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Researchers from Tohoku University and the Massachusetts Institute of Technology (MIT) have unveiled a new AI tool for high-quality optical spectra with the same accuracy as quantum simulations, but working a million times faster, potentially accelerating the development of photovoltaic and quantum materials.

Organizations are losing between $94 — $186 billion annually to vulnerable or insecure APIs (Application Programming Interfaces) and automated abuse by bots. That’s according to The Economic Impact of API and Bot Attacks report from Imperva, a Thales company. The report highlights that these security threats account for up to 11.8% of global cyber events and losses, emphasizing the escalating risks they pose to businesses worldwide.

Drawing on a comprehensive study conducted by the Marsh McLennan Cyber Risk Intelligence Center, the report analyzes over 161,000 unique cybersecurity incidents. The findings demonstrate a concerning trend: the threats posed by vulnerable or insecure APIs and automated abuse by bots are increasingly interconnected and prevalent. Imperva warns that failing to address security risks associated with these threats could lead to substantial financial and reputational damage.

Researchers have developed an algorithm that could dramatically reduce the energy consumption of artificial intelligence systems.

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Scientists at BitEnergy AI created a method called “Linear-complexity multiplication” (L-Mul) that replaces complex floating-point multiplications in AI models with simpler integer additions.

The advent of LLMs has reopened a debate about the limits of machine intelligence — and requires new benchmarks of what reasoning consists of.

One milestone along that journey is the demonstration of machine common sense.

AI-powered assistants are set to become a part of daily life, offering guidance and reminders through body-worn devices that blend AI with XR.

A student built a pangolin-inspired robot for planting, winning the University of Surrey’s robotics contest.

A California student created Plantolin, a pangolin-inspired robot for digging and planting, winning the University of Surrey’s contest.

How is AI impacting data systems? Discover the answers from experts at NVIDIA, Google, Microsoft, and Western Digital.

Despite the promising findings, the study acknowledges several limitations of quantum computing. One of the primary challenges is hardware noise, which can reduce the accuracy of quantum computations. Although error correction methods are improving, quantum computing has not yet reached the level of fault tolerance needed for widespread commercial use. Additionally, the study notes that while quantum computing has shown promise in PBPK/PD modeling and site selection, further research is needed to fully realize its potential in these areas.

Looking ahead, the study suggests several future directions for research. One of the key areas for improvement is the integration of quantum algorithms with existing clinical trial infrastructure. This will require collaboration between researchers, pharmaceutical companies and regulators to ensure that quantum computing can be effectively applied in real-world clinical settings. Additionally, the study calls for more work on developing quantum algorithms that can handle the inherent variability in biological data, particularly in genomics and personalized medicine.

The research was conducted by a team from several prominent institutions. Hakan Doga, Aritra Bose, and Laxmi Parida are from IBM Research and IBM Quantum. M. Emre Sahin is affiliated with The Hartree Centre, STFC, while Joao Bettencourt-Silva is based at IBM Research, Dublin, Ireland. Anh Pham, Eunyoung Kim, Anh Pham, Eunyoung Kim and Alan Andress are from Deloitte Consulting LLP. Sudhir Saxena and Radwa Soliman are from GNQ Insilico Inc. Jan Lukas Robertus is affiliated with Imperial College London and Royal Brompton and Harefield Hospitals and Hideaki Kawaguchi is from Keio University. Finally, Daniel Blankenberg is from the Lerner Research Institute, Cleveland Clinic.

While machine learning methods can be used for accurate flow prediction in complex environments, such as for urban structures³⁰ or turbulent fields³¹, generalizing these approaches to domains of arbitrary size and complexity remains a challenging problem. One reason is that flows near and around obstacles depend on factors associated with the fluid (i.e., Reynolds number) or domain (i.e., boundary conditions), and fixing either of these conditions puts bounds on the validity of the estimated fields. Thus, if we seek broad applicability, then we should seek the fewest set of model restrictions that together provide the most accurate flow predictions. To this end, our approach has been to deconstruct certain types of domains into individual obstacles that each maintain some level of geometrical similarity, so that a single neural network model can be used to predict flows near all structural boundaries of the domain. Flows between these structural surfaces, at a scale on the order of the obstacle diameter, are predicted using a second neural network model in series with the first. Together, this serial-modeling approach allows for rapid prediction of flows in domains that can be represented by a disjoint set of structural elements. This type of domain is common, for example, in urban and periurban areas, wherein buildings conform to a common structural motif that affects ground-level velocity fields.

Another relevant length scale is the grid size used to digitize individual domains for read-in by the model. Thus, we investigated how flow patterns can be affected when this input resolution is varied. Although our choice of grid size is somewhat arbitrary, it is dense enough to capture variation in the relevant velocity fields near individual obstacles, but not so dense that producing a large enough cohort of CFD-generated training datasets becomes computationally intractable.

Our approach can also be trained to predict flows with a variable inlet velocity, which, in the case of urban wind flow prediction, permits model parameterization in terms of current meteorological conditions. In the specific case of aerial dispersion of chemicals throughout an urban environment, our predicted flows are considered as the advective field of a drift-diffusion model of molecular dispersion. This advection field plays a central role because concentration fluctuations decorrelate in relationship with the velocity fluctuations of the advection field, and spatial heterogeneity in the flow patterns is determined by the sequence of obstacles in the flow path.