Picture Me Coding

We talk about the history of artificial neural networks and attempt to explain some of their workings with the help of our friend Bob Farzin.  

Neurons: How the Brain Communicates | Mental Health America

A LOGICAL CALCULUS OF THE IDEAS IMMANENT IN NERVOUS ACTIVITY*

THE PERCEPTRON: A PROBABILISTIC MODEL FOR INFORMATION STORAGE AND ORGANIZATION IN THE BRAIN1 (Rosenblatt)

The Rosenblatt Perceptron Algorithm

Minsky-and-Papert-Perceptrons.pdf

Learning representations by back-propagating errors

Backpropagation Applied to Handwritten Zip Code Recognition - Y. LeCun

ImageNet Classification with Deep Convolutional Neural Networks (aka Alexnet)

[1706.03762] Attention Is All You Need (Transformers)

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In this episode we explore the likelihood of existential risk from AI.  We discuss the viewpoints of doomers and skeptics, and some of the scenarios that could lead to catastrophic outcomes.  We admire the takes of the third most famous Michael Jordan. We conclude that not ALL of us will die, but maybe some of us.

Michael I. Jordan on AI: “Superintelligence is science fiction”

AI as Normal Technology

Dario Amodei — We Must Pace the Frontier

'A billion deaths': Bill Gates warns about AI in the wrong hands, saying a kill switch and self-regulation won’t be enough | Fortune

Is the AI existential threat real—and can regulatory action prevent it? | Brookings

Generative design of bacteriophages with genome language models | Science

How Might Artificial Intelligence Affect the Risk of Nuclear War?

Michael I. Jordan on AI: “Superintelligence is science fiction”

AI ‘godfather’ Yann LeCun has ‘zero concerns’ about human extinction, says Anthropic CEO Dario Amodei is ‘deluded’ | Fortune

AI 2027 - Rogue Replication Timeline — LessWrong

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This week we conclude our discussion of dynamic memory allocation with a discussion of the history and implementation of garbage collectors.  There are no mentions of raccoons.

Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I

Uniprocessor Garbage Collection Techniques?

cpython/InternalDocs/garbage_collector.md at main

Generation Scavenging: A Non-disruptlve High Perfornm.nce Storage Reclamation Algorithm 1. Introduction 2. The Relationship Betw

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This week we present the first of two episodes on dynamic memory allocation.  We were going to talk about garbage collection, but realized we should probably discuss allocators first.  Turns out they are complicated, and there are many different strategies.

Dynamic Storage Allocation: A Survey and Critical Review  (Wilson)

Hoard: a scalable memory allocator for multithreaded applications

mimalloc: A new, high-performance, scalable memory allocator for the modern era - Microsoft Research

A Scalable Concurrent malloc(3) Implementation for FreeBSD

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In this episode we interview Alan Dyke, a colleague of Erik's who got his Ph.D from the University of Wales, Aberystwyth, where he did research on Computer-Aided Software Engineering.  We talk about Alan's unique journey in software development, going from the academic environment to the defense industry, embedded software, and biotech.  We also learn about the crucial importance of calibrating your software for hurricanes.

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This week we're talking about  Mike's visit to DataCon LA 26.  We talk about the conference, various talks he was able to see, and his own experience giving a talk.

Compression Represents Intelligence Linearly

[2602.17127] The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

Instructor

spaCy

John Snow Labs

Hierarchical Navigable Small Worlds (HNSW) | Pinecone

Lance Video is a demonstration platform for analyzing and searching video content with LanceDB. · GitHub

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In this episode we discuss the Monte Carlo method, both the technique and its place in the history of computing.  The Monte Carlo method is a set of tools that use probability to solve problems that are otherwise intractable.  Programs using Monte Carlo were among the first programs created on digital computers.

The Monte Carlo Method Nicholas Metropolis; S. Ulam Journal of the American Statistical Association, Vol. 44, No. 247. (Sep.

Equation of State Calculations by Fast Computing Machines

Hitting the Jackpot: The Birth of the Monte Carlo Method | LANL

Monte Carlo Sampling Methods Using Markov Chains and Their Applications W. K. Hastings Biometrika, Vol. 57, No. 1. (Apr., 1970),

2014HaighPriestleyRope-LosAlamosBetsonENIAC.pdf

The Unheralded Contributions of Klara Dan von Neumann

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In this episode we discuss the 5 most-cited papers from the flagship journal of the Association for Computing Machinery, the JACM.   The papers include everything from distributed computing to real-time scheduling. 

Direct Search'' Solution of Numerical and Statistical Problems | Journal of the ACM 

Impossibility of distributed consensus with one faulty process | Journal of the ACM 

Robust principal component analysis? | Journal of the ACM 

Authoritative sources in a hyperlinked environment | Journal of the ACM 

Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment | Journal of the ACM

What is HITS Algorithm (Hyperlink-Induced Topic Search)?

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This episode started with the question: who first had the idea to connect computers together to make a network?  We explore JCR Licklider's ideas about an  "Intergalactic Network", some of the earliest attempts at networking, and the ideas that lead to packet switching and the Arpanet.

SAGE: Semi-Automatic Ground Environment Air Defense System | MIT Lincoln Laboratory

Memorandum for Members and Affiliates of the Intergalactic Computer Network

The Arpanet and Computer Networks

60 Years of Networking - by Bruce Davies - Systems Approach

Information Flow in Large Communication Nets (1961, Leonard Kleinrock, MIT)

On Distributed Communications Networks (1962, Paul Baran, RAND)

National Physical Laboratory Proposal for a Digital Communication Network by D. W. Davies June, 1966

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In this episode we discuss a couple of recent papers on just-in-time development and agentic systems and how we think they suggest trends that will affect software development for years to come.  We boldly predict that in 2046 people will still not want robot dentists.

The Time is Here for Just-in-Time Systems: Challenges and Opportunities

MDASH: The MS Security Thing 

Services: The New Software | Sequoia Capital

Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl

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