Agricultural equipment maker John Deere says the fully autonomous tractor combines machine learning with the company's GPS-powered auto-steer …

The autonomous tractor is a version of Deere's existing 8R series machine, the largest of which has 410 horsepower. Current 8R users can upgrade their tractors with the autonomous driving system. The Deere tractor, unveiled at the CES 2022 tech conference in Las Vegas, isn't the world's first autonomous tractor. Smaller autonomous tractors are being used in specialty-crop farms. The application of the technology to larger vehicles is just getting started and promises to be highly consequential, according to Aron Cory, research manager for world-wide agriculture at International Data Corp. "The move from conventional tractors to autonomous tractors is going to be comparable from the move from horses to the combustion engine," he said.

Agricultural hardware giant and scourge of right-to-repair advocates everywhere John Deere is ready to show off its finished, fully-autonomous tractor. Here at CES, the company is saying that this unit is going to be put into large-scale production, and will be made available to farmers later this year. When in use, a farmer can set the hardware to work and then leave it running, allowing them to tend to vital work elsewhere. The idea, so the company says, is to help make farming more efficient and more robust in the face of ever-increasing demand and dwindling resources. John Deere's ambitions in this space have been running for some time, and the company was showing off an autonomous tractor at this show in 2019.

Deere & Co. helped mechanize agriculture in 1837 with the first commercially successful steel plow. On Tuesday, the company unveiled a machine that could prove just as transformative: a fully autonomous tractor. John Deere's new 8R tractor uses six pairs of stereo cameras and advanced artificial intelligence to perceive its environment and navigate. It can find its way to a field on its own when given a route and coordinates, then plow the soil or sow seeds without instructions, avoiding obstacles as it goes. A farmer can give the machine new orders using a smartphone app.

For many companies, digitization and automation are the keys to the further development of additive manufacturing. Thus, more and more manufacturers are relying on cloud-based solutions and integrating various algorithms into their 3D printing solutions in order to exploit the full potential of the technology. As a digital process itself, 3D printing is part of Industry 4.0 and thus an important component of an era in which artificial intelligence, such as machine learning, is increasingly being used to optimize the value chain. Artificial intelligence (AI) is able to process a large amount of complex data in a very short time, which is why it is becoming increasingly important as a decision maker. We explain what machine learning is and why this form of AI is helping to shape the future of additive manufacturing.

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.

My current blog is epistem.ink. This one is here just for archival purposes. It surprises me that when people think of "software that brings about the singularity" they think of text models, or of RL agents. To me, this seems counter-intuitive, and the fact that most people researching ML are interested in subjects like vision and language is flabbergasting. For one, because getting anywhere productive in these fields is really hard, for another, because their usefulness seems relatively minimal.

The ocean is the most defining physical feature of Earth, covering 71% of the surface of this planet. It is home to incredible biodiversity, ranging from microscopic bacteria and viruses to the largest animal on Earth, the blue whale. We still do not know how many species live in the ocean, but the disappearance of an increasing number of them suggests that extinction is taking place, perhaps at a faster rate than on land. The stakes for ocean biodiversity have never been higher than they are this decade, and now more than ever, we need results. A widely promoted target is to bring 30% of marine area under protection by 2030--a major step that will contribute a great deal to marine biodiversity.

The evolution of technology and the rise of the internet have changed everything. We now live differently, see more, eat better, and do things differently. But have you ever imagined how technology, especially AI has impacted the way furniture is designed, manufactured, etc? Today, a variety of technologies can be used to promote and advertise end products in a range of furniture segments. Digital catalogs incorporate evolving technologies like augmented reality, virtuality, 3D modeling and art, mobile technology. This type of manufacturing is called additive manufacturing.

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.