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Category: information science – Page 239

If you’re interested in mind uploading, then I have an excellent article to recommend. This wide-ranging article is focused on neuromorphic computing and has sections on memristors. Here is a key excerpt:

“…Perhaps the most exciting emerging AI hardware architectures are the analog crossbar approaches since they achieve parallelism, in-memory computing, and analog computing, as described previously. Among most of the AI hardware chips produced in roughly the last 15 years, an analog memristor crossbar-based chip is yet to hit the market, which we believe will be the next wave of technology to follow. Of course, incorporating all the primitives of neuromorphic computing will likely require hardware solutions even beyond analog memristor crossbars…”

Here’s a web link to the research paper:

Computers have undergone tremendous improvements in performance over the last 60 years, but those improvements have significantly slowed down over the last decade, owing to fundamental limits in the underlying computing primitives. However, the generation of data and demand for computing are increasing exponentially with time. Thus, there is a critical need to invent new computing primitives, both hardware and algorithms, to keep up with the computing demands. The brain is a natural computer that outperforms our best computers in solving certain problems, such as instantly identifying faces or understanding natural language. This realization has led to a flurry of research into neuromorphic or brain-inspired computing that has shown promise for enhanced computing capabilities. This review points to the important primitives of a brain-inspired computer that could drive another decade-long wave of computer engineering.

Continue reading “The building blocks of a brain-inspired computer” | >

If you are interested in mind uploading, then I have a research paper for you to consider. One of the serious issues with mind uploading is the computer substrate. Simulating the brain will require a new and incredible computing capability. New techniques and new hardware are going to be required to make it practical. Of course, there is currently zero demand for mind uploading hardware, so the market is not going to provide this capability. However, there is incredible market demand for cutting edge hardware for machine learning and artificial intelligence. And it turns out that one potential technique for artificial intelligence simulates the way that the brain works: neuromorphic computing. And there is a relatively new type of electronic component that seems to mimic some of the functions of a brain’s neuron: the memristor. Memristors are relatively new, having only been fabricated for the first time by HP in 2008. So I am trying to keep up with the latest developments in memristive technology.

Here are some excerpts from the paper:

“…Artificial Neural Network (ANN) algorithms offer fast computations by mimicking the neuronal network of brains. A weight matrix is used in neural networks (NNs) for parallel processing that makes computing faster…The memristor has attracted much attention because of its potential to have linear multilevel conductance states for vector-matrix multiplication (output = weight × input), corresponding to parallel processing…”

Here is a web link to the research paper:

Continue reading “Perspective: A review on memristive hardware for neuromorphic computation” | >

The world’s first programming language based on classical Chinese is only about a month old, and volunteers have already written dozens of programs with it, such as one based on an ancient Chinese fortune-telling algorithm.

The new language’s developer, Lingdong Huang, previously designed an infinite computer-generated Chinese landscape painting. He also helped create the first and so far only AI-generated Chinese opera. He graduated with a degree in computer science and art from Carnegie Mellon University in December.

After coming up with the idea for the new language, wenyan-lang, roughly a year ago, Huang finished the core of the language during his last month at school. It includes a renderer that can display a program in a manner that resembles pages from ancient Chinese texts.

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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.

Continue reading “An ‘anti-aging’ gene therapy trial in dogs begins, and Rejuvenate Bio hopes humans will be next” | >

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.

Continue reading “Food Waste Is a Serious Problem. AI Is Trying to Solve It” | >

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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