Moving forward with machine learning for cybersecurity

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At Black Hat last week, you couldn't pass a slot machine without some cybersecurity technology vendor crowing about machine learning or artificial intelligence (AI). Yup, machine learning algorithms have great potential to help with security analytics and employee productivity, but this technology is in its infancy and not well understood. ESG asked 412 cybersecurity professionals to assess and characterize their knowledge of machine learning/AI as it relates to cybersecurity analytics and operations technologies. Of the total survey population, only 30% of respondents claim to be very knowledgeable in this area. In other words, 70% of cybersecurity professionals really don't understand where machine learning and AI fit.


Global Bigdata Conference

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While many companies are turning to machine learning tools to fight hackers, they may not be as helpful as they seem thanks to a talent shortage and a lack of transparency. By the end of 2017, some 61% of businesses had implemented artificial intelligence (AI) into their organizations--a 23% jump from the previous year, according to Narrative Science. And the incorporation of AI into business will only rise: The number of medium and large enterprises using machine learning is predicted to double by the end of 2018, said Deloitte. Machine learning is a form of AI that interprets massive amounts of data, applying algorithms to the material, and making predictions off its observations. Common technologies that employ machine learning include facial recognition, speech recognition, translation services, and object recognition.


5 ways machine learning makes life harder for cybersecurity pros

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By the end of 2017, some 61% of businesses had implemented artificial intelligence (AI) into their organizations--a 23% jump from the previous year, according to Narrative Science. And the incorporation of AI into business will only rise: The number of medium and large enterprises using machine learning is predicted to double by the end of 2018, said Deloitte. Machine learning is a form of AI that interprets massive amounts of data, applying algorithms to the material, and making predictions off its observations. Common technologies that employ machine learning include facial recognition, speech recognition, translation services, and object recognition. Major companies like Amazon, IBM, Google, and Microsoft use machine learning to improve business functionality.


A 2019 Forecast for Data-Driven Business: From AI to Ethics

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AI/Machine Learning--AI continued to grow in popularity over the past year, becoming well-institutionalized within many large enterprises. We argued in a previous post, however, that too many companies employed AI pilots and prototypes, and not enough firms had implemented production deployments. As with analytics, the use of AI is increasingly being democratized through automated machine learning (AutoML). Several contributors to KD Nuggets' review of AI and ML trends for 2019 suggested that AutoML would become more popular over the next year. It will make machine learning models easier to create for business analyst types, as well as dramatically increasing the productivity of data scientists--that is, if they can be persuaded to use it.


Storage strategies for machine learning and AI workloads

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Businesses are increasingly using data assets to accelerate their competitiveness and drive greater revenue. Part of this strategy is to use machine learning and AI tools and technologies. But AI workloads have significantly different data storage and computing needs than generic workloads. AI and machine learning workloads require huge amounts of data both to build and train the models and to keep them running. When it comes to storage for these workloads, high-performance and long-term data storage are the most important concerns.