A Japanese startup at CES is claiming to have solved one of the biggest problems in medical technology: Noninvasive continuous glucose monitoring. Quantum Operation Inc, exhibiting at the virtual show, says that its prototype wearable can accurately measure blood sugar from the wrist. Looking like a knock-off Apple Watch, the prototype crams in a small spectrometer which is used to scan the blood to measure for glucose. Quantum’s pitch adds that the watch is also capable of reading other vital signs, including heart rate and ECG.
The company says that its secret sauce is in its patented spectroscopy materials which are built into the watch and its band. To use it, the wearer simply needs to slide the watch on and activate the monitoring from the menu, and after around 20 seconds, the data is displayed. Quantum says that it expects to sell its hardware to insurers and healthcare providers, as well as building a big data platform to collect and examine the vast trove of information generated by patients wearing the device.
Quantum Operation supplied a sampling of its data compared to that made by a commercial monitor, the FreeStyle Libre. And, at this point, there does seem to be a noticeable amount of variation between the wearable and the Libre. That, for now, may be a deal breaker for those who rely upon accurate blood glucose readings to determine their insulin dosage.
The Sustainable Development Goals (SDGs) include a call for action to halve the annual rate of road deaths globally and ensure access to safe, affordable, and sustainable transport for everyone by 2030.
According to the newly launched initiative, faster progress on AI is vital to make this happen, especially in low and middle-income countries, where the most lives are lost on the roads each year.
According to the World Health Organization (WHO), approximately 1.3 million people die annually as a result of road traffic crashes. Between 20 and 50 million more suffer non-fatal injuries, with many incurring a disability.
Full Story:
A woman rushes across a busy road in Brazil., by PAHO
AI can help in different ways, including better collection and analysis of crash data, enhancing road infrastructure, increasing the efficiency of post-crash response, and inspiring innovation in the regulatory frameworks.
This approach requires equitable access to data and the ethical use of algorithms, which many countries currently lack, leaving them unable to identify road safety solutions.
Artificial intelligence (AI) is evolving—literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.
“While most people were taking baby steps, they took a giant leap into the unknown,” says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. “This is one of those papers that could launch a lot of future research.”
Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasks—for instance spotting road signs—and researchers can spend months working out how to connect them so they work together seamlessly.
Oct 8 2021 “Abstract: In this talk, we will discuss the nuts and bolts of the novel continuous-time neural network models: Liquid Time-Constant (LTC) Networks. Instead of declaring a learning system’s dynamics by implicit nonlinearities, LTCs construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. LTCs represent dynamical systems with varying (i.e., liquid) time-constants, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks compared to advance recurrent network models.”
Ramin Hasani, MIT — intro by Daniela Rus, MIT
Abstract: In this talk, we will discuss the nuts and bolts of the novel continuous-time neural network models: Liquid Time-Constant (LTC) Networks. Instead of declaring a learning system’s dynamics by implicit nonlinearities, LTCs construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. LTCs represent dynamical systems with varying (i.e., liquid) time-constants, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks compared to advance recurrent network models.
Speaker Biographies:
Dr. Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineers, and the American Academy of Arts and Sciences. She earned her PhD in Computer Science from Cornell University. Prior to joining MIT, Rus was a professor in the Computer Science Department at Dartmouth College.
Dr. Ramin Hasani is a postdoctoral associate and a machine learning scientist at MIT CSAIL. His primary research focus is on the development of interpretable deep learning and decision-making algorithms for robots. Ramin received his Ph.D. with honors in Computer Science at TU Wien, Austria. His dissertation on liquid neural networks was co-advised by Prof. Radu Grosu (TU Wien) and Prof. Daniela Rus (MIT). Ramin is a frequent TEDx speaker. He has completed an M.Sc. in Electronic Engineering at Politecnico di Milano (2015), Italy, and has got his B.Sc. in Electrical Engineering – Electronics at Ferdowsi University of Mashhad, Iran (2012).
This week, The European Parliament, the body responsible for adopting European Union (EU) legislation, passed a non-binding resolution calling for a ban on law enforcement use of facial recognition technology in public places. The resolution, which also proposes a moratorium on the deployment of predictive policing software, would restrict the use of remote biometric identification unless it’s to fight “serious” crime, such as kidnapping and terrorism.
The approach stands in contrast to that of U.S. agencies, which continue to embrace facial recognition even in light of studies showing the potential for ethnic, racial, and gender bias. A recent report from the U.S. Government Accountability Office found that 10 branches including the Departments of Agriculture, Commerce, Defense, and Homeland Security plan to expand their use of facial recognition between 2020 and 2023 as they implement as many as 17 different facial recognition systems.
Commercial face-analyzing systems have been critiqued by scholars and activists alike throughout the past decade, if not longer. The technology and techniques — everything from sepia-tinged film to low-contrast digital cameras — often favor lighter skin, encoding racial bias in algorithms. Indeed, independent benchmarks of vendors’ systems by the Gender Shades project and others have revealed that facial recognition technologies are susceptible to a range of prejudices exacerbated by misuse in the field. For example, a report from Georgetown Law’s Center on Privacy and Technology details how police feed facial recognition software flawed data, including composite sketches and pictures of celebrities who share physical features with suspects.
A virtual army of 4,000 doglike robots was used to train an algorithm capable of enhancing the legwork of real-world robots, according to an initial report from Wired. And new tricks learned in the simulation could soon see execution in a neighborhood near you.
While undergoing training, the robots mastered the up-and downstairs walk without too much struggle. But slopes threw them for a curve. Few could grasp the essentials of sliding down a slope. But, once the final algorithm was moved to a real-world version of ANYmal, the four-legged doglike robot with sensors equipped in its head and a detachable robot arm successfully navigated blocks and stairs, but had issues working at higher speeds.
It sounds like a scene from a spy thriller. An attacker gets through the IT defenses of a nuclear power plant and feeds it fake, realistic data, tricking its computer systems and personnel into thinking operations are normal. The attacker then disrupts the function of key plant machinery, causing it to misperform or break down. By the time system operators realize they’ve been duped, it’s too late, with catastrophic results.
The scenario isn’t fictional; it happened in 2,010 when the Stuxnet virus was used to damage nuclear centrifuges in Iran. And as ransomware and other cyberattacks around the world increase, system operators worry more about these sophisticated “false data injection” strikes. In the wrong hands, the computer models and data analytics—based on artificial intelligence—that ensure smooth operation of today’s electric grids, manufacturing facilities, and power plants could be turned against themselves.
Purdue University’s Hany Abdel-Khalik has come up with a powerful response: To make the computer models that run these cyberphysical systems both self-aware and self-healing. Using the background noise within these systems’ data streams, Abdel-Khalik and his students embed invisible, ever-changing, one-time-use signals that turn passive components into active watchers. Even if an attacker is armed with a perfect duplicate of a system’s model, any attempt to introduce falsified data will be immediately detected and rejected by the system itself, requiring no human response.
There’s a lot of excitement at the intersection of artificial intelligence and health care. AI has already been used to improve disease treatment and detection, discover promising new drugs, identify links between genes and diseases, and more.
By analyzing large datasets and finding patterns, virtually any new algorithm has the potential to help patients — AI researchers just need access to the right data to train and test those algorithms. Hospitals, understandably, are hesitant to share sensitive patient information with research teams. When they do share data, it’s difficult to verify that researchers are only using the data they need and deleting it after they’re done.
Secure AI Labs (SAIL) is addressing those problems with a technology that lets AI algorithms run on encrypted datasets that never leave the data owner’s system. Health care organizations can control how their datasets are used, while researchers can protect the confidentiality of their models and search queries. Neither party needs to see the data or the model to collaborate.
Frances Haugen’s testimony at the Senate hearing today raised serious questions about how Facebook’s algorithms work—and echoes many findings from our previous investigation.
Proteins are structured like folded chains. These chains are composed of small units of 20 possible amino acids, each labeled by a letter of the alphabet. A protein chain can be represented as a string of these alphabetic letters, very much like a string of music notes in alphabetical notation.
Protein chains can also fold into wavy and curved patterns with ups, downs, turns, and loops. Likewise, music consists of sound waves of higher and lower pitches, with changing tempos and repeating motifs.
Protein-to-music algorithms can thus map the structural and physiochemical features of a string of amino acids onto the musical features of a string of notes.
