AI Research1


We learned a lot about neural nets just by tinkering around with one. If you want your own neural net, we suggest Tariq Rashid’s (2016) book “Make Your Own Network,” available on Kindle. We finally got this network to operate coherently by increasing the hidden layer nodes from 3 to 300. The hidden layer of a neural network, according to Gemini, “…acts as an intermediate layer that transforms the raw input data into a more complex representation (which no one really understands, it’s probably just error correction), that the output layer can then use to make predictions.” This is why when there is a lot of data, the GPUs have to get more numerous and bigger.” The Meta network reportedly intends to add 350,000 parallel processing Nvidia H100 GPUs, within which the neural network model resides. The investment implications, however, may be quite different, because the trend now is to use smaller networks to explore more defined problems, with an increasing number of competitors.


We use a neural net to pose a simple and constant numerical question, computers represent words by numbers,  [.10,.10,.10], to a neural net where an extreme leadership sets increasingly extreme “targets.” What’s going to happen? Here is the result:


                                                                    Run 1                Run 2               Run 3                                                                                                


Target Training Range [0,0,0] to           [.10,.10,.10]       [.20,.20,.20]      [.30,.30,.30]

Question                                                [.10,.10,.10]       [.10,.10,.10]      [.10,.10,.10]


Network Answers:    x                                .10                       .17                    .26

Standard Deviation  s                                 .012                    .021                   .043



This table shows that as the range of answers to the same question become more extreme, so does the volatility of “acceptable” answers (volatility 4x), swamping the previous consensus. In Run 1, this table also suggests, for a democratic society, there has to be a continuing consensus.


Researchers in AI explore different kinds of architectures for different kinds of problems. If you are a decisionmaker, you are looking to the researcher (network architecture), the data, and the answers.






“Simplicity is complexity resolved.”  -  Constantin Brancusi