Hadi Nahari

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Founder & CEO at Cognomotiv, security expert

 http://www.cognomotiv.com
 @hadinahari


Hadi Nahari is a security professional with over 25 years of experience and extensive work in design & implementation of secure and trustworthy systems. Hadi has worked on large-scale enterprise solutions as well as embedded systems with primary focus on security, cryptography, complex systems design, data-driven systems, Artificial Intelligence and Machine Learning, and vulnerability assessment & threat analysis. Author of “Web Commerce Security: Design & Development” book published by John Wiley & Sons, Hadi is a frequent speaker in U.S. and international security events and has led various security projects for Netscape, Sun Micro, U.S. gov’t, Motorola, MontaVista, eBay, PayPal, NVIDIA, and Brocade among others. Hadi is the founder and CEO of Cognomotiv: a Silicon Valley Machine Learning, Analytics, and Cognitive Science stealth startup to drive the future of intelligent transportation and enable vehicle autonomy.

Books

Web Commerce Security: Design and Development

Recent Interviews

The sorry state of IoT security (part one)

The sorry state of IoT security (part two)

RSAC APJ – Interview with Hadi Nahari

Hadi Nahari – SIMposium 2011

YOW! 2016 Melbourne

Machine Learning: No, It Can’t Do That!

TALK – VIEW SLIDES

Artificial Intelligence (AI) in general, and Machine Learning (ML) specifically are indisputably the hottest fields in the industry at the moment and have demonstrably advanced many areas of technology and science alike: web page classification, spelling correction, search ranking, graph building, large-molecule database screening for receptor-protein binding, predictive analytics, etc. etc. However, despite all the aforementioned advances, there are classes of problems (e.g. where “baselining” is required, such as network security) where ML is not a suitable technology to apply to. In fact, there are facets of ML that we simply don’t yet understand. In this session we describe the basics of AI and ML, discuss examples in network security and why ML is not a suitable solution, and finally discuss general shortcomings ML, namely reasoning and debugging.

KEYWORDS

Machine Learning, Deep Learning, Artificial Intelligence, General Intelligence, Machine Cognition, Data Science, Analytics, Security