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50 Machine Learning and Data Science Companies That Are Revolutionizing Industries

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Nowadays it's hard to find a single industry where machine learning and data science aren't being used to improve productivity and deliver results. Indeed that is why people are so excited about the promise of artificial intelligence: it can be applied to so many diverse problem spaces effectively and it works! This list has been aggregated after analyzing over 200 company descriptions, and we've broadly organized them by the problem domain being tackled and have included a brief description of their mission. TLDR: A framework for providing data integrations and web interfaces for trained machine learning models. TLDR: Develops medical imaging tools powered by AI to help improve the efficacy of radiologists in detecting illnesses.


NIO Sets Sales Record In May But Is Still Far Behind Tesla

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The Chinese new car market has been topsy turvy lately, primarily because the government keeps playing around with its NEV (new energy vehicle) incentive program. China really, really wants people to buy electric cars -- either plug-in hybrids or battery electrics -- but found its original incentive program was costing too much money. So it modified the program, several times in fact, which caused confusion among car companies and customers. In general, people who are confused postpone buying decisions until things get clearer, and that's exactly what Chinese new car shoppers did. The second factor, of course, was production shutdowns caused by the coronavirus pandemic.


How Intelligent Is Your AI? – MIT Sloan Management Review

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Ask these four questions to tell if your AI solution is really AI. In a world where buzzwords come and go, artificial intelligence has been remarkably durable. Since it first emerged as a concept in the 1950s, there has been a relatively constant flow of technologies, products, services, and companies that purport to be AI. It is quite likely that a solution you are investing in today is being referred to as AI-enabled or machine-learning-driven. The reality today for most organizations is that AI and machine learning form a rather small piece of the overall analytics pie.


How to build a machine learning model in 7 steps

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All types of organizations are implementing AI projects for numerous applications in a wide range of industries. These applications include predictive analytics, pattern recognition systems, autonomous systems, conversational systems, hyper-personalization activities and goal-driven systems. Each of these projects has something in common: They're all predicated on an understanding of the business problem and that data and machine learning algorithms must be applied to the problem, resulting in a machine learning model that addresses the project's needs. Deploying and managing machine learning projects typically follow the same pattern. However, existing app development methodologies don't apply because AI projects are driven by data, not programming code.


Connecterra wins record funding as COVID shows food supply weakness - IoT Now - How to run an IoT enabled business

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Breed Reply, a European investor in early-stage Internet of Things (IoT) businesses, has increased its investment in Dutch agritech company, Connecterra. As part of its Series B funding round, Connecterra has secured €7.8 million from existing investors, Breed Reply and Sistema, alongside new investors including AgTech specialists ADM Capital, French food safety enterprise Kersia Group and Dutch impact investor, Pymwymic. The Series B funding round completed by Connecterra is the largest ever Series B investment raised by a European livestock tech company. The funding will be used to accelerate the development of Connecterra's predictive artificial intelligence (AI) platform, Ida. Ida is the first digital assistant for the dairy farmer, based on sensor technology, cloud computing and machine learning.


Security Think Tank: AI cyber attacks will be a step-change for criminals

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Whether or not your organisation suffers a cyber attack has long been considered a case of'when, not if', with cyber attacks having a huge impact on organisations. In 2018, 2.8 billion consumer data records were exposed in 342 breaches, ranging from credential stuffing to ransomware, at an estimated cost of more than $654bn. In 2019, this had increased to an exposure of 4.1 billion records. While the use of artificial intelligence (AI) and machine learning as a primary offensive tool in cyber attacks is not yet mainstream, its use and capabilities are growing and becoming more sophisticated. In time, cyber criminals will, inevitably, take advantage of AI, and such a move will increase threats to digital security and increase the volume and sophistication of cyber attacks.


COVID-19: China's digital health strategies against the global pandemic

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Digital health technologies are critical tools in the ongoing fight against the global COVID-19 pandemic. Artificial Intelligence (AI), big data, 5G and robotics can provide valuable and innovative solutions for patient treatment, frontline protection, risk reduction, communications and improved quality of living under lockdown as the world continues to battle the COVID-19 pandemic. Last week's AI for Good webinar, 'COVID-19: China's digital health strategies against the global pandemic,' presented different use cases from China's digital health strategy, and provided context for how AI and information and communication technologies (ICT) has supported healthcare and citizen needs for the world's most populous nation. Following the start of the COVID-19 outbreak in January 2020, China implemented a wide-reaching strategy to control and contain the virus. "With various available technologies, we [ICT engineers] can actually play a very positive supporting role in fighting the current virus," said Shan Xu, an engineer in the Smart Health Department at the China Academy of Information and Communications Technology (CAICT).


Startup Promises Immortality Through AI, Nanotechnology, and Cloning

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One of the things humans have plotted for centuries is escaping death, with little to show for it, until now. One startup called Humai has a plan to make immortality a reality. The CEO, Josh Bocanegra says when the time comes and all the necessary advancements are in place, we'll be able to freeze your brain, create a new, artificial body, repair any damage to your brain, and transfer it into your new body. This process could then be repeated in perpetuity. HUMAI stands for: Human Resurrection through Artificial Intelligence.


The surprising future of fintech

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Thanks to open banking, fintech early adopters likely already have accounts that round up transactions to boost savings or connect to third-party tools for loan applications, budget management and more. But the new wave of fintech startups are proving there's much more that can be done using open banking, the two-year-old mandate from UK regulators that required banks to easily allow their customers to share their data with third parties such as apps. "Open banking offers people the chance to get personalised, tailored support to help them manage their money by allowing regulated companies to securely analyse their bank data," says Lubaina Manji, senior programme manager at Nesta Challenges, one of the organisations behind the Open Up 2020 Challenge, alongside the Open Banking Implementation Entity (OBIE). "It's enabled the creation of new services and tools to help people with every aspect of money management – from budgeting to investing, and much, much more, all in a safe and secure way." And some of the innovations from finalists in the Open Up 2020 Challenge have surprised with their ingenuity and customer focus, she says, citing Sustainably's round-up tool for automated charity donations, and Kalgera's neuroscience-informed AI to help spot fraud targeting people with dementia – two projects that highlight the purpose-driven idea behind open banking and the aim to get financial support to show who need it the most.


Web Scraping using Selenium

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Web Scraping is a popular methodology to extract data from websites. This is often done to derive insights for Sentiment Analysis, Predicting User preferences, Cross-Selling products, etc. Some of the real-life examples of web scraping include – extracting data for pricing analysis, user ratings for movie sentiment analysis, corporate admin tasks to read and classify log files in an HTML, search bots trying to make sense of a results page. While web scraping activity does not provide intelligence of its own, as we have seen above the data extracted can be useful in multiple ways. A more common use case would be a start-up eCommerce website trying to set a price on its products based on market research on competitors.