Berlin Brown and Software Development

Numenta HTM (Hierarchical Temporal Memory) is a biologically constrained machine intelligence technology that models the structural and algorithmic properties of the mammalian neocortex https://github.com/berlinbrown/htm.java This is adding updates to the project. The visualizer simulates a room thermostat reporting 24 hourly readings each day: ```text 12 AM 64°F → 1 AM 63°F → ... → Noon 76°F → ... → 11 PM 65°F → next day ``` This example runs the complete learning path: ```text hour ─────────→ periodic ScalarEncoder ─┐                                        ├→ Spatial Pooler → active columns → Temporal Memory temperature ──→ ScalarEncoder ─────────┘ ``` The temperatures are simulated sensor readings, but they are ordinary real-world values processed by the actual encoder and Spatial Pooler. Numbers such as `[2, 3, 5]` are internal column addresses, not temperatures and not numbers the mo...

I had GPT rewrite this post. https://www.myberlinaustin-atl.com/2008/06/is-java-new-cobol-yes-what-does-that_14.html In 2008 there is a phrase floating around the software community: “Java is the new COBOL.” Usually it is followed by complaints about EJBs, XML configuration, enterprise frameworks, or somebody announcing that Ruby on Rails is going to replace everything. I think that misses the more interesting question. The important question isn't whether Java sucks. The question is what happened to COBOL, and whether Java developers should learn something from it. COBOL was created in 1959 and became enormously important for business computing. Banks, insurance companies, governments, and other large organizations built critical systems with it. Many of those systems still work. That is worth emphasizing because old technology is not necessarily bad technology. If a banking system has processed transactions correctly for decades, replacing it simply because the programming langu...

  Many years ago, in 2008, a programmer researched DNA and how the cell works and wrote a software-related analogy for the process. It was a great overview, especially because he translated a lot of the biological terminology into concepts that engineers could understand. I kept thinking about the article afterward and wanted to add my own spin to it. The author didn't really do anything wrong—the article is excellent—but I personally wished he had reduced the software analogy to a paragraph or two that an engineer could quickly visualize. As for the underlying biology, I am not a scientist, so I am certainly not trying to refute or replace his explanation. The article ends with an analogy that I particularly enjoyed: DNA is not a computer programming language, but there are some interesting similarities. We can loosely imagine a cell as a computer running its own system. Each cell contains essentially the same DNA, but different cells use different portions of that information. Wh...

 I wrote these old blog entries a while back.  Now rethinking them. https://www.myberlinaustin-atl.com/2008/07/simple-lisp-implementation-in-java-ode.html https://www.myberlinaustin-atl.com/2008/07/neophyte-schemelisp-interpreter-in.html Lisp can look strange if you come from Java, C, C++, or another procedural language. There are parentheses everywhere, functions seem to be mixed with data, and even a simple expression like (+ 1 2 3 4) does not look like the code most programmers are used to reading. Instead of starting with Lisp theory, I think it is easier to start with something every computer science student has probably implemented at some point: a linked list. A linked list has a node containing some data and a reference to the next node. You can walk through the nodes, print them, add their values, or perform some other operation on each element. Keep that basic data structure in mind because it gets us surprisingly close to understanding Lisp. Consider the Lisp expre...

2005 — Numenta Jeff Hawkins co-founds Numenta around the idea of reverse-engineering the neocortex. This eventually produces HTM and the Thousand Brains theory. 2010 — DeepMind DeepMind is founded by Demis Hassabis, Shane Legg and Mustafa Suleyman. Its path is heavily influenced by reinforcement learning, neuroscience and learning through environments/games. 2012 — AlexNet / deep-learning explosion 2013–15 — DQN / Atari DeepMind demonstrates deep reinforcement learning directly from game pixels. 2015 — OpenAI 2015–16 — AlphaGo DeepMind combines neural networks, search and reinforcement learning; AlphaGo defeats Lee Sedol in 2016. 2017 — Transformer Google researchers introduce the architecture that becomes the foundation of modern LLMs. Google DeepMind's history specifically credits the Google Brain team with the Transformer work. 2018 — GPT-1 2020 — GPT-3 / AlphaFold 2 era 2021 — Anthropic 2021 — A Thousand Brains Hawkins publishes the book laying out the Thousand ...

Random Picture of Toy AI Project There has been a lot of discussion lately about artificial intelligence ending humanity. Maybe it happens in one year. Maybe it happens in ten. Depending on whom you ask, there is some percentage chance that AI eventually wipes us out. I am not going to lean completely one way or the other. I am an engineer, not an AI safety researcher, but I do have some thoughts about the logic behind that proposition. A recent CNN article gave one of the more direct versions of the argument: https://www.cnn.com/2026/09/17/tech/how-will-ai-exterminate-humanity-cec One thing I continually hear, even when reporters interview leaders of major AI companies, is some version of this question: "Could AI destroy humanity?" And sometimes the answer is essentially: yes, it could, possibly within some number of years. What I don't hear discussed nearly enough is how . That seems like an important part of the argument. If someone says there is a meaningful probabili...