Distilling the Knowledge in a Neural Network

The AI Roles Continuum: Blurring the Boundary Between Research and...

The rapid scaling of deep neural networks and large language models has collapsed the once-clear divide between "research" and "engineering" in AI organizations. Drawing on a qualitative synthesis...

Machine Learning  |  Google for Developers

Neural Computer: A New Machine Form Is Emerging

A research essay on Neural Computer: how it differs from agents, world models, and conventional computers; what runtime and CNC would mean; what current prototypes already show; and how software and hardware might change.

Nenex: A Neural Personal Wiki Idea

Proposal for a personal wiki built on neural nets: all edits are logged & used to finetune a NN assistant in realtime.

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What is Knowledge distillation? | IBM

Knowledge distillation is a machine learning technique used to transfer the learning of a large pre-trained “teacher model” to a smaller “student model.”

Google opens Deep Review AI tool to help researchers catch manuscript errors

Google Research on Oct. 14 released Deep Review, an AI system designed to flag mathematical errors, logical gaps and unsupported claims in technical documents. The public launch follows pilots that provided feedback on more than 15,000 manuscripts, according to Google. Speaking at Google’s Research@ event in Mountain View, California, Rajesh Jayaram, a research scientist at…

Intro to Psyche - Psyche

Psyche is a system that enables distributed training of transformer-based AI models over the internet, aiming to foster collaboration between untrusted parties to create state-of-the-art machine learning models. It leverages a peer-to-peer distributed network for communication and data sharing.

The knowledge layer for collective intelligence

A portable data substrate for humans and their tools to think together.

The knowledge layer for collective intelligence

A portable data substrate for humans and their tools to think together.

The knowledge layer for collective intelligence

A portable data substrate for humans and their tools to think together.

The knowledge layer for collective intelligence

A portable data substrate for humans and their tools to think together.

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The InfoCentral project has historically been focused more heavily upon the symbolic (human-meaningful) side of AI. This included derivatives of Semantic Web and related knowledge representation research, which were dependent on human effort to curate and categorize structured data. The symbolic approach to AI has largely lost the race to the neuro/neuro-symbolic (opaque vectors, statistical) side of AI research, notably the success of the Large Language Model (LLM) paradigm. Due to its faculties with natural language, remarkably little human involvement is needed to infer structure and meaning from traditional (mostly unstructured) information. As such, some of the approaches previously explored in the InfoCentral project have lost relevance. That said, LLM-based tools can perhaps help us finish the symbolic side of the grand AI project, by doing the tedious work that humans were reluctant to do! It has long been theorized that a combination of approaches will ultimately provide the most robust and performant systems, able to work with messy transient unstructured information while also building networks of verifiable persistent structured information that both humans and machines can cross-verify. This may prove useful in both ensuring that future AI systems remain aligned to human goals and values and that inevitable abuses and failures can be easily recovered from. To that end, some research results of this project may be re-usable in a new context.

Google shakes up AI leadership as DeepMind chief shifts role

Google parent company Alphabet on Wednesday announced a leadership overhaul of its AI division, with AI chief Demis Hassabis leaving his main managerial role and several leaders of its Gemini model,...