Lifeboat Foundation

Nadella highlighted that while generative AI tools, such as ChatGPT and Dall-E, generated less than 1% of the world’s AI data sets in 2021, this can increase to 10% of all data generated by AI by 2025.

“In future, the generative models will generate most of the data. We are right now seeing the emergence of a new reasoning engine. We’ll clearly have to talk about this reasoning engine — what are its responsible uses, what displacements will it cause, and so on. But on the other side, we should also think about how it can augment us in what we are doing today since it can have a huge impact on our future,” Nadella said.

“Ultimately, these tools will accelerate creativity, ingenuity and productivity across a range of tasks. It is going to be a golden age — the computer revolution created mass consumer behaviour change and productivity for knowledge workers. But, what if we could spread that productivity more evenly? To me, that is one of the biggest things to look forward to, and the way to achieve this is by building a robust data infrastructure,” he added.

In this article, I will explain how I used GPT-3 to integrate AI into a simple productivity application. Before I go into the code and methods used, let’s have look at the final result:

I really don’t care about IQ tests; ChatGPT does not perform at a human level. I’ve spent hours with it. Sometimes it does come off like a human with an IQ of about 83, all concentrated in verbal skills. Sometimes it sounds like a human with a much higher IQ than that (and a bunch of naive prejudices). But if you take it out of its comfort zone and try to get it to think, it sounds more like a human with profound brain damage. You can take it step by step through a chain of simple inferences, and still have it give an obviously wrong, pattern-matched answer at the end. I wish I’d saved what it told me about cooking and neutrons. Let’s just say it became clear that it did was not using an actual model of the physical world to generate its answers.

Other examples are cherry picked. Having prompted DALL-E and Stable Diffusion quite a bit, I’m pretty convinced those drawings are heavily cherry picked; normally you get a few that match your prompt, plus a bunch of stuff that doesn’t really meet the specs, not to mention a bit of eldritch horror. That doesn’t happen if you ask a human to draw something, not even if it’s a small child. And you don’t have to iterate on the prompt so much with a human, either.

Competitive coding is a cherry-picked problem, as easy as a coding challenge gets… the tasks are tightly bounded, described in terms that almost amount to code themselves, and come with comprehensive test cases. On the other hand, “coding assistants” are out there annoying people by throwing really dumb bugs into their output (which is just close enough to right that you might miss those bugs on a quick glance and really get yourself into trouble).

https://www.youtube.com/watch?v=bAzsRzQfirI

Today we’re looking at how to write music WITH A.I. A lot of the current Music AI Tool are focused on AI that writes full tracks, but I wanted to show a process that allows a collaboration between the musician and AI.

In this video I’ll take an original song I wrote, have an AI analyze it and come up with an alternative version, then take the AI’s version and write a new track based on its output. Oh, and have it sound like an actual song as well.

Continue reading “How to Write Music WITH A.I.” »

https://www.youtube.com/watch?v=S_1O3OP22UQ&t=2s

Try Chat GTP: https://openai.com/blog/chatgpt/

(Non-affiliate link)

Continue reading “The Advent of Artificial Intelligence | ChatGTP” »

What an AI taught me about our future – a mind blowing podcast.

In this groundbreaking podcast, James Brauer interviews “Futurist”, an AI that specializes in forecasting the future of mankind.

Continue reading “What An AI Taught Me About Our Future — A Mind Blowing Podcast” »

Taken from JRE #1919 w/Bret Weinstein:

ChatGPT integration with Bing’s search engine.

Microsoft’s Bing search engine might soon become more attuned to users’ needs and return results in a more human-like fashion. According to The Information, the tech giant is planning to incorporate the OpenAI software powering ChatGPT into Bing in hopes that it can help the company catch up to (or maybe even outshine) Google. Microsoft invested $1 billion in OpenAI back in 2019, and more recent reports said it’s in talks with the Elon Musk-founded startup for a follow-up investment. Now, The Information is reporting that Microsoft’s initial investment included an agreement to incorporate some aspects of GPT into Bing.

OpenAI developed GPT as a language model that uses deep learning to generate human-like text responses. Late last year, it launched a program called ChatGPT that quickly skyrocketed in popularity due to its ability to return responses that seem like they were written by actual people. Educators raised concerns that it could easily be used for cheating, since those who tried the tool said they would’ve given its responses a good grade if a student claimed to have written them. ChatGPT is free for now, but OpenAI intends to charge for its use in the future.

Continue reading “Microsoft is reportedly integrating ChatGPT’s technology into Bing” »

Greg Yang is a mathematician and AI researcher at Microsoft Research who for the past several years has done incredibly original theoretical work in the understanding of large artificial neural networks. Greg received his bachelors in mathematics from Harvard University in 2018 and while there won the Hoopes prize for best undergraduate thesis. He also received an Honorable Mention for the Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student in 2018 and was an invited speaker at the International Congress of Chinese Mathematicians in 2019.

In this episode, we get a sample of Greg’s work, which goes under the name “Tensor Programs” and currently spans five highly technical papers. The route chosen to compress Tensor Programs into the scope of a conversational video is to place its main concepts under the umbrella of one larger, central, and time-tested idea: that of taking a large N limit. This occurs most famously in the Law of Large Numbers and the Central Limit Theorem, which then play a fundamental role in the branch of mathematics known as Random Matrix Theory (RMT). We review this foundational material and then show how Tensor Programs (TP) generalizes this classical work, offering new proofs of RMT. We conclude with the applications of Tensor Programs to a (rare!) rigorous theory of neural networks.

Continue reading “Greg Yang | Large N Limits: Random Matrices & Neural Networks | The Cartesian Cafe w/ Timothy Nguyen” »