We Shouldn't Be Scared of Artificial Intelligence According to Facebook

Apple confirms open secret: It's'investing heavily' in machine learning, autonomous car We Shouldn't Be Scared of Artificial Intelligence According to Facebook MIT's AI figured out how humans recognize faces Apple confirms open secret: It's'investing heavily' in machine learning, autonomous car Pedestrian killed; Phoenix off-duty officer, 2 nurses renders ai - azfamily.com Apple's letter to the NHTSA hints that there may be some truth to Project Titan Microsoft's AI will describe images for the visually disabled Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Amazon is opening up its machine-learning capabilities to third-party developers with its new AI platform, according to TechCrunch. The service, which was introduced at Amazon's re:Invent developer conference in Las Vegas on Wednesday, boasts three new tools that span the whole gamut of machine learning -- image recognition, text-to-speech, and a platform that lets developers build conversational applications: Rekognition is an image-recognition service similar to Google's, Microsoft's, and Facebook's image-recognition offerings. Image recognition is an increasingly important tool as smartphone cameras become more prevalent in search and communications. Polly is Amazon's text-to-speech service that uses machine learning and natural-language understanding (NLU) to produce more natural, life-like speech. It can distinguish between words that are spelled the same but pronounced differently, such as in "I live in Seattle" and "Live from New York," Amazon explains.

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Amazon Lex, Amazon Polly, and Amazon Rekognition are based on the same proven, highly scalable Amazon technology built by the thousands of deep learning and machine learning experts across the company. Amazon AI services all provide high-quality, high-accuracy AI capabilities that are scalable and cost-effective. Amazon AI services are fully managed services so there are no deep learning algorithms to build, no machine learning models to train, and no up-front commitments or infrastructure investments required. This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them. To learn more about Amazon Lex, Amazon Polly, or Amazon Rekognition, visit: https://aws.amazon.com/amazon-ai

Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that "the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines. Technology has already gutted many traditional manufacturing and working class jobs -- but now it may be poised to wreak similar havoc with the middle classes.

Amazon's cloud services platform Amazon Web Services recently announced three AI services it said will make it easy for developers to build apps that can understand natural language, turn text into speech, have conversations using voice or text, analyze images and recognize faces, objects and scenes. This, in turn, underscores the increasing importance of AI to consumers, brands and marketers, but also raises some questions about how it will – and should – be developed. Building apps with AI capabilities has been challenging to date because doing so requires access to vast amounts of data and specialized expertise in machine learning and neural networks, Amazon said in a press release. "The combination of better algorithms and broad access to massive amounts of data and cost-effective computing power provided by the cloud is making AI a reality for application developers," added Raju Gulabani, vice president of databases, analytics and AI at AWS, in a statement. "Thousands of machine learning and deep learning experts across Amazon have been developing AI technologies for years to predict what customers might like to read, to drive efficiencies in our fulfillment centers through robotics and computer vision technologies and to give customers our AI-powered virtual assistant, Alexa. Now, we are making the technology underlying these innovations available to any developer…we are excited to see how customers use Amazon Lex, Amazon Polly and Amazon Rekognition to build a new generation of apps that have human-like intelligence and can see, hear, speak and interact with people and their environments."

MIT's AI figured out how humans recognize faces Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Today, if you ask the Google search engine on your desktop a question like "How big is the Milky Way," you'll no longer just get a list of links where you could find the answer -- you'll get the answer: "100,000 light years." While this question/answer tech may seem simple enough, it's actually a complex development rooted in Google's powerful deep neural networks. These networks are a form of artificial intelligence that aims to mimic how human brains work, relating together bits of information to comprehend data and predict patterns. Google's new search feature's deep neural network uses sentence compression algorithms to extract relevant information from big bulks of text. Essentially, the system learned how to answer questions by repeatedly watching humans do it -- more specifically, 100 PhD linguists from across the world -- a process called supervised learning.

We explore the relation between memcomputing, namely computing with and in memory, and swarm intelligence algorithms. In particular, we show that one can design memristive networks to solve short-path optimization problems that can also be solved by ant-colony algorithms. By employing appropriate memristive elements one can demonstrate an almost one-to-one correspondence between memcomputing and ant colony optimization approaches. However, the memristive network has the capability of finding the solution in one deterministic step, compared to the stochastic multi-step ant colony optimization. This result paves the way for nanoscale hardware implementations of several swarm intelligence algorithms that are presently explored, from scheduling problems to robotics.