In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI's carbon footprint and other impacts. The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI "gold rush" remain difficult to pin down, let alone mitigate.

Then, they saw the light. Now, miniature biological robots have gained a new trick: remote control. The hybrid "eBiobots" are the first to combine soft materials, living muscle and microelectronics, said researchers at the University of Illinois Urbana-Champaign, Northwestern University and collaborating institutions. They described their centimeter-scale biological machines in the journal Science Robotics. "Integrating microelectronics allows the merger of the biological world and the electronics world, both with many advantages of their own, to now produce these electronic biobots and machines that could be useful for many medical, sensing and environmental applications in the future," said study co-leader Rashid Bashir, an Illinois professor of bioengineering and dean of the Grainger College of Engineering.

At a Call of Duty tournament in Islamabad, Pakistan, an exasperated gamer stands up from his computer and demands that the player who keeps sniping him speak up. "Who is this '$@dy'?" he bellows, referencing the player's in-game name, his eyes scanning the room in furious anticipation--but what happens next turns his anger into embarrassment, for a diminutive young woman nervously raises her hand. Now, more than 15 years later, Sadia Bashir, 33, recalls the encounter with a glint in her eye. "I was the only girl in a room full of boys, and the moment he saw me, he just sat back down again. I guess the thought of being killed by a girl really hurt his ego." At the time, Bashir was just a computer science major with a dream that she could somehow make a living in the mysterious world of video games.

The National Institutes of Standards and Technology (NIST) is a federal agency best known for measuring things like time or the number of photons that pass through a chicken. Now NIST wants to put a number on a person's trust in artificial intelligence. Trust is part of how we judge the potential for danger, and it's an important factor in the adoption of AI. As AI takes on more and more complicated tasks, officials at NIST say, trust is an essential part of the evolving relationship between people and machines. In a research paper, creators of the attempt to quantify user trust in AI say they want to help businesses and developers who deploy AI systems make informed decisions and identify areas where people don't trust AI.

The first author, Gelson Pagan-Diaz-Diaz, likens the produced tissue to a computer processing unit, which provided the basic principle to today's supercomputer. Pagan-Diaz is a graduate student in Prof. Rashid Bashir's group in the Department of Bioengineering at the Grainger College of Engineering. Bashir is also the Dean of the College. "Being able to form 3-dimensional tissue consisting of neurons can give us the ability to develop tissue models for drug screening or processing units for biological computers," Pagan-Diaz said. The brain is challenging to study in an actual person, but being able to understand how these networks develop using a 3D model outside the body promises to give researchers a new tool to better understand how it works.

Chiniki Guard, a Nigerian Information Communication Technology (ICT) startup application has won the best prize of 10,000 USD in the Artificial Intelligence category competition at the ongoing Gulf Information Technology Exhibition (GITEX). GITEX is a global annual and biggest technology gathering in the Middle East,North Africa and South Asia with theme "Synergising the Mind and Technology Economy."aqq Chiniki Guard is an Artificial Intelligence security solution for retail stores,supermarkets to monitor,detect and alert shop owners of shoplifting as well as suspicious behaviour in real time. Chiniki Guard is among the 34 startups in the category of the Supanova finalists at the GITEX Futurestars competition. Speaking with the News Agency of Nigeria (NAN)Mr Abdulhakim Bashir,the Founder of Chiniki Guard, said the opportunities at GITEX had accorded him the win and would enable him widen the scope of his business.

In another huge setback to Infosys CEO Vishal Sikka's strategy to strengthen the company's new digital and innovation capabilities, Yusuf Bashir, MD of the $500-million Infosys Innovation Fund, has resigned. The MIT-alumnus had worked closely with Sikka at SAP as VP of new products before joining Infosys in March 2015. Together with another former SAP executive, Ritika Suri, Bashir was to help Infosys take a leap into the new digital world that has become important to global clients. Bashir, based in Palo Alto, had the mandate to identify and invest in early-stage companies doing cutting-edge work in areas including AI, machine learning, big data, cloud and analytics. Acquisitions has been a key strategy used by Accenture, for instance, to build the new digital capabilities.

Human gait has been shown to be an efficient biometric measure for person identification at a distance. However, it often needs different gait features to handle various covariate conditions including viewing angles, walking speed, carrying an object and wearing different types of shoes. In order to improve the robustness of gait-based person re-identification on such multi-covariate conditions, a novel Swiss-system based cascade ranking model is proposed in this paper. Since the ranking model is able to learn a subspace where the potential true match is given the highest ranking, we formulate the gait-based person re-identification as a bipartite ranking problem and utilize it as an effective way for multi-feature ensemble learning. Then a Swiss multi-round competition system is developed for the cascade ranking model to optimize its effectiveness and efficiency. Extensive experiments on three indoor and outdoor public datasets demonstrate that our model outperforms several state-of-the-art methods remarkably.