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machine learning
While we've witnessed the rise of the Internet, smart phones, and a variety of consumer services like content streaming and online banking, most companies haven't yet embraced methodologies like lean startup, design thinking, agile engineering, or technologies like cloud architectures, IoT or machine learning.
Stories of increasingly vicious cyber-attacks have dominated the headlines this year. It seems like every day we wake up to news of another attack on the scale of the Yahoo data breach, the Democratic National Committee hack, or the NSA source code leak.
When we consider the range of cyber-threats, we generally imagine external attackers -- foreign states, criminal underworlds or lone script kiddies. But the reality is that a large proportion of vulnerabilities and "threats" that organizations face today come from legitimate network users.
If anything, the modern threat landscape encourages attacks on small, vulnerable businesses. And 60 per cent of the time, those small businesses will go under within six months of a cyber-attack. So while massive data breaches dominate the news, they only scratch the surface.
Earlier this year, a group of cyber criminals targeted two Canadian financial institutions with a hybrid type of malware, GozNym. The first time GozNym was ever seen, it stole millions of dollars from the unnamed Canadian financial institutions along with several U.S. banks.
Machine learning, and a more advanced technology called deep learning, are types of artificial intelligence that allow a computer to learn information based on the data it is given. Essentially, the more information the computer is given, the better it can learn -- and in the case of platforms like Spotify or Netflix, the more interaction you have with the program, the better it can recommend music, movies, or TV shows that you'll like.
