artificial intelligence


Synthetic fiber 'muscles' could lead to brawny robots and prosthetics

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Most attempts at giving robots muscles tend to be heavy, slow or both. Scientists might finally have a solution that's both light and nimble, though. They've developed fibers that can serve as artificial muscles for robots while remaining light, responsive and powerful. They bonded two polymers with very different thermal expansion rates (a cyclic copolymer elastomer and a thermoplastic polyethylene) that reacts with a strong pulling force when subjected to even slight changes in heat. They're so strong that just one fiber can lift up to 650 times its weight, and response times can be measured in milliseconds.



ElectrifAi, a Global Leader in Practical AI and Machine Learning, Announces New CEO and Launch of Industry's First Open Source Platform

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ElectrifAi, a global leader in Artificial Intelligence (AI) and Machine Learning (ML), today announced the launch of a new and improved open source platform and the appointment of industry veteran Edward Scott as ElectrifAi CEO. This comes alongside a comprehensive corporate rebrand for ElectrifAi, changing its name from Opera Solutions and launching a new website. In an industry-first move, ElectrifAi has re-architected its technology platform around an open source, Spark-unified computational engine that allows large-scale distributed data processing and machine learning, with embedded Zeppelin notebook capability. Now, ElectrifAi's data scientists – as well as those of its customers – can code and access data in any programming language. The incorporation of Docker Containers and Kubernetes enables ElectrifAi to build and deploy hybrid cloud enterprise solutions at scale, seeing results in weeks rather than months, thus increasing enterprise time to value dramatically.


New face of the £50 note is revealed

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Computer pioneer and codebreaker Alan Turing will feature on the new design of the Bank of England's £50 note. He is celebrated for his code-cracking work that proved vital to the Allies in World War Two. The £50 note will be the last of the Bank of England collection to switch from paper to polymer when it enters circulation by the end of 2021. The note was once described as the "currency of corrupt elites" and is the least used in daily transactions. However, there are still 344 million £50 notes in circulation, with a combined value of £17.2bn, according to the Bank of England's banknote circulation figures.


China using artificial intelligence to build economy, monitor population

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Though much of the technology was developed in the United States, President Xi Jinping wants China to be the world's AI leader by 2030.July 12, 2019


Elon Musk-backed Neuralink to detail its progress on upgrading the brain to keep pace with AI – TechCrunch

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Elon Musk's brain computer interface (BCI) venture Neuralink will provide some more insight into what they've been working on for the past two years, during which time we've heard very little in the way of updates on their progress. In 2017, we learned that Neuralink's overall driving mission was to help humans keep pace with rapid advancements in AI, ensuring that we can continue to work with ever-more advanced technology by closing the input and output gap between ourselves and computers. Musk has famously forewarned of the potential dangers of artificial intelligence, and what happens when it becomes more powerful relative to our own ability to control and understand it. He also founded OpenAI alongside Sam Altman and others as a research organization hoping to collaborate on the development of AI specifically designed to benefit, not harm humanity. At Recode's Code conference in 2016, and again in 2017 at an event in Dubai, Musk discussed how BCI could help people communicate with computers with much higher bandwidth and lower latency than is possible now, using our relatively primitive input methods (keyboard, mice and touch all introduce a surprising amount of lag and fidelity loss if you think about it).


The best of both worlds: How to solve real problems on modern quantum computers

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In recent years, quantum devices have become available that enable researchers--for the first time--to use real quantum hardware to begin to solve scientific problems. However, in the near term, the number and quality of qubits (the basic unit of quantum information) for quantum computers are expected to remain limited, making it difficult to use these machines for practical applications. A hybrid quantum and classical approach may be the answer to tackling this problem with existing quantum hardware. Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory and Los Alamos National Laboratory, along with researchers at Clemson University and Fujitsu Laboratories of America, have developed hybrid algorithms to run on quantum machines and have demonstrated them for practical applications using IBM quantum computers (see below for description of Argonne's role in the IBM Q Hub at Oak Ridge National Laboratory [ORNL]) and a D-Wave quantum computer. "This approach will enable researchers to use near-term quantum computers to solve applications that support the DOE mission. For example, it can be applied to find community structures in metabolic networks or a microbiome," says Yuri Alexeev, principal project specialist, Computational Science division The team's work is presented in an article entitled "A Hybrid Approach for Solving Optimization Problems on Small Quantum Computers" that appears in the June 2019 issue of the Institute of Electrical and Electronics Engineers (IEEE) Computer Magazine.


A mind of its own: Russia unveils terrifying new AI 'superweapon'

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The race to incorporate artificial intelligence in modern weapons threatens to outstrip the technology's capabilities -- and the world's ability to control them. The Commander-in-Chief of Russia's air force, Viktor Bondarev, has told a gathering at the MAKS-2017 international airshow his aircraft would soon be getting cruise missiles with artificial intelligence capable of analysing their environment and opponents and make "decisions" about altitude, speed, course -- and targets. "Work in this area is under way," Russian news agency TASS reports Tactical Missiles Corporation CEO Boris Obnosov as adding. "As of today, certain successes are available, but we'll still have to work for several years to achieve specific results." While neither indicated which missiles were slated to get such enhanced artificial intelligence, there are two apparent contenders among the "super weapons" President Vladimir Putin bragged about last year: the "Avangard" hypersonic glide vehicle and the "Burevestnik" nuclear-powered cruise missile.


AI in manufacturing: adopt early to gain market share - The Manufacturer

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With research suggesting artificial intelligence in manufacturing could become mainstream within 24 months, what can manufacturers gain from taking an early adopter approach? With AI and advanced analytics to identify patterns and trends in the wealth of data generated by the IoT, the barriers between operational technology and information technology are breaking down. Manufacturers can become data-driven in all aspects of business, enabling the companies to transform operations, restructure supply chains, improve efficiency, address skills shortages and create entirely new revenue streams and business models. Despite the many benefits, the Manufacturing Leadership Council's'Factories of the Future' survey revealed that less than one in 10 (8%) of manufacturers are currently using AI – though a further 50% expect to deploy it within two years. AI is still nascent in manufacturing today, yet these results suggest it could become mainstream in under 24 months.


TensorWatch: A debugging and visualization system for machine learning

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The rise of deep learning is accompanied by ever-increasing model complexity, larger datasets, and longer training times for models. When working on novel concepts, researchers often need to understand why training metrics are trending the way they are. So far, the available tools for machine learning training have focused on a "what you see is what you log" approach. As logging is relatively expensive, researchers and engineers tend to avoid it and rely on a few signals to guesstimate the cause of the patterns they see. At Microsoft Research, we've been asking important questions surrounding this very challenge: What if we could dramatically reduce the cost of getting more information about the state of the system?