Finding alien planets orbiting other stars is easy. Astronomers have found over 6,000 of them in just the last decade, but very few are considered even possibly habitable. Scientists have dozens of telescopes on the ground and in space that can find them and now even study their atmospheres for signs of life. Most are around small, dim red dwarf stars simply because current technology makes it difficult to study objects around bright Sun-like stars.
The next great objective in planetary science? Send a spacecraft to explore the surface of one of them, of course.
If we want to find Earth 2.0 we’re going to have to play the long game and visit star systems most like our own, says a white paper proposing a multi-century mission.
Researchers used AI to analyze genes and discovered that aging is caused by unbalanced genes.
Researchers have discovered a breakthrough in what causes people to age. The research team, from Northwestern University in Evanston, Illinois, found a previously unknown factor that leads to aging.
The research team used AI to analyze tissue samples.
Tylim/iStock.
The team discovered that the length of genes can account for most molecular-level changes that happen as animals get older. The study used artificial intelligence to assess data from various tissue that was collected from humans, rodents, and fish.
More than three years ago, this editor sat down with Sam Altman for a small event in San Francisco soon after he’d left his role as the president of Y Combinator to become CEO of the AI company he co-founded in 2015 with Elon Musk and others, OpenAI.
At the time, Altman described OpenAI’s potential in language that sounded outlandish to some. Altman said, for example, that the opportunity with artificial general intelligence — machine intelligence that can solve problems as well as a human — is so great that if OpenAI managed to crack it, the outfit could “maybe capture the light cone of all future value in the universe.” He said that the company was “going to have to not release research” because it was so powerful. Asked if OpenAI was guilty of fear-mongering — Musk has repeatedly called all organizations developing AI to be regulated — Altman talked about the dangers of not thinking about “societal consequences” when “you’re building something on an exponential curve.”
The audience laughed at various points of the conversation, not certain how seriously to take Altman. No one is laughing now, however. While machines are not yet as intelligent as people, the tech that OpenAI has since released is taking many aback (including Musk), with some critics fearful that it could be our undoing, especially with more sophisticated tech reportedly coming soon.
It’s proving tough to reign in systems like ChatGPT
Did a human.
Did a human write that, or ChatGPT? It can be hard to tell — perhaps too hard, its creator OpenAI thinks, which is why it is working on a way to “watermark” AI-generated content.
In a lecture at the University of Austin, computer science professor Scott Aaronson, currently a guest researcher at OpenAI, revealed that OpenAI is developing a tool for “statistically watermarking the outputs of a text [AI system].” Whenever a system — say, ChatGPT — generates text, the tool would embed an “unnoticeable secret signal” indicating where the text came from.
OpenAI engineer Hendrik Kirchner built a working prototype, Aaronson says, and the hope is to build it into future OpenAI-developed systems.
Keith Downing is a professor of Computer Science at the Norwegian University of Science and Technology, specializing in Artificial Intelligence and Artificial Life. He has a particular interest in evolutionary algorithms, which have applications ranging from the development of the Mars Rover antenna, patented circuits, early driverless cars, to even art. For computer scientists to learn from nature, he believes there needs to be a shift in our traditional ways thinking.
About TEDx, x = independently organized event. In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations).
Thin films made of carbon nanotubes hold a lot of promise for advanced optoelectronics, energy and medicine, however with their manufacturing process subject to close supervision and stringent standardization requirements, they are unlikely to become ubiquitous anytime soon.
“A major hindrance to unlocking the vast potential of nanotubes is their multiphase manufacturing process which is extremely difficult to manage. We have suggested using artificial neural networks (ANN) to analyze experimental data and predict the efficiency of single-walled carbon nanotubes synthesis,” explains one of the authors of the study and Skoltech researcher, Dmitry Krasnikov.
In their work published in the prestigious Carbon journal, the authors show that machine learning methods, and, in particular, ANN trained on experimental parameters, such as temperature, gas pressure and flow rate, can help monitor the properties of the carbon nanotube films produced.
For years, Shepherd’s Organic Robotics Lab has used stretchable fiber-optic sensors to make soft robots and related components – from skin to wearable technology – as nimble and practical as possible.
In fiber-optic sensors, light from a LED is sent through an optical waveguide, and a photodiode detects changes in the beam’s intensity to determine when the material is being deformed. One of the virtues of the technology is that waveguides are still able to propagate light if they are punctured or cut.
The researchers combined the sensors with a polyurethane urea elastomer that incorporated hydrogen bonds, for rapid healing, and disulfide exchanges, for strength.
Responsive material changes its behavior based on earlier conditions.
Inspired by living systems, a new material has been developed that changes its electrical behavior based on previous experience, effectively giving it a basic form of adaptive memory. Such adaptive materials could play a vital role in the next generation of medical and environmental sensors, as well as in soft robots or active surfaces. The breakthrough was achieved by researchers at Aalto University in Finland.
Responsive materials have become common in a range of applications, from glasses that darken in sunlight to drug delivery systems. However, existing materials always react in the same way each time. Their response to a change doesn’t depend on their history, nor do they adapt based on their past.
Discusses the possibility of Femtotech and the technological possibilities it may unlock. Not long ago nanotechnology was a fringe topic; now it’s a flourishing engineering field, and fairly mainstream. For example, while writing this article, I happened to receive an email advertisement for the “Second World Conference on Nanomedicine and Drug Delivery,” in Kerala, India. It wasn’t so long ago that nanomedicine seemed merely a flicker in the eyes of Robert Freitas and a few other visionaries!
But nano is not as small as the world goes. A nanometer is 10–9 meters – the scale of atoms and molecules. A water molecule is a bit less than one nanometer long, and a germ is around a thousand nanometers across. On the other hand, a proton has a diameter of a couple femtometers – where a femtometer, at 10–15 meters, makes a nanometer seem positively gargantuan. Now that the viability of nanotech is widely accepted (in spite of some ongoing heated debates about the details), it’s time to ask: what about femtotech? Picotech or other technologies at the scales between nano and femto seem relatively uninteresting, because we don’t know any basic constituents of matter that exist at those scales. But femtotech, based on engineering structures from subatomic particles, makes perfect conceptual sense, though it’s certainly difficult given current technology.
The nanotech field was arguably launched by Richard Feynman’s 1959 talk “There’s Plenty of Room at the Bottom.” As Feynman wrote there.
“It is a staggeringly small world that is below. In the year 2000, when they look back at this age, they will wonder why it was not until the year 1960 that anybody began seriously to move in this direction.
Why cannot we write the entire 24 volumes of the Encyclopedia Brittanica on the head of a pin? ”
Bio: Hugo de Garis (born 1947, Sydney, Australia) is a researcher in the sub-field of artificial intelligence (AI) known as evolvable hardware. He became known in the 1990s for his research on the use of genetic algorithms to evolve neural networks using three dimensional cellular automata inside field programmable gate arrays. He claimed that this approach would enable the creation of what he terms “artificial brains” which would quickly surpass human levels of intelligence.
The software system competed against human coders in programming contests.
A novel system called AlphaCode uses artificial intelligence (AI) to create computer code, and has recently participated in programming competitions, using critical thinking, algorithms, and natural language comprehension. The AI system performed extremely well in competitions.
AlphaCode can create code quickly and efficiently
AlphaCode is an AI software system created by DeepMind, a subsidiary of the company Alphabet, the parent company of Google. The software generates code in Python or C++, while filtering out any bad coding. It has the ability to generate code at an exceptional rate.
