![]() Machines are less adaptable than us, so their tasks can’t change suddenly. It uses stats and probability, so it never delivers a single optimal solution. AI can’t deliver on any task where a thorough, clear explanation is required. Science recently shared that AI needs masses of data and well-defined inputs and outputs to do well. In fact, we’re a long way from replacing humans in fields that need creativity and insight. While many are afraid of losing their jobs or relevance in this new world, we need to be realistic about what’s really possible today. Look at history and think about computers becoming personal, and spaceflight becoming something where private citizens can innovate. These smaller entities have found they can leverage the new data, processing, and machine learning resources available to them, and have access to products and services that amplify their workforce. Problems that used to be solely in the realm of the largest governments and enterprises are now addressable by individuals and small- and medium-sized businesses. ![]() However, AI and ML can leverage a small number of humans to mine the data and pull out relevant trends for further analysis. It’s great to have all this data, but what are we doing with it? We can’t afford to hire armies of technologists and analysts to find patterns and draw conclusions. Scarcity of Technologists / Huge Volume of Data For example, NASA’s planet-hunting Kepler space telescope recently detected an exoplanet using Google’s ML technologies. Neural networks, deep learning, and algorithm improvements have all been brought to bear. We can spin up a few cloud instances to experiment, and then scale them up to production when we achieve success.īy focusing on solving pragmatic problems, ML advances have produced steady-state improvements in many fields. These same trends that have reduced data costs have also brought more processing power to problems, and an ability to scale up and down dynamically. We also can keep this data in the cloud, which enables redundancy and processing power never before available. We’re now able to gather and retain information in much more depth than ever before, because networking and storage costs have dropped. There are many factors that have converged to enable this leap forward – let’s look at some of them.ĭata Gathering and Processing Power in the Cloud If they don’t, the competition will pass them up or make them irrelevant soon. Self-driving automobiles, commercial space flight, facial recognition, targeted ads, medical advances, and fake news detection all benefit from advances in AI.Īlmost every software and information product and practice are thinking not “if,” but “when and how” to apply AI and ML. He holds a Bachelor of Science in Aeronautics and Astronautics from MIT.Īrtificial Intelligence (AI) and Machine Learning (ML) are getting a lot of press coverage these days and for good reason - it’s a broad and rapidly advancing technical frontier. Lonas has held key engineering management positions with Websense (WBSN), ADP and others, and has co-authored several patents. He joined Webroot via the acquisition of BrightCloud, where he was a founder and VP Engineering. Previously the Senior VP of Product Engineering for Webroot, Lonas has 25+ years of experience in enterprise software and engineering. Hal Lonas is CTO at Webroot, a privately held internet security company that provides state-of-the-art, cloud-based software as a service (SaaS) solutions spanning threat intelligence, detection and remediation. There are many factors that have converged to enable this leap forward – let’s look at some of them. In this special guest feature, Hal Lonas, Chief Technology Officer at Webroot, suggests that almost every software and information product and practice are thinking not “if,” but “when and how” to apply AI and ML.
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