Lifeboat Foundation

There’s no shortage of emerging applications and projects that promise increased productivity, new levels of automation, and cutting-edge innovation. But all too often, AI initiatives within the enterprise fail to get off the ground, and there can be vast and costly unintended consequences when this technology is applied to the wrong use cases or falls into the wrong hands.

In the case of cyber defense, widespread accessibility to generative AI tools, as well as the increasing sophistication of nation-state actors, means that threats are more personalized and convincing than ever. In an era of algorithms fighting algorithms, human defenders must effectively team up with AI to build cyber resiliency and prevent business disruption.

Presented by expert stakeholders from industry, academia, and government, this event is designed to offer practical guidance for security teams to cut through the noise and unleash the power of AI responsibly and effectively.

The immune system employs different immune cells to target infection and disease throughout the body. Immunologists, who study the immune system, have worked on therapies to get more of these cells to the site of infection and at a faster rate. Currently, it is still unclear how effectively the immune system operates in age-and sex-related research. A group at the University of Birmingham have demonstrated specific sex-related differences associated with the immune system in older female mice. This novel research introduces age and sex into the equation and will change the way we study the immune system and improve patient treatment.

A recent publication in the Journal of Leukocyte Biology, by Dr. Myriam Chimen and colleagues found that age is a significant factor that determines cell movement to the major organs in the stomach cavity. More specifically, immune cells were not going to the site of infection, but “leaking” into the stomach cavity from blood vessels. This study has found a clear difference between sexes associated with immunity, as it was previously believed women’s immune system deteriorates faster compared to men. Chimen and colleagues have confirmed this long-standing belief through their work on immune system sex-related differences.

Chimen and colleagues show that the increased immune cell presence in the stomach cavity is from “leaky” blood vessels. “Leaky” is a term used to described blood vessels that do not maintain strong structural integrity. The idea of “leaky” blood vessels occurs in inflammatory diseases such as cancer. Cancer cells travel through the blood system and commonly “leak” out of the blood stream to other sites in the body. The trafficking of cells to other sites allows the spread of cancer throughout the body, further promoting tumor growth.

Researchers from Duke University and associated partners have uncovered the atomic mechanics that render a group of substances, known as argyrodites, promising prospects for solid-state battery electrolytes and thermoelectric energy converters.

Their findings, made possible through a machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.

Intel announced the next step on its road to quantum with the release of its latest quantum chip, a 12-qubit, silicon-based chip the company is calling “Tunnel Falls”. No, no, it’s okay, you can keep those greenbacks in your wallet: Intel isn’t in the commercialization phase yet. Instead, Tunnel Falls is meant to be a research test chip: it’s still a stepping stone towards the actual Quantum Processing Units of the future. Hopefully, those will be more like Intel’s own Tunnel Falls than Iran’s Amazon-based “quantum computing” technology.

“Tunnel Falls is Intel’s most advanced silicon spin qubit chip to date and draws upon the company’s decades of transistor design and manufacturing expertise. The release of the new chip is the next step in Intel’s long-term strategy to build a full-stack commercial quantum computing system. While there are still fundamental questions and challenges that must be solved along the path to a fault-tolerant quantum computer, the academic community can now explore this technology and accelerate research development.” says Jim Clarke, Intel’s director of Quantum Hardware.

While it may be underwhelming to know that Tunnel Falls is just a research test chip, it’s also an often overlooked necessity for any new technology. Before any work can be done within the quantum computers of the future, the algorithms, the learning and the how-to have to be started today. One issue with that is the difficulty in producing quantum computing hardware; there’s a reason such a small number of big companies — from Intel to Microsoft, IBM, IonQ, and Google — are actively developing quantum computing hardware.

Artificial intelligence (AI) and its latest contribution to the development of anti-aging drugs has paved the way for breakthrough discoveries in modern medicine.

Researchers, using AI technology, have successfully identified three chemicals that specifically target malfunctioning cells, believed to be associated with certain cancers and Alzheimer’s disease.

A group of scientists from the University of Edinburgh developed an AI algorithm to screen a collection of over 4,300 chemical compounds.

Understanding the mechanisms of interaction between plasma and microparticles is of a critical importance in various fields, including astrophysics, microelectronics, and plasma medicine. A common experimental approach for studying interactions between plasma and microparticles is to place microparticles in a flowing plasma of a gas discharge. In order to achieve a more accurate understanding of the processes occurring in such systems, scientists need fast and efficient tools for calculating forces acting on microparticles in a plasma flow.

Typically, plasma-physicists have to independently develop software tailored to a specific task, which is a significant investment of time and resources. Existing open-source programs frequently encounter challenges related to installation, documentation, and sluggish performance. A group of scientists from the JIHT, the HSE and, MIPT have developed a novel solution: a fast, open-source code which is easy to install and extensively documented.

The outcome—OpenDust—performs ten times faster than existing analogues. In order to accelerate calculations, the algorithm uses multiple GPUs simultaneously.

In short, data-driven solutions themselves are only part of the overall approach. It is the effective integration of this fast-evolving technology into existing workflows and processes that leads to successful business outcomes.

The first step to integrating AI is identifying places and processes where it can help increase efficiency or accuracy. Businesses should step back and identify their pain points, creating a list of processes that are slow, tedious, cumbersome or suffering from a lack of staff. They should also analyze where additional data or information could help make better decisions.

In the pharma industry, data-driven AI solutions have been widely adopted in sales and marketing processes. For example, by analyzing patient and physician data, electronic medical records and demographic information, AI algorithms can identify trends, patterns and insights that help sales representatives tailor their messaging and presentations to specific HCPs.

AI isn’t programming’s future, it’s its present.

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.