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Category: information science

Well, it’s a good thing, but not what I was hoping for. 3 gene therapies though Church is otherwise testing 45. But this is not the rejuvenation I was getting optimistic about. Still, I’m sure as I am getting older that I will be grateful when a treatment comes my way for something when I am elderly. But frankly this was overhyped from the start and I was part of that equation spreading a “2025” figure for some time.

Gene Therapy.

An ‘anti-aging’ gene therapy trial in dogs begins, and Rejuvenate Bio hopes humans will be next.

The startup, spun out of George Church’s lab, has tested an experimental therapy that treats four age-related diseases in mice.

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YouTube’s “next video” is a profit-maximizing recommendation system, an A.I. selecting increasingly ‘engaging’ videos. And that’s the problem.

“Computer scientists and users began noticing that YouTube’s algorithm seemed to achieve its goal by recommending increasingly extreme and conspiratorial content. One researcher reported that after she viewed footage of Donald Trump campaign rallies, YouTube next offered her videos featuring “white supremacist rants, Holocaust denials and other disturbing content.” The algorithm’s upping-the-ante approach went beyond politics, she said: “Videos about vegetarianism led to videos about veganism. Videos about jogging led to videos about running ultramarathons.” As a result, research suggests, YouTube’s algorithm has been helping to polarize and radicalize people and spread misinformation, just to keep us watching.”

By teaching machines to understand our true desires, one scientist hopes to avoid the potentially disastrous consequences of having them do what we command.

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Inspired by the functioning of the human brain and based on a biological mechanism called neuromodulation, it allows intelligent agents to adapt to unknown situations.

Artificial Intelligence (AI) has enabled the development of high-performance automatic learning techniques in recent years. However, these techniques are often applied task by task, which implies that an intelligent agent trained for one task will perform poorly on other tasks, even very similar ones. To overcome this problem, researchers at the University of Liège (ULiège) have developed a based on a called . This algorithm makes it possible to create intelligent agents capable of performing tasks not encountered during training. This novel and exceptional result is presented this week in the magazine PLOS ONE.

Despite the immense progress in the field of AI in recent years, we are still very far from . Indeed, if current AI techniques allow to train computer agents to perform certain tasks better than humans when they are trained specifically for them, the performance of these same agents is often very disappointing when they are put in conditions (even slightly) different from those experienced during training.

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Not everything is knowable. In a world where it seems like artificial intelligence and machine learning can figure out just about anything, that might seem like heresy – but it’s true.

At least, that’s the case according to a new international study by a team of mathematicians and AI researchers, who discovered that despite the seemingly boundless potential of machine learning, even the cleverest algorithms are nonetheless bound by the constraints of mathematics.

“The advantages of mathematics, however, sometimes come with a cost… in a nutshell… not everything is provable,” the researchers, led by first author and computer scientist Shai Ben-David from the University of Waterloo, write in their paper.

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Circa 2019

Technology has long been helping to hack world hunger. These days most conversations about tech’s impact on any sector of the economy inevitably involves artificial intelligence—sophisticated software that allows machines to make decisions and even predictions in ways similar to humans. Food waste tech is no different.

A report from the Ellen MacArthur Foundation and Google estimates that technologies employing AI to “design out food waste” could help generate up to $127 billion a year by 2030. These technologies range from machine vision that can spot when fruit is ready to be picked to algorithms that forecast demand in order to ensure retailers don’t overstock certain foods.

One London-based startup that has been generating headlines by reducing food waste is Winnow Solutions. The company took in $20 million in October from equity investments and loans to scale its AI platform, Winnow Vision, which identifies and weighs food waste for commercial kitchens. It then automatically assigns a dollar value to each scraped plate of fettuccine Alfredo or bowl of carrots dumped into its smart waste bin.

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IBM and the University of Tokyo will form the Japan – IBM Quantum Partnership, a broad national partnership framework in which other universities, industry, and government can engage. The partnership will have three tracks of engagement: one focused on the development of quantum applications with industry; another on quantum computing system technology development; and the third focused on advancing the state of quantum science and education.

Under the agreement, an IBM Q System One, owned and operated by IBM, will be installed in an IBM facility in Japan. It will be the first installation of its kind in the region and only the third in the world following the United States and Germany. The Q System One will be used to advance research in quantum algorithms, applications and software, with the goal of developing the first practical applications of quantum computing.

IBM and the University of Tokyo will also create a first-of-a-kind quantum system technology center for the development of hardware components and technologies that will be used in next generation quantum computers. The center will include a laboratory facility to develop and test novel hardware components for quantum computing, including advanced cryogenic and microwave test capabilities.

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Editor’s note: Geoff Woollacott is Senior Strategy Consultant and Principal Analyst at Technology Business Research. IBM and NC State are coperating on quantum computing development.

HAMPTON, N.H. – JPMorgan Chase announced on Jan. 22 the hiring of Marco Pistoia from IBM. A 24-year IBM employee with numerous patents to his credit, Pistoia most recently led an IBM team responsible for quantum computing algorithms. Algorithm development will be key to developing soundly engineered quantum computing systems that can deliver the business outcomes enterprises seek at a faster and more accurate pace than current classical computing systems.

A senior hire into a flagship enterprise in the financial services industry is the proverbial canary in the coal mine, as TBR believes such actions suggest our prediction of quantum achieving economic advantage by 2021 remains on target. Quantum executives discuss the three pillars of quantum commercialization as being:

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Grey hair seems to be driven by stem cell exhaustion, one of the suggested reasons we age. One researcher believes we can turn back the clock on greying hair.

rep melissa harris 550px

Melissa Harris’s research points to a new paradigm for hair graying. “We thought that once you go gray the stem cells are all lost — there’s no going back,” Harris said. “But presumably they can be reactivated.”

Molecular biology is not usually the kind of science you can do with the naked eye. Sure enough, Melissa Harris, Ph.D., runs a lab that leans into CRISPR gene-editing tools, single-cell sequencing studies and network-analysis algorithms. But all she needs is a glance to diagnose the state of your melanocyte stem cells.

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Artificial intelligence helped detect an outbreak of the coronavirus one week before the CDC issued a warning.

The BlueDot algorithm scours news reports and airline ticketing data to predict the spread of diseases like those linked to the flu outbreak in China.

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The algorithm lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices.

The news: A team at Facebook AI has created a reinforcement learning algorithm that lets a robot find its way in an unfamiliar environment without using a map. Using just a depth-sensing camera, GPS, and compass data, the algorithm gets a robot to its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration. This is a big improvement over previous best efforts.

Why it matters: Mapless route-finding is essential for next-gen robots like autonomous delivery drones or robots that work inside homes and offices. Some of the best robots available today, such as Spot and Atlas made by Boston Dynamics and Digit made by Agility Robotics, are packed with sensors that make them pretty good at keeping their balance and avoiding obstacles. But if you dropped them off at an unfamiliar street corner and left them to find their way home, they’d be screwed. While Facebook’s algorithm does not yet handle outside environments, it is a promising step in that direction and could probably be adapted to urban spaces.

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