Google DeepMind at ICLR 2024

Creating next-gen AI brokers, exploring new modalities, and pioneering foundational studying

Subsequent week, AI researchers from across the globe will converge on the twelfth International Conference on Learning Representations (ICLR), set to happen Might 7-11 in Vienna, Austria.

Raia Hadsell, Vice President of Analysis at Google DeepMind, will ship a keynote reflecting on the final 20 years within the subject, highlighting how classes realized are shaping the way forward for AI for the good thing about humanity.

We’ll additionally supply reside demonstrations showcasing how we convey our foundational analysis into actuality, from the event of Robotics Transformers to the creation of toolkits and open-source fashions like Gemma.

Groups from throughout Google DeepMind will current greater than 70 papers this yr. Some analysis highlights:

Drawback-solving brokers and human-inspired approaches

Massive language fashions (LLMs) are already revolutionizing superior AI instruments, but their full potential stays untapped. As an illustration, LLM-based AI brokers able to taking efficient actions may remodel digital assistants into extra useful and intuitive AI instruments.

AI assistants that comply with pure language directions to hold out web-based duties on individuals’s behalf could be an enormous timesaver. In an oral presentation we introduce WebAgent, an LLM-driven agent that learns from self-experience to navigate and handle advanced duties on real-world web sites.

To additional improve the overall usefulness of LLMs, we centered on boosting their problem-solving abilities. We exhibit how we achieved this by equipping an LLM-based system with a historically human method: producing and using “tools”. Individually, we current a coaching method that ensures language fashions produce extra constantly socially acceptable outputs. Our approach makes use of a sandbox rehearsal area that represents the values of society.

Pushing boundaries in imaginative and prescient and coding

Till lately, massive AI fashions principally centered on textual content and pictures, laying the groundwork for large-scale sample recognition and knowledge interpretation. Now, the sphere is progressing past these static realms to embrace the dynamics of real-world visible environments. As computing advances throughout the board, it’s more and more vital that its underlying code is generated and optimized with most effectivity.

If you watch a video on a flat display screen, you intuitively grasp the three-dimensional nature of the scene. Machines, nonetheless, wrestle to emulate this capability with out express supervision. We showcase our Dynamic Scene Transformer (DyST) mannequin, which leverages real-world single-camera movies to extract 3D representations of objects within the scene and their actions. What’s extra, DyST additionally allows the technology of novel variations of the identical video, with person management over digital camera angles and content material.

Emulating human cognitive methods additionally makes for higher AI code turbines. When programmers write advanced code, they usually “decompose” the duty into less complicated subtasks. With ExeDec, we introduce a novel code-generating method that harnesses a decomposition method to raise AI programs’ programming and generalization efficiency.

In a parallel spotlight paper we discover the novel use of machine studying to not solely generate code, however to optimize it, introducing a dataset for the robust benchmarking of code performance. Code optimization is difficult, requiring advanced reasoning, and our dataset allows the exploration of a spread of ML strategies. We exhibit that the ensuing studying methods outperform human-crafted code optimizations.

Advancing foundational studying

Our analysis groups are tackling the large questions of AI – from exploring the essence of machine cognition to understanding how superior AI fashions generalize – whereas additionally working to beat key theoretical challenges.

For each people and machines, causal reasoning and the power to foretell occasions are carefully associated ideas. In a highlight presentation, we discover how reinforcement learning is affected by prediction-based training objectives, and draw parallels to adjustments in mind exercise additionally linked to prediction.

When AI brokers are in a position to generalize nicely to new eventualities is it as a result of they, like people, have realized an underlying causal mannequin of their world? It is a important query in superior AI. In an oral presentation, we reveal that such fashions have indeed learned an approximate causal model of the processes that resulted of their coaching knowledge, and focus on the deep implications.

One other important query in AI is belief, which partially will depend on how precisely fashions can estimate the uncertainty of their outputs – a vital issue for dependable decision-making. We have made significant advances in uncertainty estimation within Bayesian deep learning, using a easy and basically cost-free technique.

Lastly, we discover recreation idea’s Nash equilibrium (NE) – a state by which no participant advantages from altering their technique if others preserve theirs. Past easy two-player video games, even approximating a Nash equilibrium is computationally intractable, however in an oral presentation, we reveal new state-of-the-art approaches in negotiating offers from poker to auctions.

Bringing collectively the AI group

We’re delighted to sponsor ICLR and assist initiatives together with Queer in AI and Women In Machine Learning. Such partnerships not solely bolster analysis collaborations but in addition foster a vibrant, various group in AI and machine studying.

In the event you’re at ICLR, you’ll want to go to our sales space and our Google Research colleagues subsequent door. Uncover our pioneering analysis, meet our groups internet hosting workshops, and have interaction with our consultants presenting all through the convention. We stay up for connecting with you!