MAXPOOL

Things you are not supposed to talk about:

- There is no "moat" (lasting, easy-to-defend technological edge) in AI model businesses. There are just short-term advantages.

- An AI business is a capital-intensive business, just like old factories. Data centers are expensive, models are energy-hungry, and the hardware inside must be replaced every 3–4 years.

- Smaller, specialized models eat margins from below. Transcription, voice, or image detection do not need large models.

There is no reason to expect high margins like you can in traditional software business. Benefits of AI go mostly to consumers.

edit: There is potential for economies of scale. Few megacorps can strive for cost advantage when they achieve scale (Microsoft, Google, Amazon and Meta)

If you take a birds eye view, fundamental breakthroughs don't happen that often. "Attention Is All You Need" paper also came out in 2017. It has now been 7 years without breakthrough at the same level as transformers. Breakthrough ideas can take decades before they are ready. There are many false starts and dead ends.

Money and popularity are orthogonal to pathfinding that leads to breakthroughs.

That's is based on old assumption of neuron function.

Firstly, Kurzweil underestimates the number connections by order of magnitude.

Secondly, dentritic computation changes things. Individual dentrites and the dendritic tree as a whole can do multiple individual computations. logical operations low-pass filtering, coincidence detection, ... One neuronal activation is potentially thousands of operations per neuron.

Single human neuron can be equivalent of thousands of ANN's.

> deep learning architectures have been crafted to create inductive biases matching invariances and spatial dependencies of the data. Finding corresponding invariances is hard in tabular data, made of heterogeneous features, small sample sizes, extreme values

Transformers with positional encoding have embeddings are invariant to the input order. CNN's have translation invariance and can have little rotational invariance.

It's harder to find similar invariances to tabular data. Maybe applying methods from GNN's would help?

Effect of exercise for depression: systematic review and network meta-analysis of randomised controlled trials.

Conclusions Exercise is an effective treatment for depression, with walking or jogging, yoga, and strength training more effective than other exercises, particularly when intense. Yoga and strength training were well tolerated compared with other treatments. Exercise appeared equally effective for people with and without comorbidities and with different baseline levels of depression. To mitigate expectancy effects, future studies could aim to blind participants and staff. These forms of exercise could be considered alongside psychotherapy and antidepressants as core treatments for depression.