It’s the best day of the week guys! Thursday! Whoooooooooooooooooo!

Let’s go learn about AI. Bill Gates is looking happy this morning. Find out why!

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Here is everything you need to know about AI today in 5 minutes.

Read it while you tuck into a delicious croissant.

TODAY IN MINDSTREAM:

Microsoft has dropped a huge bombshell in the AI arms race, unveiling two AI chips yesterday, which they claim can compete with Nvidia’s best.

The first and most important chip, is the Maia 100, which will compete with Nvidia’s highly sought-after H100 and soon-to-launch H200 chip.

Microsoft’s Maia 100 is designed for running cloud AI workloads, like large language model training and inference. It will be used to power some of the company’s largest AI workloads on Azure, including parts of the multibillion-dollar partnership with OpenAI where Microsoft powers all of OpenAI’s workloads.

The software giant has been collaborating with OpenAI on the design and testing phases of Maia, hinting that it could soon be powering ChatGPT and more.

The second, a Cobalt 100 Arm chip, is aimed at general computing tasks and could compete with Intel processors. This could see the transition of general computing, or at least Windows, to Arm chips. Apple has already led the way with their incredible M-series chips powering Macs since 2021.

Both chips will launch in 2024, although whether they can provide the same power and performance ratio as Nvidia’s best remains to be seen.

Read more in The Verge.

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Get smarter about AI with our regular explainers of key concepts and phrases.

Recruitment Neural Network

A Recurrent Neural Network (RNN) is a type of AI model that's good at handling sequences, like sentences in a conversation or a series of stock prices over time. Unlike other AI models that treat all inputs separately, RNNs remember what they've processed before. This memory helps the RNN understand the context and make better predictions.

AI models are fed on huge amounts of data. This has led to the question - how can we determine who should be compensated for this data?

Technology pioneer Jaron Lanier says AI companies should work to determine which individuals contributed the most useful data to their artificial intelligence systems, and compensate them accordingly.

Lanier says that AI companies need to understand where the data comes from and who contributed - with the goal of compensating people more fairly.

“In order to do it, we have to calculate and present the provenance of which human sources were the most important to a given AI output,” he said. “We don't currently do that. We can do it efficiently and effectively, it's just that we're not. It has to be a societal decision to shift to doing that.”

This could lead to a complete shift in how we understand and use data.

Maybe OpenAI will start paying us for the documents we upload to ChatGPT? Hey, I’m an optimist.

More on this in Bloomberg (paywalled).

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