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A machine learning pipeline named ARTEMIS captures how the landscape of repeat genomic sequences shifts in patients with cancer, and could facilitate earlier detection and monitoring of tumor progression.

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ARTEMIS is a new approach to characterizing genome-wide repeat elements in cancer and cell-free DNA.

Apple acquired Canada-based company DarwinAI earlier this year to build out its AI team, reports Bloomberg. DarwinAI created AI technology for inspecting components during the manufacturing process, and it also had a focus on making smaller and more efficient AI systems.

DarwinAI’s website and social media accounts have been taken offline following Apple’s purchase. Dozens of former DarwinAI companies have now joined Apple’s artificial intelligence division. AI researcher Alexander Wong, who helped build DarwinAI, is now a director in Apple’s AI group.

Devin, SIMA, Figure 1, all in 24 hours. What does it mean and are AI models taking the wheel? I’ll go through 5 relevant papers and 11 articles to get you all the relevant details, from what exactly Devin accomplished, and didn’t, to DeepMind’s new AGI-attempt-in-3D (SIMA) to just how far AI agents have come and what that means for the future of jobs. They’ll also be a guest star … discussing … me?

AI Insiders [Exclusive videos, Discord, Interviews and More]: / aiexplained.

Continue reading “AI Agents Take the Wheel: Devin, SIMA, Figure 01 and The Future of Jobs” »

Waabi says its new model can anticipate how pedestrians, trucks, and bicyclists move using lidar data.

Last summer, Breakthrough Energy, Google Research, and American Airlines announced some promising results from a research collaboration, as first reported in the New York Times. They employed satellite imagery, weather data, software models, and AI prediction tools to steer pilots over or under areas where their planes would be likely to produce contrails. American Airlines used these tools in 70 test flights over six months, and subsequent satellite data indicated that they reduced the total length of contrails by 54%, relative to flights that weren’t rerouted.

There would, of course, be costs to implementing such a strategy. It generally requires more fuel to steer clear of these areas, which also means the flights would produce more greenhouse-gas emissions (more on that wrinkle in a moment).

More fuel also means greater expenses, and airlines aren’t likely to voluntarily implement such measures if it’s not relatively affordable.

AI is expanding across the continent and new policies are taking shape. But poor digital infrastructure and regulatory bottlenecks could slow adoption.

Pilot season has officially begun for the world of humanoid robotics. Last year, Amazon began testing Agility’s Digit robots in select fulfillment centers, while this January, Figure announced a deal with BMW. Now Apptronik is getting in on the action, courtesy of a partnership with Mercedes-Benz.

According to the Austin-based robotics startup, “as part of the agreement Apptronik and Mercedes-Benz will collaborate on identifying applications for highly advanced robotics in Mercedes-Benz Manufacturing.” Specific figures have not been disclosed, as is customary for these sorts of deals. Generally, the actual number of systems included in a pilot are fairly small — understandably so, given the early nature of the technology.

Even so, these deals are regarded as a win-win for both parties. Apptronik can demonstrate clear interest from a leading automotive name, while Mercedes signals to customers and shareholders alike that it’s looking to the future. What comes next is what really matters. Should the pilot go well, causing the carmaker to put in a big order, that would be a massive feather in Apptronik’s cap — and the industry at large.

Apple has added another AI startup to its acquisition list with Canada-based DarwinAI, which specializes in vision-based tech to observe components during manufacturing to improve efficiency, Bloomberg reported.

While Apple and DarwinAI haven’t announced this deal, several members of the startup’s team joined Apple’s machine learning teams in January, as per their LinkedIn profiles.

DarwinAI had raised over $15 million in funding across various rounds from investors, including BDC Capital’s Deep Tech Venture Fund, Honeywell Ventures, Obvious Ventures and Inovia Capital. BDC Capital confirms on its website that it has received an exit from DarwinAI, whereas Obvious Ventures has updated its portfolio to reflect that the startup has been acquired.

Just like Taylor Swift’s wildly successful Eras tour, Nvidia has taken center stage in their own widely successful AI Era tour. From Wall Street to Main Street, everyone is talking about Nvidia, and rightfully so. By powering the latest innovations in AI, Nvidia has achieved 126% revenue growth and 286% net income growth in the past fiscal year, an achievement most companies can only dream about, to become one of the most world’s most valuable companies. All of this is a result of being able to take existing core competencies like their GPU expertise and successfully applying it to an adjacent, yet still emerging use case like artificial intelligence (AI).

Much of Nvidia’s success can be attributed to one of its founders and the only CEO the company has ever had, Jensen Huang. Mr. Huang was recently recognized as one of the world’s most accomplished engineers with his election to the National Academy of Engineering (NAE), a nonprofit organization with more than 2,000 peer-elected members from industry, academia, and government that “provides engineering leadership in service to the nation.” This is a huge career achievement, one of the highest professional distinctions possible for an engineer.

Mr. Huang likes to say that “Nvidia innovates at the speed of light.” To his credit, Mr. Huang has continued to drive this kind of innovation at Nvidia since its inception. Nvidia was one of many companies developing graphics in the early days of PC gaming and one of the few to survive. Nvidia pioneered the Graphics Processing Unit (GPU) and was the first company to promote the concept of using GPUs for general computing purposes, which became known as GPGPU compute and led to the development of the Compute Unified Device Architecture (CUDA) software framework aimed at fully utilizing the massively parallel processing capabilities of Nvidia GPUs. With the advent of deep-learning techniques to train neural network models, Nvidia quickly adapted both its hardware and software solutions to enable an exponential growth in processing capabilities that led to the traditional and generative AI innovations that are sweeping the world today.