BEHAVIOR


Is machine learning the future of marketing?

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Will it be entirely automated by smart AIs that fully understand human nuance? Will it be entirely manual and managed only by individual people without the aid of technology? As we'll discuss shortly, the top influencers in marketing put their heads together on this very topic, and the results may surprise you. In any case, it's safe to say that marketing probably isn't going back to the old days of billboards, newspapers, and radio spots. The numbers don't lie: the future of marketing is definitely digital.


AI and machine learning: Looking beyond the hype -- FCW

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In every federal agency, critical insights are hidden within the massive data sets collected over the years. But because of a shortage of data scientists in the federal government, extracting value from this data is time consuming, if it happens at all. Yet with advances in data science, artificial intelligence (AI) and machine learning, agencies now have access to advanced tools that will transform information analysis and agency operations. From predicting terror threats to detecting tax fraud, a new class of enterprise-grade tools, called automated machine learning, have the power to transform the speed and accuracy of federal decision-making through predictive modeling. Technologies like these that enable AI are changing the way the federal government understands and makes decisions.


The difference between AI, machine learning and digital assistants

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We hear a lot about artificial intelligence (AI), machine learning and digital assistants these days. But what does it all mean? Microsoft, Google, Amazon, Apple, Facebook and others are aggressively imbuing their products and services with "intelligence." We're told that computer systems will become smarter, will anticipate our needs, proactively serve us, and understand us in various contexts and via multiple methods including voice, text, inking and motion. The digital "face" of this promising tech comes in the form of assistants like Cortana, Assistant, Siri, Alexa, and Bixby.


How Artificial Intelligence Will Personalize How We Work

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Artificial intelligence in the workplace is here to stay. However, as enterprise technologies continue to develop and evolve, we must understand how AI will affect our roles and responsibilities at work. The unknowns about the impact of AI has led to the fear that this emerging technology could be a substitute for – or entirely eradicate – existing jobs. Depending on which stats you refer to, AI will replace over 40% of jobs by 2030, or that 165 million Americans could be out of work before 2025. Yet it is not all doom and gloom.


How Machine Learning Can Help Identify Cyber Vulnerabilities

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People are undoubtedly your company's most valuable asset. But if you ask cybersecurity experts if they share that sentiment, most would tell you that people are your biggest liability. Historically, no matter how much money an organization spends on cybersecurity, there is typically one problem technology can't solve: humans being human. Gartner expects worldwide spending on information security to reach $86.4 billion in 2017, growing to $93 billion in 2018, all in an effort to improve overall security and education programs to prevent humans from undermining the best-laid security plans. But it's still not enough: human error continues to reign as a top threat.


Teaching Self-Learning Machines to Forget

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Many tasks in which humans excel are extremely difficult for robots and computers to perform. Especially challenging are decision-making tasks that are non-deterministic and, to use human terms, are based on experience and intuition rather than on predetermined algorithmic response. A good example of a task that is difficult to formalize and encode using procedural programming is image recognition and classification. For instance, teaching a computer to recognize that the animal in a picture is a cat is difficult to accomplish using traditional programming. Artificial intelligence (AI) and, in particular, machine learning technologies, which date back to the 1950s, use a different approach.


The First AI-Driven Solution for SAP Cybersecurity CRM Daily

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Nov. 21, 2017 -- ERPScan, the most innovative ERP cybersecurity provider, announces the release of the first and only AI-driven SAP cybersecurity platform at "Cybersecurity for SAP Customers" conference in Las Vegas. The new platform leverages Machine Learning and Deep Learning to provide predictive, preventive, detective and responsive capabilities thus covering all aspects of SAP Security in one platform. Gartner predicted, "Through 2022, AI will be a major battleground for technology leadership," and we already started to drive it [1]. While cyberattacks are looming large over enterprises, it is inappropriate to rely on the detection and patching of vulnerabilities alone but crucial to detect any potential attack. Business applications are customized in the way that building the signature-based threat detection is ineffective, and, as a matter of fact, traditional approaches can hardly help.


Four AI Applications Banks and Credit Unions Must Implement Now

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As artificial intelligence (AI) and machine learning are woven into banking's fold, their potential is almost too vast to predict. The real benefit is in financial institutions' ability to understand where and how it makes sense to apply these tools first, and where they can derive the greatest value in the fastest way. While a few industry leaders do get it, many discussions around artificial intelligence (AI) in banking shows that the industry at large still views AI in very abstract terms. While banks seem to be thinking about AI more and more, there still seems to be a consistent struggle in understanding when or where to apply this analytic tool. This struggle often leads to hesitation to actually testing and implementing the benefits of AI at financial institutions.


Social bots are ruining the internet for the rest of us

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We've all seen the stories and allegations of Russian bots manipulating the 2016 U.S. presidential election and, most recently, hijacking the FCC debate on net neutrality. Yet far from such high stakes arenas, there's good reason to believe these automated pests are also contaminating data used by firms and governments to understand who we (the humans) are, as well as what we like and need with regard to a broad range of things. Social bots -- which is what we're talking about here; "bot" is a catch-all term for many different types of AI -- can be a nuisance for social media platforms. A recent report estimated as many as 48 million Twitter accounts are actually bots, and they are responsible for as many as 1 in 4 tweets. Depressingly for Taylor Swift fans, a study in 2015 revealed that 67 percent of her followers were bots, and a new study from the University of Cambridge revealed that celebrities with more than 10 million followers behave in bot-like ways themselves.


Introducing marketing science – a truly personalized marketing framework powered by AI

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Marketing automation and advertising technologies are often presented as the silver bullet to all your marketing needs. However, the reality is often disappointing, as messaging is targeted at predefined segments and sets of rules, falling short of actual individual user needs and wants. This isn't true personalization, as it doesn't scale to the individual, and it isn't capable of predicting the context, needs, behavior (or aberrations in behavior) of a single human being. Fortunately, with the rise of sophisticated artificial intelligence capabilities, particularly machine learning, this can all change. We're already seeing some of the more advanced players move away from formulaic segment-based messaging towards a more truly personalized way of marketing.