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Category: robotics/AI – Page 1,317

Deep learning tool’s ‘computational microscope’ predicts protein interactions, potential paths to new antibiotics

Though it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.

In a new paper published in eLife, researchers in the School of Biological Sciences and the School of Computer Science have shown that AF2Complex may be able to lend a hand.

Building on the models of DeepMind’s AlphaFold 2, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex—short for AlphaFold 2 Complex—is a deep learning tool designed to predict the physical interactions of multiple proteins. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.

How can artificial intelligence fuel the logistics industry?

Artificial Intelligence is the buzzword of the year with many big giants in almost every industry trying to explore this cutting-edge technology. Right from self-checkout cash registers to AI-based applications to analyse large data in real-time to advanced security check-ins at the airport, AI is just about everywhere.

Currently, the logistics industry is bloated with a number of challenges related to cost, efficiency, security, bureaucracy, and reliability. So, according to the experts, new age technologies like AI, machine learning, the blockchain, and big data are the only fix for the logistics sector which can improve the supply chain ecosystem right from purchase to internal exchanges like storage, auditing, and delivery.

AI is an underlying technology which can enhance the supplier selection, boost supplier relationship management, and more. When combined with big data analytics AI also helps in analysing the supplier related data such as on-time delivery performance, credit scoring, audits, evaluations etc. This helps in making valuable decisions based on actionable real-time insights.

The Physics Principle That Inspired Modern AI Art

The first important generative models for images used an approach to artificial intelligence called a neural network — a program composed of many layers of computational units called artificial neurons. But even as the quality of their images got better, the models proved unreliable and hard to train. Meanwhile, a powerful generative model — created by a postdoctoral researcher with a passion for physics — lay dormant, until two graduate students made technical breakthroughs that brought the beast to life.

DALL·E 2 is such a beast. The key insight that makes DALL·E 2’s images possible — as well as those of its competitors Stable Diffusion and Imagen — comes from the world of physics. The system that underpins them, known as a diffusion model, is heavily inspired by nonequilibrium thermodynamics, which governs phenomena like the spread of fluids and gases. “There are a lot of techniques that were initially invented by physicists and now are very important in machine learning,” said Yang Song, a machine learning researcher at OpenAI.

A Challenge to Test Dall.E 2: Even We Were Shocked by the Results

The goal of this activity was to have fun & boost everyone’s imagination to the limit. Everyone was shocked by Dall. E 2’s creative scope & infinite possibilities.

As this session was interactive & thought-provoking, it turned the usual tiresome process of learning into an energetic experience.

For those unfamiliar with Dall. E 2, it is Open AI’s newest tool that helps generate images from text inputs in seconds. The name “Dall. E 2” is a combination of the Spanish artist Salvador “Dali” & Pixar’s “Wall-E”. Dall. E 2 uses GPT 3 (Third generation Generative Pre-trained transformer) which is Open AI’s newest software release.

TIMELAPSE OF ROBOTS IN THE FUTURE (2030 — 3000+)

– 3000+)

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We’ve all heard and brushed off those crazy seeming futurists claims that robots replace most human activities in the future. But when we look at the pace at which AI and technology is growing, the thought doesn’t seem so crazy afterall.

#Robot #Timelapse #Future.

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New radar allows cars to spot hazards around corners

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.”

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

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

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

Dr Alan D. Thompson is a world expert in artificial intelligence (AI), specialising in the augmentation of human intelligence, and advancing the evolution of ‘integrated AI’. Alan’s applied AI research and visualisations are featured across major international media, including citations in the University of Oxford’s debate on AI Ethics in December 2021.

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Music:
Under licence.

Liborio Conti — Looking Forward (The Memo outro)
https://no-copyright-music.com/

Generative models like Dall-E, ChatGPT to give rise to a ‘golden age’: Satya Nadella

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.

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