Professor Patrick Winston, former director of MIT's Artificial Intelligence Laboratory, dies at 76

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Patrick Winston, a beloved professor and computer scientist at MIT, died on July 19 at Massachusetts General Hospital in Boston. A professor at MIT for almost 50 years, Winston was director of MIT's Artificial Intelligence Laboratory from 1972 to 1997 before it merged with the Laboratory for Computer Science to become MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). A devoted teacher and cherished colleague, Winston led CSAIL's Genesis Group, which focused on developing AI systems that have human-like intelligence, including the ability to tell, perceive, and comprehend stories. He believed that such work could help illuminate aspects of human intelligence that scientists don't yet understand. "My principal interest is in figuring out what's going on inside our heads, and I'm convinced that one of the defining features of human intelligence is that we can understand stories,'" said Winston, the Ford Professor of Artificial Intelligence and Computer Science, in a 2011 interview for CSAIL.


Game changer: the Commodore 64 concert

The Guardian

My grandfather, a lover of classical music, was president of the Hull Philharmonic Orchestra for many years. When I was 15, I played him an orchestrated version of Nobuo Uematsu's To Zanarkand, from the video game Final Fantasy X. "This isn't real music if it's from a video game," he told me at the time. I don't think he could ever have imagined that 12 years later, the Hull orchestra to which he had devoted so many years would be performing music from 1980s video games, in front of a packed hall. In the past, video game music concerts were a promotional novelty, but today they are regular and well-attended billings in venues across the world. From The Legend of Zelda: Symphony of the Goddess to Final Fantasy: Distant Worlds, Assassin's Creed Symphony to the recent debut by the London Video Game Orchestra and even a performance by the BBC Concert Orchestra hosted by lauded composer Jessica Curry, fans are flocking to concert halls to hear their favourite video game melodies played live.


12 Best Machine Learning Slack Groups for Data Scientists Lionbridge AI

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Slack is a growing chat client that allows teams to communicate and collaborate on projects in one place. You can make group channels (group chats) for different teams within an organization, where the members can also share documents and comments. You can also make a secure private channel where you direct message one or more people. Over the past few years, Slack has been gaining popularity for web developers, data scientists, engineers, bloggers, digital marketers, etc. There are now 10 million daily active users on Slack.


How Marketers Are Using AI And Machine Learning To Grow Audiences

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When technologists first developed the concept of artificial intelligence decades ago, they wanted to create a technology that could mimic human intelligence. But what artificial intelligence (AI) has been able to do in the wake of big data and analytics has far outpaced any human ability. Indeed, big data would be completely useless if we relied on human brains to process it. One of the largest groups to benefit from AI's superhuman powers are marketers using AI and machine learning to better grow their audiences. Whether they work B2B or B2C, marketers are using AI and machine learning to reach and engage customers in increasingly personal ways.


firmai/industry-machine-learning

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Please add your tools and notebooks to this Google Sheet. Highlight in YELLOW to get your pacakge added, you can also just add it yourself with a pull request. A curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.


Evaluating the Potential and Promise of Machine Learning for UI Testing

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As the qualitative focus is getting more intense with new and granular level parameters, user interface testing for the vast majority of apps is now trying every new methodology and means to ensure optimum output. The artificial intelligence and Machine Learning in recent times have come as the two most promising technologies to allow automation in the field of UI testing without really compromising on the output. While Machine Learning for mobile apps will continue to prosper as a groundbreaking technology, it is in the context of user interface testing that we can see the highest impact. What Machine Learning as a new technology promises for unit testing professionals is really groundbreaking in many respects. Thanks to this technology, the testing professionals now can write unit test programs based on new test cases learned from the user inputs in interacting with the machines.


Design & Build an End-to-End Data Science Platform

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This research describes the best practices and tools that data scientists and data engineers can use to build a data science platform that combines existing data stores with cutting edge machine learning (ML) frameworks like TensorFlow.


Buy Where Will Man Take Us?: The bold story of the man technology is creating Book Online at Low Prices in India

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Where Will Man Take Us? looks at the primary drivers of this change – artificial intelligence, bio-engineering and nanotechnology. It looks at how in our quest to bring human-like cognition to AI, we are forced to look at ourselves and answer some of our oldest questions – what is it to be human, what is self-awareness, what is consciousness. AI's ability to crunch data and math's ability to find patterns, could also help us unravel some of our greatest mysteries – astrology, aliens, the secret to unbroken eternal happiness. The book also looks at the advancements in genetics – the ability to edit the genome truly marks the beginning of man's next avatar. All of this is impacting some of our greatest ideas and institutions.


Microsoft invests $1 billion in OpenAI to develop AI technologies on Azure

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Microsoft today announced that it would invest $1 billion in OpenAI, the San Francisco-based AI research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, Elon Musk, and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman. In a blog post, Brockman said the investment will support the development of artificial general intelligence (AGI) -- AI with the capacity to learn any intellectual task that a human can -- with "widely distributed" economic benefits. To this end, OpenAI intends to partner with Microsoft to jointly develop new AI technologies for the Seattle company's Azure cloud platform and will enter into an exclusivity agreement with Microsoft to "further extend" large-scale AI capabilities that "deliver on the promise of AGI." Additionally, OpenAI will license some of its technologies to Microsoft, which will commercialize them and sell them to as-yet-unnamed partners, and OpenAI will train and run AI models on Azure as it works to develop new supercomputing hardware while "adhering to principles on ethics and trust." "AI is one of the most transformative technologies of our time and has the potential to help solve many of our world's most pressing challenges," said Microsoft CEO Satya Nadella.


AI protein-folding algorithms solve structures faster than ever

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Predicting protein structures from their sequences would aid drug design.Credit: Edward Kinsman/Science Photo Library The race to crack one of biology's grandest challenges -- predicting the 3D structures of proteins from their amino-acid sequences -- is intensifying, thanks to new artificial-intelligence (AI) approaches. At the end of last year, Google's AI firm DeepMind debuted an algorithm called AlphaFold, which combined two techniques that were emerging in the field and beat established contenders in a competition on protein-structure prediction by a surprising margin. And in April this year, a US researcher revealed an algorithm that uses a totally different approach. He claims his AI is up to one million times faster at predicting structures than DeepMind's, although probably not as accurate in all situations. More broadly, biologists are wondering how else deep learning -- the AI technique used by both approaches -- might be applied to the prediction of protein arrangements, which ultimately dictate a protein's function.