Lifeboat Foundation

Using radar commonly deployed to track speeders and fastballs, researchers have developed an automated system that will allow cars to peer around corners and spot oncoming traffic and pedestrians.

The system, easily integrated into today’s vehicles, uses Doppler radar to bounce radio waves off surfaces such as buildings and parked automobiles. The radar signal hits the surface at an angle, so its reflection rebounds off like a cue ball hitting the wall of a pool table. The signal goes on to strike objects hidden around the corner. Some of the radar signal bounces back to detectors mounted on the car, allowing the system to see objects around the corner and tell whether they are moving or stationary.

“This will enable cars to see occluded objects that today’s lidar and camera sensors cannot record, for example, allowing a self-driving vehicle to see around a dangerous intersection” said Felix Heide, an assistant professor of computer science at Princeton University and one of researchers. “The radar sensors are also relatively low-cost, especially compared to lidar sensors, and scale to mass production.”

The Memo: https://lifearchitect.ai/memo/

Demo site: https://muse-model.github.io/
Read the paper: https://arxiv.org/abs/2301.

Continue reading “First look — Muse by Google AI/Research — Launched 2/Jan/2023 — (3B + 4.6B T5-XXL) — Google Muse” »

Proving you’re a human on a web flooded with generative AI content.

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).

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.” »

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” »