Lifeboat Foundation

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.

Common sense priors are essential to make decisions under uncertainty in real-world settings. Let’s say they want to give the scenario in Fig. 1 some labels. As a few key elements are recognized, it becomes evident that the image shows a restroom. This assists in resolving some of the labels for certain more difficult objects, such as the shower curtain in the scene rather than the window curtain and the mirror instead of the portrait on the wall. In addition to visual tasks, prior knowledge of expected item or event co-occurrences is crucial for navigating new environments and comprehending the actions of other agents. Moreover, such expectations are essential to object categorization and reading comprehension.

Unlike robot demos or segmented pictures, vast text corpora are easily accessible and include practically all aspects of the human experience. Current machine learning models use task-specific datasets to learn about the previous distribution of labels and judgments for the majority of problem domains. When training data is skewed or sparse, this can lead to systematic mistakes, particularly on uncommon or out-of-distribution inputs. How might they provide models with broader, more adaptable past knowledge? They suggest using learned distributions over natural language strings known as language models as task-general probabilistic priors.

LMs have been employed as sources of prior knowledge for tasks ranging from common-sense question answering to modeling scripts and tales to synthesizing probabilistic algorithms in language processing and other text production activities. They frequently give higher diversity and fidelity than small, task-specific datasets for encoding much of this information, such as the fact that plates are found in kitchens and dining rooms and that breaking eggs comes before whisking them. It has also been proposed that such language monitoring contributes to common-sense human knowledge in areas that are challenging to learn from first-hand experience.

Session kindly contributed by Silvester Sabathiel in SEMF’s 2021 Numerous Numerosity Workshop: https://semf.org.es/numerosity/

ABSTRACT
With the rise and advances in the field of artificial intelligence, opportunities to understand the finer-grained mechanisms involved in mathematical cognition have increased. A vast scope of related research has been conducted on machine learning systems that learn solving differential equations, algebraic equations and integrals or proofing complex theorems, all for which the preprocessed symbolic representations form the input and output types. However on the search for cognitive mechanisms that match the scope of humans when it comes to generalizability and applicability of mathematical concepts in the external world, a more grounded approach might be required. This involves starting with fundamental mathematical concepts that are earliest acquired in the human development and learning these within an interactive and multimodal environment. In this talk we are going to examine how artificial neural network systems within such a framework provide a controlled setup to discover possible cognitive mechanisms for intuitive numerosity perception or culturally acquired numerical concepts, such as counting. First we review impactful research results from the past, before I present the contributions of the work myself was involved in. Finally we can discuss the upcoming challenges for the field of numerical cognition and where this research journey could evolve to.

Continue reading “Neural Network Models of Mathematical Cognition | Silvester Sabathiel | Numerosity Workshop 2021” »

Humanity may soon generate more data than hard drives or magnetic tape can handle, a problem that has scientists turning to nature’s age-old solution for information-storage—DNA.

In a new study in Science, a pair of researchers at Columbia University and the New York Genome Center (NYGC) show that an algorithm designed for streaming video on a cellphone can unlock DNA’s nearly full storage potential by squeezing more information into its four base nucleotides. They demonstrate that this technology is also extremely reliable.

DNA is an ideal storage medium because it’s ultra-compact and can last hundreds of thousands of years if kept in a cool, dry place, as demonstrated by the recent recovery of DNA from the bones of a 430,000-year-old human ancestor found in a cave in Spain.

But with so many signals that could happen, how can scientists possibly sift through all of them to try to find a possible alien transmission?

Well, it turns out, scientists found the answer: Don’t. Instead, let AI do it for you.

That’s exactly what the researchers behind this study did, utilizing an AI algorithm to look through signals from a designated 820 different star systems. These targets were made from the Hipparcos catalogue, a collection of data from 118,200 stars made by the European Space Agency’s (ESA) Hipparcos satellite, and totaled over 480 hours of data.