This is a pretty staggering number to anyone who has made an online transaction, but the amount of attacks that were stopped is much higher, so it’s worth some optimism.
In 2019, more than 627 million online records were comprised due to hacking and other types of cyber attacks. This is a pretty staggering number to anyone who has made an online transaction, but the amount of attacks that were stopped is much higher, so it’s worth some optimism. As COVID-19 has pushed many companies into the remote work world, online transactions and records are growing exponentially, and most experts believe that remote work will continue to be very popular even after stay-at-home orders get lifted and life goes back to some form of normal.
The pros of remote work for businesses are plenty, and for many employees, it is now a requirement when trying to land the best jobs they can find. Costs are lower when employees are remote, employees are generally happier, meaning less employee turnover and the associated time and costs. However, one of the biggest cons is, indeed, increased threats to a business’s cyber security. As the means by which hackers conduct their business continue to evolve, so must the ways that companies protect against them. Today, artificial intelligence and machine learning are two key components in the fight against cyber threats.
Most people have heard the term artificial intelligence. Machine learning, in a way, takes it a step further. To use videogames as an example: a gamer can play against the computer, but generally a pre-programmed computer doesn’t start to learn the trends of the gamer, so ultimately the gamer can improve and win against the computer with time. With machine learning, the artificial intelligence (no matter the medium… shying away from video games now) is programmed to recognize trends and “learn” from them, evolving it’s level of intelligence as time goes on.
A good example of this is when an online order offers you up “other things you might like.” The computer in this scenario has utilized data and algorithms from other purchasers in an attempt to show you other items that similarly minded people have bought.
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Deep Q-Networks have revolutionized the field of Deep Reinforcement Learning, but the technical prerequisites for easy experimentation have barred newcomers until now.
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Emojify - Create your own emoji with Deep Learning. We will classify human facial expressions to filter and map corresponding emojis or avatars.