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Where Machine Learning meets rule-based verification


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.

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


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


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


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.

Constraints on the Cloud: why we need machine learning at the Edge


We are entering a new wave of technological innovation driven by artificial intelligence (AI), with machine learning (ML) at the forefront. Even today, ML is an important aspect of any device experience, powering all kinds of tasks, features, and applications. From on-device security, like face unlock, facepay and fingerprint recognition, to smartphone camera and audio functions that allow users to have more intelligent and fun experiences through apps such as Socratic, Snapchat, FaceApp and Shazam, there are a variety of ML-based features used regularly by consumers. However, for ML-based tasks that create massive amounts of data, these are often shifted to the cloud for processing before being sent back to the device with the action. This begs the question: wouldn't it be simpler and quicker for ML processing to happen on the device?