"“Data is being created in a lot of different places, and that data has its own gravity: It’s going to cost you money and time to move it,” Tso explains. “That means the end state is a distributed cloud that reaches out to edge devices and servers. You have to bring the cloud to the data, not the data to the cloud.” Cloudian-helps-data-storage-keep-up-with-ai-revolution
Machine Learning in Computational biology
Task specification - " writing concrete chunks of code/comments myself and in the right part of the code is a high bandwidth way of communicating "task specification" to the LLM, i.e. it's primarily about task specification bits - it takes too many bits and too much latency to communicate what I want in text, and it's faster to just demonstrate it in the code and in the right place. "
From chatgpt:
User Device
|
| 📡 TLS 1.3
v
API Gateway / CDN
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| 🔒 mTLS (optional)
v
LLM Frontend (e.g., OpenAI API)
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| 🔐 Internal encrypted service calls (gRPC, mTLS)
v
LLM Core Services (Tokenizer, Model, Storage, Cache)
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| 💾 At-rest encryption via AES-256
v
Cloud Storage / Logs / Backups (with KMS/HSM)
meta prompt - " I’d advise meta-prompting GPT-5 Pro first. Tell it to design a maximally helpful prompt for solving a complex mathematical problem. Then apply the prompt to your specific case. "
justinskycak on how to learn and learn by testing instead of reviewing notes
llm as a train, once you get on it , it keeps on going.
chain of thought
That’s a deep and provocative insight — and it aligns remarkably well with modern trends in AI, biology, and even physics. Let’s explore what you mean and why it matters.
Let’s unpack this.
Evolution operates over millions of trials, but through slow, iterative improvement, not random chaos.
This means evolved systems (e.g. proteins, languages, behaviors) exhibit regularities, constraints, and patterns.
These patterns can often be learned — that’s what machine learning models like AlphaFold or LLMs exploit.
If nature can "find" a solution via mutation and selection, then a model can often learn to approximate or simulate that search process — much faster.
| Domain | Evolutionary Origin? | Efficiently Modeled? |
|---|---|---|
| Protein folding | Yes (natural selection) | Yes (AlphaFold) |
| Language | Yes (cultural evolution) | Yes (GPT, LLMs) |
| Behavior & strategy | Yes (biological & cultural) | Yes (AlphaGo, RL agents) |
| Vision / perception | Yes (biological) | Yes (ConvNets, ViTs) |
| Gene regulation | Yes | Partially (deep genomics) |
Here’s the logic:
Evolution is a search algorithm.
If it can find good solutions in a massive space (over time), then those solutions must exist in a compressed, learnable form.
So if you give a learning system:
Enough examples (data from evolution),
The right inductive bias (architecture),
And compute (training time),
…it can often shortcut the evolutionary process by learning the underlying patterns.
Nature explored folds through mutation + selection.
AlphaFold learned the statistical patterns in those folds.
Result: it predicts in seconds what nature took billions of years to refine.
This idea hints at something deeper:
Evolution is slow, modeling is fast.
Evolution finds instances, modeling builds generalizations.
If a phenomenon is too chaotic or non-evolved (e.g. turbulence, quantum randomness), we might not be able to model it efficiently.
Some systems may not be evolved in the same way:
Chaotic systems (weather, turbulence)
Truly random or adversarial systems
Non-biological emergent systems (e.g. stock markets)
Even here, though, parts of the system might be modeled locally, even if not globally.
“If evolution can find it, a model can learn it.”
This principle underlies much of modern AI — from LLMs to AlphaFold to reinforcement learning agents. It’s not just poetic; it’s a working assumption of deep learning's success.
Would you like to explore how this applies to cultural evolution, or maybe how it informs artificial life or AGI discussions?
Great — let’s take the idea, “Anything that can be evolved can be efficiently modeled,” and explore how it extends into cultural evolution, artificial life, and even AGI (Artificial General Intelligence).
Just like biological evolution shapes genes and proteins, culture evolves through:
Transmission (learning, imitation)
Variation (in ideas, tools, behaviors)
Selection (via utility, popularity, survival)
This means language, technology, art, norms, and even religions are shaped by long evolutionary processes — and they exhibit the same compressible patterns.
Language is culturally evolved and shaped by usage.
LLMs (like GPT) are efficient statistical models of evolved language.
These models learn cultural priors: metaphors, logic, storytelling, ethics.
“If language evolved, it can be modeled. If it’s modeled, it can be reasoned about.”
So, tools like LLMs are not just text predictors — they’re cultural mirrors that reflect accumulated human intelligence.
A-Life explores digital organisms that evolve in silico:
Mutation + selection → novel code, behavior, structures
Simulations like Tierra, Avida, or even Neuroevolution
If digital evolution creates complex forms, then models can learn to predict or control them.
Neural networks can mimic evolved behaviors, and sometimes even outperform them in optimization.
In a way, A-Life systems provide training data for models of evolution itself.
AGI aims to replicate the general-purpose intelligence shaped by biological and cultural evolution.
Here’s where your principle becomes profound:
If human-level intelligence evolved, and if the pressure that shaped it can be simulated or learned from, then AGI may be achieved by modeling that evolutionary process.
Reinforcement learning = model of reward-seeking behavior.
LLMs = model of linguistic and conceptual evolution.
Meta-learning = model of learning-to-learn (like evolution’s shortcut).
A scalable, general-purpose learning system trained on the full stack of evolutionarily-shaped data (sensory, language, motor, memory, goals) could approximate or even exceed evolved biological intelligence.
| Concept | Evolution Does It | Models Learn It |
|---|---|---|
| Biological structure | Protein folding | AlphaFold |
| Behavior & planning | Animal intelligence | RL agents (AlphaGo) |
| Language & culture | Cultural transmission | LLMs (GPT, Claude, etc.) |
| Tool use | Human innovation | Foundation models |
| Learning ability | Evolution of brains | Meta-learning / AGI |
Evolution is nature's brute-force algorithm.
Machine learning is our compression of its results.
The closer we can get to modeling what evolution has already discovered — whether in biology, language, or cognition — the more powerful and general our artificial systems will become.
Would you like to go even deeper — perhaps explore:
The idea of evolution as computation?
Or whether LLMs themselves are evolving through training loops and feedback?
"with the Alpha X projects, so I’m thinking AlphaGo, of course, AlphaFold, what they really are is we are building models of very combinatorially, high dimensional spaces that if you try to brute force a solution, find the best move and go, or find the exact shape of a protein, and if you enumerated all the possibilities, there wouldn’t be enough time in the time of the universe."
You're absolutely right — both AlphaGo and AlphaFold tackled combinatorially explosive problems where brute-force enumeration is impossible. What made the Alpha projects successful is not just computational power, but smart modeling of massive search spaces. Here's how they approached it:
Both problems involve astronomical possibilities:
| Problem | Size of Search Space |
|---|---|
| Go | >10¹⁷⁰ possible board configurations |
| Protein folding | ~10³⁰⁰ possible conformations for a small protein |
Searching these spaces directly is like trying to find a needle in a universe of haystacks. So they didn't search directly — they learned patterns that make intelligent guesses far more efficient than brute force.
AlphaGo used:
Policy network – predicts the most promising next moves (reducing branching factor).
Value network – estimates who is winning from any given board state.
Monte Carlo Tree Search (MCTS) – selectively explores move sequences using the above networks to prioritize paths.
Key Insight: Don’t simulate every move — simulate only the most relevant ones using learned heuristics.
AlphaFold (especially AlphaFold 2) used:
Large-scale training on protein databases – learned statistical patterns in protein sequences and structures.
Evoformer – a deep neural architecture that infers pairwise relationships between amino acids.
Structure module – predicts 3D structure from inferred relationships.
Key Insight: It’s not necessary to simulate every quantum force or physical interaction — statistical modeling of evolutionary patterns is enough to approximate structure.
| Feature | AlphaGo | AlphaFold |
|---|---|---|
| Problem Type | Planning / Optimization | Structural Prediction |
| Strategy | Guided simulation (MCTS + NN) | Learned statistical approximation |
| Brute-force Feasibility | Impossible | Impossible |
| Key Innovation | Deep learning + Tree search | Transformer-based structure prediction |
| Use of Domain Knowledge | Yes (rules of Go, board symmetry) | Yes (biology, multiple sequence alignment) |
Rather than "solve" the entire space, they learned from data how to:
Recognize patterns that compress the space.
Prioritize plausible solutions (moves or folds).
Generalize from past data without needing full enumeration.
This is exactly why brute force is out, and learning is in — deep models are function approximators that give you good answers fast.
Would you like a deeper dive into AlphaFold 2’s architecture, or a visual analogy of how these spaces are compressed through learning?
Great! Let's break this down with two things:
A visual analogy to understand how deep learning compresses huge spaces.
A simplified architecture breakdown of AlphaFold 2, since it's a pinnacle of this idea.
Imagine you're dropped into a dark mountain range the size of a planet. You're told to find the highest peak — but you can't see more than a few feet around you, and there's no map.
You try every path up every hill.
You’d die of old age before covering 0.000000001% of the area.
Before you even start, you’re trained on thousands of aerial photos of mountain ranges.
You learn to spot subtle signs of elevation changes.
When dropped into a new mountain range, you instantly guess the best few paths to try first — and you’re right 95% of the time.
Deep learning doesn’t "solve" the space — it learns a compressed representation of it, guided by experience.
This is how AlphaFold can predict a protein’s shape without evaluating all 10³⁰⁰ possibilities — it’s been "shown enough mountain ranges" (i.e., protein families and structures) to generalize well.
AlphaFold 2 is like a three-step system:
Aligns your input protein sequence with related sequences from evolution (across species).
This gives clues about which amino acids evolve together, implying they might be close in 3D space.
Imagine tracing the ancestry of words across languages to infer grammar rules — that’s what MSA is doing with proteins.
A Transformer-based neural network (like what powers GPT, but specialized).
It passes information back and forth between:
MSA features
Pairwise residue relationships (a 2D map of how likely two amino acids are to be near each other)
Think of this as massive relational reasoning: figuring out if A is near B, and B is near C, then what’s A’s relation to C?
It’s like a very smart Sudoku solver — piecing together spatial relationships between parts.
Takes that 2D map and predicts the 3D atomic coordinates of the protein.
Uses a differentiable 3D geometry model — it knows how to build shapes from angles and distances.
Like going from a 2D floor plan to a full 3D house model.
It models structure and sequence jointly, not separately.
It iteratively refines predictions — feeding output structure back into the model to improve it.
Trained on a massive protein structure database (PDB) + evolutionary data.
They didn’t brute-force the universe. They learned to see the shortcuts.
AlphaGo: Learns which moves matter in a vast game space.
AlphaFold: Learns which patterns in sequences matter for folding.
Meta-ai-custom-silicon-olivia-wu
"As we work on the next generations of MTIA chips, we’re constantly looking at bottlenecks in the system, such as memory and communication across different chips so that we can put together a well-balanced solution to scale and future-proof our silicon."
Constructure Computer architecture
Computer Architecture Carnegie Mellon
Efficient Processing of Deep Neural Networks — by Sze, Chen, Yang, and Emer
Covers memory bottlenecks, reuse strategies, and on-chip dataflows in DNN accelerators.
🔥 Highly cited and foundational.
Google TPU Architecture Whitepapers
TPU v1: Google TPU v1
TPU v2/v3: Google TPU v2 & v3
Focuses on systolic arrays, memory bandwidth trade-offs, and system balance.
Meta’s MTIA Architecture Overview
Discusses how Meta balances compute and memory, and bottleneck analysis behind their chip design.
NVIDIA Hopper or Ampere Architecture Deep Dives
Whitepapers cover NVLink, sparsity handling, HBM integration.
Example: Hopper Whitepaper (PDF)
IEEE MICRO – Special Issues on DNN Accelerators
Look for 2018–2022 issues for real-world chip case studies from companies like Cerebras, Graphcore, Google, NVIDIA, etc.
MIT 6.S897: Machine Learning Hardware
Playlist: YouTube Link
Includes full lectures on memory architectures, dataflow mapping, and interconnect design.
Stanford CS316: Advanced Chip Design for ML
Covers real-world ML accelerator design, power/memory trade-offs.
Hot Chips Conference Talks (YouTube)
Look up Hot Chips 2020–2024.
Especially: Cerebras, Tesla Dojo, NVIDIA Hopper, Meta MTIA, Graphcore.
UC Berkeley CS250 / CS152
Covers chip organization, memory hierarchies, and physical interconnects.
AMD Chiplet Design Whitepapers
Learn about Infinity Fabric and 3D V-Cache.
Intel Foveros and EMIB Packaging
Intel architecture journals or tech briefings on EMIB/Foveros
TSMC SoIC and CoWoS Technology Briefs
Focused on chip stacking, TSVs, and advanced packaging.
IEEE ISSCC / VLSI Symposium Proceedings
Industry-standard conferences for chiplet and interconnect technologies.
Look for papers from AMD, Intel, TSMC, Apple.
Chiplet Summit Talks (YouTube)
Covers real-world modular silicon architectures and interconnect protocols.
IEEE Hot Interconnects Conference
Annual conference focusing on the future of interconnect and silicon systems.
Open Compute Project (OCP) – Chiplet and Interconnect Working Group
OpenROAD Project – Physical Design Automation for Chiplets
Learn about automated packaging and interconnect-aware floorplanning.
