Google DeepMind at ICML 2024

Exploring AGI, the challenges of scaling and the way forward for multimodal generative AI

Subsequent week the synthetic intelligence (AI) neighborhood will come collectively for the 2024 Worldwide Convention on Machine Studying (ICML). Working from July 21-27 in Vienna, Austria, the convention is a global platform for showcasing the newest advances, exchanging concepts and shaping the way forward for AI analysis.

This yr, groups from throughout Google DeepMind will current greater than 80 analysis papers. At our sales space, we’ll additionally showcase our multimodal on-device mannequin, Gemini Nano, our new household of AI fashions for schooling known as LearnLM and we’ll demo TacticAI, an AI assistant that may assist with soccer techniques.

Right here we introduce a few of our oral, highlight and poster shows:

Defining the trail to AGI

What’s synthetic basic intelligence (AGI)? The phrase describes an AI system that’s no less than as succesful as a human at most duties. As AI fashions proceed to advance, defining what AGI might seem like in observe will turn out to be more and more necessary.

We’ll current a framework for classifying the capabilities and behaviors of AGI fashions. Relying on their efficiency, generality and autonomy, our paper categorizes methods starting from non-AI calculators to rising AI fashions and different novel applied sciences.

We’ll additionally present that open-endedness is vital to constructing generalized AI that goes past human capabilities. Whereas many latest AI advances had been pushed by current Web-scale information, open-ended methods can generate new discoveries that stretch human data.

Scaling AI methods effectively and responsibly

Creating bigger, extra succesful AI fashions requires extra environment friendly coaching strategies, nearer alignment with human preferences and higher privateness safeguards.

We’ll present how utilizing classification as a substitute of regression strategies makes it simpler to scale deep reinforcement studying methods and obtain state-of-the-art efficiency throughout completely different domains. Moreover, we suggest a novel strategy that predicts the distribution of penalties of a reinforcement studying agent’s actions, serving to quickly consider new eventualities.

Our researchers current an alignment-maintaining strategy that reduces the necessity for human oversight, and a new strategy to fine-tuning massive language fashions (LLMs), based mostly on recreation concept, higher aligns a LLM’s output with human preferences.

We critique the strategy of coaching fashions on public information and solely fine-tuning with “differentially non-public” coaching, and argue this strategy could not provide the privateness or utility that’s typically claimed it does.

New approaches in generative AI and multimodality

Generative AI applied sciences and multimodal capabilities are increasing the artistic potentialities of digital media.

We’ll current VideoPoet, which makes use of an LLM to generate state-of-the-art video and audio from multimodal inputs together with photos, textual content, audio and different video.

And share Genie (generative interactive environments), which may generate a variety of playable environments for coaching AI brokers, based mostly on textual content prompts, photos, photographs, or sketches.

Lastly, we introduce MagicLens, a novel picture retrieval system that makes use of textual content directions to retrieve photos with richer relations past visible similarity.

Supporting the AI neighborhood

We’re proud to sponsor ICML and foster a various neighborhood in AI and machine studying by supporting initiatives led by Incapacity in AI,
Queer in AI,
LatinX in AI and
Ladies in Machine Studying.

For those who’re on the convention, go to the Google DeepMind and Google Analysis cubicles to fulfill our groups, see stay demos and discover out extra about our analysis.