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.


Meet IRpair & Phantom; powerful anti-facial recognition glasses

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Facial recognition technology is the single biggest tool for authorities to keep an eye on suspected (and unsuspected) individuals; but thanks to Snowden leaks, it would appear that most of the victims of such technologies have been unsuspected users. The growing use of facial recognition technology at airports in the United States to its misuse in China to track minorities; it all raises serious concerns over user privacy and in particular, just how much do authorities know about you. See: One out of Two American Adults Part of the FBI's Facial Recognition Database For instance, in Southeast China, the police used facial recognition technology to locate and detain a suspect among a crowd of over 60,000 people. The incident occurred at a pop concert where the popular Hong Kong singer Jacky Cheung was performing, a concert attended by the suspected fugitive. The problem is that the same technology can be used to track and oppress anyone hiding from brutal regimes.


Microsoft invests $1 billion in AI startup

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Microsoft Corp. MSFT, 1.32% is making a huge bet on artificial intelligence by sinking $1 billion into AI startup OpenAI, the companies announced Monday. Under a partnership, OpenAI will run all of its services on Microsoft's Azure cloud platform and use Microsoft as its preferred partner for commercializing new AI tech. On its website, OpenAI says its mission is "to ensure that artificial general intelligence (AGI) -- by which we mean highly autonomous systems that outperform humans at most economically valuable work -- benefits all of humanity." Microsoft, which is currently the only U.S. company valued at more than $1 trillion, recently reported strong earnings growth driven by Azure. Shares were up 1.2% to $138.19 in recent trading Monday, compared with a flat Dow Jones Industrial Average DJIA, 0.07% a 0.3% gain in the S&P 500 index SPX, 0.28% and a 0.7% gain in the tech-heavy Nasdaq Composite Index COMP, 0.71%