Lifeboat Foundation

Lean Co-pilot for LLM-human collaboration to write formal mathematical proofs that are 100% accurate.

Top right: LeanDojo extracts proofs in Lean into datasets for training machine learning models. It also enables the trained model to prove theorems by interacting with Lean’s proof environment.

Top left: The proof tree of a Lean theorem ∀n∈N, gcd n n = n, where gcd is the greatest common divisor. When proving the theorem, we start from the original theorem as the initial state (the root) and repeatedly apply tactics (the edges) to decompose states into simpler sub-states, until all states are solved (the leaf nodes). Tactics may rely on premises such as mod_self and gcd_zero_left defined in a large math library. E.g., mod_self is an existing theorem ∀n∈N, n % n = 0 used in the proof to simplify the goal.

The eccentric pro-tech movement known as “Effective Accelerationism” wants to unshackle powerful A.I., and party along the way.

Spotify made a name for itself in the audio-streaming business through its hyper-personalized user experience, thanks to artificial intelligence and a team of 9,800 staffers at the end of 2022.

But after three rounds of layoffs in one year: 590 positions in January, 200 in June, and another 1,500 this week, Spotify’s investments into AI to boost margins for its podcasting and audiobook divisions look like a complete overhaul in strategy that Wall Street seems confident can work.

“Spotify is leveraging AI across its platform, launching AI DJ, simulating a traditional radio experience, in 50 additional markets and rolling out AI Voice Translation for podcasts,” said Justin Patterson, equity research analyst at KeyBanc Capital Markets, in a research note. “Coupled with audiobooks rolling out to Premium Subscribers, we believe Spotify has several opportunities to drive engagement and eventually stronger monetization.”

Continue reading “Spotify slashes staff to move faster into AI — and Wall Street loves it” »

At the end of last year, we published a list of 10 predictions for the world of artificial intelligence in 2023.

Let’s revisit our 2023 predictions for AI to see how things actually played out.

In the exercise, an engineer equipped with a set of virtual reality (VR) goggles is orchestrating the robot’s actions.

Advanced proposition.

Continue reading “IHMC’s Nadia: A task-ready humanoid robot with a boxing edge” »

“This architecture means Australia could develop its own sovereign chip manufacturing without exclusively relying on international foundries for the value-add process.”

Researchers at the University of Sydney Nano Institute have introduced a compact silicon semiconductor chip that seamlessly integrates electronics with photonic components. The innovation promises to significantly expand radio-frequency (RF) bandwidth and the ability to accurately control information flowing within the chip.

The chip, built using cutting-edge silicon photonics technology, boasts integration capabilities for diverse systems on semiconductors less than 5 millimeters wide. Pro-Vice-Chancellor (Research) Professor Ben Eggleton described the process as akin to assembling Lego building blocks, where new materials are integrated through advanced packaging of electronic ‘chiplets’, in a statement.

Continue reading “Sydney researchers debut new lego-style chip with enhanced bandwidth” »

The greater the ability of a CEO to develop and execute firm strategy, the more valuable they become when quick decision-making is vital to that firm’s survival.

Still agi could be a big problem because it could have 1 million iq and we barely have 140 iq maximum.

AI researcher Geoffrey Hinton, widely regarded as the “Godfather of AI,” is worried that AI might take over the world one day.

GPT-4 and other models rely on transformers. With StripedHyena, researchers present an alternative to the widely used architecture.

With StripedHyena, the Together AI team presents a family of language models with 7 billion parameters. What makes it special: StripedHyena uses a new set of AI architectures that aim to improve training and inference performance compared to the widely used transformer architecture, used for example in GPT-4.

The release includes StripedHyena-Hessian-7B (SH 7B), a base model, and StripedHyena-Nous-7B (SH-N 7B), a chat model. These models are designed to be faster, more memory efficient, and capable of processing very long contexts of up to 128,000 tokens. Researchers from HazyResearch, hessian. AI, Nous Research, MILA, HuggingFace, and the German Research Centre for Artificial Intelligence (DFKI) were involved.