Autonomous therapeutic systems are one of the most significant future medical technologies. These systems take over patient care from (human) providers by analyzing, determining and autonomously controlling conditions and treatments. Thus, we need to have a precise simulation of a patient’s medical condition—the patient’s bio digital twin. This should reduce human error and medical care costs. – Kazuhiro Gomi, NTT Research 18 New And Emerging Biotech Developments Everyone Should Know About (forbes.com)

Researchers at MIT in the US and the Technische Universitat Berlin have built a optical neural network processor with embedded lasers that slashes the power consumption of large language model AI such as GPT4.0. Chen, Hamerly, and Englund have filed for a patent on the work, which was sponsored by the US Army Research Office and NTT Research in Japan as well as the Volkswagen Foundation in Germany. Optical AI processor to cut data centre power consumption … (eenewseurope.com)

MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems. The current work is the latest achievement in a drumbeat of progress over the last few years by Englund and many of the same colleagues. For example, in 2019 an Englund team reported the theoretical work that led to the current demonstration. The first author of that paper, Ryan Hamerly, now of RLE and NTT Research Inc., is also an author of the current paper. Machine-learning system based on light could yield more powerful, efficient large language models | MIT News | Massachusetts Institute of Technology

The surge of interest in the overall category of digital twins has led to some confusion, as tends to happen during “hype” cycles. A closer look at the cardiovascular bio-digital twin (one area of focus for my research organization) can help clarify matters. First, the existence of this niche says something. A bio-digital twin does not have to represent an entire person. Just as you can build twins of subsystems such as aircraft wings or landing gears, you can also model physiological systems—something less than an entire human body. Bio-Digital Twin: Getting The Heart Right (forbes.com)

A new processor that uses light, instead of electrons, has the potential to perform its computations up to 100 times more powerfully and efficiently than existing processors inside machine-learning computer systems. That’s according to a new USC and MIT study published in the journal Nature Photonics. Zaijun Chen of USC, Ryan Hamerly of NTT Research, and Dirk Englund are the corresponding authors. Other co-authors who also contributed significantly are Alexander Sludds, Ronald Davis, Ian Christen, Liane Bernstein, and Lamia Ateshian of RLE. The key component of this computing system, VCSEL arrays, is fabricated and provided by Tobias Heuser, Niels Heermeier, James A. Lott, and Stephan Reitzensttein of Technische Universitat Berlin. New Laser-Array Processor Could Vastly Improve AI Computing Efficiency (photonicsonline.com)

Quantum approach: NTT DATA used Quantum Annealing technology with superconducting qubits from D-Wave. NTT Research’s CIM (Coherent Ising Machine) then simulated the technology. The comparison was based on the measurement of computational efficiency, accuracy, and scalability indicators. Quantum News Briefs July 31: OQC and Cogniframe partner to enable users, regardless of their level of expertise, to test drive quantum; Researchers from ORNL use Quantinuum H1-1 quantum computer to identify molecular candidate for development of more efficient solar cells; Why India risks a quantum tech brain drain; Researchers from ORNL use Quantinuum H1-1 quantum computer to identify molecular candidate for development of more efficient solar cells + MORE – Inside Quantum Technology

The impact artificial intelligence will have on society is similar to or greater than that of the discovery of nuclear energy, so robust ethical guidelines need to be put in place. And it’s much easier to access AI than nuclear energy, which makes AI more difficult to control. A lot of thought must go into developing ways to detect AI’s harms and correct them. We don’t currently have many effective measures for this. – Kazuhiro Gomi, NTT Research 16 Current And Potential Ethical Crises In Technology (forbes.com)