face_with_colon_three circa 2021.
Microsoft has developed an AI program called BugLab, which can help you find and fix bugs in your code. Here’s how it works!
Improving the efficiency of algorithms for fundamental computations is a crucial task nowadays as it influences the overall pace of a large number of computations that might have a significant impact. One such simple task is matrix multiplication, which can be found in systems like neural networks and scientific computing routines. Machine learning has the potential to go beyond human intuition and beat the most exemplary human-designed algorithms currently available. However, due to the vast number of possible algorithms, this process of automated algorithm discovery is complicated. DeepMind recently made a breakthrough discovery by developing AplhaTensor, the first-ever artificial intelligence (AI) system for developing new, effective, and indubitably correct algorithms for essential operations like matrix multiplication. Their approach answers a mathematical puzzle that has been open for over 50 years: how to multiply two matrices as quickly as possible.
AlphaZero, an agent that showed superhuman performance in board games like chess, go, and shogi, is the foundation upon which AlphaTensor is built. The system expands on AlphaZero’s progression from playing traditional games to solving complex mathematical problems for the first time. The team believes this study represents an important milestone in DeepMind’s objective to improve science and use AI to solve the most fundamental problems. The research has also been published in the established Nature journal.
Matrix multiplication has numerous real-world applications despite being one of the most simple algorithms taught to students in high school. This method is utilized for many things, including processing images on smartphones, identifying verbal commands, creating graphics for video games, and much more. Developing computing hardware that multiplies matrices effectively consumes many resources; therefore, even small gains in matrix multiplication efficiency can have a significant impact. The study investigates how the automatic development of new matrix multiplication algorithms could be advanced by using contemporary AI approaches. In order to find algorithms that are more effective than the state-of-the-art for many matrix sizes, AlphaTensor further leans on human intuition. Its AI-designed algorithms outperform those created by humans, which represents a significant advancement in algorithmic discovery.
The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine.
The present essay explores key similarities and differences in the process of computation by the brains of animals and by digital computing, by anchoring the exploration on the essential properties of a Universal Turning Machine, the abstract foundation of modern digital computing. In this context, we try to explicitly distance XVIIIth century mechanical automata from modern machines, understanding that when computation allows recursion, it changes the consequences of determinism. A mechanical device is usually both deterministic and predictable, while computation involving recursion is deterministic but not necessarily predictable. For example, while it is possible to design an algorithm that computes the decimal digits of π, the value of any finite sequence following the nth digit, cannot (yet) be computed, hence predicted, with n sufficiently large.
AI programs are not crystal balls.
If you’ve ever wondered what the apocalypse would look like in the United States, artificial intelligence has been asked to predict it.
Popular TikTok accounts like “Robot Overloards” have been asking AI to predict futuristic events, including the demise of humanity and the apocalypse.
Continue reading “Creepiest things AI has predicted — from the apocalypse to last selfie on Earth” »
The social media platform announced on Friday that it identified more than 400 malicious Android and iOS apps this year which target internet users in order to steal their login credentials.
Meta Platforms Inc. reveals that it would notify one million Facebook users that their account credentials may have been compromised due to security issues with apps downloaded from Alphabet Inc. and Apple Inc.’s software store.
https://www.livemint.com/technology/apps/facebook-warns-agai…5206859852.
The device weighs less than 3 lbs.
Researchers from Edinburgh’s National Robotarium have developed an AI helmet to help firefighters navigate a smoke-filled environment and rescue victims more quickly. The team created the device using sensors, thermal cameras, and radar technologies, according to a press release published by Heriot-Watt University, Edinburgh, Scotland, last week.
Firefighters are heroes. Everyone knows that.
Rideshare provider Uber has signed a 10-year, multi-market commercial agreement with Motional – a developer of driverless robotaxis. As part of the agreement, Uber will deploy Motional’s IONIQ 5 electric robotaxis in select markets at first, with the potential to reach millions of customers by providing both ride-hailing and delivery services autonomously.
Motional is an autonomous driving technology developer that exists as a joint venture between Hyundai Motor Group and Aptiv – specialists in advanced safety, electrification, and vehicle connectivity. It is headquartered in Boston with more recent offices in Santa Monica, California, where the company has been testing its driverless robotaxis built upon Hyundai IONIQ 5 EVs.
In 2021, the public got the first glimpse of Motional’s IONIQ 5 robotaxis, which have completed a fully-autonomous cross-country drive in the US, in addition to 100,000 public rides. Prior to today’s news, Motional has already had a working relationship with Uber Technologies ($UBER) that has consisted of autonomous food deliveries in the Los Angeles area.
This technique involves having participants place their finger over the camera and flash of a smartphone, which uses a deep-learning algorithm to decipher the blood oxygen levels from the blood flow patterns in the resulting video.
Conditions like asthma or COVID-19 make it harder for bodies to absorb oxygen from the lungs. This leads to oxygen saturation percentages dropping to 90% or below, indicating that medical attention is needed.
In a clinic, doctors monitor oxygen saturation using pulse oximeters — those clips you put over your fingertip or ear. But monitoring oxygen saturation at home multiple times a day could help patients keep an eye on COVID symptoms, for example.
A new study has revealed that researchers have used artificial intelligence to create a map that allows them to predict the distribution of dark matter throughout the universe.
The new study has been published in the Astrophysical Journal and shows that researchers have taken a different approach to creating a model of the distribution of dark matter. So far, researchers know that dark matter makes up 80% of the universe, and creating a model of the distribution of dark matter allows cosmologists to construct what is called a “cosmic web”.
With this cosmic web, cosmologists and researchers will be able to see how dark matter impacts the motion of galaxies in the past, present, and future. Researchers in the new study used machine learning, a branch of artificial intelligence, to construct a new model. The AI was fed a large set of galaxy simulations that include galaxies, dark matter, visible matter, and gases.
