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Tech Companies Are Training AI to Read Your Lips


The task is incredibly challenging--even expert human lip readers are actually pretty poor at word-for-word interpretation. In 2018, Google subsidiary Deepmind published research unveiling its latest full-sentence lip-reading system. The AI achieved a word error rate (the percent of words it got wrong) of 41 percent on videos containing full sentences. Human lip readers viewing a similar sample of video-only clips had word error rates of 93 percent when given no context about the subject matter and 86 percent when given the video's title, subject category, and several words in the sentence. That study was conducted using a large, custom-curated dataset.

This High Schooler Created a Drug Discovery Search Engine


Between his mom's place in Manhattan, his dad in Queens, and his high school in the Bronx, Noah Getz is on the subway a lot. It gives him time to read and to think. Our first coronavirus summer was waning, and he'd been wrestling with a weighty science problem: using machine learning to hunt down tiny molecules that may help treat Alzheimer's. Thus far, his AI had been spitting out results that were "almost comically bad." The problem was that the algorithms Getz was using did their best when they had massive amounts of data to sift through and discover patterns in. Getz' data set was far smaller; he was working with one lab at Mount Sinai, not a multinational pharmaceutical company with a galaxy-sized drug library.

Global Artificial Intelligence and Machine Learning Market Analysis, Size, Share, Growth, Trends and Forecast to 2026


The Artificial Intelligence and Machine Learning Market research report is an in-depth analysis of the latest developments, market size, status, upcoming technologies, industry drivers, challenges, regulatory policies, with key company profiles and strategies of players. The research study provides market overview, Artificial Intelligence and Machine Learning market definition, regional market opportunity, sales and revenue by region, manufacturing cost analysis, Industrial Chain, market effect factors analysis, Artificial Intelligence and Machine Learning market size forecast, market data Graphs and Statistics, Tables, Bar & Pie Charts, and many more for business intelligence. The up-to-date report of Artificial Intelligence and Machine Learning market presents an in-depth evaluation of all the crucial factors such as key growth drivers, impediments, and opportunities to understand the industry behavior. Moving ahead, insights into competitive landscape with regards to the top firms, emerging contenders, and new entrants is taken into account. Moreover, the document sheds light on the effects of COVID-19 pandemic on this marketplace and puts forth various strategies for effective risk management and strong profits in the upcoming years.

Machine Learning at the Edge: TinyML Is Getting Big


Is it $61 billion and 38.4% compound annual growth rate (CAGR) by 2028 or $43 billion and 37.4% CAGR by 2027? Depends on which report outlining the growth of edge computing you choose to go by, but in the end it is not that different. What matters is that edge computing is booming. There is growing interest by vendors, and ample coverage, for good reason. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple.

AI system outperforms humans in designing floorplans for microchips


Success or failure in designing microchips depends heavily on steps known as floorplanning and placement. These steps determine where memory and logic elements are located on a chip. The locations, in turn, strongly affect whether the completed chip design can satisfy operational requirements such as processing speed and power efficiency. So far, the floorplanning task, in particular, has defied all attempts at automation. It is therefore performed iteratively and painstakingly, over weeks or months, by expert human engineers.

Machine learning speeds up simulations in material science


Research, development, and production of novel materials depend heavily on the availability of fast and at the same time accurate simulation methods. Machine learning, in which artificial intelligence (AI) autonomously acquires and applies new knowledge, will soon enable researchers to develop complex material systems in a purely virtual environment. How does this work, and which applications will benefit? In an article published in the Nature Materials journal, a researcher from Karlsruhe Institute of Technology (KIT) and his colleagues from Göttingen and Toronto explain it all. Digitization and virtualization are becoming increasingly important in a wide range of scientific disciplines.