Big Data Meltdown: How Unclean, Unlabeled, and Poorly Managed Data Dooms AI

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We may be living in the fourth industrial age and on cusp of huge advances in automation powered by AI. But according to the latest data, our great future will be less rosy if enterprises don't start doing something about one thing in particular: the poor state of data. That's the gist of several reports to make the rounds recently, as well as interviews with industry experts. Time after time, the lack of clean, well-managed, and labeled data was cited as a major impediment for enterprises getting value out of AI. Last month, Figure Eight (formerly CrowdFlower) released a study about the state of AI and machine learning.


The Fundamental Differences Between ML Model Development and Traditional Enterprise Software Development - DZone AI

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Academic literature on machine learning modeling does not explicitly address how enterprises across industries can utilize ML algorithms. And many companies, even after investing in foundational ML tools, still often get puzzled when defining business use cases for their AI apps, customizing general purpose machine learning models for domain-specific tasks, converting business requirements into data requirements, etc. In this post, we'll talk about key differences between traditional enterprise software development and ML model building and offer some ML lifecycle management tips (chiefly concerning data preparation and feature engineering) for those seeking to harness AI. In traditional software development we write out explicit instructions for a computer to follow and, therefore, the applications we end up with are deterministic. In machine learning, which is probabilistic in nature, we rely on data to write our if-then statements.


How machine learning is transforming healthcare

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Deep-learning algorithms help uncovering insights that were previously hidden away in the dark. A mind-boggling amount of healthcare data is generated annually, namely about a trillion gigabytes of data, according to latest estimates. Thanks to the emergence of advanced analytical approaches such as machine learning and deep learning, it has only recently become possible to make sense of such massive data sets. They may help lift the veil on the complexity of human biology. There are still no curative therapies available to date.


Machine learning tutorial: How to create a recommendation engine

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What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage. Recommendation engines can be gamed because they amplify the effects of thought bubbles. The 2016 US presidential election showed how important it is to understand how recommendation engines work and the limitations and strengths they offer.


AI Deployment Challenges: 5 Tips to Help Overcome the Hurdles

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Everyone is talking about the power of AI and it's slowly invading our lives--emphasis on slowly. While you might have a few AI assistants and connected devices in your house, the business world hasn't fully jumped on board yet with AI. Sure, the forward-thinking companies have and those are the headline we are seeing, but I'm talking about full adoption from the mom and pop shops all the way up to the enterprise level, spanning across all industries. We all understand the power and the potential of AI, but we don't seem to discuss the AI deployment challenges that many businesses are likely facing. We see statistics like 61 percent of companies with an innovation strategy are using AI to identify opportunities in data and think that a majority of companies must be adopting AI.


Nokia 2.2 offers the latest advances in AI and Android at an accessible price

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HMD Global, the home of Nokia phones, has announced the new Nokia 2.2, delivering sophisticated AI powered low light imaging and Google Assistant at the press of a button, all at a truly astonishing price. Nokia 2.2 is the first 2 series Nokia smartphone to be part of the Android One programme, delivering the latest full Android experience on a modern 5.7" screen with a discreet selfie-notch. Shipping with Android 9 Pie, Nokia 2.2 is Android Q ready and will receive two years of OS upgrades and three years of monthly security updates, ensuring access to all the latest innovations from Android. Sanmeet Singh Kochhar, General Manager - Middle East, HMD Global, said: "We continue to offer secure and innovative smartphone experience that keeps getting better with two years of OS updates and three years of monthly security updates. And today, we've brought the pinnacle of AI experiences to more people than ever before with the Nokia 2.2, which joins our Android One family.


Designing for Speech – Frank's World of Data Science & AI

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Here's a great talk from Build 2019 about the importance of design in creating Voice and chat virtual assistants. Designing a natural language interface can be difficult, is the interface supposed to be able to interpret every single nuance of speech? Or should we aim more towards forced language and make our users learn how to interact with simple commands? All the big companies are making huge investments in AI personal assistants. Amazon has Alexa, Google has Google assistant, Apple has Siri and Microsoft has Cortana to name a few.


Globots and telemigrants: The new language of the future of work

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To describe the future of work, Richard Baldwin is developing a new lexicon. The professor of international economics at the Graduate Institute in Geneva warns that we are unprepared for the ways in which new technology is changing the nature of globalization. Baldwin's new book, The Globotics Upheaval: Globalization, Robotics, and the Future of Work, is a natural follow-up to his 2016 book, The Great Convergence. Three years ago, he explained how a third wave of globalization--a collapse in the cost of the movement of people thanks to technology--would be the most disruptive, because it hits workers in the service sector. Baldwin's new book, published earlier this year, breaks down what this disruption will entail.


Volvo picks up Nvidia to assist with AI for self-driving vehicles

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Volvo and Nvidia have formed a partnership that will see the pair collaborate on AI technology for self-driving vehicles. Speaking to investors and media at Volvo's annual event for the capital-markets community, Volvo Group CEO Martin Lundstedt said: "Partnership is the new leadership. If we are to succeed in the future with speed, quality, and safety – and to gain benefits of autonomous driving – we need to partner up with the best guys. In this world of unknowns, you need a partnership built on trust." Volvo is the world's second-largest truckmaker after Daimler.


Volvo picks up Nvidia to assist with AI for self-driving vehicles

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Volvo and Nvidia have formed a partnership that will see the pair collaborate on AI technology for self-driving vehicles. Speaking to investors and media at Volvo's annual event for the capital-markets community, Volvo Group CEO Martin Lundstedt said: "Partnership is the new leadership. If we are to succeed in the future with speed, quality, and safety – and to gain benefits of autonomous driving – we need to partner up with the best guys. In this world of unknowns, you need a partnership built on trust." Volvo is the world's second-largest truckmaker after Daimler.