If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."
Informatica on Tuesday is officially unveiling its intelligent data management cloud (IDMC), an AI-powered platform designed to serve a broad base of users working with data in multi-cloud environments. Along with that, the company is announcing a series of partnerships and integrations with Microsoft Azure, Amazon Web Services and Google Cloud Platform. Informatica has been in the business of data management tools for more than 20 years, and in that span of time, data has become increasingly valuable, Informatica Chief Product Officer Jitesh Ghai told ZDNet. "We recognize our community of data-led practitioners has grown well beyond technical ETL experts, well beyond data engineers and data scientists," he said. Now, it includes "non-technical users who want to operate with facts. Gut-based decision making has been laid bare as insufficient moving forward."
The agricultural marketplace -- and where farmers will be willing to spend their money on AI and ML technology -- will ultimately decide the winners and losers. But there are things agriculture companies can do to position themselves to take the lead, and it all starts with the kind of purpose that drove John Deere to become one of the most well-known names in a global industry for centuries. But first, what's AI got to do with agriculture? For crop producers, data has changed how they do business. It's been both a blessing and a curse since precision ag technology entered the industry around 25 years ago.
"Artificial intelligence" is now a household term, whether it's powering driving directions, spotting tumors in cancer patients or driving big discussions over ethics, bias, autonomous weapons or the future of work. But despite the fact that the first neural network was created in the late 1950s, a lot of what I just described has taken place over only about 10 years. In his new book, "Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World," New York Times tech correspondent Cade Metz writes about the history of AI and the corporate forces that have shaped it since the mid-2000s. He told me AI pioneer Geoffrey Hinton really rebranded neural networks as "deep learning," and that happened just as a bunch of other factors were coming together. The following is an edited transcript of our conversation.
Modzy, the leading enterprise AI platform, announced that DarwinAI is now a model partner at the Modzy AI Model Marketplace. DarwinAI is expected to deploy numerous models using its GenSynth platform, including COVID-Net, an open source deep neural network for detecting COVID-19 infections from chest X-rays. DarwinAI has been featured in the MIT Technology Review, AI in Healthcare, and VentureBeat for their innovations in combating COVID-19 using AI. "We're really excited for this partnership with DarwinAI," said Norm Litterini, Head of Partnerships at Modzy. "The quality of their work is reflected in their solid reputation and industry acknowledgment. DarwinAI's models will enable customers to quickly operationalize AI into strategic initiatives while building out our marketplace offering, particularly for biomedical applications, where there is critical need."
It's "time to wake up and do a better job," says publisher Tim O'Reilly--from getting serious about climate change to building a better data economy. And the way a better data economy is built is through data commons--or data as a common resource--not as the giant tech companies are acting now, which is not just keeping data to themselves but profiting from our data and causing us harm in the process. "When companies are using the data they collect for our benefit, it's a great deal," says O'Reilly, founder and CEO of O'Reilly Media. "When companies are using it to manipulate us, or to direct us in a way that hurts us, or that enhances their market power at the expense of competitors who might provide us better value, then they're harming us with our data." And that's the next big thing he's researching: a specific type of harm that happens when tech companies use data against us to shape what we see, hear, and believe. It's what O'Reilly calls "algorithmic rents," which uses data, algorithms, and user interface design as a way of controlling who gets what information and why. Unfortunately, one only has to look at the news to see the rapid spread of misinformation on the internet tied to unrest in countries across the world. We can ask who profits, but perhaps the better question is "who suffers?" According to O'Reilly, "If you build an economy where you're taking more out of the system than you're putting back or that you're creating, then guess what, you're not long for this world." That really matters because users of this technology need to stop thinking about the worth of individual data and what it means when very few companies control that data, even when it's more valuable in the open. After all, there are "consequences of not creating enough value for others." We're now approaching a different idea: what if it's actually time to start rethinking capitalism as a whole? "It's a really great time for us to be talking about how do we want to change capitalism, because we change it every 30, 40 years," O'Reilly says. He clarifies that this is not about abolishing capitalism, but what we have isn't good enough anymore. "We actually have to do better, and we can do better. And to me better is defined by increasing prosperity for everyone."
Written by Greg Vert, Senior Manager at Deloitte Consulting. If there's a silver lining to be found amid the devastation of the global pandemic it might be the way it's snapped everyone's priorities into sharp focus. From a business perspective, the boundaries between work and life have disintegrated for virtual workers, and frontline workers are busier than ever. Organizations need to respond by reimagining the way they support and empower their workforces using technology. Thankfully, this urgent need to evolve workforce experiences coincides with the continued evolution and availability of AI solutions, presenting opportunities to make renewed investments in well-being.
Matching demand to supply in internet marketplaces (e-commerce, ride-sharing, food delivery, professional services, advertising) is a global inference problem that can be formulated as a Linear Program (LP) with (millions of) coupling constraints and (up to a billion) non-coupling polytope constraints. Until recently, solving such problems on web-scale data with an LP formulation was intractable. Recent work (Basu et al., 2020) developed a dual decomposition-based approach to solve such problems when the polytope constraints are simple. In this work, we motivate the need to go beyond these simple polytopes and show real-world internet marketplaces that require more complex structured polytope constraints. We expand on the recent literature with novel algorithms that are more broadly applicable to global inference problems. We derive an efficient incremental algorithm using a theoretical insight on the nature of solutions on the polytopes to project onto any arbitrary polytope, that shows massive improvements in performance. Using better optimization routines along with an adaptive algorithm to control the smoothness of the objective, improves the speed of the solution even further. We showcase the efficacy of our approach via experimental results on web-scale marketplace data.
Last September, Gartner published its Hype Cycle for AI in which it identified two emerging trends (and five new AI solutions) that would have an impact on the workplace. One of those trends was what Gartner described as the democratization of AI. While there are many ways that this can be interpreted, in simple terms what it means for workers is the general distribution and use of AI across the digital workplace to achieve business goals. In the enterprise, the target deployment of AI is now likely to include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals. As AI reaches a larger set of employees and partners, it requires new enterprise roles to deliver it to a wider audience.
For the channel, 2020 was a tale of two cities. On one hand, customers and governments recognized partners as an essential service and central to their ability to rapidly respond to a worsening pandemic. On the other, customer demand shifted to automation, cloud acceleration, customer/employee experience, and e-commerce/marketplaces, where many technology channel parts were left in the cold. The industry experienced a "K-shaped" recovery where partners who had skills, resources, and prebuilt practices around the business needs of their customers excelled with double- (and sometime triple-) digit growth. Yet many smaller VARs and MSPs were down by double digits, relying on government, vendor, and distributor funding to survive.