Well File:

3 signs that your AI project is doomed

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"Among the machine learning strategy consultations you've done, which kinds of product team were the most challenging to work with?" After consulting on hundreds of machine learning projects, I've learned to pay attention to early warning signs that the client is in danger of shooting themselves in the foot. There's a lot of hype and nonsense about AI out there, so when teams come to me straight from the latest As-Seen-On-TV session ("…but if you call now, we'll throw in 50 free deep neural networks which you can compose into an unholy ensemble!") I know I'm going to have to undo all kinds of unrealistic expectations. Science fiction is a terrible teacher… but an ace of sales.


DeepVerge creating at least 60 highly skilled jobs in Co Cork

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DeepVerge, an Irish-founded, London-listed scientific research group, is to create 60 highly skilled jobs in Co Cork. It has announced a major expansion at its Fermoy-based scientific data management company Rinocloud with the development of a new artificial intelligence (AI) centre of excellence. The company is recruiting for roles in data science, physics and microbiology. Fin Murray, managing director of Rinocloud and chief operating office at DeepVerge, said the new centre would pay a key role in the real-time detection of pathogens such as E-coli and the coronavirus SARS-CoV-2, which leads to Covid. The new facility, which is to be located in Moorepark Technology Centre, includes laboratory and engineering production units to uncover such pathogens in water and wastewater systems.


The AI-Powered Micro-Business with Ash Fontana

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Artificial Intelligence is already part of our lives in the tools and services we use every day. As AI development accelerates, how can authors and small businesses use it as leverage to expand income and opportunities? Ash Fontana gives some ideas in this interview on The AI-First Company.


Detecting colon cancer early with artificial intelligence

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NORFOLK, Va. - News 3 is taking action for your health! March is National Colorectal Cancer Awareness Month. Be aware - the Hampton Roads area was identified as a "hot spot" for colon cancer deaths, according to the American Cancer Society. In Norfolk, a trial is currently underway, adding the tool of artificial intelligence in the quest for prevention. As the video indicates, AI technology is like an extra set of eyes; it highlights areas of interest when a patient undergoes a colonoscopy.


How a hybrid workforce can save up to 20 hours a month - Cloud computing news

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How productive would your company employees be if they could save two hours a day on regular tasks? With the growth and evolution of today's digital economy, companies face the challenge of managing increasingly complex business processes that involve massive amounts of data. This has also led to repetitive work, like requiring employees to manually perform data-intensive tasks when there are technologies available that could help free their time and automate tasks. According to a WorkMarket report, 53 percent of employees believe they could save up to two hours a day by automating tasks; that equates to roughly 20 hours a month. Working on tasks that could easily be automated is probably not the best use of employees' time, especially if your business is trying to improve productivity or customer service.



Machine Learning vs. Artificial Intelligence: Which Is the Future of Data Science? - Dataconomy

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When we imagine the future of AI, we may think of the fiction we see in cinema: highly advanced robots that can mimic humans so well as to be indistinguishable from them. It is true that the ability to quickly learn, process, and analyze information to make decisions is a key feature of artificial intelligence. But what most of us have come to know as AI actually belongs to a subdiscipline called machine learning. Artificial intelligence has become a catch-all term for several algorithmic fields of mathematics and computer science. There are some key differences between them that are important to understand to maximize their advancement potential.


Introduction: How to Build an ASI

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First things first: what is an ASI? ASI is short for Artificial Superintelligence, which informally means any non-biological thing (in practice, a computer, embodied or not) that's more intelligent than even Albert Einstein, John von Neumann and your personal favorite genius. Intelligent here refers to one's ability to reach one's goals in a wide variety of environments; we will go deeper into this definition in a later post, but this will do for now. Given our brain's small size and low signaling speed, it seems unlikely that peak human intelligence is the maximum level physically possible. We'll go into more detail about this in a later post as well. So why is ASI interesting?


Now DeepMind is using AI to transform football

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In March 1950, an RAF wing commander and trained accountant called Charles Reep turned his eye for numbers to football. Reep, who had become interested in the sport in the 1930s and was fascinated by Herbert Chapman's pioneering Arsenal team, had returned from the Second World War to find that the tactical revolution he'd witnessed before had stalled. Finally, at half-time during a drab Division Three game between Swindon Town and Bristol City during which he watched countless attacks amount to nothing, Reep's patience ran out. He grabbed a notebook and a pencil and began furiously jotting down what happened on the pitch – he started counting the number of passes and shots, in one of the first systematic attempts to use data to analyse football. Seven decades later, the data revolution has reached the grassroots – fans are fluent in xG and net spend, and the top teams pluck statistics PhD students straight from university in the search for an edge.


Question Generation using Natural Language processing

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This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.