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"Scammers are sneaky and sly. They can target anyone, from youngsters to retirees. They can also target businesses. No one is immune to fraud. Our group of superheroes has found a way to see through the scams. Their secret is simple: knowledge is power!   Read on to find out how you can also become a fraud-fighting superhero. Share this booklet with family and friends and start powering up! Follow: Twitter Facebook LinkedIn YouTube"

Advantages: Keeps system running smooth Protection against Internet frauds

"The groundwork for machine learning was laid down in the middle of last century. But increasingly powerful computers - harnessed to algorithms refined over the past decade - are driving an explosion of applications in everything from medical physics to materials, as Marric Stephens discovers When your bank calls to ask about a suspiciously large purchase made on your credit card at a strange time, it's unlikely that a kindly member of staff has personally been combing through your account. Instead, it's more likely that a machine has learned what sort of behaviours to associate with criminal activity - and that it's spotted something unexpected on your statement. Silently and efficiently, the bank's computer has been using algorithms to watch over your account for signs of theft. Monitoring credit cards in this way is an example of "machine learning" - the process by which a computer system, trained on a given set of examples, develops the ability to perform a task flexibly and autonomously. As a subset of the more general field of artificial intelligence (AI), machine-learning techniques can be applied wherever there are large and complex data sets that can be mined for associations between inputs and outputs. In the case of your bank, the algorithm will have analysed a vast pool of both legitimate and illegitimate transactions to produce an output ("suspected fraud") from a given input ("high-value order placed at 3 a.m."). But machine learning isn't just used in finance. It's being applied in many other fields too, from healthcare and transport to the criminal-justice system. Indeed, Ge Wang - a biomedical engineer from the Rensselaer Polytechnic Institute in the US who is one of those pioneering its use in medical imaging - believes that when it comes to machine learning, we're on the cusp of a revolution."

"From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns.  The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules. These seven patterns are then applied individually or in various combinations depending on the specific solution to which AI Is being applied."

Think 100 times before taking help from local PC repair shop, if you once had sensitive files

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