- 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.
Without looking the answer, what is your intuition about the size of the VC-dimension of ReLU networks as a function of a number of weights and layers?
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.
AGI is a scientific problem of the hardest kind, not an engineering problem where you just use existing knowledge to build better and better things.
Marving Minsky once said that in mathematics just five axioms is enough to provide the amount of complexity that overwhelms the best minds for centuries. AGI could be messy practical problem that depends on 10 or 25 fundamental 'axioms' that work together to produce general intelligence. "I bet the human brain is a kludge." - Marvin Minsky
The idea that if many people think this problem very hard, the problem is solved in our lifetime is prevalent. It's not true in math and physics, so why would AI be any different? Progress is made but you can't know if there is breakthrough tomorrow or if it happens 100 years from now. Just adding more computational capability is not going to solve AI.
Currently it's the engineering applications and the use of the science what is exploding and getting funded. In fact, I think some of the best brains are lured from the fundamental research into applied science with high pay and resources. What the current state of the art can do now has not been utilized fully in the economy and this brings in the investments and momentum.
- 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)