideas, theories and creations have shaped the 21st century?

A little context.

John Nash: The Game Theorist

John Nash revolutionized economics, mathematics, and strategic decision-making through his groundbreaking work in game theory. His Nash Equilibrium describes how parties, whether they be countries, companies, or individuals, can find optimal strategies in competitive situations. Today, his work influences fields as diverse as economics, politics, and evolutionary biology. NOTE: Computational Consensus Not So Hard; Carbon (Human) Consensus Nigh Impossible.

The Nash equilibrium is the set of degradation strategies

$(E_i^*,E_j^*)$

such that, if both players adopt it, neither player can achieve a higher payoff by changing strategies. Therefore, two rational agents should be expected to pick the Nash equilibrium as their strategy.

If Nash were alive today, he would be amazed at how game theory has permeated decision-making in technology, particularly in algorithms used for machine learning, cryptocurrency trading, and even optimizing social networks. His equilibrium models are at the heart of competitive strategies used by businesses and governments alike. With the rise of AI systems, Nash might ponder the implications of intelligent agents learning to “outplay” human actors and question what ethical boundaries should be set when AI is used in geopolitical or financial arenas.

Claude Shannon: The Father of Information Theory

Claude Shannon’s work on information theory is perhaps the most essential building block of the digital age. His concept of representing and transmitting data efficiently set the stage for everything from telecommunications to the Internet as we know it. Shannon predicted the rise of digital communication and laid the foundations for the compression and encryption algorithms protecting our data. He also is the father of my favorite equation mapping the original entropy equation from thermodynamics to channel capacity:

$H=-1/N \sum_{i=1}^{N} P_i\,log_2\,P_i$

The shear elegance and magnitude is unprecedented. If he were here, Shannon would witness the unprecedented explosion of data, quantities, and speeds far beyond what was conceivable in his era. The Internet of Things (IoT), big data analytics, 5G/6G networks, and quantum computing are evolutions directly related to his early ideas. He might also be interested in cybersecurity challenges, where information theory is critical in protecting global communications. Shannon would likely marvel at the sheer volume of information we produce yet be cautious of the potential misuse and the ethical quandaries regarding privacy, surveillance, and data ownership.

Alan Turing: The Architect of Artificial Intelligence

Alan Turing’s vision of machines capable of performing any conceivable task laid the foundation for modern computing and artificial intelligence. His Turing Machine is still a core concept in the theory of computation, and his famous Turing Test continues to be a benchmark in determining machine intelligence.

In today’s world, Turing would see his dream of intelligent machines realized—and then some. From self-driving cars to voice assistants like Siri and Alexa, AI systems are increasingly mimicking human cognition human capabilities in specific tasks like data analysis, pattern recognition, and simple problem-solving. While Turing would likely be excited by this progress, he might also wrestle with the ethical dilemmas arising from AI, such as autonomy, job displacement, and the dangers of creating highly autonomous AI systems as well as calling bluff on the fact that LLM systems do not reason in the same manner as human cognition on basing the results on probabilistic convex optimizations. His work on breaking the Enigma code might inspire him to delve into modern cryptography and cybersecurity challenges as well. His reaction-diffusion model called Turings Metapmorphsis equation, is foundational in explaining biological systems:

Turing’s reaction-diffusion system is typically written as a system of partial differential equations (PDEs):

$\frac{\partial u}{\partial t} &= D_u \nabla^2 u + f(u, v),$

$\frac{\partial v}{\partial t} &= D_v \nabla^2 v + g(u, v),$

$where:$

$\begin{itemize}\item $u$ and $v$ are concentrations of two chemical substances (morphogens),\item $D_u$ and $D_v$ are diffusion coefficients for $u$ and $v$,\item $\nabla^2$ is the Laplacian operator, representing spatial diffusion,\item $f(u, v)$ and $g(u, v)$ are reaction terms representing the interaction between $u$ and $v$.\end{itemize}$

In addition to this, his contributions to cryptography and game theory alone are infathomable.
In his famous paper, “Computing Machinery and Intelligence,” Turing posed the question, “Can machines think?” He proposed the Turing Test as a way to assess whether a machine can exhibit intelligent behavior indistinguishable from a human. This test has been a benchmark in AI for evaluating a machine’s ability to imitate human intelligence.

Given the recent advances made with large language models, I believe he would find it amusing, not that they think or reason.

Norbert Wiener: The Father of Cybernetics

Norbert Wiener’s theory of cybernetics explored the interplay between humans, machines, and systems, particularly how systems could regulate themselves through feedback loops. His ideas greatly influenced robotics, automation, and artificial intelligence. He wrote the books “Cybernetics” and “The Human Use of Humans”. During World War II, his work on the automatic aiming and firing of anti-aircraft guns caused Wiener to investigate information theory independently of Claude Shannon and to invent the Wiener filter. (The now-standard practice of modeling an information source as a random process—in other words, as a variety of noise—is due to Wiener.) Initially, his anti-aircraft work led him to write, with Arturo Rosenblueth and Julian Bigelow, the 1943 article ‘Behavior, Purpose and Teleology. He was also a complete pacifist. What was said about those who can hold two opposing views?

If Wiener were alive today, he would be fascinated by the rise of autonomous systems, from drones to self-regulated automated software, and the increasing role of cybernetic organisms (cyborgs) through advancements in bioengineering and robotic prosthetics. He, I would think, would also be amazed that we could do real-time frequency domain filtering based on his theories. However, Wiener’s warnings about unchecked automation and the need for human control over machines would likely be louder today. He might be deeply concerned about the potential for AI-driven systems to exacerbate inequalities or even spiral out of control without sufficient ethical oversight. The interaction between humans and machines in fields like healthcare, where cybernetics merges with biotechnology, would also be a keen point of interest for him.

John von Neumann: The Architect of Modern Computing

John von Neumann’s contributions span so many disciplines that it’s difficult to pinpoint just one. He’s perhaps most famous for his von Neumann architecture, the foundation of most modern computer systems, and his contributions to quantum mechanics and game theory. His visionary thinking on self-replicating machines even predated discussions of nanotechnology.

Von Neumann would likely be astounded by the ubiquity and power of modern computers. His architectural design is the backbone of nearly every device we use today, from smartphones to supercomputers. He would also find significant developments in quantum computing, aligning with his quantum mechanics work. As someone who worked on the Manhattan Project (also Opphenhiemer), von Neumann might also reflect on the dual-use nature of technology—the incredible potential of AI, nuclear power, and autonomous weapons to both benefit and harm humanity. His early concerns about the potential for mutual destruction could be echoed in today’s discussions on AI governance and existential risks.

What Would They Think Overall?

Together, these visionaries would undoubtedly marvel at how their individual contributions have woven into the very fabric of today’s society. The rapid advancements in AI, data transmission, computing power, and autonomous systems would be thrilling, but they might also feel a collective sense of responsibility to ask:

Where do we go from here?

Once again Oh Dear Reader You pre-empt me….

A colleague sent me this paper, which was the impetus for this blog:

My synopsis of said paper:

“The Tensor as an Informational Resource” discusses the mathematical and computational importance of tensors as resources, particularly in quantum mechanics, AI, and computational complexity. The authors propose new preorders for comparing tensors and explore the notion of tensor rank and transformations, which generalize key problems in these fields. This paper is vital for understanding how the foundational work of Nash, Shannon, Turing, Wiener, and von Neumann has evolved into modern AI and quantum computing. Tensors offer a new frontier in scientific discovery, building on their theories and pushing the boundaries of computational efficiency, information processing, and artificial intelligence. It’s an extension of their legacy, providing a mathematical framework that could revolutionize our interaction with quantum information and complex systems. Fundamental to systems that appear to learn where the information-theoretic transforms are the very rosetta stone of how we perceive the world through perceptual filters of reality.

This shows the continuing relevance in ALL their ideas in today’s rapidly advancing AI and fluid computing technological landscape.

They might question whether today’s technology has outpaced ethical considerations and whether the systems they helped build are being used for the betterment of all humanity. Surveillance, privacy, inequality, and autonomous warfare would likely weigh heavily on their minds. Yet, their boundless curiosity and intellectual rigor would inspire them to continue pushing the boundaries of what’s possible, always seeking new answers to the timeless question of how to create the future we want and live better, more enlightened lives through science and technology.

Their legacy lives on, but so does their challenge to us: to use the tools they gave us wisely for the greater good of all.

Or would they be dismayed that we use all of this technology to make a powerpoint to save time so we can watch tik tok all day?

Until Then,

#iwishyouwater <- click and see folks who got the memo

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

Music To blog by: Bach: Mass in B Minor, BWV 232. By far my favorite composer. The John Eliot Gardiner and Monterverdi Choir version circa 1985 is astounding.

What Is Love?

Love is a complex and multifaceted emotion that is often defined differently depending on cultural, social, and individual perspectives. At its core, love typically involves strong feelings of affection, caring, and attachment towards another person, often accompanied by a desire for intimacy, closeness, and mutual support.

Love can take many different forms, including romantic love, familial love, platonic love, and self-love. It can also involve a range of different experiences, such as joy, happiness, excitement, passion, and sometimes even pain and heartbreak.

Ultimately, love is a powerful and transformative force that can have a profound impact on our lives, shaping our relationships, beliefs, and sense of self.
~ ChatGPT

First Oh Dear Reader, i trust you are safe. Second, we are taking a slight detour into squishy, non-exact, messy, and truly complicated land: Emotions. This comes on the heels of Snake_Byte[14] Coding In Philosophical Frameworks. Also if you haven’t seen the news, the Cognitive Computing Arms Race with Large Language Models is in full swing. Boy Howdy Do We Love Us Some Anthropomorphism! ( i personally think its funny the parlor trick of auto-typing makes ChatGPT and all the other ones appear human to most. )

Turns out there is a class at Stanford on Computational Philosophy which is in the same realm as what i would love to discuss however slightly obtuse. It is a good read.

If we can compute Philosophical frameworks, could we or do we need to compute emotions in the future?

It was not random i started with the terseness of Stoicism. As this philosophy trends towards being objective in your emotions, akin to a more recent philosophy, “Objectivism.” We can quantify the aspects thereof. Or at least we think we can quantify them to some extent.

We know from a physiological standpoint that bodily metrics and chemicals can be measured for systematic responses to emotions. Dopamine, Vasopressin, Oxytocin, Serotonin, heart rate, sweat level, skin clamminess, and eye dilation are just a few measurable considerations.

However what does it mean?

Let’s take some bookend emotions: Love, Hate, Fear.

Some consider Fear to be the only true emotion. Flight or Fight. i’m also positive we can duplicate this in a machine or at least make it plausible to appear the machine is scared. (Also, it will make humans “feel” better one day but that is for a different discussion and different blog.)

Being that Spring Is In The Air let us choose LOVE.

There are so many types and kinds of Love.

Love of a Mother to a child
The Love you have for your pets
The Love of eating a doughnut.

i’m not referring to these types i am referencing:

You are the Love of My Life Companionship – Love

Books, signs, poetry, and yes, humans’ most deplorable creation War has been created over Love of Another. It is genuinely innate in the Human_Condition.

People acting a fool, if you will. You have seen it many times, and maybe (hopefully) you have done it.

As the famous song says, “What does love have to do with IT? What is love but some secondhand emotion?”. Great questioning Tina. We love you.

Have you ever been in The Love? How do you know that person Loves YOU?

i asked one of my progeny that question:

Me: “Do i Love you?”

Them: “Yes.”

Me” “How do you know?”

Them: “Dad i just know.”

This is not to be confused with infatuation, which is in and of itself an extremely intense feeling usually associated with a physically idealized version of the receiving person. Infatuation is initially overwhelming but then quickly fades. Love, on the other hand, appears to be a much deeper, almost seemingly “infinite forever-giving” emotion.

i was wondering how many times “Love” has been mentioned over the years. Below is a plot thereof:

i thought the drop in the 1980’s was interesting. If anyone was around in the United States in the 80’s that was the period of certain illegal chemicals, neon, 80’s metal and madonna. The Me Generation.

So what has happened in this area with using technology of Love? Well we scan our noggin:

**From The Journal of Frontiers in Human Neuroscience**

When I take you in my arms gathered forever. Sometimes it feels like a dream. Sometimes it feels like a dream; to be with you.
~ Feels Like A Dream Lyrics

Research has shown that certain brain regions are activated when a person is in love or experiencing strong feelings of affection, including the insula, anterior cingulate cortex, and striatum. These regions are associated with reward, motivation, and emotional processing. Brain scans have also revealed that the levels of certain neurotransmitters, like dopamine and oxytocin, can change in response to being in love. So while there is no one-size-fits-all image of someone in love, there are certain brain patterns and chemical changes that can be detected in those experiencing the emotion.

Furthermore on the chemical brain related activity research has shown that during the early stages of romantic love, there is a decrease and increase in serotonin levels in the brain^[1]. Serotonin can have direct affects on anxiety, insomnia and depression. For men there is a decrease and for women and increase in serotonin. Strangely this is similar to what is observed in individuals with obsessive-compulsive disorder, suggesting that the intense focus and preoccupation with the loved one may be related to changes in serotonin levels. Additionally, it has been found that long-term couples who remain in love have higher levels of serotonin than those who have fallen out of love. This suggests that serotonin may play a role in maintaining long-term relationships.

Our study provides the first evidence of love-related alterations in the underlying architecture of the brain, and the results shed new light on the mechanisms of romantic love.
~ Professor Xiaochu Zhang

So if there are (and there are injectable adjuncts to all of these chemicals) that can be augmented and tailored with specific dosages, this led me to ask the questions:

can we compute a model of Love?
can we fool the brain into thinking it is in Love?
do we want to augment the brain so we feel like we are in Love?
Do we want to make Love a thing of the past with future tech?
The list could go on…..

Did my heart love till now? Forswear it, sight / For I ne’er saw true beauty till this night.
~ That Romeo Guy

When people meet, go on a date, shack up, get hitched or get married, i have heard it said:

“You will grow to love them.”
“Well, the person doesn’t really have the attributes i am looking for in a mate, but i can adapt.”
“They share the same interest as you!”
“They can cook or fix stuff for you!”

Why would we want to change or have that person change? Are social norms so strong that there are unwritten rules that we as a so-called society must follow, With a hard rule of it must be two consenting adults in a relationship, what is it of anyone’s business? Do most want to do what they feel they should do? Do we want a best friend to watch the big game with or play golf? Why not a companion that you can dance with now and when your old and just look at each other? To idealized? Oh life gets in the way. i see it is hard and you have stuff to do – yep i get it. Then is Love that important? If not then what is?

Love in a relationship is it needed?

How do you know when you’re really in Love? Do violins play when you’re touching the one your Lovin?
~ The Tubes

Most often, love is due to their connection with their partner. This connection can be emotional, physical, spiritual, or intellectual (or hopefully all of them). People may also fall in love or grow into love because they appreciate their partner’s qualities or values, such as kindness, honesty, or intelligence. Sometimes, people are drawn to others who are different from themselves because they find them intriguing or exciting.

This room is bare
This night is cold
We’re far apart, and I’m growing old
But while we live
We’ll meet again
So then, my love
We may whisper once more
It’s you I adore
~ Palabras de Amor from Queen

Other factors that contribute to love include shared experiences, common interests, and mutual respect. When people feel like they’re a team with their partner and can rely on them, it can deepen their love. Additionally, physical attraction and chemistry can significantly influence romantic love.

However, what if it is all of these with the same partner? Isn’t that what you truly desire? ALL THE THINGS! To truly know that there is one – what would you do to have that person show up on your porch?

This is what i believe we need to understand as we trend toward a world of distributed disconnected artificial intelligence.

In one kiss, you’ll know all I haven’t said.
~ Pablo Neruda^[3]

Even post the pandemic, we see people hug and kiss less. There is less physicality which as we all know for most, a hug feels good for both and releases several proof-positive chemicals. i would tend to think at some point you want to rub against your significant other instead of smooching in cyberspace or sleeping in separate beds.

Is Love now an algorithm? i know in the age of match.com and tinder this appears to be monday morning quarterbacking but at least i believe there is something more to it than a list of attributes. In fact one of the startups i worked at started with the premise, “What are the three main questions you ask someone on a blind date?” Here they are:

What is your favorite travel spot?
What is your favorite wine (assuming one drinks)
What type of music do you like?

From a cognitive standpoint the most difficult one was music recommendations. This by the way Oh Dear Reader was circa 2001 before Pandora, Shazam or any other music recommendation system. We based it on a cognitive model of music perception and cognitive psychology of inter-relations^[2].

If we already know the future and can match it aren’t we living in a world of no surprises? What happens to that special glance? That special smile or touch or a good morning hug? That true everlasting one that loves you for you and only you not because your father mother or your in-laws approved. (If course it has been said you marry the family not the person. )

i am sure by now you have heard of the term Twin Flame. This is different than a soul mate. A Twin Flame is a mirror of oneself, a yin-yang type entity. The biggest distinction between a twin flame and any other type of soul mate is that twin flames are two halves of the same whole. A soul mate, on the other hand, can be thought of as someone cut from the same cloth as you. There’s a deep familiarity, and you feel very close, but you aren’t the same soul.

i wonder if we can compute and create a Twin Flame Agent of oneself?

i’m not sure i want to work on those future algorithms folks. Some things i want to be a mystery^[4].

i’d love to hear some stories, comments, and possibly even rants as this is some very esoteric area we have paddled into so to speak. i’d love to know if you have the love of your life, twin flame or a tinder swipe of the night, i hope you just know.

Until Then,

#iwshyouwater <- alexy molchanov sets a new world record in Bonaire. i was just in the same spot freediving.

Muzak To Blog By: All The Love Songs by Queen. i miss you Freddy and recently brian may was knighted.

References:

[1] Scans Say Its True Love Link

[2] For the record, i tried the system, and no one liked strict cathedral organ works.

[3] Pablo Neruda is the most prolific poet when it comes to love and beaches i have ever read. there is a connection there.

[4] In blade runner, harrison ford knows full well racheal is a replicant yet he Loves her. Question is – Is He? That would be a twin flame. robot love.

Snake_Byte:[14] Coding In Philosophical Frameworks

Your vision will only become clear when you can look into your heart. Who looks outside, dreams; who looks inside, awakes. Knowing your own darkness is the best method for dealing with the darknesses of other people. We cannot change anything until we accept it.
~ C. Jung

(Caveat Emptor: This blog is rather long in the snakes tooth and actually more like a CHOMP instead of a BYTE. tl;dr)

First, Oh Dear Reader i trust everyone is safe, Second sure feels like we are living in an age of Deus Ex Machina, doesn’t it? Third with this in mind i wanted to write a Snake_Byte that have been “thoughting” about for quite some but never really knew how to approach it if truth be told. I cant take full credit for this ideation nor do i actually want to claim any ideation. Jay Sales and i were talking a long time after i believe i gave a presentation on creating Belief Systems using BeliefNetworks or some such nonsense.

The net of the discussion was we both believed that in the future we will code in philosophical frameworks.

Maybe we are here?

So how would one go about coding an agent-based distributed system that allowed one to create an agent or a piece of evolutionary code to exhibit said behaviors of a philosophical framework?

Well we must first attempt to define a philosophy and ensconce it into a quantized explanation.

Stoicism seemed to me at least the best first mover here as it appeared to be the tersest by definition.

So first those not familiar with said philosophy, Marcus Aurelius was probably the most famous practitioner of Stoicism. i have put some references that i have read at the end of this blog¹.

Stoicism is a philosophical school that emphasizes rationality, self-control, and inner peace in the face of adversity. In thinking about this i figure To build an agent-based software system that embodies Stoicism, we would need to consider several key aspects of this philosophy.

Stoics believe in living in accordance with nature and the natural order of things. This could be represented in an agent-based system through a set of rules or constraints that guide the behavior of the agents, encouraging them to act in a way that is in harmony with their environment and circumstances.

Stoics believe in the importance of self-control and emotional regulation. This could be represented in an agent-based system through the use of decision-making algorithms that take into account the agent’s emotional state and prioritize rational, level-headed responses to stimuli.

Stoics believe in the concept of the “inner citadel,” or the idea that the mind is the only thing we truly have control over. This could be represented in an agent-based system through a focus on internal states and self-reflection, encouraging agents to take responsibility for their own thoughts and feelings and strive to cultivate a sense of inner calm and balance.

Stoics believe in the importance of living a virtuous life and acting with moral purpose. This could be represented in an agent-based system through the use of reward structures and incentives that encourage agents to act in accordance with Stoic values such as courage, wisdom, and justice.

So given a definition of Stoicism we then need to create a quantized model or discrete model of those behaviors that encompass a “Stoic Individual”. i figured we could use the evolutionary library called DEAP (Distributed Evolutionary Algorithms in Python ). DEAP contains both genetic algorithms and genetic programs utilities as well as evolutionary strategy methods for this type of programming.

Genetic algorithms and genetic programming are both techniques used in artificial intelligence and optimization, but they have some key differences.

This is important as people confuse the two.

Genetic algorithms are a type of optimization algorithm that use principles of natural selection to find the best solution to a problem. In a genetic algorithm, a population of potential solutions is generated and then evaluated based on their fitness. The fittest solutions are then selected for reproduction, and their genetic information is combined to create new offspring solutions. This process of selection and reproduction continues until a satisfactory solution is found.

On the other hand, genetic programming is a form of machine learning that involves the use of genetic algorithms to automatically create computer programs. Instead of searching for a single solution to a problem, genetic programming evolves a population of computer programs, which are represented as strings of code. The programs are evaluated based on their ability to solve a specific task, and the most successful programs are selected for reproduction, combining their genetic material to create new programs. This process continues until a program is evolved that solves the problem to a satisfactory level.

So the key difference between genetic algorithms and genetic programming is that genetic algorithms search for a solution to a specific problem, while genetic programming searches for a computer program that can solve the problem. Genetic programming is therefore a more general approach, as it can be used to solve a wide range of problems, but it can also be more computationally intensive due to the complexity of evolving computer programs².

So returning back to the main() function as it were, we need create a genetic program that models Stoic behavior using the DEAP library,

First need to define the problem and the relevant fitness function. This is where the quantized part comes into play. Since Stoic behavior involves a combination of rationality, self-control, and moral purpose, we could define a fitness function that measures an individual’s ability to balance these traits and act in accordance with Stoic values.

So lets get to the code.

To create a genetic program that models Stoic behavior using the DEAP library in a Jupyter Notebook, we first need to install the DEAP library. We can do this by running the following command in a code cell:

pip install deap

Next, we can import the necessary modules and functions:

import random
import operator
import numpy as np
from deap import algorithms, base, creator, tools

We can then define the problem and the relevant fitness function. Since Stoic behavior involves a combination of rationality, self-control, and moral purpose, we could define a fitness function that measures an individual’s ability to balance these traits and act in accordance with Stoic values.

Here’s an example of how we might define a “fitness function” for this problem:

# Define the fitness function.  NOTE: # i am open to other ways of defining this and other models
# the definition of what is a behavior needs to be quantized or discretized and 
# trying to do that yields a lossy functions most times.  Its also self referential

def fitness_function(individual):
    # Calculate the fitness based on how closely the individual's behavior matches stoic principles
    fitness = 0
    # Add points for self-control, rationality, focus, resilience, and adaptability can haz Stoic?
    fitness += individual[0]  # self-control
    fitness += individual[1]  # rationality
    fitness += individual[2]  # focus
    fitness += individual[3]  # resilience
    fitness += individual[4]  # adaptability
    return fitness,

# Define the genetic programming problem
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

# Initialize the genetic algorithm toolbox
toolbox = base.Toolbox()

# Define the genetic operators
toolbox.register("attribute", random.uniform, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=5)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", fitness_function)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)

# Run the genetic algorithm
population = toolbox.population(n=10)
for generation in range(20):
    offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    population = toolbox.select(offspring, k=len(population))
    
# Print the best individual found
best_individual = tools.selBest(population, k=1)[0]

print ("Best Individual:", best_individual)

Here, we define the genetic programming parameters (i.e., the traits that we’re optimizing for) using the toolbox.register function. We also define the evaluation function (stoic_fitness), genetic operators (mate and mutate), and selection operator (select) using DEAP’s built-in functions.

We then define the fitness function that the genetic algorithm will optimize. This function takes an “individual” (represented as a list of five attributes) as input, and calculates the fitness based on how closely the individual’s behavior matches stoic principles.

We then define the genetic programming problem via the quantized attributes, and initialize the genetic algorithm toolbox with the necessary genetic operators.

Finally, we run the genetic algorithm for 20 generations, and print the best individual found. The selBest function is used to select the top individual fitness agent or a “behavior” if you will for that generation based on the iterations or epochs. This individual represents an agent that mimics the philosophy of stoicism in software, with behavior that is self-controlled, rational, focused, resilient, and adaptable.

Best Individual: [0.8150247518866958, 0.9678037028949047, 0.8844195735244268, 0.3970642186025506, 1.2091810770505023]

This denotes the best individual with those best balanced attributes or in this case the Most Stoic,

As i noted this is a first attempt at this problem i think there is a better way with a full GP solution as well as a tunable fitness function. In a larger distributed system you would then use this agent as a framework amongst other agents you would define.

i at least got this out of my head.

until then,

#iwishyouwater <- Alexey Molchanov and Dan Bilzerian at Deep Dive Dubai

Muzak To Blog By: Phil Lynott “The Philip Lynott Album”, if you dont know who this is there is a statue in Ireland of him that i walked a long way with my co-founder, Lisa Maki a long time ago to pay homage to the great Irish singer of the amazing band Thin Lizzy. Alas they took Phil to be cleaned that day. At least we got to walk and talk and i’ll never forget that day. This is one of his solo efforts and i believe he is one of the best artists of all time. The first track is deeply emotional.

References:

[1] A list of books on Stoicism -> click HERE.

[2] Genetic Programming (On the Programming of Computers by Means of Natural Selection), By Professor John R. Koza. There are multiple volumes i think four and i have all of this but this is a great place to start and the DEAP documentation. Just optimizing a transcendental functions is mind blowing what GP comes out with using arithmetic

January 9, 2023 TCTJr Artificial_Intelligence|Books|Direct Thoughts|General Discussion

A Book Review – Scythe

I am the blade that is swung by your hand,

Slicing a rainbow’s arc,

I am the clapper; but you are the bell,

Tolling the gathering dark.

If you are the singer, then I am the song,

A threnody, requiem dirge.

You’ve made me the answer for all the world’s need,

Humanity’s undying urge.
~ “Threnody,” from the collected works of H.S. Socrates

First as always i hope everyone is safe. Second, i hope everyone had an indulgent and superlative holiday season heading into the New Year! Third, i decided i wanted to write a book review since i haven’t in quite some time and given the number of books i have read recently figured hey lets do a book review!

This review deserves a little context. My middle progeny was assigned this book for a winter break reading assignment. i believe it is important to take an interest in your progenies’ activities and well reading is definitely one to promote and take interest. Thus when i was talking to her about what she was assigned, she said, “i think you will like this book.” She handed it to me, and i opened it to this page:

“It is the most difficult thing a person can be asked to do. And know what it is for the greater good doesn’t make it any easier. People used to die naturally. Old age used to be a terminal affliction, not a temporary state. There were invisible killers called “diseases” that broke the body down. Aging couldn’t be reversed, and there were accidents from which there was no return. Planes fell out from the sky. Cars actually crashed. There was pain, misery, and despair. It’s hard for most of us to imagine a world so unsafe, with dangers lurking in every unseen, unplanned corner. All of that is behind us now, and yet a single simple truth remains: People must die.”

~ From the gleaning journal of H.S. Currie

My daughter knows me well. So i said ok let us read it together but don’t think this is a race I’ll probably have this book finished in a week.

Ok this had my attention. So immediately i thought of Soylent Green meets Logan’s Run but there is a twist. In reading the back cover it paraphrases a world with no hunger, no disease, no war, no misery. Humanity has conquered all those things and has even conquered death. However, who are these scythes that are mentioned, and are the only ones who can “glean” life? So with the context out of the way let us get down to business.

The cover above depicts a Scythe. As most know or should know from Websters we have the following definition:

scythe (pronounced /sīT͟H/)

noun: a tool used for cutting crops such as grass or wheat, with a long curved blade at the end of a long pole attached to which are one or two short handles.

verb: cut with a scythe as in scythed.

Given many aspects of our so-called society today and social normalizations i believe this is a wonderful teenage adventure novel that sets the stage for some more esoteric readings in science fiction such as 1984, Something Wicked This Way Comes, Brave New World, Do Androids Dream Of Electric Sheep (DADOES), Snowcrash and Neuromancer.

The book’s premise is that humans now exist in a conflict-free world where human-kind have conquered death. The world they live in is a Post Age of Morality world where one no longer has true crimes against humanity; poverty is not an issue, and hunger is solved via synthetic food engineering. As such over-population has overrun Mother Earth and elected Scythes must cull the human population. This culling process is known in the vernacular as “gleaning”. The Age of Mortality is the duration of time before the scythedom, revival centers, and the Thunderhead were established.

To this end, artificial intelligence has been amplified via the “ThunderHead” which monitors, recommends, and predicts AllTheThings. As such, there is no need for the concept or construction of a government.

Tyger shrugged, “One Splat Too Many. They gave up. Now I am a ward of the ThunderHead.”

“I’m sorry Tyger”

“Hey don’t be. Believe it or not, the ThunderHead’s a better father than my father was. I get good advice now and get asked how my day was from someone who actually seems to care.”

Just like everything else about the ThunderHead its parenting skills were indisutable.

~ Apprentice Rowan

Two teens find themselves volunteered as apprentice-Scythes which leads to a world of corruption greed and the finality of death.

Scythes are volunteered as apprentices and taught the ways of all of the classics, philosophy, chemistry (poisons), neural linguistic programming (person-reading), and of course, skilled in all the ways one can end a person’s life or if you will extreme social engagement called “killcraft”.

Scyhtes choose which lives to glean based on statistics of past Age Of Morality morbidity rates and behaviors, social class, and ethnicities. They however cannot show bias.

Scythes are ruled by a worldwide committee and meet on a quarterly basis where concerns are raised, appretences are tested, and old friendships are renewed. Did i mention that humans now live indefinitely and can rewind physical age and appearance to no lower than 21? However, given that most Sychtes choose ages between 35-45.

The Scythes lived by the following commandments:

Thou Shalt Kill
Thou shalt kill with no bias, bigotry or malice of afterthought
Thou shalt grant an annum of immunity to the beloved of those who accept your coming and to anyone else you deem worthy.
Thou shalt kill the beloved of those who resist.
Thou shalt serve humanity for the full span of thy days and thy family shall have immunity as recompense for as long as you live.
Thou shalt lead an exemplary life in word and deed and keep a journal of each and every day.
Thou shalt kill no scythe beyond thyself.
Thou shalt claim no earthly possessions save thy robe, ring and journal.
Thou shalt have neither spouse or spawn.
Thou shalt be beholden to no laws beyond these.

So we must ask ourselves that if in fact we solve all the so called woes of the Human Condition will we solve the root cause of the Human Condition? If we take away mortality (and morality) and can save and upload our memories then what is meant to be Human? Passion and Lust (of life)? Is compassion still needed?

Upon giving me the book to read my daughter laughed and said “Daddy maybe you are one.”

Then again, reflecting on what my daughter said to me when she was referencing the text, at the core maybe we all are Sycthes.

So if your in the market for a good book for your children or you just want a quick read that will be a good catalyst for your thoughts for our future, pick this book up. Here i even will provide the link to the ThunderHead Book Club In The Sky. Note: this is book one of a triology.

Until Then,

#iwishyouwater <- some footage from the recent 50 year storm on the left coast.

@tctjr

Muzak To Blog By: Tchaikovsky’s Symphony No. 6 in B minor, Valery Abisalovich Gergiev conducting the Vienna Philharmonic. Spectacular piece Symphony No. 6 in B minor, Op. 74, also known as the Pathétique Symphony (a.k.a. The Passionate Symphony). I recently got to see this performed by the Charleston Symphony with Jonathan Heyward conducting it was spectacular. I was sitting there thinking how someone who has certain sexual proclivities or other passions in their life that at the time were not tolerated in society could create such a work of art; then again, it just goes to show the extreme lengths humans will go to make their true passions as it were, incarnate. i also think it very ironic that this composer would probably not trend in the position of Tchaikovsky due to his political beliefs, but one never knows, does one?

Look Up Down All Around!

Your Brain 3D Printed ^[1]

The effects of technology do not occur at the level of opinions or concepts. Rather they alter patterns of perception steadily and without any resistance.
~ Marshall McLuhan

First i hope everyone is safe. Second, this blog is more meta-physical in nature. The above picture is a present i received from a dear friend who 3D printed it for me. A transhumanist pictorial if you will for accelerating our wetware. This brings us to the current matter at hand.

i was traveling recently and i couldn’t help but notice how many humans are just sitting, walking, running and even biking looking at their mobile devices. Families no longer talk to each other, couples no longer kiss. Kids no longer day dream. All no longer LOOK UP, DOWN and ALL AROUND.

i must confess at this juncture that, as a technologist, i am conflicted. As they say we make the guns, but we don’t pull the trigger. As a technologist, i truly love using and creating with mathematics, hardware, and software. it is an honor as far as i am concerned, and i treat it as such, yet when i have time to sit and ponder i think of the time i held the first telegraph in my hands. Yes, the FIRST telegraph that read:

What hath God wrought!

Invented and sent by Samuel Finley Breese Morse 24 May 1844. I held it. Of course it was behind plexiglass, and this is a link to said telegraph.

Why is this important? It converted numbers (morse code in this case) into a readable document, content if you will. Even if you do not believe in higher-order deities or some theistic aspects what was transmitted and received via the message of the telegraph herewith was multi-modal and carried some weight to the message.

There seems to be a trend toward a kind of primitive outlook on life a more tribal attitude and i think its a natural reaction to industrialization. Unfortunately i think it is a bit naive because the future is going to become more mechanized, computerized as you call it and i dont think there is any turning back.
~ Jim Morrison

Intelligence it seems, is now but a search engine away or if you will a “tic-tok” away. It also seems due to this immediate gratification of content and information that, we no longer talk to anyone. “The Pandemic” seems to have modified several aspects of our existence. The results of this i believe will take decades of evolution before this change is truly understood from a systems theory and first principles engineering view.

We have been sequestered into a living environment tethered to the LazyWeb_^(TM). Per my commentary about seeing families with their heads buried in their phones during all modes of so-called social engagement, this is creating considerable fractures in how we deal with friends, families, and most importantly ourselves.

Now in recent times, Humans are going into the office or “back to the hybrid workplace” and taking a zoom call in the adjacent meeting room to where the REAL PHYSICAL meeting is occurring. So the more i pondered, the more i thought i would post a bunch of pictures and talk about cyberspace vs real space.

i have read all the books: “Neuromancer, Cyberspace, SnowCrash,Do Androids Dream of Electric Sheep (DADOES), Super Intelligence, 1984, Brave New World, Realware etc”, i first worked on full Virtual Reality applications in 1993. Yes there were computers back then, big red ones called Silicon Graphics Crimson machines. These augmented with fixed point digital signal processing equipment created the first six degrees of freedom ( 6DOFS) head tracked stereoscopic renderings complete with spatial audio. So it is nothing new just executed in a different fashon.

i recently went to the NASA Astronaut Training Experience at the Kennedy Space Center with my eldest daughter and we took a walk on Mars and did some trivial tasks. It was tethered environment with mono-based audio however it was impressive from a simulation standpoint. When the alert system informed me that a sandstorm was coming, i was non-plussed. Having worked on top-secret systems, i understand the need for simulations entirely. Simulate all the emergencies over and over again that you can think of when going into an environment of conflict.

On a regular basis “Humans being” and living do not constitute simulation unless you buy into Bostrom’s theory that we are living in a simulation, then what of it? Please make the most of IT. Talk to that person across from you. What color do they love? What is their favorite food? Do they like puppies? If they are close friends and family, above all – show them how you feel. Hug them.

I believe that computers have taken over the world. I believe that they
have in many ways ruined our children. I believe that kids used to love
to go out and play. I believe that social graces are gone because
manners are gone because all people do is sit around and text. I think
it’s obnoxious.
~ Stevie Nicks

If you are not the talkative type go outside build a fire, Walk through the city. Go sit under a tree. If you live in a place where you can see the sky go outside and just stare at the sky and let your eyes adjust. The stars will come out and think about the fact you are made of the same substances.

Reflect on and into yourself. Shut down all the noise and chatter. Listen. What do YOU hear?

I can’t fax you my love.
I can’t e-mail you my heart.
I can’t see your face in cyberspace,
I don’t know where to start.
~ Jimmy Buffet

When you get up in the morning, don’t start the Doom Scroll. Contemplate. Get a notebook and write some thoughts. The visceral act of writing activates differing neural patterns that allow us to remember and learn. Think about what you would like to accomplish. Hopefully, you made your bed. That is at least one thing you can check off that you did accomplish, and your parents would be proud.

i wrote a blog a while ago called Its An Honor To Say Goodbye. Many seemed to enjoy it for several different reasons. As you look up from your phone and are around, folks play a game. What if that person just disappeared as though they were shot by a BFG (Big F-in Gun) in one of the first-person shooting games and could not re-frag? Just gone from the simulation? Poof!

How would you feel?

i’ll have to say if this is a simulation, it is pretty good and has to be some quantum information theoretic manifestation^[2]. Yet! Feeling that embrace from a friend or loved one, feeling the spray from a wave, smelling and touching a rose, A dog licking you in the face, tasting that steak, the carnality and sensuality of it all transcend, at least for me, the “meta” aspects of the online experience.

Go Outside! The Graphics are Great!
~ Sensai Todd

So folks, when in doubt, put that device down for a bit. Go for a walk. Say hello to that person across the room and ask how the day is going, and mean it and listen. Go outside and sit against a tree at night, or take a walk near the ocean or body of water (my favorite). Draw. Shut your eyes and deeply listen to music. Dance. Make stupid sounds. Try something you have never done before. Do something besides being fed programmed content.

Look UP DOWN and ALL AROUND.

So question for all of you:

Q: Would you prefer a telegraph, facsimile or simulation of this life?

Until Then,

tctjr

#iwishyouwater <- Nathan Florence on a hellish scottish slab paddle out. He aint worried about who clicked like….

Muzak To Blog By Forestt “Into The Woods”. i would classify this as Martial Folk if i may use genre classification liberally.

[1] Someone i really respect technically and now consider a dear friend printed this out for me. He also prints body parts. Heavy stuff. He is a practicing ER doctor and also codes.

[2] On the above commentary concerning simulations, i do believe in the Minowski multi-verse theory and view of The Universe. Its all happening NOW with multiple probabilities, our noggin cant sample fast enough to reconstruct all of the information simultaneously. Also, remember, girls and boys, YOU are the universe.

[3] i took all of the pictures included herewith except the last one.

references:

[1] this is a great interview with The Lizard King (aka Jim Morrison when he was 26 in 1970. Listen. This isn’t hippie stuff. Click HERE.

Ted ℂ. Tanner Jr. (@tctjr) / X

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