ParallelM, one of the fastest-growing companies in machine learning operationalization (MLOps), today announced that it has been named a "Cool Vendor" based on the September 11, 2018 report titled, "Cool Vendors in Data Science and Machine Learning," by Peter Krensky, Svetlana Sicular, Jim Hare, Erick Brethenoux, and Austin Kronz at Gartner, Inc. Gartner's report notes, "While the democratization of machine learning platforms is proliferating model creation, the need to operationalize models at scale is still a looming challenge. Vendors focusing on this piece of the machine learning life cycle can answer a growing demand in the market." "We believe it is an honor to be named as one of Gartner's'Cool Vendors' this year in the areas of machine learning and data science," said Sivan Metzger, CEO of ParallelM. "As the interest in these areas continues to grow at a rapid pace, technology has brought us to a point where ML models are being created and tested at scale. How can companies actually operationalize these models and derive the value out of ML? With our solution, companies are finally able to automate the deployment and scale of their machine learning applications, finally unlocking their true business value."

Oracle has been named a leader in notebook-based Predictive Analytics and Machine Learning (PAML) solutions by Forrester Research, earning the highest average current offering score as well as the highest possible score for its solution roadmap. The Forrester Wave: Notebook-Based Predictive Analytics and Machine Learning Solutions, Q3 2018 report recognizes that Oracle Autonomous Data Science Cloud Service "provides the standardization and controls that enterprises need" and "makes it easy to put models into production by offering visual tools to create APIs with automatic load balancing." According to Forrester, PAML solutions are defined as "Software that provides enterprise data scientist teams and stakeholders with 1) tools to analyze data; 2) workbench tools to build predictive models using statistical and machine learning algorithms; 3) a platform to train, deploy, and manage analytical results and models; and 4) collaboration tools for extended enterprise teams including businesspeople, data engineers, application developers, DevOps, and AI engineers." Forrester evaluated the strengths and weaknesses of the top notebook-based PAML vendors across 24 evaluation criteria, which were grouped into three categories: current offering, strategy and market presence. Of the nine vendors Forrester evaluated, Oracle was one of the two companies recognized as a leader. "With Oracle Autonomous Data Science Cloud Service, Oracle has a winning solution for our customers to build and deploy artificial intelligence and machine learning models on the Oracle Cloud," said Greg Pavlik, Senior Vice President and Chief Technology Officer, Oracle Cloud Platform.

Would you go into business with a robot? Whatever the reason, Brits are currently loving their artificial intelligence, so much so that a new study has found that 53% of employed workers would be happy to work for a robot. Perhaps ironically, nearly 1 in 10 believe that the smart technology company would be more enjoyable company than that of a human colleague. But watch out entrepreneurs, as almost a third of Brits (32%) would welcome a robot CEO. And it seems that Millennials are the generation welcoming such radical moves with 8 out of 10 surveyed happy to bring technology into the office whereas only 6% of baby boomers would trust a robot. The news is therefore slightly at odds with a previous survey commissioned by the government who found more than 6million UK workers were afraid that their roles would be replaced by machines in the near future.

The Indian edition of dating app Tinder is trialing a new feature which gives women an additional level of scrutiny and security before they allow men to start messaging conversations, with a view to rolling the function out globally. The'My Move' feature allows women to choose in their settings that only they can start a conversation with a male match after both have approved each other with Tinder's swiping function. Normally, the app gives both parties to a successful match - where both have swiped yes on the other's photograph - the right to text each other immediately. The'My Move' feature allows women to choose in their settings that only they can start a conversation with a male match after both have approved each other with Tinder's swiping function. Tinder has been testing the function in India for several months and plans to spread it worldwide if the full rollout proves successful.

The accurate measurement of security metrics is a critical research problem because an improper or inaccurate measurement process can ruin the usefulness of the metrics, no matter how well they are defined. This is a highly challenging problem particularly when the ground truth is unknown or noisy. In contrast to the well perceived importance of defining security metrics, the measurement of security metrics has been little understood in the literature. In this paper, we measure five malware detection metrics in the {\em absence} of ground truth, which is a realistic setting that imposes many technical challenges. The ultimate goal is to develop principled, automated methods for measuring these metrics at the maximum accuracy possible. The problem naturally calls for investigations into statistical estimators by casting the measurement problem as a {\em statistical estimation} problem. We propose statistical estimators for these five malware detection metrics. By investigating the statistical properties of these estimators, we are able to characterize when the estimators are accurate, and what adjustments can be made to improve them under what circumstances. We use synthetic data with known ground truth to validate these statistical estimators. Then, we employ these estimators to measure five metrics with respect to a large dataset collected from VirusTotal. We believe our study touches upon a vital problem that has not been paid due attention and will inspire many future investigations.

Now that businesses have seen the kind of revolutionary impact AI and automation can bring, most of them are making a leap towards the advanced technologies. AI has transformed functions, tasks, workforce skills, results and there is much more to come. With sales and marketing functions being the easy playground for AI, business leaders are getting anxious about the immediate impacts of technology on the workforce and job prospects. Their concern remains that AI may take away the jobs of the sales reps. There is a great deal of tension and discussion around this subject.

When it comes to AI-enhanced marketing, the future is already happening. How far are we from hyper-intelligent robots becoming a part of our daily lives' AI-enabled'assistants' like Siri, Cortana, and Alexa have already been adopted into many people's homes and businesses. But they are becoming very common. More relevantly, we are also seeing the use of advanced analytics and Artificial Intelligence in many business functions. It doesn't matter what industry we're talking about; AI is being used to deliver results at previously impossible speeds.

In this profile series, we interview AI innovators on the front-lines - those who have dedicated their life's work to improving the human condition through technology advancements. He is also a founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian's background is in research where he focuses on large- scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. Damian talks about his realization in deep learning and shares why integrating his work with deep learning is an important part to help prevent future natural disasters. What has your journey been like in deep learning?

In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future. As I described in a recent post, workflow automation will happen in stages. With that in mind, chatbots and intelligent assistants are bound to improve as underlying algorithms, technologies, and training data get better.

The following is an interview with Daniel Wagner, ahead of the release of his new book, AI Supremacy: Winning in the Era of Machine Learning. Can you briefly explain the differences between artificial intelligence, machine learning, and deep learning? Artificial intelligence (AI) is the overarching science and engineering associated with intelligent algorithms, whether or not they learn from data. However, the definition of intelligence is subject to philosophical debate-even the terms algorithms can be interpreted in a wide context. This is one of the reasons why there is some confusion about what AI is and what is not, because people use the word loosely and have their own definition of what they believe AI is. People should understand AI to be a catch-all term for technology which tends to imply the latest advances in intelligent algorithms, but the context in how the phrase is used determines its meaning, which can vary quite widely. Machine learning (ML) is a subfield of AI that focuses on intelligent algorithms that can learn automatically (without being explicitly programmed) from data.