They claimed that artificial intelligence can actually solve some of the hardest challenges that affect the delivery of dementia treatment to old people, especially those with Alzheimer’s disease.
In 2021, the National Library of Medicine revealed that more than 6.2 million U.S. residents are suffering from Alzheimer’s.
Researchers from TU Delft have constructed the smallest flow-driven motors in the world. Inspired by iconic Dutch windmills and biological motor proteins, they created a self-configuring flow-driven rotor from DNA that converts energy from an electrical or salt gradient into useful mechanical work. The results open new perspectives for engineering active robotics at the nanoscale.
The article is now published in Nature Physics (“Sustained unidirectional rotation of a self-organized DNA rotor on a nanopore”).
Rotary motors have been the powerhouses of human societies for millennia: from the windmills and waterwheels across the Netherlands and the world to today’s most advanced off-shore wind turbines that drive our green-energy future.
Researchers have developed a new chip-based beam steering technology that provides a promising route to small, cost-effective and high-performance lidar (or light detection and ranging) systems. Lidar, which uses laser pulses to acquire 3D information about a scene or object, is used in a wide range of applications such as autonomous driving, free-space optical communications, 3D holography, biomedical sensing and virtual reality.
“Optica l beam steering is a key technology for lidar systems, but conventional mechanical-based beam steering systems are bulky, expensive, sensitive to vibration and limited in speed,” said research team leader Hao Hu from the Technical University of Denmark. “Although devices known as chip-based optical phased arrays (OPAs) can quickly and precisely steer light in a non-mechanical way, so far, these devices have had poor beam quality and a field of view typically below 100 degrees.”
In Optica, Hu and co-author Yong Liu describe their new chip-based OPA that solves many of the problems that have plagued OPAs. They show that the device can eliminate a key optical artifact known as aliasing, achieving beam steering over a large field of view while maintaining high beam quality, a combination that could greatly improve lidar systems.
AI Philosophy
The AI model was trained using answers from Dennett on a range of questions about free will, whether animals feel pain and even favorite bits of other philosophers. The researchers then asked different groups of people to compare the AI’s responses and Dennett’s real answers and see if they could tell them apart. They used responses from 302 random people online who followed a link from Schwitzgebel’s blog, 98 confirmed college graduates from the online research platform Prolific, and 25 noted Dennett experts. Immersion in Dennett’s philosophy and work didn’t prevent anyone from struggling to identify the source of the answers, however.
The research platform participants only managed an average success rate of 1.2 out of 5 questions. The blog readers and experts answered ten questions, with the readers hitting an average score of 4.8 out of 10. That said, not a single Dennett expert was 100% correct, with only one answering nine correctly and an average of 5.1 out of 10, barely higher than the blog readers. Interestingly, the question whose responses most confused the Dennett experts was actually about AI sentience, specifically if people could “ever build a robot that has beliefs?” Despite the impressive performance by the GPT-3 version of Dennett, the point of the experiment wasn’t to demonstrate that the AI is self-aware, only that it can mimic a real person to an increasingly sophisticated degree and that OpenAI and its rivals are continuing to refine the models so that similar quizzes will likely get harder to pass.
A model for autonomous underwater robots could help them navigate previously-unexplored waters.
Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.
Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.
The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.
Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.
Often, an expert operator must use manual trial-and-error—possibly making thousands of prints—to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.
MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.
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Imagine knowing the future. Being able to predict what’s going to happen next. It feels that this concept is merely a dream, but in reality, this dream is underway. Modeling and simulation, data analytics, AI and machine learning, distributed systems, social dynamics and human behavior simulation are fast becoming the go-to tools, and their qualities could offer significant advantages for the battlespace of tomorrow.
According to army-technology.com, London-based technology provider Improbable has been working closely with the UK Ministry of Defense (MoD) since 2018 to explore the utility of synthetic environments (SEs) for tactical training and operational and strategic planning. At the core of this work is Skyral, a platform that supports an ecosystem of industry and academia enabling the fast construction of new SEs for almost any scenario using digital entities, algorithms, AI, historic and real-time data.
Got a protein? This AI will tell you what it looks like.
AlphaFold was recognized by the journal Science as 2021’s Breakthrough of the Year, beating out candidates like Covid-19 antiviral pills and the application of CRISPR gene editing in the human body. One expert even wondered if AlphaFold would become the first AI to win a Nobel Prize.
The breakthroughs have kept coming.
Last week, DeepMind announced that researchers from around the world have used AlphaFold to predict the structures of some 200 million proteins from 1 million species, covering just about every protein known to human beings. All of that data is being made freely available on a database set up by DeepMind and its partner, the European Molecular Biology Laboratory’s European Bioinformatics Institute.
Artificial Intelligence has many potential applications and consequences for strategic risk. Here is NTI’s work on it so far.
