AI is developing along three dimensions, and progress is wildly different in each.
Most of the breakthroughs this year have been in generating new media. Dall-E, Stable Diffusion, and GPT-3 all generate text or images from prompts. The models are shockingly good at this task. The transformation is purely digital - someone types a prompt and the model produces a piece of content. If you believe that AI is mostly about the data then the progress here is unsurprising. The internet provides a nearly endless repository of content on which to train.
The second dimension takes sensor data from the physical world and maps it to a digital twin which can be further manipulated. Cashierless checkout, weather prediction, classifying radiology images, predicting machine failure, or any system which intakes physical data and uses AI to map it to something understandable by further software systems or humans fits this second dimension. Progress here has been steady but as you can imagine, training datasets are more limited.
The dimension with the slowest progress are closed-loop systems, or those which take in data from the physical world and then manipulate that world based on AI decisions. Self driving cars and robotics fit this third type. As we enter 15 years since the first Darpa competition it may be tempting to think that there have been self driving breakthroughs or that robotics has come a long way. But the reality is that it has been more of a grind. To date I am not aware of any robot which is commercially deployed to do things like clean a home. The story with cars looks better but a future where children never learn to drive still looks like a long way off.
When we think about investing or building in any of these dimensions, the areas with the most fundamental progress are the places where we can move to building applications and software interfaces to obtain widespread adoption. So it is viable to focus on building the perfect prompt input to generate copy for ads. Or build a saas service which writes blogs for companies. It would not make sense at this juncture to build applications for self driving cars - such as a car communication protocol to coordinate the most efficient commute in a city. Too much fundamental research is still to be done.
However that represents another opportunity. We know that teams which make research breakthroughs are very attractive acquisitions or they may be SPAC’d or see some liquidity event based only on the fundamental discoveries they have made. Lidar companies would fit this category. So it is important to know which dimension you are building for and where progress stands because this fundamentally impacts the go to market strategy for the company.