Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
NASA'S Planetary Defense Coordination Office uses the Catalina Sky Survey facility in Tucson,... [ ] Arizona, to catalog space objects Even in this age of high-speed data analysis, a keen human eye normally can't be beaten when poring over images of potential asteroidal impactors. But Artificial Intelligence (A.I.) could soon change all that. The El Segundo, Calif.-based Aerospace Corporation is now testing A.I. software designed to help astronomers speed up the process of identifying and tracking threatening Near-Earth Objects (NEOs). NASA's Planetary Defense Coordination Office already uses numerous telescopes to find and monitor NEOs that might have the potential to impact Earth. But the non-profit Aerospace Corporation's A.I. team is working with NASA on implementing software dubbed NEO AID (Near-Earth Object Artificial Intelligence Detection) to differentiate false positives from asteroids and comets that might be real threats.
A few days back, Joe McKendrick wrote about an IBM study showing seven business areas that are ripe for AI. It's just part of the onrush of developments that are making AI mainstream. And so it's easy to get jaded when you hear an announcement of yet another AI-enhanced tool. So when we saw an announcement from the tiny startup Obviously AI, we were expecting to see yet another refinement in what we term guided analytics. That's analytics where machine learning is employed to help you choose the best data sets, ask the right questions, and frame the narrative with the best visualizations.
Is artificial intelligence getting too smart (and intrusive) for its own good? A growing number of nations have concluded that it's time to take a close look at AI's impact on an array of critical issues, including privacy, security, human rights, crime, and finance. A proposal for an international oversight panel, the Global Partnership on AI, already has the support of six members of The Group of Seven (G7), an international organization comprised of nations with the largest and most advanced economies. The G7's dominant member, the United States, remains the only holdout, claiming that regulation could hamper the development of AI technologies and hurt US businesses. The Global Partnership on AI and OECD's G20 AI principles represent a good first step toward building a worldwide AI regulatory structure, noted Robert L. Foehl, an executive-in-residence for business law and ethics at Ohio University.
India is rising and shining bright when it comes to adopting new and emerging technologies. Enterprises from almost all major industry verticals are hiring data science experts to help them garner actionable insights from big data. The analytics sector has witnessed a sharp increase in demand for highly-skilled professionals who understand both the business world as well as the tech world. Organisations today are on a constant lookout for such professionals who can fill this ever-growing dearth in talent. The stark reality, however, is that there is a lot of confusion regarding this profession among aspiring professionals.
Both approaches have their pros and cons. The blog post Machine Learning and Real-Time Analytics in Apache Kafka Applications and the Kafka Summit presentation Event-Driven Model Serving: Stream Processing vs. RPC with Kafka and TensorFlow discuss this in detail. There are more and more applications where the analytic model is directly embedded into the event streaming application, making it robust, decoupled, and optimized for performance and latency. The model can be loaded into the application when starting it up (e.g., using the TensorFlow Java API). Model management (including versioning) depends on your build pipeline and DevOps strategy. For example, new models can be embedded into a new Kubernetes pod which simply replaces the old pod. Another commonly used option is to send newly trained models (or just the updated weights or hyperparameters) as a Kafka message to a Kafka topic.
"It's machine learning's job to find patterns based on the data you give it to help you focus on the data points most likely to lead to conversion." Elizabeth Gallagher, chief revenue officer at Lineate talks about how machine learning (ML) and artificial intelligence (AI) are changing the game for ecommerce brands. With the use of predictive analytics, marketers can create personalized marketing campaigns. In this edition of MarTalk Connect, Gallagher shares the key data points marketers should use to provide personalized recommendations. She stresses how data-driven automation and machine learning are strategic assets to enhance the customer journey.
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A next generation AI-Analytics decisioning solution should be able to tell retailers precisely how their business is doing across various departments and metrics far better than traditional business intelligence solutions can do. More importantly, the AI platform can explain why something happened (causation), and what you should do about it. Traditional business intelligence can't do this! What are examples of what can be done better with predictive and prescriptive AI-analytics capabilities? AI Demand Forecasting can provide highly accurate day-of-week and time-of-day forecasting at an item/store level.
"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.
Teikametrics, a leading SaaS provider of AI-powered optimization for brands and sellers on Amazon and Walmart, today announced the completion of a $15 million strategic funding round backed by new and existing investors. The announcement follows Teikametrics' selection as one of Walmart's first exclusive advertising optimization partners, and the addition of Srinivas Guddanti, a 14-year senior Amazon veteran, as its Chief Product Officer. Jump Capital led the round and were joined by follow-on investments from Granite Point Capital, MIT Professor of Econometrics, Jerry Hausman, and the former Head of Growth at Facebook and Uber, Ed Baker. "We're thrilled to lead this new round of capital in Teikametrics," said Michael McMahon, founding partner of Jump Capital. "The Company has grown rapidly, and the success of its proprietary AI technology for Amazon is a strong proof point for a broader ecommerce platform opportunity. The partnership with Walmart is a landmark event and we are excited to fund the expansion of the Teikametrics platform across multiple ecommerce channels."