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[Online] AI/Machine Learning for beginners

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This is a 1-week/10 hours long, part-time and instructor-led training offered in evening time (New York Timezone) by 6FS.io, a San Francisco based technology company. This training program is built based on 6FS team's years of experience in building large-scale solutions using various various Big Data and AI/ML technologies. This is not a book-based training, rather a hands-on, interactive experience app building apps using AI/ML, delivered by experienced startup CTOs. While learning basic concepts like Python, Jupyter notebooks, and training models and human powered labeling, you'll also learn practical problems and solutions, based on how Dean and Adrian built technology stacks in their previous startups. Let's build a project to gather data from human labeling service like AWS Sage maker GroundTruth.


How to quickly solve machine learning forecasting problems using Pandas and BigQuery Google Cloud Blog

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In the rest of this blog, we'll use an example to provide more detail into how to build a forecasting model using the above workflow. Machine learning is all about running experiments. The faster you can run experiments, the more quickly you can get feedback, and thus the faster you can get to a Minimum Viable Model (MVM). Let's build a model to forecast the median housing price week-by-week for New York City. We spun up a Deep Learning VM on Cloud AI Platform and loaded our data from nyc.gov into BigQuery.


Gary Marcus on Why AI Needs a Reboot

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Artificial intelligence (AI) has emerged from relative dormancy to a worldwide renaissance--fueled by significant investments and arousing interest across nearly all sectors and industries. Amid this global ground swell of enthusiasm, a few voices are going against popular opinion, and are calling for a reboot. Robust.AI CEO Gary Marcus and NYU professor of computer science Ernest Davis, sound a warning bell for AI in their book Rebooting AI, released in September 2019. Gary Marcus is a modern-day polymath. He is a cognitive scientist, successful technology entrepreneur, prolific author, keynote speaker, professor emeritus at New York University (NYU), juggler, unicyclist and erstwhile guitarist who literally wrote the book on it with his bestseller Guitar Zero: The Science of Becoming Musical at Any Age.


RE•WORK AI in Finance Federated AI, Reinforcement and Transfer Learning

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The financial sector has been among the fastest adaptors of AI algorithms, which are well suited to the industry's complex and fast-moving environment. At last week's Re•Work AI in Finance Conference in New York, researchers and engineers from banks and academia alike shared their thoughts on current AI research and applications in the finance world. IBM has built a blockchain-based infrastructure for federated AI, enabling institutions to leverage transaction data across branches to improve decision making. Alan King is an IBM AI and Blockchain Solutions engineer. In his presentation King spoke of the advantages of using federated AI on transaction data.


'Money Honey' Maria Bartiromo on Trump, artificial intelligence and the future of work

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Maria Bartiromo, host of "Maria Bartiromo's Wall Street" on Fox Business Network will host at documentary on the future of artificial intelligence. On a recent Friday, Maria Bartiromo, the financial television icon, offered a visitor a tour of her new corner office in the News Corporation Building in midtown Manhattan. The office, lined with floor-to-ceiling windows, is furnished with a cognac leather sofa, chairs and a large desk. Bartiromo, who has been with Fox News and Fox Business for more than five years, extended her tenure with the networks with a multi-year deal earlier this week. By 10 a.m., she'd already logged four hours of on-camera work.


RE•WORK AI in Insurance Summit NYC 2019: AI Underwriting, Fraud Detection, and More

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The Re•Work AI in Insurance Summit in New York City was held September 5–6 and saw 60 speakers from AVIVA, Travelers, GoCompare, Prudential and other insurance-related companies cover a wide range of topics -- from detecting claims fraud to applying machine learning to underwriting and maximizing revenue. Today's specialty and commercial insurance underwriters face an overwhelming number of challenges. AXIS Capital Senior Data Scientist Min Yu believes artificial intelligence (AI) will transform the specialty and commercial insurance underwriting from a "detect and repair" mode to "predict and prevent" mode. In her talk on Machine Learning to Specialty Insurance Underwriting, Yu outlined the AI process as follows: receive a submission, retrieve data, analyze risk, automate quote and quick binding. Manual underwriting would be mainly used for review, or on complicated or emerging risks.


Incoming EU Leaders Plan New AI, Data Use Laws PYMNTS.com

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Evidence is being gathered on U.S. big tech companies as the European Commission prepares for new leadership, The Wall Street Journal reported on Tuesday (Sept. Within 100 days of taking office on Nov. 1, President-elect Ursula von der Leyen and her team indicate there will be new laws governing artificial intelligence (AI) and how tech companies like Facebook use big data. Big tech investigations were already initiated by commissioner Margrethe Vestager and could end with multimillion-dollar fines. Facebook and Amazon deny wrongdoing. Alphabet's Google has already been hit with $9.4 billion in fines resulting from three separate EU investigations, and a fourth is underway.


Viral App Highlights the Insensitive Logic of a System at the Heart of the Current AI Boom

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The tool, called ImageNet Roulette, detects human faces in any uploaded photo and assign them labels using ImageNet, an academic training set with millions of pictures depicting almost anything imaginable, and WordNet, the corresponding text tags. As viral examples on Twitter have shown, the results of this process are more often than not completely useless--nonsensical at best and racist or otherwise offensive at worst. In some cases, it would label black men as "offenders" or "wrongdoers," while other times it would spit out racial slurs against Asians or outdated and offensive terms for black people. I might have a bad sense of humor but I don't think this particularly funny #imagenetroulette pic.twitter.com/RR578nhCOU The offensiveness was more or less the point, says co-creator, Kate Crawford, who is also a co-founder of New York University's AI Now Institute, which studies the social implications of artificial intelligence.


AI Mimics CEOs Voice To Steal £201000 – GMA

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A recent Wall Street Journal report has highlighted how, in March this year, a group of hackers were able to use AI software to mimic an energy company CEO's voice in order to steal £201,000. Reports indicate that the CEO of an unnamed UK-based energy company received a phone call from someone that he believed to be the German chief executive of the parent company. The person on the end of the phone ordered the CEO of the UK-based energy company to immediately transfer €220,000 (£201,000) into the bank account of a Hungarian supplier. The voice was reported to have been so accurate in its sound, that the CEO of the energy company even recognised what he thought was the subtleties of the German accent of his boss, and even "melody" of the accent. The call was so convincing that the energy company made the transfer of funds as requested.


Daily Digest September 16, 2019 – BioDecoded

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Reseachers benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. The general-purpose support vector machine classifier has overall the best performance across the different experiments. Researchers present a novel algorithm for predicting genetic ancestry using only variables that are routinely captured in electronic health records (EHRs), such as self-reported race and ethnicity, and condition billing codes. Using patients that have both genetic and clinical information at Columbia University / New York-Presbyterian Irving Medical Center, they developed a pipeline that uses only clinical data to predict the genetic ancestry of all patients of which more than 80% identify as other or unknown.