Epistemic status: head spinning, suddenly unsure of everything in alignment. And unsure of these predictions.

I’m following the suggestions in 10 reasons why lists of 10 reasons might be a winning strategy in order to get this out quickly (reason 10 will blow your mind!). I’m hoping to prompt some discussion, rather than try to do the definitive writeup on this topic when this technique was introduced so recently. Read More

The news and data giant has — with a relatively small team — built a generative AI that it says outperforms the competition on its own specific information needs.

If you were going to predict which news company would be the first out with its own massive AI model, Bloomberg would’ve been a good bet. For all its success expanding into consumer-facing news over the past decade, Bloomberg is fundamentally a data company, driven by $30,000/year subscriptions to its terminals.

On Friday, the company announced it had built something called BloombergGPT. Think of it as a computer that aims to “know” everything the entire company “knows.” Read More

The Paper

It’s the inaugural week of the second annual edition of Automation April, and to celebrate the occasion, I’ve been working on something special: today, I’m introducing S-GPT, an advanced conversational shortcut for ChatGPT that bridges OpenAI’s assistant to native system features of iOS, iPadOS, macOS, and watchOS.

S-GPT (which stands for Shortcuts-GPT) is free to use for everyone, but it requires an OpenAI account with an associated pay-as-you-go billing plan since it takes advantage of OpenAI’s developer API, which has a cost. S-GPT was built with the latest ChatGPT API, and it can be used both with the existing ChatGPT 3.5 model or – if you have access to it – the ChatGPT 4 API.

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… Computers are, at their core, incredibly dumb; a transistor, billions of which lie at the heart of the fastest chips in the world, are simple on-off switches, the state of which is represented by a 1 or a 0. What makes them useful is that they are dumb at incomprehensible speed; the Apple A16 in the current iPhone turns transistors on and off up to 3.46 billion times a second.

… It is mathematical logic that reduces all of math to a series of logical statements, which allows them to be computed using transistors.

… ChatGPT does great at the “human-like parts”, where there isn’t a precise “right answer”. But when it’s “put on the spot” for something precise, it often falls down. But the whole point here is that there’s a great way to solve this problem—by connecting ChatGPT to Wolfram|Alpha and all its computational knowledge “superpowers”.

… That’s exactly what OpenAI has done, by adding support for plug-ins to ChatGPT. Read More

Artificial intelligence chipmaker Cerebras Systems Inc. today announced it has trained and now released seven GPT-based large language models for generative AI, making them available to the wider research community.

The new LLMs are notable as they are the first to be trained using CS-2 systems in the Cerebras Andromeda AI supercluster, which are powered by the Cerebras WSE-2 chip that is specifically designed to run AI software. In other words, they’re among the first LLMs to be trained without relying on graphics processing unit-based systems. Cerebras said it’s sharing not only the models, but also the weights and training recipe that was used, via a standard Apache 2.0 license. Read More

We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. In this paper, we define and explore a comprehensive list of advanced vision tasks that are intriguing to solve, but may exceed the capabilities >of existing vision and vision-language models. To achieve such advanced visual intelligence, MM-REACT introduces a textual prompt design that can represent text descriptions, textualized spatial coordinates, and aligned file names for >dense visual signals such as images and videos. MMREACT’s prompt design allows language models to accept, associate, and process multimodal information, thereby facilitating the synergetic combination of ChatGPT and various vision experts. Zero-shot experiments demonstrate MM-REACT’s effectiveness in addressing the specified capabilities of interests and its wide application in different scenarios that require advanced visual understanding. Furthermore, we discuss and compare MM-REACT’s system paradigm with an alternative approach that extends language models for multimodal scenarios through joint finetuning. Code, demo, video, and visualization are available at https://multimodal-react.github.io/.

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