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Where Machine Learning meets rule-based verification

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This whole topic – where (and how) should ML and rule-based verification meet – has been on my mind for a while, but I still don't have good answers. I do think it deserves significant attention from researchers and practitioners. The next three chapters will discuss why I expect ML to keep growing in dynamic verification, why there will always be an unavoidable, irreducible non-ML part, and some ideas about connecting the two. Finally, the last chapter will talk about rules in ML-based systems, explainable AI and all that. If you are not into verification, just go directly there. Please take a quick look at my Dynamic verification in one picture post.


Taming Machine Learning on AWS with MLOps: A Reference Architecture

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Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently. How can teams tame complexity and live up to the expectations placed on them? There is no one size fits all when it comes to implementing an MLOps solution on Amazon Web Services (AWS).


Security and Privacy considerations in Artificial Intelligence & Machine Learning -- Part 4: The…

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Note: This is part-4 of a series of articles on'Security and Privacy in Artificial Intelligence & Machine Learning'. In this article we will take a closer look at use of AI&ML in various security-related use cases. We will cover not only cybersecurity but also some general security scenarios and how solutions based on AI&ML are becoming increasingly prevalent in all such areas. Towards the end we will also explore ways that attackers are likely to circumvent these security techniques. So let us begin with a look at some interesting areas where security features are benefiting from ML&AI.


How Cybercriminals are Attacking Machine Learning

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In recent years, machine learning has made tremendous strides in the fight against cybercrime. But it's not foolproof, and criminals have developed techniques to undermine its effectiveness. In today's adversarial environment, organizations must deploy technologies that are resilient to attacks against machine learning. Machine learning (ML) is getting a lot of attention these days. Search engines that autocomplete, sophisticated Uber transportation scheduling, and recommendations from social sites and online storefronts are just a few of the daily events that ML technologies make possible.


More to Machine Learning than Meets the Eye - insideBIGDATA

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In this special guest feature, Kevin Gidney, Co-Founder and CTO at Seal Software, explores four main factors that go into creating advanced machine learning technology. There is a lot of required training and work that goes into developing successful machine learning solutions and not all ML is created equal. Kevin, a founder of Seal Software, has held various senior technical positions within Legato, EMC, Kazeon, Iptor and Open Text. His roles have included management, solutions architecture and technical pre-sales, with a background in electronics and computer engineering, applied to both software and hardware solutions. It's no secret that machine learning is dominating the enterprise, across a wide variety of industries.