Today, two labs separately announced programs that use diffusion models to generate designs for novel proteins with more precision than ever before. Generate Biomedicines, a Boston-based startup, revealed a program called Chroma, which the company describes as the "DALL-E 2 of biology." At the same time, a team at the University of Washington led by biologist David Baker has built a similar program called RoseTTAFold Diffusion. In a preprint paper posted online today, Baker and his colleagues show that their model can generate precise designs for novel proteins that can then be brought to life in the lab. "We're generating proteins with really no similarity to existing ones," says Brian Trippe, one of the co-developers of RoseTTAFold. These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function.

Conventional digital computers, by prioritizing reliability, have missed out, said Turing Award winner Geoffrey Hinton, on "all sorts of variable, stochastic, flakey, analog, unreliable properties of the hardware, which might be very useful to us." Machine-learning forms of artificial intelligence are going to produce a revolution in computer systems, a new kind of hardware-software union that can put AI in your toaster, according to AI pioneer Geoffrey Hinton. Learn about the leading tech trends the world will lean into over the next 12 months and how they will affect your life and your job. Hinton, offering the closing keynote Thursday at this year's Neural Information Processing Systems conference, NeurIPS, in New Orleans, said that the machine learning research community "has been slow to realize the implications of deep learning for how computers are built." He continued, "What I think is that we're going to see a completely different type of computer, not for a few years, but there's every reason for investigating this completely different type of computer." All digital computers to date have been built to be "immortal," where the hardware is engineered to be reliable so that the same software runs anywhere.

Kay talks to us about why AI Ethics matter during her presentation at the RE•WORK Applied AI Virtual Summit. But the second piece is really, well, if you are not creating trusted AI, then you're going to have a loss of brand value if something goes wrong, so the risks have to really be weighed with the benefits of AI. I also I don't think that you could ignore trusted AI or AI ethics anymore because we've got over 160 different sets of ethical principles out there, from Beijing to Montreal to everywhere in the world. We also worked with the Singaporean Government on how companies could ethically use AI. A few other places where we have high-risk case uses of AI and lots of ethical issues involved is obviously facial recognition technology, we're working on a project with France around that.

While artificial intelligence long ago surpassed human capability in Chess, and more recently Go -- and let us not forget Doom -- other more complex board games still present a challenge to computer systems. Until very recently, Stratego and Diplomacy were two of those games, but now AI has become table-flipping good at the former and passably human at the latter. On the surface, you might think that it's just because these games require a certain level of long-term planning and strategy. But so do Go and Chess, just in a different way. The crucial difference is actually that Stratego and Diplomacy are games of strategy based on imperfect information.

Monarch Tractor this morning announced the delivery of its first MK-V unit. The "smart tractor" is electric and what the Bay Area-based company refers to as "driver optional" (terms like "autonomous" and "self-driving" come with their own unique baggage). We'll just refer to it as a "robot tractor" from here on. The system was unveiled a bit under two or so years ago. The timing was certainly right.

Object detection has been an important task in the computer vision domain in recent decades. The goal is to detect instances of objects, such as humans, cars, etc., in digital images. Hundreds of methods have been developed to answer a single question: What objects are where? Traditional methods tried to answer this question by extracting hand-crafted features like edges and corners within the image. Most of these approaches used a sliding-window approach, meaning that they kept checking small parts of the image in different scales to see if any of these parts contained the object they were looking for.

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. JumpStart also offers example notebooks that use Amazon SageMaker features like spot instance training and experiments over a large variety of model types and use cases. These example notebooks contain code that shows how to apply ML solutions by using SageMaker and JumpStart. They can be adapted to match to your own needs and can thus speed up application development. Recently, we added 10 new notebooks to JumpStart in Amazon SageMaker Studio.

Saha noted that customers approach machine learning in different ways, so AWS seeks to meet them where they are in their implementation. According to Saha, customers fall into one of three layers of development, and AWS offers services for each layer. "At the bottom layer are the machine learning infrastructure services. This is where we provide the machine learning hardware and software that customers can use to build their own machine learning infrastructure," he said. "This is meant for customers with highly custom needs, and that is why they want to build their own machine learning infrastructure."

This strategy differs from standard centralized machine learning methods, which store all local datasets on a single server.

New research describes how two machine-learning tools developed by Johns Hopkins researchers can determine where seizures begin and the success of …