"A Generalist Agent": New DeepMind Publication

My main takeaway from Gato: If we can build specialized AI agents for 100s/1000s of tasks, it's now pretty straightforward to make a general agent that can do it all in a single model. Just tokenize data from all the tasks and feed into a transformer.

Wild. One important note is that the model is trained with labeled examples of successful performance on the target task, rather than learning the tasks from scratch by trial and error like MuZero and OpenAI Five. For example, here's the training description for the DeepMind Lab tasks:

We collect data for 255 tasks from the DeepMind Lab, 254 of which are used during training, the left out task was used for out of distribution evaluation. Data is collected using an IMPALA (Espeholt et al., 2018) agent that has been trained jointly on a set of 18 procedurally generated training tasks. Data is collected by executing this agent on each of our 255 tasks, without further training.

Gato then achieves near-expert performance on >200 DM Lab tasks (see Figure 5). It's unclear whether the model could have learned superhuman performance training from scratch, and similarly unclear whether the model could learn new tasks without examples of expert performance. 

More broadly, this seems like substantial progress on both multimodal transformers and transformer-powered agents, two techniques that seem like they could contribute to rapid AI progress and risk. I don't want to downplay the significance of these kinds of models and would be curious to hear other perspectives. 

Would it be fair to call this AGI, albeit not superintelligent yet?

Gato performs over 450 out of 604 tasks at over a 50% expert score threshold.

👀

Possibly the first truly AGI paper. 

Even though it is just exploiting the fact that all the narrow problems can be solved as sequence problems via tokenisation, it's remarkable that the tasks do not interferee distructively between each other. My gut feeling is that this is due the very high dimensional space of the embedding vectors.

It leaves ample room for grow.

From the paper:

Technical AGI safety (Bostrom, 2017) may also become more challenging when considering generalist agents that operate in many embodiments. For this reason, preference learning, uncertainty modeling and value alignment (Russell, 2019) are especially important for the design of human-compatible generalist agents. It may be possible to extend some of the value alignment approaches for language (Kenton et al., 2021; Ouyang et al., 2022) to generalist agents. However, even as technical solutions are developed for value alignment, generalist systems could still have negative societal impacts even with the intervention of well-intentioned designers, due to unforeseen circumstances or limited oversight (Amodei et al., 2016). This limitation underscores the need for a careful design and a deployment process that incorporates multiple disciplines and viewpoints.

What I think I'm most interested in, is does training on one task improve or degrade it's performance on other tasks?

It's not very surprising if you take a model and throw a bunch of tasks at it and it eventually learns how to do all tasks. But if it can apply lessons from one task to general situations, that's impressive!

I'd be curious to hear more thoughts on how much we could already scale it right now. Looks like that data might be a bottleneck?

Some thoughts on compute:

Gato estimate: 256 TPUv3 chips for 4 days a 24hours = 24'574 TPUv3-hours (on-demand costs are $2 per hour for a TPUv3) =$49'152

In comparison, PaLM used 8'404'992 TPUv4 hours and I estimated that it'd cost $11M+. If we'd assume that someone would be willing to spend the same compute budget on it, we could make the model 106x bigger (assuming Chinchilla scaling laws). Also tweeted about this here.

The size of the model was only(?) limited due to latency requirements for the robotics part.

The primary question on my mind is something like this:

How much retraining is needed for Gato to learn a new task? Given a task, such as "Stack objects and compose a relevant poem" which combines skills it has already learned, yet is a fundamentally different task, does it quickly learn how to perform well at it?

If not, then it seems Deepmind 'merely' managed to get a single agent to do a bunch of tasks we were previously only able to do with multiple agents. If it is also quicker at learning new tasks in similar domains, than an agent trained solely to do it, then it seems like a big step towards general intelligence.

Alarming that it freely "lies" (?) or hallucinates or whatever is going on, rather than replying "I don't know".

Looking at the image captioning and text prediction responses, it doesn't appear to be very good at either...