Connectionism and symbolism: The fall of the symbolists
The big tech layoffs happen, unfortunately and entirely by coincidence, at a time of incredibly elevated expectations regarding machine learned generative models: ChatGPT may not be the 'best' language model out there, but due to the hard work by OpenAI to turn it into an easy to use product, and the huge amount of resources made available for free so that a very large audience could play with it, has in a very short time managed to captured the imagination of many and the conversation. I would say, rightfully. The way ChatGPT was released led to a shock in the sense that we are right now dazed and confused about what effect this technology will have on the world.
And while we are still in the middle of processing this shock, large scale strategic decisions regarding many projects and people were made. Anyone in big tech who worked on symbolic approaches in natural language processing, knowledge representation and reasoning, and other fields of artificial intelligence had a hard time to keep their job. It feels right now like large language models will make all of these symbolic approaches superfluous (I think, this might be true, but is more likely to turn out to be mistaken).
It is always difficult to predict how events will be viewed historically. The advent of wide-spread deep learning approaches in the 2010s, culminating in the well-deserved recognition of Hinton, LeCun, and Bengio with the Turing Award show clearly what dominated the research agenda and the attention in AI in the last decade. But until now it felt like symbolic approaches still had some space left, that the growth in deep learning was in addition to other approaches. Symbolic approaches were ready to offer impulses and work on ideas for a field which might well be climbing towards a local maximum.
But a good number of the teams that were disbanded in the layoffs were exactly teams working with such symbolic approaches, and it feels like these parts of AI are now entering a bitter-cold winter.
A lot of knowledge is being lost right now, and many paths to innovative ideas are being buried. I have no doubt that there are still a lot of breakthroughs to be had in machine learning, and that there is immense value to be collected from the research results in machine learning from the last few years. And with immense I mean tens and hundreds of billions of dollars.
Nevertheless I expect that we will hit a wall. Reach a local maximum. Run into problems and limitations. And it would be good to keep a wider net to cast. To keep a larger search space alive. Alas, it seems it is not meant to be. In this abundance of capital and potential value, we seem to be on the way to starve research, optimise away alternatives, and to give everything to the mainstream ideas.
22 years of Wikipedia
Ina Kramer (1948-2023)