Advancing best-in-class massive fashions, compute-optimal RL brokers, and extra clear, moral, and truthful AI techniques
The thirty-sixth Worldwide Convention on Neural Info Processing Techniques (NeurIPS 2022) is going down from 28 November – 9 December 2022, as a hybrid occasion, primarily based in New Orleans, USA.
NeurIPS is the world’s largest convention in synthetic intelligence (AI) and machine studying (ML), and we’re proud to help the occasion as Diamond sponsors, serving to foster the alternate of analysis advances within the AI and ML neighborhood.
Groups from throughout DeepMind are presenting 47 papers, together with 35 exterior collaborations in digital panels and poster periods. Right here’s a quick introduction to a number of the analysis we’re presenting:
Finest-in-class massive fashions
Massive fashions (LMs) – generative AI techniques educated on enormous quantities of knowledge – have resulted in unimaginable performances in areas together with language, textual content, audio, and picture era. A part of their success is all the way down to their sheer scale.
Nonetheless, in Chinchilla, we’ve created a 70 billion parameter language mannequin that outperforms many bigger fashions, together with Gopher. We up to date the scaling legal guidelines of huge fashions, displaying how beforehand educated fashions have been too massive for the quantity of coaching carried out. This work already formed different fashions that comply with these up to date guidelines, creating leaner, higher fashions, and has received an Excellent Foremost Observe Paper award on the convention.
Constructing upon Chinchilla and our multimodal fashions NFNets and Perceiver, we additionally current Flamingo, a household of few-shot studying visible language fashions. Dealing with pictures, movies and textual knowledge, Flamingo represents a bridge between vision-only and language-only fashions. A single Flamingo mannequin units a brand new state-of-the-art in few-shot studying on a variety of open-ended multimodal duties.
And but, scale and structure aren’t the one components which are essential for the ability of transformer-based fashions. Knowledge properties additionally play a big function, which we focus on in a presentation on knowledge properties that promote in-context studying in transformer fashions.
Optimising reinforcement studying
Reinforcement studying (RL) has proven nice promise as an method to creating generalised AI techniques that may handle a variety of complicated duties. It has led to breakthroughs in lots of domains from Go to arithmetic, and we’re at all times on the lookout for methods to make RL brokers smarter and leaner.
We introduce a brand new method that reinforces the decision-making talents of RL brokers in a compute-efficient manner by drastically increasing the dimensions of data accessible for his or her retrieval.
We’ll additionally showcase a conceptually easy but normal method for curiosity-driven exploration in visually complicated environments – an RL agent referred to as BYOL-Discover. It achieves superhuman efficiency whereas being strong to noise and being a lot easier than prior work.
Algorithmic advances
From compressing knowledge to operating simulations for predicting the climate, algorithms are a elementary a part of fashionable computing. And so, incremental enhancements can have an infinite influence when working at scale, serving to save power, time, and cash.
We share a radically new and extremely scalable technique for the automated configuration of laptop networks, primarily based on neural algorithmic reasoning, displaying that our extremely versatile method is as much as 490 instances quicker than the present state-of-the-art, whereas satisfying the vast majority of the enter constraints.
Throughout the identical session, we additionally current a rigorous exploration of the beforehand theoretical notion of “algorithmic alignment”, highlighting the nuanced relationship between graph neural networks and dynamic programming, and the way finest to mix them for optimising out-of-distribution efficiency.
Pioneering responsibly
On the coronary heart of DeepMind’s mission is our dedication to behave as accountable pioneers within the subject of AI. We’re dedicated to creating AI techniques which are clear, moral, and truthful.
Explaining and understanding the behaviour of complicated AI techniques is a vital a part of creating truthful, clear, and correct techniques. We provide a set of desiderata that seize these ambitions, and describe a sensible method to meet them, which includes coaching an AI system to construct a causal mannequin of itself, enabling it to clarify its personal behaviour in a significant manner.
To behave safely and ethically on the earth, AI brokers should be capable of cause about hurt and keep away from dangerous actions. We’ll introduce collaborative work on a novel statistical measure referred to as counterfactual hurt, and exhibit the way it overcomes issues with customary approaches to keep away from pursuing dangerous insurance policies.
Lastly, we’re presenting our new paper which proposes methods to diagnose and mitigate failures in mannequin equity attributable to distribution shifts, displaying how essential these points are for the deployment of secure ML applied sciences in healthcare settings.
See the complete vary of our work at NeurIPS 2022 right here.
