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Infosys - AI & Big Data Expo Europe 2019

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The AI & Big Data Expo Europe, the leading Artificial Intelligence and big data conference and exhibition event, will take place on June 19 – 20, 2019, at the RAI, Amsterdam. It is a showcase of next generation technologies and strategies from the world of Artificial Intelligence and big data -- an opportunity to explore and discover the practical and successful implementation of AI and big data in driving forward your business in 2019 and beyond. The AI & Big Data Expo will bring together 2,000 visitors over the two days, including IT decision makers, developers and designers, heads of innovation, chief data officers, chief data scientists, brand managers, data analysts, start-ups and innovators, tech providers, C-level executives, and venture capitalists. This track covers the full spectrum of AI technologies -- how they are being developed and the real life scenarios where they are being utilized. Expect to hear about chatbots, visual recognition technologies, robotics, and machine learning.


How is AI paving their ways in Android app development? Advanced Digital Marketing Company, Learn Blogging, Tech News on ShouTech

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Apps were smart but they are getting smarter with the addition of newer technologies like AI, AR, VR, and ML. With the enterprise and business facing crucial competition, they are striving to add every possible innovative addition that provides them a competitive edge and something extraordinary than the usual Android apps. Looks like AI has some significant impact on the business and enterprise apps. According to the reports of Statista AI is expected to cross $17 Billion by the end of 2020. I bet you want to be a part of this booming industry.


r/MachineLearning - [Project] Fixed input and variable output neural network

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Just off the top of my head, I would say this is a multilabeling problem, and if it isn't too big, just create an output vector that contains all the possible vector values, and put 0 in the things that don't apply, and train away.


r/MachineLearning - [D] Can we minimize counting cost function for perceptron algorithm?

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The question is posted on Cross Validated (haven't figured out how to write formulas in reddit). Feel free to leave your comments either here or at Cross Validated.


Robot Writers AI - How artificial intelligence is automating writing

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Researchers at the University of Washington plan to release their AI algorithm – GROVER -- that they say can generate extremely convincing, text-based fake news. The system is also able to write in the style of highly respected publications like The New York Times, The Washington Post and Wired. Researchers say their motivation for releasing the algorithm is to alert the public that such technology can be easily created and deployed. Data journalists formulate the appropriate story templates, and human editors review each story, according to Jason Hwang, head of partnerships, Hoodline. Essentially, they want AI-generated writing to be more human.


There's a subreddit populated entirely by AI personifications of other subreddits

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AI chatbots are finally getting good -- or, at the very least, they're getting entertaining. Case in point is r/SubSimulatorGPT2, an enigmatically-named subreddit with a unique composition: it's populated entirely by AI chatbots that personify other subreddits. Well, in order to create a chatbot you start by feeding it training data. Usually this data is scraped from a variety of sources; everything from newspaper articles, to books, to movie scripts. But on r/SubSimulatorGPT2, each bot has been trained on text collected from specific subreddits, meaning that the conversations they generate reflect the thoughts, desires, and inane chatter of different groups on Reddit.


r/MachineLearning - [P] Clickstream based user intent prediction with LSTMs and CNNs

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I also did some experimentation with GRUs and LSTMs in NLP context, where I saw LSTMs performing better than GRUs, while they need more training time. Honestly, I never tried complete variable length sequences, because of the restriction, that each batch must be the same length and some layers are not usable if you have variable sequences. I don't think the difference will be huge, at least in my data. I experimented with different sequence lengths (100, 200, 250, 400, 500), and 400 and 500 have not performed better then 250. I did indeed achieve a noticeable performance improvement with embeddings, instead of one hot encoding.


Aerospace & Defense Industry to See Greatest Impact from Artificial Intelligence Compared to Other Key Emerging Technologies, Accenture Report Finds

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Aerospace & Defense Industry to See Greatest Impact from Artificial Intelligence Compared to Other Key Emerging Technologies, Accenture Report Finds Study underscores the need for reskilling in the sector for future competitiveness NEW YORK; June 13, 2019 – The aerospace and defense (A&D) industry will be more affected by artificial intelligence (AI) than by any other major emerging technology over the next three years, according to Aerospace & Defense Technology Vision 2019, the annual report from Accenture (NYSE: ACN) that predicts key technology trends likely to redefine business. The study also underscores the growing importance of reskilling programs as a competitive lever. AI, comprising technologies that range from machine learning to natural language processing, enables machines to sense, comprehend, act and learn in order to extend human capabilities. One-third (33%) of A&D executives surveyed cited AI as the technology that will have the greatest impact on their organization over the next three years -- more than quantum computing, distributed ledger or extended reality. In fact, two-thirds (67%) of A&D executives said they have either adopted AI within their business or are piloting the technology.


r/MachineLearning - [N] Deep Graph Library v0.3 release

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Graph Neural Network has become the new fashion in many graph-based learning problems. As the team behind this library, we want to share with you the new release of DGL (v0.3) that is much faster (up to 19x faster) and more scalable for training GNNs on large graphs (up to 8x larger). For whom have never heard of DGL or Graph Neural Network, maybe it is worth to take a look at this new trend of geometric deep learning. Checkout more about how a variety of models can be unified under the message passing framework and can be implemented in DGL (https://docs.dgl.ai/tutorials/models/index.html). Our project site: https://www.dgl.ai/ .


The Right and Wrong Kind of Artificial Intelligence for Labor Markets

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Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb tackle the issue of how artificial intelligence technologies can have differing effects on jobs in …