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Researchers discover more than 5,500 new RNA virus species in the ocean

Our next challenge, then, was to determine the evolutionary connections between these genes. The more similar the two genes were, the more likely viruses with those genes were closely related. Because these sequences had evolved so long ago (possibly predating the first cell), the genetic signposts indicating where new viruses may have split off from a common ancestor had been lost to time. A form of artificial intelligence called machine learning, however, allowed us to systematically organize these sequences and detect differences more objectively than if the task were done manually.

We identified a total of 5,504 new marine RNA viruses and doubled the number of known RNA virus phyla from five to 10. Mapping these new sequences geographically revealed that two of the new phyla were particularly abundant across vast oceanic regions, with regional preferences in either temperate and tropical waters (the Taraviricota, named after the Tara Oceans expeditions) or the Arctic Ocean (the Arctiviricota).

MIT launches cross-disciplinary program to boost AI hardware innovation

MIT has launched a new academia and industry partnership called the AI Hardware Program that aims to boost research and development.


“A sharp focus on AI hardware manufacturing, research, and design is critical to meet the demands of the world’s evolving devices, architectures, and systems,” says Anantha Chandrakasan, dean of the MIT School of Engineering, and Vannevar Bush Professor of Electrical Engineering and Computer Science.

AI system inspects astronauts’ gloves for damage in real-time

Microsoft and Hewlett Packard Enterprise (HSE) are working with NASA scientists to develop an AI system for inspecting astronauts’ gloves.

Space is an unforgiving environment and equipment failures can be catastrophic. Gloves are particularly prone to wear and tear as they’re used for just about everything, including repairing equipment and installing new equipment.

Currently, astronauts will send back images of their gloves to Earth to be manually examined by NASA analysts.

Artificial intelligence is already upending geopolitics

The TechCrunch Global Affairs Project examines the increasingly intertwined relationship between the tech sector and global politics.

Geopolitical actors have always used technology to further their goals. Unlike other technologies, artificial intelligence (AI) is far more than a mere tool. We do not want to anthropomorphize AI or suggest that it has intentions of its own. It is not — yet — a moral agent. But it is fast becoming a primary determinant of our collective destiny. We believe that because of AI’s unique characteristics — and its impact on other fields, from biotechnologies to nanotechnologies — it is already threatening the foundations of global peace and security.

The rapid rate of AI technological development, paired with the breadth of new applications (the global AI market size is expected to grow more than ninefold from 2020 to 2028) means AI systems are being widely deployed without sufficient legal oversight or full consideration of their ethical impacts. This gap, often referred to as the pacing problem, has left legislatures and executive branches simply unable to cope.

A Cosmic Camera is Being Sent to the Moon’s South Pole

The International Lunar Observatory Association (ILOA) in Hawai’i is preparing to launch a dual-camera system attached to a Moon lander whose primary purpose will be to photograph the cosmos.

ILOA is preparing its precursor science education payload for integration on a pioneering commercial Moon lander later this year, while also continuing to advance more robust observatories for future long-term astronomy, science, and exploration missions.

The International Lunar Observatory (ILO) missions have been in development for the better part of a decade. In 2013, ILOA and the Moon Express corporation announced the private enterprise mission in 2013 that would have both scientific and commercial purposes with the goal of delivering the ILO to the Moon’s South Pole aboard a robotic lander. The hope is that it would establish permanent astrophysical observations and lunar commercial communications systems for professional and amateur researchers.

Researchers at MIT and IBM Propose an Efficient Machine Learning Method That Uses Graph Grammar to Generate New Molecules

Chemical engineers and materials scientists are continuously looking for the following groundbreaking material, chemical, or medication. The emergence of machine-learning technologies has accelerated the discovery process, which may typically take years. Ideally, the objective is to train a machine-learning model on a few known chemical samples and then let it build as many manufacturable molecules of the same class with predictable physical attributes as feasible. You can develop new molecules with ideal characteristics if you have all of these components and the know-how to synthesize them.

However, current approaches need large datasets for training models. Many class-specific chemical databases only contain a few example compounds, restricting their capacity to generalize and construct biological molecules that might be generated in the real world.

This issue was addressed by a team of researchers from MIT and IBM by employing a generative graph model to create new synthesizable compounds within the same training data’s chemical class. The research was presented in a research paper. They model the production of atoms and chemical bonds as a graph and create a graph grammar — a linguistic analog of systems and structures for word ordering — that provides a set of rules for constructing compounds like monomers and polymers.