Groks Idea on My Idea: Church Of The Bitchin’ Breakers
It is not tragic to die doing something you love.
~ Mark Foo
Recently, while on a brief holiday somewhere on the east coast of Florida, I was reminded of the sheer magnitude of truth in the world of binary representations. Zero = OFF; one = ON.
The Computing world operates on the world of binary mathematics. We are to thank Gottfried Wilhelm Leibniz (1646-1716), the co-inventor of Calculus, who published his invention in 1701. However, the actual discovery occurred more than 20 years earlier.
There are many precepts, many sports and many social constructs that operate within the binary world.
Either you make it or you don’t. On/Off, GetHit/Not Hit etc.
Back to my holiday. I had a new surfboard. The air was mid-80s, the water was upper-seventies, and the swell was easy, rolling 3-4 ft (1-1.5 meters) (think chest-shoulder high).
i have surfed this spot for decades. i thought, hey, this will be easy.
Never think a paddle out or for that matter, anything in life is easy.
I leisurely paddled out and was immediately rocked by a constant set of waves. My board slipped out from under me. I casually gathered it back. I kept paddling. When I turned around, I had gone nowhere. I got out and sat there on the sand, dejected and rejected. NOTE: In surfing, it is called the walk of shame.
Here is where the plot thickens.
Riding the Wave: Surfing, Startups, and the Art of Life
Surfing is my ultimate passion, and my second is freediving; as the stakes get higher (think bigger), the world of binary representation starts to get amplified. Even though we have a world of simple ON/OFF [0,1], the consequences are enormous. The following video features Peahi (also known as Jaws), considered one of the premier meccas of tow-in and paddle-in surfing. The video below is a historic day when the professionals paddled into the waves not towing into the waves. In contrast, surfing by jet ski allows the surfer to achieve higher speeds and match the speed of the open ocean swell, thereby reducing the risk of missing the wave.
In the footage below, some of the world’s best big wave surfers opt for paddle only to fully experience the wave and increase the adrenaline rush of “making the drop.” At around 4:00, the video starts showing some situations where the waves are not being caught. The carnage begins.
Of particular interest is Isaac Stant
Out THERE, where the ocean seemingly appears to kiss the sky, a surfer paddles into the swell. The board cuts through the chaos of foam and salt, chasing a wave that’s equal parts promise and peril. It’s a dance, raw, unscripted, and relentless. Oh, and by the way, buttercup, you are no longer the apex predator. Sharks and, in some cases, crocodiles abound, not to mention jellyfish, crabs, and stingrays. Or just slamming into reefs and rocks. Controlled Chaos. Sound familiar? (NOTE: For those mathematically inclined and with respect to the binary system we trend toward high entropy.)
If you’ve ever built a startup or company, it should. Everyone should attempt to start a company based on their passions or constant flow of ideas. Surfing isn’t just a sport; it’s a metaphor for the entrepreneurial grind and, if you squint hard enough, a framework for life itself. Let’s break it down, wave by wave.
I am the Nose (Ha-ah, hah-ah, ha-ah) I don’t swim I don’t dance (Ha-ah, hah-ah, ha-ah) I don’t swim or dance (Biohumanyouloop) I’m too cool to swim, or dance I might get my hair wet (Underwater boogie baby, underwater boogie baba!) (Ha-ah, ha-ah, ha-ah!)
~ Parliment, Aqua Boogie (A Psychoalphadiscobetabioaquadoloop)
The Paddle: Grit Before Glory
Every surfer knows the wave doesn’t come to you; you go to it. Arms burning, lungs screaming, you paddle through the chop, fighting currents that don’t care about YOUR dreams. Start-ups are the same. The early days aren’t sexy; they’re a slog. Instead of paddling through clear blue warm water, you are crawling through glass.
You’re pitching to skeptics, scraping by on fumes, and debugging code at 3 a.m. while the world sleeps. Success isn’t handed out, it’s earned through the unglamorous grind.
Life’s no different. The good stuff, meaning love and purpose, doesn’t wash up on your shore. You’ve got to paddle out, past the breakers of doubt and distraction. The people who sit on the beach say it is too big to paddle out or get out to catch any waves. It’s impossible to do what you are suggesting to make with your startup, your passion. The framework starts here: Embrace the resistance. It’s not punishment; it’s preparation.
(Aqua boogie baby) Never learned to swim (underwater boogie baby) Can’t catch the rhythm of the stroke (Aqua boogie baby) Why should I hold my breath (underwater boogie baby) Feelin’ that I might choke (Ha-ah, ha-ah, ha-ah!)
~ Parliment, Aquaboogie (A Psychoalphadiscobetabioaquadoloop)
The Pop-Up: Timing Is Everything
Catch the wave too early, and it fizzles and dies beneath you. Too late, and it buries you in whitewater. The pop-up; leaping from prone to standing, is a split-second bet on instinct and readiness. Startups live or die by this, too. Launch too soon, and your half-baked idea flops. Wait too long, and the market’s moved on. Timing isn’t luck; it’s the alchemy of preparation meeting opportunity.
Zoom out to life: When do you leap to pop-up? When do you hold and pull into the tub, the glorious place that is akin to a unicorn or exit in a startup or a major life decision? The surfer’s lesson is to trust your gut, but train it first. Study the patterns, waves, winds, weather, market trends, and your rhythms. Then, when the moment aligns, stand up and ride.
Remember: As in most things in life and nature, hesitation kills.
If It All Blows Up and Goes To Hell, Oh The Stories We Can Tell.
~ Jimmy Buffet
The Ride: Balance in Chaos
On the wave, you’re not in control but in conversation with the essence of nature. Lean too far forward, and you nose-dive (called pearling). Too far back, you stall. It’s a tightrope of adjustments, a negotiation with forces more significant than you. Startups feel this daily: pivot or perish, scale or sink. You’re riding a beast of cash flow, customer whims, and competitor moves while pretending you’ve figured it out.
Life’s a wave, too unpredictable, beautiful, and brutal. The framework here is flow, not force. Balance isn’t static; it’s dynamic. Relationships, children, families, the job; You don’t conquer the chaos; you surf it. Fall off? Laugh, paddle back, try again. Cycle, rinse, repeat.
The Wipeout: Failure as Teacher
Even the best surfers eat it sometimes. The wave slams you down, spins you like a sock in a dryer, and you’re left gasping, board leash tugging at your ankle. (NOTE: I’ve personally thought i was dead at least 4 times that i can remember). Here’s the secret: Wipeouts aren’t the end; they’re the curriculum. Every tumble teaches you something about the ocean’s mood or your own limits. Startups crash too -90 % of them, by the stats. The ones that rise again do so because they learn: bad hires sharpen your instincts, busted deals hone your hustle. Most of “talking story” before and after surfing revolves around the insane wipeouts and maybe a good wave or two.
Life’s wipeouts, lost jobs, broken hearts, health setbacks, and trauma sting just as hard. The surfer’s creed applies: Get back on the board. Paddle Back Out and Into the Swell. Failure is not final unless you let it be. The framework demands that resilience scars are just tattoos with better stories. The worst scar that will never heal is the mental one of “i should have done <INSERT THING HERE>” while you are old sitting there in the lazyboy watching the TV of someone who did THE THING.
The Lineup: Community in Solitude
Surfing looks solitary, one rider, one wave, but the lineup tells a different tale. It’s a tribe of misfits, nodding at each other between sets, sharing stoke and unspoken rules. Startups thrive on this too: founders, coders, dreamers huddled in garages or Slack channels, pushing each other forward. No one rides alone, even if it feels that way. There are extreme written and unwritten rules.
Life’s framework leans on this: Find your people. Find YOUR TRIBE. They’re the ones who cheer your wins, fish you out of the riptide, and remind you why you paddled out. Solitude builds strength; community keeps it alive.
The Framework Writ Large
So here’s the takeaway, distilled from salt and spray: Life, like surfing, startups, and life, is a rhythm of effort, instinct, and adaptation. Paddle hard, time your moves, ride the chaos, learn from the falls, and lean on your crew. It’s not about mastering the wave, it’s about mastering yourself in its grip.
Next time you’re staring down a monster set, a pitch deck, or a heartbreak in life, channel the inner surfer. The ocean doesn’t care if you’re ready, but it’ll teach you how to be. That’s the beauty of the paddle out and hopefully THE RIDE.
Here are some rules of the ocean and an analogy for your company. i apply these to life.
The Thou Shalts Of The Church Of The Bitchen Breakers
#1
Never Turn Your Back On The Ocean: Sounds Simple doesnt it? You would be surprised. Always walk out of the ocean facing the ocean. Trust me. I have probably close to 100 stitches in various parts to prove it. In life never take anything for granted. At a company that yu are running never assume everything is ok.
#2
Always Paddle Out: At the very least give it a shot. Don’t sit on the sand. You can’t catch waves on the beach. Go try it is better to crash and burn and have a story than just say it cannot be done. The Courage to Create in Life.
#3
There Is Always One: At some point during a session, there will always be the one you catch. Just one. Same in life or a company, there is always one event, happening, occurrence that is totally amazing.
#4
Waves Stop Conversation: This is true,. Hey man sorry your mom died, Oh OUTSIDE! (paddles and takes off). Same with situation in life or a company you are running. Situations happen that immediately cut off chit chat or even serious conversations, overriding everything.
#5
Always Take One In: It doesnt have to be pretty. Could be on your belly. Could be the best wave of the day. Doesnt matter ride one in all the way to the beach and deeply know you have tried your best.
Oh yes, and back to the beginning of this blog. i sucked up my pride got back on my new board, paddled back out and caught some great waves.
MUZAK TO Blog By: Opeth ‘Ghost Reveries”. Deep Purple On Steroids. Thanks Dr. Miller.
NOTE: Foo’s oft-repeated quote was: “To get the ultimate thrill, you have to be willing to pay the ultimate price.” Sadly, that is exactly what happened when he died surfing at Mavericks on December 23, 1994. December 23 is my birthday and i was surfing a place a couple miles from it called Montara State Beach on the same swell.
NOTE: Sir Nose is a technology-assisted human who has made it his mission to rid the world of music and the desire to dance. He is the self-proclaimed arch-nemesis of Dr. Funkenstein and his most advanced clone, Star Child. Sir Nose later becomes the leader of his own team of supervillains called The Unfunkables. Obviously, he can’t get the stroke right underwater on the way to Atlantis. He specializes in Propaganda, Brainwashing, and Manipulation to the point of complete consumerism. He appears on Motor Booty Affair by Parliament, where Sir Nose announces he can’t swim and hates water, to which he is immediately thrown into the sea.
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.
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
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.
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!
~ 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:
Using Wolfram Mathematica to visualize this function (as i didn’t know what it looked like…) relative to solving for :
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()
So this optimized the minima of the function relative to the bounds of and and .
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.
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 LazyWebTM 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 and a transition matrix , where each element represents the probability of transitioning from state to state .
Provide The Goal: Sampling from a Probability Distribution
In many applications (e.g., statistical mechanics, Bayesian inference, as mentioned), we are interested in sampling from a complex probability distribution . 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 .
Ok Now: The Metropolis Algorithm
The Metropolis Algorithm is one of the simplest MCMC algorithms to generate samples from . It works by constructing a Markov Chain whose stationary distribution is the desired probability distribution . 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 , a point in the state space. This point can be chosen randomly or based on prior knowledge.
Proposal Step
From the current state , propose a new state using a proposal distribution , which suggests a candidate for the next state. This proposal distribution can be symmetric (e.g., a normal distribution centered at ) or asymmetric.
Acceptance Probability
Calculate the acceptance probability for moving from the current state to the proposed state :
In the case where the proposal distribution is symmetric (i.e., , the formula simplifies to:
Acceptance or Rejection
Generate a random number from a uniform distribution If , accept the proposed state , i.e., . If , reject the proposed state and remain at the current state, i.e., .
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 , meaning the samples generated by the algorithm will approximate 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 , 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 , 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 ScriptFigure 1.0
Ok, What’s going on here?
For the Target Distribution:
The function 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 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 (in red).
The plot shows the Metropolis samples over time, which should cluster around the time-varying mean 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 .
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 ScriptFigure 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 . 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
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
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:
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):
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?
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.
Dalle 3’s idea of an Abstract Syntax Tree in space
If you would know strength and patience, welcome the company of trees.
~ Hal Borland
First, I hope everyone is safe. Second, I am changing my usual SnakeByte [] stance process. I am pulling this from a website I ran across. I saw the library mentioned, so I decided to pull from the LazyWebTM instead of the usual snake-based tomes I have in my library.
As a Python developer, understanding and navigating your codebase efficiently is crucial, especially as it grows in size and complexity. Trust me, it will, as does Entropy. Traditional search tools like grep or IDE-based search functionalities can be helpful, but they cannot often “‘understand” the structure of Python code – sans some of the Co-Pilot developments. (I’m using understand here *very* loosely Oh Dear Reader).
This is where pyastgrep it comes into play, offering a powerful way to search and analyze your Python codebase using Abstract Syntax Trees (ASTs). While going into the theory of ASTs is tl;dr for a SnakeByte[] , and there appears to be some ambiguity on the history and definition of Who actually invented ASTs, i have placed some references at the end of the blog for your reading pleasure, Oh Dear Reader. In parlance, if you have ever worked on compilers or core embedded systems, Abstract Syntax Trees are data structures widely used in compilers and the like to represent the structure of program code. An AST is usually the result of the syntax analysis phase of a compiler. It often serves as an intermediate representation of the program through several stages that the compiler requires and has a strong impact on the final output of the compiler.
So what is the Python Library that you speak of? i’m Glad you asked.
What is pyastgrep?
pyastgrep is a command-line tool designed to search Python codebases with an understanding of Python’s syntax and structure. Unlike traditional text-based search tools, pyastgrep it leverages the AST, allowing you to search for specific syntactic constructs rather than just raw text. This makes it an invaluable tool for code refactoring, auditing, and general code analysis.
Why Use pyastgrep?
Here are a few scenarios where pyastgrep excels:
Refactoring: Identify all instances of a particular pattern, such as function definitions, class instantiations, or specific argument names.
Code Auditing: Find usages of deprecated functions, unsafe code patterns, or adherence to coding standards.
Learning: Explore and understand unfamiliar codebases by searching for specific constructs.
I have a mantra: Reduce, Refactor, and Reuse. Please raise your hand of y’all need to refactor your code? (C’mon now no one is watching… tell the truth…). See if it is possible to reduce the code footprint, refactor the code into more optimized transforms, and then let others reuse it across the enterprise.
Getting Started with pyastgrep
Let’s explore some practical examples of using pyastgrep to enhance your code analysis workflow.
Installing pyastgrep
Before we dive into how to use pyastgrep, let’s get it installed. You can install pyastgrep via pip:
(base)tcjr% pip install pyastgrep #dont actually type the tctjr part that is my virtualenv
Example 1: Finding Function Definitions
Suppose you want to find all function definitions in your codebase. With pyastgrep, this is straightforward:
pyastgrep 'FunctionDef'
This command searches for all function definitions (FunctionDef) in your codebase, providing a list of files and line numbers where these definitions occur. Ok pretty basic string search.
Example 2: Searching for Specific Argument Names
Imagine you need to find all functions that take an argument named config. This is how you can do it:
pyastgrep 'arg(arg=config)'
This query searches for function arguments named config, helping you quickly locate where configuration arguments are being used.
Example 3: Finding Class Instantiations
To find all instances where a particular class, say MyClass, is instantiated, you can use:
pyastgrep 'Call(func=Name(id=MyClass))'
This command searches for instantiations of MyClass, making it easier to track how and where specific classes are utilized in your project.
Advanced Usage of pyastgrep
For more complex queries, you can combine multiple AST nodes. For instance, to find all print statements in your code, you might use:
pyastgrep 'Call(func=Name(id=print))'
This command finds all calls to the print function. You can also use more detailed queries to find nested structures or specific code patterns.
Integrating pyastgrep into Your Workflow
Integrating pyastgrep into your development workflow can greatly enhance your ability to analyze and maintain your code. Here are a few tips:
Pre-commit Hooks: Use pyastgrep in pre-commit hooks to enforce coding standards or check for deprecated patterns.
Code Reviews: Employ pyastgrep during code reviews to quickly identify and discuss specific code constructs.
Documentation: Generate documentation or code summaries by extracting specific patterns or structures from your codebase.
Example Script
To get you started, here’s a simple Python script using pyastgrep to search for all function definitions in a directory:
import os from subprocess import run
def search_function_definitions(directory): result = run(['pyastgrep', 'FunctionDef', directory], capture_output=True, text=True) print(result.stdout)
if __name__ == "__main__": directory = "path/to/your/codebase" #yes this is not optimal folks just an example. search_function_definitions(directory)
Replace "path/to/your/codebase" with the actual path to your Python codebase, and run the script to see pyastgrep in action.
Conclusion
pyastgrep is a powerful tool that brings the capabilities of AST-based searching to your fingertips. Understanding and leveraging the syntax and structure of your Python code, pyastgrep allows for more precise and meaningful code searches. Whether you’re refactoring, auditing, or simply exploring code, pyastgrep it can significantly enhance your productivity and code quality. This is a great direct addition to your arsenal. Hope it helps and i hope you found this interesting.
Until Then,
#iwishyouwater <- The best of the best at Day1 Tahiti Pro presented by Outerknown 2024
𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X
MUZAK to Blog By: SweetLeaf: A Stoner Rock Salute to Black Sabbath. While i do not really like bands that do covers, this is very well done. For other references to the Best Band In Existence ( Black Sabbath) i also refer you to Nativity in Black Volumes 1&2.
The frequency domain of mind (a mind, it must be stressed, is an unextended, massless, immaterial singularity) can produce an extended, spacetime domain of matter via ontological Fourier mathematics, and the two domains interact via inverse and forward Fourier transforms.
I am Optimus Transformer Ruler Of The AutoCorrelation Bots
First i trust everyone is safe. i haven’t written technical blog in a while so figured i would write a Snake_Byte on one of my favorite equations The Fourier Transform:
More specifically we will be dealing with the Fast Fourier Transform which is an implementation of The Discrete Fourier Transform. The Fourier Transform operates on continuous signals and while i do believe we will have analog computing devices (again) in the future we have to operate on 0’s and 1’s at this juncture thus we have a discrete version thereof. The discrete version:
where:
The Discrete Fourier Transform (DFT) is a mathematical operation. The Fast Fourier Transform (FFT) is an efficient algorithm for the evaluation of that operation (actually, a family of such algorithms). However, it is easy to get these two confused. Often, one may see a phrase like “take the FFT of this sequence”, which really means to take the DFT of that sequence using the FFT algorithm to do it efficiently.
The Fourier sequence is a kernel operation for any number of transforms where the kernel is matched to the signal if possible. The Fourier Transform is a series of and which makes it really useful for audio and radar analysis.
For the FFT it only takes for the sequence computation and as one would imagine this is a substantial gain. The most commonly used FFT algorithm is the Cooley-Tukey algorithm, which was named after J. W. Cooley and John Tukey. It’s a divide and conquer algorithm for the machine calculation of complex Fourier series. It breaks the DFT into smaller DFTs. Other FFT algorithms include the Rader’s algorithm, Winograd Fourier transform algorithm, Chirp Z-transform algorithm, etc. The only rub comes as a function of the delay throughput.
There have been amazing text books written on this subject and i will list them at the end of the blarg[1,2,3]
So lets get on with some code. First we do the usual houskeeping on import libraries as well as doing some majik for inline display if you are using JupyterNotebooks. Of note ffpack which is a package of Fortran subroutines for the fast Fourier transform. It includes complex, real, sine, cosine, and quarter-wave transforms. It was developed by Paul Swarztrauber of the National Center for Atmospheric Research, and is included in the general-purpose mathematical library SLATEC.
# House keeping libraries imports and inline plots:
import numpy as np
from scipy import fftpack
%matplotlib inline
import matplotlib.pyplot as pl
We now set up some signals where we create a sinusoid with a sample rate. We use linspace to set up the amplitude and signal length.
#frequency in cycles per second or Hertz
#this is equivalent to concert A
Frequency = 20
# Sampling rate or the number of measurements per second
# This is the rate of digital audio
Sampling_Frequency = 100
# set up the signal space:
time = np.linspace(0,2,2 * Sampling_Frequency, endpoint = False)
signal = np.sin(Frequency * 2 * np.pi * time)
We now plot the transformed sinusoid depicting the frequencies we generated:
# plot frequency components of the signal:
fig, ax = plt.subplots()
ax.stem(Frequency_Component, np.abs(X_Hat)) # absolute value of spectrum
ax.set_xlabel ('Frequency in Hertz [HZ] Of Transformed Signal')
ax.set_ylabel ('Frequency Domain (Spectrum) Magnitude')
ax.set_xlim(-Sampling_Frequency / 2, Sampling_Frequency / 2)
ax.set_ylim(-5,110)
To note you will see two frequency components, this is because there are positive and negative (real and imaginary) components to the transform which is what we see using the stem plots as expected. This is because the kernel as mentioned before is both and .
So something really cool happens when using the FFT. It is called the convolution theorem as well as Dual Domain Theory. Convolution in the time domain yields multiplication in the frequency domain. Mathematically, the convolution theorem states that under suitable conditions the Fourier transform of a convolution of two functions (or signals) is the poin-twise (Hadamard multiplication) product of their Fourier transforms. More generally, convolution in one domain (e.g., time domain) equals point-wise multiplication in the other domain (e.g., frequency domain).
Where:
So there you have it. A little taster on the powerful Fourier Transform.
Muzak To Blarg by: Voyager Essential Max Ricther. Phenomenal. November is truly staggering.
References:
[1] The Fourier Transform and Its Applications by Dr Ronald N Bracewell. i had the honor of taking the actual class at Stanford University from Professor Bracewell.
[2] The Fourier Transform and Its Applications by E. Roan Brigham. Graet book on butterfly and overlap-add derivations thereof.
[3] Adaptive Digital Signal Processing by Dr. Claude Lindquist. A phenomenal book on frequency domain signal processing and kernel analysis. A book ahead of its time. Professor Lindquist was a mentor and had a direct effect and affect on my career and the way i approach information theory.
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.
[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
Even if we crash and burn and loose everthing the experience is worth ten times the cost.
~ S. Jobs
As always, Oh Dear Readers, i trust this finds you safe. Second, to those affected by the SVB situation – Godspeed.
Third, i was inspired to write a blog on “Doing versus Thinking,” and then i decided on the title “Execution Is Everything”. This statement happens to be located at the top of my LinkedIn Profile.
The impetus for this blog came from a recent conversation where an executive who told me, “I made the fundamental mistake of falling in love with the idea and quickly realized that ideas are cheap, it is the team that matters.”
This is taken directly from the book in his words, but condensed for space, here are some examples of the types of lists James makes:
10 olds ideas I can make new
10 ridiculous things I would invent (e.g., the smart toilet)
10 books I can write (The Choose Yourself Guide to an Alternative Education, etc).
10 business ideas for Google/Amazon/Twitter/etc.
10 people I can send ideas to
10 podcast ideas or videos I can shoot (e.g., Lunch with James, a video podcast where I just have lunch with people over Skype and we chat)
10 industries where I can remove the middleman
10 things I disagree with that everyone else assumes is religion (college, home ownership, voting, doctors, etc.)
10 ways to take old posts of mine and make books out of them
10 people I want to be friends with (then figure out the first step to contact them)
10 things I learned yesterday
10 things I can do differently today
10 ways I can save time
10 things I learned from X, where X is someone I’ve recently spoken with or read a book by or about. I’ve written posts on this about the Beatles, Mick Jagger, Steve Jobs, Charles Bukowski, the Dalaï Lama, Superman, Freakonomics, etc.
10 things I’m interested in getting better at (and then 10 ways I can get better at each one)
10 things I was interested in as a kid that might be fun to explore now (like, maybe I can write that “Son of Dr. Strange” comic I’ve always been planning. And now I need 10 plot ideas.)
10 ways I might try to solve a problem I have. This has saved me with the IRS countless times. Unfortunately, the Department is Motor Vehicles is impervious to my superpowers
Is your brain tired of just “thinking” about doing those gymnastics?
i cannot tell you how many people have come to me and said “hey I have an idea!” Great, so do you and countless others. What is your plan of making it a reality? What is your maniacal passion every day to get this thing off the ground and make money?
The statement “Oh I/We thought about that 3 years ago” is not a qualifier for anything except that fact you thought it and didn’t execute on said idea. You know why?
Creating software from an idea that runs 24/7 is still rather difficult. In fact VERY DIFFICULT.
“Oh We THOUGHT about that <insert number of days or years ago here>. i call the above commentary “THOUGHTING”. Somehow the THOUGHT is manifested from Ideas2Bank? If that is a process, i’d love to see the burndown chart on that one. No Oh Dear Readers, THOUGHTING is about as useful as that overly complex PowerPoint that gets edited ad nauseam, and people confuse the “slideware” with “software”. The only code that matters is this:
Code that is written with the smallest OPEX and Highest Margins thereby increasing Revenue Per Employee unless you choose to put it in open source for a wonderful plethora of reasons or you are providing a philanthropic service.
When it comes to creating software, “Execution is everything.” gets tossed around just like the phrase “It Just Works” as a requirement. At its core, this phrase means that the ability to bring an idea to life through effective implementation is what separates successful software from failed experiments.
The dynamic range between average and the best is 2:1. In software it is 50:1 maybe 100:1 very few things in life are like this. I’ve built a lot of my sucess on finding these truly gifted people.
~ S. Jobs
In order to understand why execution is so critical in software development, it’s helpful first to consider what we mean by “execution.” Simply put, execution refers to the process of taking an idea or concept and turning it into a functional, usable product. This involves everything from coding to testing, debugging to deployment, and ongoing maintenance and improvement.
When we say that execution is everything in software development, what we’re really saying is that the idea behind a piece of software is only as good as the ability of its creators to make it work in the real world. No matter how innovative or promising an idea may seem on paper, it’s ultimately worthless if it can’t be brought to life in a way that users find valuable and useful.
You can fail at something you dislike just as easily as something you like so why not choose what you like?
~ J. Carey
This is where execution comes in. In order to turn an idea into a successful software product, developers need to be able to navigate a complex web of technical challenges, creative problem-solving, and user feedback. They need to be able to write code that is clean, efficient, and scalable. They need to be able to test that code thoroughly, both before and after deployment. And they need to be able to iterate quickly and respond to user feedback in order to improve and refine the product continually.
The important thing is to dare to dream big, then take action to make it come true.
~ J. Girard
All of these factors require a high degree of skill, discipline, and attention to detail. They also require the ability to work well under pressure, collaborate effectively with other team members, and stay focused on the ultimate goal of creating a successful product.
The importance of execution is perhaps most evident when we consider the many examples of software projects that failed despite having what seemed like strong ideas behind them. From buggy, unreliable apps to complex software systems that never quite delivered on their promises, there are countless examples of software that fell short due to poor execution.
On the other hand, some of the most successful software products in history owe much of their success to strong execution. Whether we’re talking about the user-friendly interface of the iPhone or the robust functionality of Paypal’s Protocols, these products succeeded not just because of their innovative ideas but because of the skill and dedication of the teams behind them.
The only sin is mediocrity[1].
~ M. Graham
In the end, the lesson is clear: when it comes to software development, execution really is everything. No matter how brilliant your idea may be, it’s the ability to turn that idea into a functional, usable product that ultimately determines whether your software will succeed or fail. By focusing on the fundamentals of coding, testing, and iterating, developers can ensure that their software is executed to the highest possible standard, giving it the best chance of success in an ever-changing digital landscape.
So go take that idea and turn it into a Remarkable Viable Product, not a Minimum Viable Product! Who likes Minimum? (thanks R.D.)
Be Passionate! Go DO! Go Create!
Go Live Your Personal Legend!
A great video stitching of discussions from Steve Jobs on execution, and passion – click here-> The Major Thinkers Steve Jobs
Until then,
#iwishyouwater <- yours truly hitting around 31 meters (~100ft) on #onebreath
@tctjr
Muzak To Blog By: Todd Hannigan “Caldwell County.”
[1] The only sin is mediocrity is not true if there were a real Sin it should be Stupidity but the quote fits well in the narrative.
![\[f(x, y) = (x^2 + y - 11)^2 + (x + y^2 - 7)^2\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-23ec65f33d158fdb3b13461e54d04f1a_l3.png "Rendered by QuickLaTeX.com")
:
and 
and 
.
and a transition matrix 
, where each element 
represents the probability of transitioning from state 
to state 
.
, a point in the state space. This point can be chosen randomly or based on prior knowledge.
, propose a new state 
using a proposal distribution 
, which suggests a candidate for the next state. This proposal distribution can be symmetric (e.g., a normal distribution centered at 
for moving from the current state ![\[\alpha = \min \left(1, \frac{\pi(x^) q(x_t | x^)}{\pi(x_t) q(x^* | x_t)} \right)\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-d0021c453163124b132e82a38dbf20bb_l3.png "Rendered by QuickLaTeX.com")
, the formula simplifies to:![\[\alpha = \min \left(1, \frac{\pi(x^*)}{\pi(x_t)} \right)\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-52d53f2c5d02dc0c354ef060a927b5de_l3.png "Rendered by QuickLaTeX.com")
from a uniform distribution 
, accept the proposed state 
.
, reject the proposed state and remain at the current state, i.e., 
.
, 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.
', 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()
defines a time-varying mean for the distribution. In this example, it follows a sinusoidal pattern.
![\[(E_i^*,E_j^*)\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-4b7772a3028c53b1f55a34079971f64d_l3.png "Rendered by QuickLaTeX.com")
![\[H=-1/N \sum_{i=1}^{N} P_i\,log_2\,P_i\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-5a3422c3ff70f85af819c83d0790d495_l3.png "Rendered by QuickLaTeX.com")
![\[\frac{\partial u}{\partial t} &= D_u \nabla^2 u + f(u, v),\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-99147d396c77a8652cc77ac0cab65ffe_l3.png "Rendered by QuickLaTeX.com")
![\[\frac{\partial v}{\partial t} &= D_v \nabla^2 v + g(u, v),\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-a0acfd5f636e96e9c05ed136c210b906_l3.png "Rendered by QuickLaTeX.com")
![\[\begin{itemize}\item $u$ and $v$ are concentrations of two chemical substances (morphogens),\item $D_u$ and $D_v$ are diffusion coefficients for $u$ and $v$,\item $\nabla^2$ is the Laplacian operator, representing spatial diffusion,\item $f(u, v)$ and $g(u, v)$ are reaction terms representing the interaction between $u$ and $v$.\end{itemize}\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-3c0fe86ddd76baee1115898f2a4cd86e_l3.png "Rendered by QuickLaTeX.com")
𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X
space
![\[\hat{f} (\xi)=\int_{-\infty}^{\infty}f(x)e^{-2\pi ix\xi}dx\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-4dfceb039152febfe9d05a834affcd1a_l3.png "Rendered by QuickLaTeX.com")
![\[F(x) &= f\f[k] &= \sum_{j=0}^{N-1} x[j]\left(e^{-2\pi i k/N}\right)^j\0 &\leq k < N\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-1824d6e88be0eea8520d0874ddd199c6_l3.png "Rendered by QuickLaTeX.com")
![\[f[k] &= f_e[k]+e^{-2\pi i k/N}f_o[k]\f[k+N/2] &= f_e[k]-e^{-2\pi i k/N}f_o[k]\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-acc567b5572c2d346d8b2f83e18c00a5_l3.png "Rendered by QuickLaTeX.com")
and 
which makes it really useful for audio and radar analysis.
![\[x(t)*h(t) &= y(t)\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-f1aef3d24f6d2ce949b8fc4b8db98b93_l3.png "Rendered by QuickLaTeX.com")
![\[X(f) H(f) &= Y(f)\]](https://www.tedtanner.org/wp-content/ql-cache/quicklatex.com-739e46b1cd83c2130983eef987f18794_l3.png "Rendered by QuickLaTeX.com")
