Lifeboat Foundation

Will a machine learning AI be the way we find out we are not alone in the Universe?

In a January 2023 published paper in Nature Astronomy, a collaboration by authors from universities in Toronto, Canada, Berkeley in California, Manchester in the United Kingdom, Malta, Queensland and Western Australia, and the SETI Institute, created a machine learning algorithm variational autoencoder, a type of neural network that learns through the unsupervised study of unlabelled data. They used it to try and find technosignatures contained within 150 Terabytes of radio traffic from 820 nearby stars. The data source came from the Green Bank Telescope in West Virginia, the world’s largest steerable radio telescope. This data had previously been searched in 2017 using traditional techniques.

Radio signals are abundant throughout the Universe and they represent the most effective way for us to find out if we are a solo act or one of many technical civilizations. Our contribution to radio traffic has been going on for more than a century which means an alien civilization within a hundred light-years from us with technology similar to ours can now detect us.

Continue reading “Will AI Make First Contact with Extra-Terrestrial Intelligence?” »

OAKLAND, Calif. Feb 28 (Reuters) — Intel Corp (INTC.O) on Tuesday released a software platform for developers to build quantum algorithms that can eventually run on a quantum computer that the chip giant is trying to build.

The platform, called Intel Quantum SDK, would for now allow those algorithms to run on a simulated quantum computing system, said Anne Matsuura, Intel Labs’ head of quantum applications and architecture.

Quantum computing is based on quantum physics and in theory can perform calculations quicker than conventional computers.

As chatbot responses begin to proliferate throughout the Internet, they will, in turn, impact future machine learning algorithms that mine the Internet for information, thus perpetuating and amplifying the impact of the current programming biases evident in ChatGPT.

ChatGPT is admittedly a work in progress, but how the issues of censorship and offense ultimately play out will be important. The last thing anyone should want in the future is a medical diagnostic chatbot that refrains from providing a true diagnosis that may cause pain or anxiety to the receiver. Providing information guaranteed not to disturb is a sure way to squash knowledge and progress. It is also a clear example of the fallacy of attempting to input “universal human values” into AI systems, because one can bet that the choice of which values to input will be subjective.

If the future of AI follows the current trend apparent in ChatGPT, a more dangerous, dystopic machine-based future might not be the one portrayed in the Terminator films but, rather, a future populated by AI versions of Fahrenheit 451 firemen.

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy.

Humans and animals detect different stimuli such as light, sound, and odor through nerve cells, which then transmit the information to the brain. Nerve cells must be able to adjust to the wide range of stimuli they receive, which can range from very weak to very strong. To do this, they may become more or less sensitive to stimuli (sensitization and habituation), or they may become more sensitive to weaker stimuli and less sensitive to stronger stimuli for better overall responsiveness (gain control). However, the exact way this happens is not yet understood.

To better understand the process of gain control, a research team led by Professor Kimura at Nagoya City University in Japan studied the roundworm C. elegans. They found that, when the worm first smells an unpleasant odor, its nerve cells exhibit a large, quickly increasing, and continuous response to both weak and strong stimuli. However, after exposure to the odor, the response is smaller and slower to weak stimuli but remains large to strong stimuli, similar to the response to the first exposure to the odor. Because the experience of odor exposure causes more efficient movement of worms away from the odor, the nerve cells have changed their response to better adapt to the stimulus using gain control.

Then the researchers used mathematical modeling to understand this process. Mathematical modeling is a powerful tool that can be used to better understand complex biological processes. They found that the “response to first smell” consists of fast and slow components, while the “response after exposure” only consists of the slow component, meaning that the odor experience inhibits the fast component to achieve gain control. They further found that both responses could be described by a simple differential equation and that the slow and fast components correspond to the leaky integration of a first and second derivative term of the odor concentration that the worm senses, respectively. The results of this study showed that the prior odor experience only appears to inhibit the mechanism required for the fast component.

AI might not take your job any time soon, but companies are already using it to help them decide who to lay off.

That’s according to a November Capterra survey of 300 US human resources leaders, which found that 98% of respondents plan to use software and algorithms to help them make any layoff decisions in 2023.

While many companies have access to a wide range of employee data — including information on employee attendance, pay, and experience — the HR leaders said “skills” and “performance” data would be most likely to be used in a layoff decision, with 70% of the leaders saying each of these would be considered.

The CRYSTALS-Kyber public-key encryption and key encapsulation mechanism recommended by NIST in July 2022 for post-quantum cryptography has been broken. Researchers from the KTH Royal Institute of Technology, Stockholm, Sweden, used recursive training AI combined with side channel attacks.

A side-channel attack exploits measurable information obtained from a device running the target implementation via channels such as timing or power consumption. The revolutionary aspect of the research (PDF) was to apply deep learning analysis to side-channel differential analysis.

“Deep learning-based side-channel attacks,” say the researchers, “can overcome conventional countermeasures such as masking, shuffling, random delays insertion, constant-weight encoding, code polymorphism, and randomized clock.”

For the first time, scientists have used machine learning to create brand-new enzymes, which are proteins that accelerate chemical reactions. This is an important step in the field of protein design, as new enzymes could have many uses across medicine and industrial manufacturing.

“Living organisms are remarkable chemists. Rather than relying on toxic compounds or extreme heat, they use enzymes to break down or build up whatever they need under gentle conditions. New enzymes could put renewable chemicals and biofuels within reach,” said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine and recipient of the 2021 Breakthrough Prize in Life Sciences.

As reported Feb, 22 in the journal Nature, a team based at the Institute for Protein Design at UW Medicine devised machine-learning algorithms that can create light-emitting enzymes called luciferases. Laboratory testing confirmed that the new enzymes can recognize specific chemicals and emit light very efficiently. This project was led by two postdoctoral scholars in the Baker Lab, Andy Hsien-Wei Yeh and Christoffer Norn.

In a new study, researchers at the Indian Institute of Science (IISc) show how a brain-inspired image sensor can go beyond the diffraction limit of light to detect miniscule objects such as cellular components or nanoparticles invisible to current microscopes. Their novel technique, which combines optical microscopy with a neuromorphic camera and machine learning algorithms, presents a major step forward in pinpointing objects smaller than 50 nanometers in size. The results are published in Nature Nanotechnology.

Since the invention of optical microscopes, scientists have strived to surpass a barrier called the diffraction limit, which means that the microscope cannot distinguish between two objects if they are smaller than a certain size (typically 200–300 nanometers).

Their efforts have largely focused on either modifying the molecules being imaged, or developing better illumination strategies—some of which led to the 2014 Nobel Prize in Chemistry. “But very few have actually tried to use the detector itself to try and surpass this detection limit,” says Deepak Nair, Associate Professor at the Center for Neuroscience (CNS), IISc, and corresponding author of the study.