Amazon is reportedly investing millions in training Olympus.
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The Information reported that Olympus will be Amazon’s answer to the AI leaps made by OpenAI and Microsoft in selling similar software to corporate customers. Another report by Reuters said that the company is investing millions in training Olympus, according to the two people familiar with the matter.
Android Studio, like so much of Google’s product portfolio, is getting its infusion of AI today at the company’s annual I/O developer conference. Android Studio Hedgehog, the upcoming version of Android Studio currently in the canary release channel, will be the first to add support for the new conversational experience in Android Studio meant to help developers write code and fix bugs and answer more general coding questions.
Built on top of Codey, Google’s new PaLM 2-based foundation model specifically trained for coding, the Studio Bot will roll out to developers in the U.S. first, with a wider rollout expected over time.
face_with_colon_three year 2022.
Sozos and co-workers present and numerically evaluate photonic neuromorphic hardware using recurrent optical spectrum slicing for use in ultra-fast optical applications. The approach extends optical signal transmission reach to more than four-fold that of two state-of-the-art digital equalizers and reduces power consumption tenfold.
More than 10 Chinese companies this year have revealed innovations related to humanoid robots, he noted, adding that China already has some supporting facilities from developing industrial robots.
Beijing has set aside about 10 billion yuan (about $1.4 billion) to fund the robotic development. On Nov. 6, China opened the first provincial-level innovation center on humanoid robots in the country’s capital to work on solving pressing “key common problems,” including an operation control system, open source software, and robot prototypes.
At least one Chinese company, Jiangsu Miracle Logistics System Engineering Co., has promised to introduce its first humanoid robot by the end of the year. Chinese securities brokerage firm Zheshang Securities estimates that the humanoid robot market will have a demand for 1.77 million machines by 2030.
In particular, many have griped over their original work being used to train these AI models — a use they never opted into, and for which they’re not compensated.
But what if artists could “poison” their work with a tool that alters it so subtly that the human eye can’t tell, while wreaking havoc on AI systems that try to digest it?
That’s the idea behind a new tool called “Nightshade,” which its creators say does exactly that. As laid out in a yet-to-be-peer-reviewed paper spotted by MIT Technology Review, a team of researchers led by University of Chicago professor Ben Zhao built the system to generate prompt-specific “poison samples” that scramble the digital brains of image generators like Stable Diffusion, screwing up their outputs.
Researchers at the University of Barcelona have made a sweet discovery: Honeybees make great subjects when studying the dynamic of group behavior and decision-making.
In a recently released study, Professor M. Carmen Miguel, who has previously studied leadership activity among schooling fish and social interactions among flocks of birds, said a group of mini robots were trained to reach a consensus on tasks by mimicking processes displayed by bees.
The intricate behavior of bees has long been a subject of great interest among researchers. There are more than 4,000 species of the insect, and they have been around for more than 100 million years.
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MIT scientists have developed a deep learning system, Air-Guardian, designed to work in tandem with airplane pilots to enhance flight safety. This artificial intelligence (AI) copilot can detect when a human pilot overlooks a critical situation and intervene to prevent potential incidents.
The backbone of Air-Guardian is a novel deep learning system known as Liquid Neural Networks (LNN), developed by the MIT Computer Science and Artificial Intelligence Lab (CSAIL). LNNs have already demonstrated their effectiveness in various fields. Their potential impact is significant, particularly in areas that require compute-efficient and explainable AI systems, where they might be a viable alternative to current popular deep learning models.
