Lifeboat Foundation

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.

Long before researchers discovered the electron and its role in generating electrical current, they knew about electricity and were exploring its potential. One thing they learned early on was that metals were great conductors of both electricity and heat.

And in 1,853, two scientists showed that those two admirable properties of metals were somehow related: At any given temperature, the ratio of electronic conductivity to thermal conductivity was roughly the same in any metal they tested.

This so-called Wiedemann-Franz law has held ever since — except in quantum materials, where electrons stop behaving as individual particles and glom together into a sort of electron soup.

This is the BEST and most complete deep dive on the financial models for Tesla’s 11 major business units! James from InvestAnswers comes with data and analysis of the seasonality of Tesla stock showing its volatile movement as it compares to the macro economy and the S and P 500. Together we adjust the assumptions and metrics in his dynamic financial model. Come join us! The spreadsheet used in the video will be made available to everyone once James finalizes it first James runs a very successful YouTube channel called InvestAnswers where he shares insights on financial freedom, real estate, crypto, stocks, and options.

Pick-up truck fans let’s goooooo!! The Cybertruck is back once again, and it’s going head-to-head against one of the most powerful pick-ups money can buy — the Ford F-150 Raptor R! So let’s check out how these two almighty machines compare. Starting with the Tesla, we’ve got our hands on the ‘Cyberbeast’ tri-motor edition, which can produce 845hp and 930Nm of torque. This power is sent to all four wheels, and if you’re looking to pick one up it’ll cost you around $96,000.

It’s also pretty damn heavy, tipping the scales at 3,084kg!! Then alongside it we have the F-150. This Raptor R edition comes with a huge 5.2-litre supercharged V8 under the bonnet that can put down 700hp and 870Nm of torque. This power is sent to all four wheels via a 10-speed automatic gearbox, and this truck also comes in lighter than the Cybertruck, weighing in at 2,703kg.

Researchers from Tsinghua University, Shanghai Artificial Intelligence Laboratory, and 01.AI have developed a new framework called OpenChat to improve open-source language models with mixed data quality.

Open-source language models such as LLaMA and LLaMA2, which allow anyone to inspect and understand the program code, are often refined and optimized using special techniques such as supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT).

However, these techniques assume that all data used is of the same quality. In practice, however, a data set typically consists of a mixture of optimal and relatively poor data. This can hurt the performance of language models.

To best move in their surrounding environment and tackle everyday tasks, robots should be able to perform complex motions, effectively coordinating the movement of individual limbs. Roboticists and computer scientists have thus been trying to develop computational techniques that can artificially replicate the process through which humans plan, execute, and coordinate the movements of different body parts.

A research group based at Intel Labs (Germany), University College London (UCL, UK), and VERSES Research Lab (US) recently set out to explore the motor control of autonomous robots using hierarchical generative models, computational techniques that organize variables in data into different levels or hierarchies, to then mimic specific processes.

Their paper, published in Nature Machine Intelligence, demonstrates the effectiveness of these models for enabling human-inspired motor control in autonomous robots.

Large language models combined with confidence scores help them recognize uncertainty. That could be key to making robots safe and trustworthy.

Recent advances allow imaging of neurons inside freely moving animals. However, to decode circuit activity, these imaged neurons must be computationally identified and tracked. This becomes particularly challenging when the brain itself moves and deforms inside an organism’s flexible body, e.g. in a worm. Until now, the scientific community has lacked the tools to address the problem.

Now, a team of scientists from EPFL and Harvard have developed a pioneering AI method to track neurons inside moving and deforming animals. The study, now published in Nature Methods, was led by Sahand Jamal Rahi at EPFL’s School of Basic Sciences.

The new method is based on a convolutional neural network (CNN), which is a type of AI that has been trained to recognize and understand patterns in images. This involves a process called “convolution”, which looks at small parts of the picture – like edges, colors, or shapes – at a time and then combines all that information together to make sense of it and to identify objects or patterns.

South Korean conglomerate LG’s AI research division launched an AI-run ETF with Qraft Technologies in November.