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How Artificial Intelligence Detects Faces?

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You might've heard about face recognition and its different applications. A face recognition system can identify people in videos or static images to put it in simple terms. Many fields use the technology for surveillance and tracking people. Some countries are using face recognition systems more widely than others. But while you may hear about it more frequently now, the technology has been in existence for decades.


DeepMind AI Says Will Release Structure of Every Protein Known

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DeepMind, an artificial intelligence (AI) subsidiary of Google parent Alphabet, said it has been successful in predicting the shape of nearly every protein in the human body as well as thousands of other proteins found in 20 additional organisms that scientists rely on for their research, including yeast, fruit flies, and mice. This breakthrough is likely to assist researchers to understand human diseases better and find new drugs to treat or cure them. Some scientists have compared the DeepMind project to the international effort to map every human gene. DeepMind said in a blog post it is releasing the database for free. To set up and run the database, it has partnered with the European Molecular Biology Laboratory.


Huge protein structure database could transform biology

Science

Earlier this month, two groups unveiled the culmination of years of work by computer scientists, biologists, and physicists: advanced modeling programs that can predict the precise 3D atomic structures of proteins. Last week, the biggest payoff of that work arrived. One team used its newly minted artificial intelligence (AI) programs to solve the structures of 350,000 proteins from humans and 20 model organisms, such as Escherichia coli bacteria, yeast, and fruit flies, all mainstays of biological research. In the coming months, the group says it plans to expand its efforts to all cataloged proteins—some 100 million molecules. “It's pretty overwhelming,” says John Moult, a protein folding expert at the University of Maryland, Shady Grove, who runs a biennial competition called the Critical Assessment of protein Structure Prediction (CASP). Moult says structural biologists have dreamed for decades that accurate computer models would one day augment slow, painstaking experimental methods, such as x-ray crystallography, that map protein shapes with extreme precision. “I never thought the dream would come true,” Moult says. The computer model, called AlphaFold, is the work of researchers at DeepMind, a U.K. AI company owned by Alphabet, the parent company of Google. In fall of 2020, AlphaFold swept the CASP competition, tallying a median accuracy score of 92.4 out of 100 for its predicted structures, well ahead of the next closest competitor ( Science , 4 December 2020, p. [1144][1]). But because DeepMind researchers didn't reveal AlphaFold's underlying computer code, other teams were left frustrated, unable to build on the progress. That began to change this month ( Science , 16 July, p. [262][2]). On 15 July, researchers led by Minkyung Baek and David Baker at the University of Washington, Seattle, reported online in Science that they had created a competing system: a highly accurate protein structure prediction program called RoseTTAFold, which they released publicly. The same day, Nature rushed out details of AlphaFold in a paper by DeepMind researchers led by Demis Hassabis and John Jumper. Both programs use AI to spot folding patterns in vast databases of solved protein structures. The programs compute the most likely structure of unknown proteins by applying those patterns and also considering basic physical and biological rules governing how neighboring amino acids in a protein interact. In their paper, Baek and Baker used RoseTTAFold to create a structure database of hundreds of G-protein coupled receptors, a class of common drug targets. Now, DeepMind researchers report in Nature that they have amassed 350,000 predicted structures—more than twice as many as experimenters have solved in many decades of work. AlphaFold's structures for which the researchers say they have high confidence cover nearly 44% of all human proteins. AlphaFold determined that many of the remaining human proteins were “disordered,” meaning their shape doesn't adopt a single structure. Such disordered proteins may ultimately adopt a structure when they bind to a protein partner, Baker says. They may also naturally adopt multiple conformations, says David Agard, a structural biologist at the University of California, San Francisco. A database of DeepMind's new protein predictions, assembled with collaborators at the European Molecular Biology Laboratory (EMBL), is freely accessible online. “It's fantastic they have made this available,” Baker says. “It will really increase the pace of research.” Because the 3D structure of a protein largely dictates its function, the DeepMind library is apt to help biologists sort out how thousands of unknown proteins do their jobs. “We at EMBL believe this will be transformative to understanding how life works,” says the lab's director general, Edith Heard. “This will be one of the most important data sets since the mapping of the human genome,” adds Ewan Birney, director of EMBL's European Bioinformatics Institute. DeepMind collaborators say that by making it possible to quickly assess how a change in a protein's sequence alters its structure and function, AlphaFold has already spurred the development of novel enzymes for breaking down plastic waste. It has also prompted efforts to better target parasitic diseases. The impacts aren't likely to stop there. The predictions will help experimentalists who solve structures, Baek says. Data from x-ray crystallography and cryo–electron microscopy experiments can be difficult to interpret, Baek and others say, and having a model can help pinpoint the correct structure. “In the short term, it will boost structure determination efforts,” she predicts. “And over time it will also slowly replace [experimental] structural determination efforts.” If that happens, structural biologists won't find themselves out of work. Baker notes that both experimental and computational scientists are already beginning to turn their efforts to the more complex challenge of understanding exactly which proteins interact with one another and what molecular changes happen during these interactions. The new tools will “reset the field,” Baker says. “It's a very exciting time.” [1]: http://www.sciencemag.org/content/370/6521/1144 [2]: http://www.sciencemag.org/content/373/6552/262


Getting Banned From Riding In AI Self-Driving Cars For The Rest Of Your Entire Life

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People are increasingly getting onto those banned no-fly types of lists, which could happen with ... [ ] self-driving cars too. People keep getting banned for doing the darndest and seemingly dumbest of acts. Oftentimes getting banned for the rest of their entire life. You might have heard or seen the recent brouhaha in major league baseball when a spectator in Yankee Stadium seated above leftfield opted to throw a baseball down onto the field that then struck the Boston Red Sox player Alex Verdugo in the back. He was not hurt, but you can imagine the personal dismay and shock at suddenly and unexpectedly having a projectile strike him from behind, seemingly out of nowhere. Turns out that Alex had earlier tossed the same baseball up into the stands as a memento for a young Red Sox cheering attendee. By some boorish grabbing, it had ended up in the hands of a New York Yankees fan. Next, after some hysterical urging by other frenetic Yankees to toss it back, the young man did so. Whether this act of defiance was intentionally devised to smack the left-fielder is still unclear and it could have been a happenstance rather than a purposeful aim.


Synthetic Data May Be The Solution to AI Privacy Concerns

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AI is hungry for data. Training and testing the machine-learning tools to perform desired tasks consumes huge lakes of data. More data often means better AI. Yet gathering this data, especially data concerning people's behavior and transactions, can be risky. For example, In January of this year, the US FTC reached a consent order with a company called Everalbum, a developer of photography apps.


Council Post: How To Build A Perfect AI Team

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Artificial intelligence (AI) is now on a mission to permeate every industry. From e-commerce and healthcare to travel and finance, AI has made its way to just about every type of industry. In fact, the adoption rate of AI has increased by more than 270%, according to Gartner, Inc. Moreover, 37% of all types of businesses are now using AI-driven technologies such as natural language processing, predictive analysis, machine learning and robotic process automation. Therefore, if you're still not using AI in your business, then it's highly likely your competitors are already doing so, and very soon, you'll be left behind. That's why we have come up with this article that will allow you to build your own AI team to eliminate your existing bottlenecks and achieve your business goals.


Introduction to Machine Learning

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Humans learn from their experiences and gain expertise. Machine Learning is concerned with computer programs that imitate this process. It is a field of study that gives computers the capability to learn without being explicitly programmed. Can you imagine how Netflix makes those recommendations? The program learns from your past activities and tries to gain expertise in predicting your behavior.


How BMW Powers its Processes with Artificial Intelligence

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In this decade, companies across the globe have embraced the potential of artificial intelligence for digital transformation and enhanced customer experience. One important application of AI is enabling companies to use the pools of data available with them for smart business use. BMW is one of the world's leading manufacturers of premium automobiles and mobility services. BMW uses artificial intelligence in critical areas like production, research and development, and customer service. BMW also runs a project dedicated to this technology called Project AI, for efficient use of artificial intelligence.


Synthetic Data May Be The Solution to AI Privacy Concerns

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AI is hungry for data. Training and testing the machine-learning tools to perform desired tasks consumes huge lakes of data. More data often means better AI. Yet gathering this data, especially data concerning people's behavior and transactions, can be risky. For example, In January of this year, the US FTC reached a consent order with a company called Everalbum, a developer of photography apps.


AI's human protein database a 'great leap' for research

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Scientists on Thursday unveiled the most exhaustive database yet of the proteins that form the building blocks of life, in a breakthrough observers said would "fundamentally change biological research". Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome -- the complete sequence of human genes that encode cellular life -- the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate -- the shape in which they end up, or "fold" into -- within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking.