Graphcore is continuing to build out its AI applications and services ecosystem, launching a new partnership with AI platform company Pienso to make its powerful text analysis solution available on IPUs in the cloud. Pienso uses natural language processing to help businesses extract actionable insights from written text such as comments posted on social media, transcripts of customer service phone calls, news articles and documents. Pienso Graphcore is aimed at enterprise users such as media and entertainment companies, consumer internet - including social networks and e-commerce businesses, telecoms providers, and anyone trying to get high quality, high speed insights from large amounts of written data. No coding or ML skills are needed to build and run models in Pienso, meaning it can be used by subject matter experts and strategic decision makers within a business, removing reliance on in-demand AI engineers. Thanks to the IPU's designed-for-AI architecture and world leading performance in Natural Language Processing, Pienso runs considerably faster and with finer granularity and precision on IPUs than on other compute platforms; a performance gain that makes an already powerful solution truly transformative for its users.

DEEP IDEAS and STORIES!… Lucid AI written stories and articles with opposing points of view, skepticism, and AI written perspectives, on EVERYTHING. CAUTION: All Italic text at this site is generated by A.I. AI at this science fiction and fantasy magazine gets its "intelligence" from processing vast, overwhelming datasets of collaborative human information and so it could be thought of as "human", in a way. It is a vast conglomeration of human information, analyzed and digested by "intelligent" algorithms. The answer is "Yes, AI can be human" and in fact it is already the most human thing on Earth. AI is more human than any individual because it is a vast collective of humanity, assembled and processed by computers.

New episode of the Retail Rundown #podcast is LIVE! @Emarsys' Global Head of Product #Marketing & Marketing Solutions @meghann_york is on the show to discuss why marketers need more than just predictions--they need real strategy & insights.

Radiology has always been on the cutting edge of technology in medicine, and artificial intelligence (AI) has made some progress within this specialty. But AI is at an inflection point. To make use of AI as a more accessible, trusted reality, radiology will need six critical components: 1) Reassurance that AI will never replace a physician With the growing body of evidence that shows the extraordinary potential of AI in healthcare, I find it interesting that widespread adoption remains slow. Although I hear multiple reasons for the reluctance of radiologists to embrace AI, one of the most common that I hear is the misperception that AI is going to put us radiologists out of business. This simply isn’t realistic. At the end of the day, algorithms address technology; radiologists take care of people, with all the inherent responsibilities, nuances and complexities. Algorithms aren’t designed to do that. However, It’s also increasingly unrealistic to expect radiologists to manage…

The $2.5 trillion fashion industry seems vulnerable to a long drawn out recovery, due to the discretionary nature of consumer spending on clothes. Retailers will now experience first-hand, the need for a digital-first mentality to even stay afloat. Empowered by AI insights, designers, merchandisers and buyers could soon predict customer preferences, way before they become fashionable. Even before the coronavirus pandemic crashed the global economy, disrupting supply chains and stalling consumer demand, the fashion industry was not looking forward to walking the 2020 ramp. Headwinds such as slowing demand, rising competition, shifting demographic-driven preferences and the rising carbon footprint of fast fashion, were already hanging over the sector's head.

In the latest McKinsey Global Survey on AI we noted a significant year-over-year jump in companies using AI across multiple areas of the business. And while most survey respondents said their companies have gained value from AI, some are attaining greater scale, revenue increases, and cost savings than the rest. Based on our research and experience, this is no accident; how companies build their business strategy, what foundations they put in place, and how they tackle AI adoption in the workplace can all impact their potential for transformation. Many companies that have spent years developing AI technologies are facing the stark reality that successfully scaling AI requires more than just deploying AI technology. We find that those companies finding more success in scaling efforts are more likely than others to apply a core set of practices.

In the latest McKinsey Global Survey on AI we noted a significant year-over-year jump in companies using AI across multiple areas of the business. And while most survey respondents said their companies have gained value from AI, some are attaining greater scale, revenue increases, and cost savings than the rest. Based on our research and experience, this is no accident; how companies build their business strategy, what foundations they put in place, and how they tackle AI adoption in the workplace can all impact their potential for transformation. Many companies that have spent years developing AI technologies are facing the stark reality that successfully scaling AI requires more than just deploying AI technology. We find that those companies finding more success in scaling efforts are more likely than others to apply a core set of practices.

In the world of AI (artificial intelligence) there are a lot of exciting companies making their mark in a pretty impressive way in the automotive industry. Some of these disruptive startups are truly giving us all a glimpse of where the AI market is headed. Ford and its Ford City Insights program, which uses its AI and data from traffic cameras, parking garages, and other sources to share mobility-related information is a perfect example. This AI-powered database is able to predict where collisions are more likely to happen or where microtransit solutions will be most valuable within city limits. Three of the six new cities are Austin, Indianapolis, and Detroit, and reports suggest the other three will be Miami, Pittsburgh, and Grand Rapids.

These posts represent my personal views on enterprise governance, regulatory compliance, and legal or ethical issues that arise in digital transformation projects powered by the cloud and artificial intelligence. Unless otherwise indicated, they do not represent the official views of Microsoft. This week I'd like to take a break from regular blogging to introduce you to a new series of online publications called AI Insights that my team at Microsoft is producing. Each title is presented as a media rich digital publication with photos, charts, and embedded videos. The adaptive design meets customers where they are--displaying equally well on desktops, laptops, tablets, and phones.

Your enterprise organization probably has set up data management and data analytics operations to get the most out of the data generated by your organization. Chances are you are also at least exploring or piloting machine learning as part of your data operations. But is your data telling you the whole story? Sometimes incorporating a new data stream from outside the organization can add a whole new level of insight and value to your analytics or machine learning efforts. A Gartner survey of 196 organizations released in February 2018 revealed that 46% of organizations are using external data sources.