After its ambitious Feb. 18 landing on Mars, NASA’s Perseverance Mars rover is already getting critical help from the multiple onboard AI systems that are designed to guide its two-year exploratory mission on the red planet.
Perseverance successfully completed its 293-million-mile voyage from Earth to Mars and landed safely on the Martian surface at 3:55 p.m. EST Thursday.
There’s a reason why most of the talk about artificial intelligence is around its potential instead of its accomplishments. To function at peak levels it often needs more computing power than current technology can deliver. Too much data is needed too quickly for its operations to run smoothly. This is why so many AI programs are stuck in the ‘research’ or ‘development’ phase. The present just can’t keep up with the future.
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All it took was a ball of discarded Scotch tape in the waste basket… The discovery was serendipitous — but just like many other accidental material discoveries, it could significantly impact our world, leading to flexible circuits and unbreakable touchscreens, more powerful gadgets, improved bulletproof vests and more efficient medical, chemical and industrial processes.
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The Large Hadron Collider (LHC) is one of the most intricate machines mankind has ever built. When it’s running, every second approximately one billion particles smash into each other at velocities close to the speed of light, probing physics beyond the edges of the current standard model of particle physics.
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Might it be time to create an “FDA for algorithms? In the United States, there is currently no federal institution that protects the public from harmful algorithms. We can buy eggs, get a vaccine, and drive on highways knowing there are systems in place to protect our safety: the USDA checks our eggs for salmonella, the FDA checks vaccines for safety and effectiveness, the NHTSA makes sure highway turns are smooth and gentle for high speeds. But what about when we run a Google search or look up a product on Amazon?
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Superhuman Innovation: Transforming Business with Artificial Intelligence by Chris DuffeyKiranmayi GFeb 3
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Natural language processing is perhaps the most talked-about subfield of data science. It’s interesting, it’s promising, and it can transform the way we see technology today. Not just technology, but it can also transform the way we perceive human languages.
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Technologies like artificial intelligence and human genome editing “reveal and reflect even more about the complex and sometimes dangerous social architecture that lies beneath the scientific progress that we pursue,” said Dr. Alondra Nelson today as part of the introduction of the Biden administration science team. On Friday, the Biden transition team appointed Nelson to the position of OSTP deputy director for science and society. Biden will be sworn in Wednesday to officially become the 46th president of the United States.
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The mind tries to understand the world in terms of concepts, most of which are dressed in language and in some cases, in mathematics. But our conceptual understanding of the world suffers from a chicken-and-egg problem: where do the concepts in which this understanding is modeled come from in the first place? How can you build a new theory with old terminology and make sure that it doesn’t suffer from the implicit assumptions inherent in the terminology?
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I remember my first IBM patent. I also knew it had contributed to IBM’s patent leadership that year. Just like during the 13 years before and 15 years after, IBM has been getting more patents granted than any other company in the US.
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Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.
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According to a new study published in journal Nature Communications, our only natural satellite Moon has way more craters than we thought. By using data gathered by Chinese lunar orbiters, Artificial Intelligence (AI) has detected more than 100,000 new lunar craters in the mid and low latitude regions.
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Our mission at IBM is to help our clients change the way the world works. There’s no better example of that than IBM’s annual “5 in 5” technology predictions.
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“Every second of every day, our senses bring in way too much data than we can possibly process in our brains.” — Peter Diamandis, Chairman/CEO, X-Prize Foundation. AI startups for Data Aggregation and Analysis during Drug Development.
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For natural language processing and natural language understanding, there are many steps in the process. One of these is Stemming. In this blog we will look to understand Stemming, review various examples and see how stemming works.
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In this blog, we talk about the core to Persoana-ization. We will discuss Content-based Recomender Systems, what are they, the different types, and how these systems work. We will also create a content-based recommender system for movies.
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The new plan unifies some of the behind-the-scenes work of sending exposure notifications. In the original scheme, state health authorities were responsible for setting up servers to send exposure alerts to people who had been near others who had tested positive for Covid-19.
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To start, try getting a clear idea of what a machine learning solution would have to achieve. Then go through the flowchart.
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Encoding the semantics of words and sentences is something we take for granted that state-of-the-art NLP systems are capable of.
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The importance of recognizing non-identifiability. identifiability is a property that a model must satisfy in order for precise inference to be possible.
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