Google DeepMind at ICLR 2024

Growing 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 Could 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 discipline, 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 might rework digital assistants into extra useful and intuitive AI instruments.

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

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

Pushing boundaries in imaginative and prescient and coding

Till just lately, giant AI fashions largely targeted on textual content and pictures, laying the groundwork for large-scale sample recognition and information interpretation. Now, the sector 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 necessary that its underlying code is generated and optimized with most effectivity.

If you watch a video on a flat display, you intuitively grasp the three-dimensional nature of the scene. Machines, nonetheless, battle to emulate this capability with out specific 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 era of novel variations of the identical video, with person management over digicam angles and content material.

Emulating human cognitive methods additionally makes for higher AI code mills. When programmers write complicated code, they sometimes “decompose” the duty into easier subtasks. With ExeDec, we introduce a novel code-generating method that harnesses a decomposition method to raise AI methods’ 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 complicated reasoning, and our dataset allows the exploration of a variety of ML strategies. We show that the ensuing studying methods outperform human-crafted code optimizations.

Advancing foundational studying

Our analysis groups are tackling the massive 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 flexibility 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 capable of generalize effectively to new eventualities is it as a result of they, like people, have realized an underlying causal mannequin of their world? This can be a vital 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 information, and focus on the deep implications.

One other vital query in AI is belief, which partly relies 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 methodology.

Lastly, we discover sport idea’s Nash equilibrium (NE) – a state by which no participant advantages from altering their technique if others keep 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 help initiatives together with Queer in AI and Women In Machine Learning. Such partnerships not solely bolster analysis collaborations but additionally foster a vibrant, various group in AI and machine studying.

In case you’re at ICLR, remember to go to our sales space and our Google Analysis 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 sit up for connecting with you!