Vijay Sathish @Vslowsprinter
contrarian; systems thinker; cross pollinator; AI/ML @blockinc @stanford @nvidia; check highlights for artificial × biological intelligence deep dives 👇 California, USA Joined February 2013-
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@LilitaMaslaton Agree. Most people don't consider Evolution an algorithm, especially engineers steeped in AI/ML. These comparisons are meant as first order comparisons to help engineers build mental models of biology.
Evolution is a vastly inefficient optimization algorithm compared to backprop+gradient descent; yet it has 3 tricks up its sleeve 1 the timescale (2 billion years) 2 massive parallel search 3 compounding by retaining & stacking successful edits. For unique bio x AI takes 👇
@geckones Read more in the deep dive: x.com/Vslowsprinter/…
@geckones Search is random and embarrassingly parallel, but beneficial changes compound thru fitness (survival+reproductive), so they stack.
The deep dive article here:
@jaya11235 Philosophically, they are both optimization algorithms. I will post about the brain vs neural net training too in the near future. The brain does not have backprop either. It uses local hebbian plasticity learning and spike timing dependent plasticity.
@gregisenberg A good majority of these ideas just seem like work about the work (tools for monitoring, correcting, facilitating, trusting agents etc).
@RExpunged It very much is. I recommend 'the blind watchmaker' or 'the selfish gene' by Richard Dawkins if you want to learn more. And it's truly magnificent.
@HerbertPotdevin Thanks for the really detailed and thoughtful response Herbert. Agree on evolution not chasing a specific loss function. Evolution's 'loss function' of 'survive and replicate' allows for vastly more exploration and diversity, albeit at the cost of efficiency.
@_____Peter_ Haha, evolution has the opportunity to explore more via it's algorithm and produces more diversity for sure. Backprop has no exploration component, only extreme optimization efficiency.
The spike gets the spotlight. The pump pays the bill. Neuron signaling is 'free' because its like discharging a battery. The brain's energy (ATP) bill comes from restoring the charge. The life lesson: the real work always happens behind the scenes, away from the spotlight. Follow @Vslowsprinter for unique AI x bio takes and deep dives:
This is age old principle that is going to propagate up the entire stack - when constraints start to matter - chips, models, routers, data, agents, tools, apps and everything in between get specialized and the system complexity increases. x.com/Vslowsprinter/…
In the '90s, general-purpose chips wiped out the specialists. The original Thinking Machines, Cray Computer, and a wave of others shut down, and their best ideas got absorbed into other chips. Zain Asgar of @gimletlabs argues the conditions that made generalists win have
@a16z @gimletlabs This is age old principle that is going to propagate up the entire stack - when constraints start to matter - chips, models, routers, data, agents, tools, apps and everything in between get specialized and the system complexity increases:
The brain has a key design principle - "complicate". You heard that right, complicate, not simplify. Why? Because, the brain has to optimize for efficiency: under extreme space, energy and compute constraints. AI's constraints will ultimately boil down to similar constraints so
@subyroy Not sure I follow your comment. What is the reference to html here in the context of this post?
Robotics stole hierarchical control from the brain. But a significant gap still exists. Answer pinned to my profile 👇
@MEmelianchenko Care to share the link? Caching generally works when the new request is EXACTLY identical as before. Ex: pick up a cup is the same task in robotics, but cup size, weight, shape, orientation, material changes with each task instance, but is still handled efficiently in brain.
Remember Kahneman's Thinking, Fast and Slow? The brain decides when to rely on learned patterns (system 1-fast/Cerebellum) vs. deliberate thinking (system 2-slow/Prefrontal Cortex). Energy and compute constraints necessitates extreme efficiency. Where is the System 1 for RL?
Robotics stole hierarchical control from the brain. But a significant gap still exists. Read thread to find out 👉 x.com/Vslowsprinter/…
Robotics stole hierarchical control from the brain. But a significant gap still exists. Read on to see what’s missing 🧵 👇🏽 (Follow me (@Vslowsprinter) for regular AI x bio insights; only images are AI gen; thoughts my own)
Today’s hard problem should become tomorrow’s easy skill. Follow me (@Vslowsprinter) for regular AI x bio insights)
8/9 Google’s recently demoed using reasoning to generate training experience (i.e. policy distillation). The bigger gap, in my view, is making this loop continuous and online. Can a robot turn effortful deliberation and repeated execution into reliable, reusable skills?
7/9 Robotics also mirrors this hierarchical control In Gemini Robotics, the Embodied Reasoning model plans, reasons and breaks complex goals into tasks. The Vision Language Action model senses and executes them, while the reasoning model monitors progress. The hierarchy exists.
Robotics stole hierarchical control from the brain. But a significant gap still exists. Read on to see what’s missing 🧵 👇🏽 (Follow me (@Vslowsprinter) for regular AI x bio insights; only images are AI gen; thoughts my own)
6/9 The cerebellum predicts the consequences of actions. Comparing predictions with sensory feedback generates an error signal that is used to adjust motor system in real time. Fine control doesn’t require replanning the entire task.
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