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.
Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive to get a place in the report. Gartner recently published its magic quadrant report on data science and machine learning (DSML) platforms. The market landscape for DS, ML and AI is extremely fragmented, competitive, and complex to understand.
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.
National guidance is urgently needed to oversee the police's use of data-driven technology amid concerns it could lead to discrimination, a report has said. The study, published by the Royal United Services Institute (Rusi) on Sunday, said guidelines were required to ensure the use of data analytics, artificial intelligence (AI) and computer algorithms developed "legally and ethically". Forces' expanding use of digital technology to tackle crime was in part driven by funding cuts, the report said. Officers are battling against "information overload" as the volume of data around their work grows, while there is also a perceived need to take a "preventative" rather than "reactive" stance to policing. Such pressures have led forces to develop tools to forecast demand in control centres, "triage" investigations according to their "solvability" and to assess the risks posed by known offenders.
"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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