Demis Hassabis from Google Deepmind says that what they’re developing will eclipse ChatGPT.
The company is working on a system called Gemini that will draw on techniques that powered AlphaGo to a historic victory over a Go champion in 2016.
Category: information science – Page 90
Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks.
Italian fashion start-up Cap_able has launched a collection of knitted clothing that protects the wearer’s biometric data without the need to cover their face.
Named Manifesto Collection, the clothing features various patterns developed by artificial intelligence (AI) algorithms to shield the wearer’s facial identity and instead identify them as animals.
Cap_able designed the clothing with patterns – known as adversarial patches – to deceive facial recognition software in real-time.
Data science has been around for a long time. But the failure rates of big data projects and AI projects remain disturbingly high. And despite the hype, companies have yet to cite the contributions of data science to their bottom lines.
Why is this the case? In many companies, data scientists are not engaging in enough of softer, but more difficult, work, including gaining a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address concerns among those impacted.
Managers must do four things to get more from their data science programs? First, clarify your business objectives and measure progress toward them. Second, hire data scientists best suited to the problems you face and immerse them in the day-in, day-out work of your organization. Third, demand that data scientists take end-to-end accountability for their work. Finally, insist that data scientists teach others, both inside their departments and across the company.
Background
Many everyday tasks can fall under the mathematical class of “hard” problems. Typically, these problems belong to the complexity class of nondeterministic polynomial (NP) hard. These tasks require systematic approaches (algorithms) for optimal outcomes. In the case of significant complex problems (e.g., the number of ways to fix a product or the number of stops to be made on a delivery trip), more computations are required, which rapidly outgrows cognitive capacities.
A recent Science Advances study investigated the effectiveness of three popular smart drugs, namely, modafinil (MOD), methylphenidate (MPH), and dextroamphetamine (DEX), against the difficulty of real-life daily tasks, i.e., the 0–1 knapsack optimization problem (“knapsack task”). A knapsack task is basically a combinatorial optimization task, the class of NP-time challenging problems.
The compelling feature of this new breed of quasiparticle, says Pedram Roushan of Google Quantum AI, is the combination of their accessibility to quantum logic operations and their relative invulnerability to thermal and environmental noise. This combination, he says, was recognized in the very first proposal of topological quantum computing, in 1997 by the Russian-born physicist Alexei Kitaev.
At the time, Kitaev realized that non-Abelian anyons could run any quantum computer algorithm. And now that two separate groups have created the quasi-particles in the wild, each team is eager to develop their own suite of quantum computational tools around these new quasiparticles.
Researchers have used a machine learning model to identify three compounds that could combat aging. They say their approach could be an effective way of identifying new drugs, especially for complex diseases.
Cell division is necessary for our body to grow and for tissues to renew themselves. Cellular senescence describes the phenomenon where cells permanently stop dividing but remain in the body, causing tissue damage and aging across body organs and systems.
Ordinarily, senescent cells are cleared from the body by our immune system. But, as we age, our immune system is less effective at clearing out these cells and their number increases. An increase in senescent cells has been associated with diseases such as cancer, Alzheimer’s disease and the hallmarks of aging such as worsening eyesight and reduced mobility. Given the potentially deleterious effects on the body, there has been a push to develop effective senolytics, compounds that clear out senescent cells.
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.