Musk’s plan to use Chinese data to train algorithms aligns with the country’s ambitions to lead in autonomous driving technologies.
If you use the web for more than just browsing (that’s pretty much everyone), chances are you’ve had your fair share of “CAPTCHA rage,” the frustration stemming from trying to discern a marginally legible string of letters aimed at verifying that you are a human. CAPTCHA, which stands for “Completely Automated Public Turing test to tell Computers and Humans Apart,” was introduced to the Internet a decade ago and has seen widespread adoption in various forms — whether using letters, sounds, math equations, or images — even as complaints about their use continue.
A large-scale Stanford study a few years ago concluded that “CAPTCHAs are often difficult for humans.” It has also been reported that around 1 in 5 visitors will leave a website rather than complete a CAPTCHA.
Researchers at the University of Basel have developed a new method for calculating phase diagrams of physical systems that works similarly to ChatGPT. This artificial intelligence could even automate scientific experiments in the future.
A year and a half ago, ChatGPT was released, and ever since, there has been hardly anything that cannot be created with this new form of artificial intelligence: texts, images, videos, and even music. ChatGPT is based on so-called generative models, which, using a complex algorithm, can create something entirely new from known information.
A research team led by Professor Christoph Bruder at the University of Basel, together with colleagues at the Massachusetts Institute of Technology (MIT) in Boston, have now used a similar method to calculate phase diagrams of physical systems.
An advanced new technique combines machine-learning algorithms with measurements of vibrations for monitoring tool wear.
By making use of randomness, a team has created a simple algorithm for estimating large numbers of distinct objects in a stream of data.
Oscar Wilde once said that sarcasm was the lowest form of wit, but the highest form of intelligence. Perhaps that is due to how difficult it is to use and understand. Sarcasm is notoriously tricky to convey through text—even in person, it can be easily misinterpreted. The subtle changes in tone that convey sarcasm often confuse computer algorithms as well, limiting virtual assistants and content analysis tools.
Longevity businessman Peter Diamandis said he prioritized his body composition over everything else last year.
“Big machine learning models have to consume lots of power to crunch data and come out with the right parameters, whereas our model and training is so extremely simple that you could have systems learning on the fly,” said Robert Kent.
How can machine learning be improved to provide better efficiency in the future? This is what a recent study published in Nature Communications hopes to address as a team of researchers from The Ohio State University investigated the potential for controlling future machine learning products by creating digital twins (copies) that can be used to improve machine learning-based controllers that are currently being used in self-driving cars. However, these controllers require large amounts of computing power and are often challenging to use. This study holds the potential to help researchers better understand how future machine learning algorithms can exhibit better control and efficiency, thus improving their products.
“The problem with most machine learning-based controllers is that they use a lot of energy or power, and they take a long time to evaluate,” said Robert Kent, who is a graduate student in the Department of Physics at The Ohio State University and lead author of the study. “Developing traditional controllers for them has also been difficult because chaotic systems are extremely sensitive to small changes.”
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A novel algorithm enables robots to flexibly squish, bend, or stretch for tasks such as obstacle avoidance or item retrieval.
