Neural Information Processing Systems http://nips.cc/

OpenAI, Anthropic, Google, and a host of other major tech companies have found common ground in F/ai, a new startup accelerator based out of Paris. The largest western AI labs are taking a break from sniping at one another to partner on a new accelerator program for European startups building applications on top of their models. Paris-based incubator Station F will run the program, named F/ai. On Tuesday, Station F announced it had partnered with Meta, Microsoft, Google, Anthropic, OpenAI and Mistral, which it says marks the first time the firms are all participating in a single accelerator. Other partners include cloud and semiconductor companies AWS, AMD, Qualcomm, and OVH Cloud.

In spite of its promise, though, inferring the structure of differentiable programs is a fundamentally hard problem.

We introduce a neural network layer API and library for sequence modeling, designed for easy creation of sequence models that can be executed both layer-by-layer (e.g., teacher-forced training) and step-by-step (e.g., autoregressive sampling). To achieve this, layers define an explicit representation of their state over time (e.g., a Transformer KV cache, a convolution buffer, an RNN hidden state), and a step method that evolves that state, tested to give identical results to a stateless layer-wise invocation. This and other aspects of the SequenceLayers contract enables complex models to be immediately streamable, mitigates a wide range of common bugs arising in both streaming and parallel sequence processing, and can be implemented in any deep learning library.

In this paper, we study a functional programming approach to natural language semantics, allowing us to increase the expressiveness of a more traditional denotation style. We will formalize a category based type and effect system to represent the semantic difference between syntactically equivalent expressions. We then construct a diagrammatic calculus to model parsing and handling of effects, providing a method to efficiently compute the denotations for sentences.

Computer programs are often factored into pure components -- simple, total functions from inputs to outputs -- and components that may have side effects -- errors, changes to memory, parallel threads, abortion of the current loop, etc. We make the case that human languages are similarly organized around the give and pull of pure values and impure processes, and we'll aim to show how denotational techniques from computer science can be leveraged to support elegant and illuminating analyses of natural language composition.

ChatGPT on macOS is about to become more useful for coding. ChatGPT can now edit code directly within an integrated development environment -- no need to copy and paste. You can find the full list of supported IDEs on OpenAI's website, but some of the more notable inclusions are Apple's own Xcode, Visual Code Studio and offshoots of Jetbrains like Android Studio and PyCharm. According to OpenAI, IDE integration has been one of the most-requested features from macOS users since the company released its "works with app" framework back in November. If you're a Plus, Pro or Team subscriber, you can start using the integration today.

This paper proposes a formal framework based on symbolic compression, integrating combinatory logic, information-theoretic optimal encoding, and context-aware inference techniques to achieve a step-change improvement in token efficiency while preserving semantic integrity. We establish a mathematical framework within a functional programming paradigm, derive the quantitative relationship between symbolic density and model interpretability, and propose a differentiable compression factor metric to evaluate encoding efficiency. Furthermore, we leverage parameter-efficient fine-tuning (PEFT) techniques to achieve a low-cost application of the GAEL language. Experimental results show that this method achieves a 78.3% token compression rate in code generation tasks while improving logical traceability by 62% through structural explicitness. This research provides new theoretical tools for efficient inference in LLMs and opens a symbolic path for model interpretability research.

On 16 May 2023, Sam Altman, OpenAI's charming, softly spoken, eternally optimistic billionaire CEO, and I stood in front of the US Senate judiciary subcommittee meeting on AI oversight. We were in Washington DC, and it was at the height of AI mania. Altman, then 38, was the poster boy for it all. Raised in St Louis, Missouri, Altman was the Stanford dropout who had become the president of the massively successful Y Combinator startup incubator before he was 30. A few months before the hearing, his company's product ChatGPT had taken the world by storm. All through the summer of 2023, Altman was treated like a Beatle, stopping by DC as part of a world tour, meeting prime ministers and presidents around the globe. US Senator Kyrsten Sinema gushed: "I've never met anyone as smart as Sam… He's an introvert and shy and humble… But… very good at forming relationships with people on the Hill and… can help folks in government understand AI." Glowing portraits at the time painted the youthful Altman as sincere, talented, rich and interested in nothing more than fostering humanity.