H/T Stephen Wolfram.
Particularly given its recent surprise successes, there’s a somewhat widespread belief that eventually AI will be able to “do everything”, or at least everything we currently do.
Stephen Wolfram explores the potential—and limitations—of AI in science. See cases in which AI will be a useful tool, and in others a less ideal tool.
For decades, scientists and pathologists have tried, without much success, to come up with a way to determine which individual lung cancer patients are at greatest risk of having their illness spread, or metastasize, to other parts of the body.
Now a team of scientists from Caltech and the Washington University School of Medicine in St. Louis has fed that problem to artificial intelligence (AI) algorithms, asking computers to predict which cancer cases are likely to metastasize. In a novel pilot study of non-small cell lung cancer (NSCLC) patients, AI outperformed expert pathologists in making such predictions.
These predictions about the progression of lung cancer have important implications in terms of an individual patient’s life. Physicians treating early-stage NSCLC patients face the extremely difficult decision of whether to intervene with expensive, toxic treatments, such as chemotherapy or radiation, after a patient undergoes lung surgery. In some ways, this is the more cautious path because more than half of stage I–III NSCLC patients eventually experience metastasis to the brain. But that means many others do not. For those patients, such difficult treatments are wholly unnecessary.
With a mobile app powered by artificial intelligence (AI), Caitlin Hicks, MD, MS, reviews selfies of patients’ feet in real time to track their wounds as part of a clinical trial. The app saves time for Hicks, a vascular surgeon at Johns Hopkins Medicine, but also reduces clinic trips for her patients with diabetes in inner-city Baltimore, many of whom are elderly and less mobile or have other socioeconomic barriers to care. Hicks knows that for these patients, wound vigilance is the linchpin to preventing infection, hospitalization, or, worse, amputation or even death.
Despite their crushing toll, diabetic foot infections remain stubbornly hard to treat, but multidisciplinary care teams, new drugs and devices on the horizon, and practical solutions to socioeconomic factors could budge the needle.
Summary: A recent study showcases a significant leap in the study of brain oscillations, particularly ripples, which are crucial for memory organization and are affected in disorders like epilepsy and Alzheimer’s. Researchers have developed a toolbox of AI models trained on rodent EEG data to automate and enhance the detection of these oscillations, proving their efficacy on data from non-human primates.
This breakthrough, stemming from a collaborative hackathon, offers over a hundred optimized machine learning models, including support vector machines and convolutional neural networks, freely available to the scientific community. This development opens new avenues in neurotechnology applications, especially in diagnosing and understanding neurological disorders.
Something to look forward to: AMD’s FSR image upscaling technology has avoided using AI until now, which has been a double-edged sword in its competition against Nvidia’s DLSS and Intel’s XeSS. A recent interview with AMD’s CTO indicates that the company plans to pivot sharply toward AI in 2024, with gaming upscaling as one area of focus.
AMD has confirmed that it’s developing a method to play games with AI-based image upscaling. Although further details are scarce, this could potentially bring the company’s solution closer to Nvidia’s. In an interview on the No Priors podcast, CTO Mark Papermaster explained how AMD has deployed AI acceleration throughout its product stack and plans to introduce new applications to utilize it this year. “We are enabling gaming devices to upscale using AI and 2024 is a really huge deployment year,” he said.
Nvidia DLSS, Intel XeSS, and AMD FSR allow gamers to increase the resolution at which they play while minimizing the performance impact. However, while DLSS and XeSS utilize hardware-assisted AI, FSR relies only on spatial and temporal information.
The idea of objects seamlessly disappearing, not just in controlled laboratory environments but also in real-world scenarios, has long captured the popular imagination. This concept epitomizes the trajectory of human civilization, from primitive camouflage techniques to the sophisticated metamaterial-based cloaks of today.
Recently, this goal was further highlighted in Science, as one of the “125 questions: exploration and discovery.” Researchers from Zhejiang University have made strides in this direction by demonstrating an intelligent aero amphibious invisibility cloak. This cloak can maintain invisibility amidst dynamic environments, neutralizing external stimuli.
Despite decades of research and the emergence of numerous invisibility cloak prototypes, achieving an aero amphibious cloak capable of manipulating electromagnetic scattering in real-time against ever-changing landscapes remains a formidable challenge. The hurdles are multifaceted, ranging from the need for complex-amplitude tunable metasurfaces to the absence of intelligent algorithms capable of addressing inherent issues such as non-uniqueness and incomplete inputs.
A machine-learning tool that transforms text prompts into detailed video has generated excitement—and skepticism.
Quantum computers, which can solve several complex problems exponentially faster than classical computers, are expected to improve artificial intelligence (AI) applications deployed in devices like autonomous vehicles; however, just like their predecessors, quantum computers are vulnerable to adversarial attacks.
A team of University of Texas at Dallas researchers and an industry collaborator have developed an approach to give quantum computers an extra layer of protection against such attacks. Their solution, Quantum Noise Injection for Adversarial Defense (QNAD), counteracts the impact of attacks designed to disrupt inference—AI’s ability to make decisions or solve tasks.
The team will present research that demonstrates the method at the IEEE International Symposium on Hardware Oriented Security and Trust held May 6–9 in Washington, D.C.
Scientists at the University of Florida have pioneered a method for using semiconductor technology to manufacture processors that significantly enhance the efficiency of transmitting vast amounts of data across the globe. The innovation, featured on the current cover of the journal Nature Electronics, is poised to transform the landscape of wireless communication at a time when advances in AI are dramatically increasing demand.
Traditionally, wireless communication has relied on planar processors, which, while effective, are limited by their two-dimensional structure to operate within a limited portion of electromagnetic spectrum. The UF-designed approach leverages the power of semiconductor technology to propel wireless communication into a new dimension—quite literally.
Researchers have successfully transitioned from planar to three-dimensional processors, ushering in a new era of compactness and efficiency in data transmission.