{"id":38058,"date":"2017-03-03T13:31:51","date_gmt":"2017-03-03T21:31:51","guid":{"rendered":"http:\/\/world.edu\/?p=38058"},"modified":"2017-03-03T13:31:51","modified_gmt":"2017-03-03T21:31:51","slug":"survival-guide-coming-ai-revolution","status":"publish","type":"post","link":"https:\/\/world.edu\/survival-guide-coming-ai-revolution\/","title":{"rendered":"A survival guide for the coming AI revolution"},"content":{"rendered":"

\"campaign_ai\"<\/a><\/p>\n

If the popular media are to be believed, artificial intelligence<\/a> (AI) is coming to steal your job<\/a> and threaten life as we know it<\/a>. If we do not prepare now<\/a>, we may face a future where AI runs free and dominates humans in society<\/a>.<\/p>\n

The AI revolution<\/a> is indeed underway. To ensure you are prepared to make it through the times ahead, we\u2019ve created a handy survival guide for you.<\/p>\n

Step 1: Recognising AI<\/strong><\/p>\n

The first step in every conflict is knowing your target. It is crucial to acknowledge that AI is not in the future; it is already here.<\/p>\n

You are most likely using it on a daily basis. AI is the magic glue behind the ranking of your Facebook timeline<\/a>, how Netflix knows what to suggest you watch next<\/a>, and how Google predicts where you are headed<\/a> when you jump in your car.<\/p>\n

AI is not a new concept. It was born in the summer of 1956<\/a>, when a group of pioneers came together with a dream to build machines as intelligent as humans. AI encompasses disciplines such as machine learning, which can find patterns in data<\/a> and learn to predict phenomena, as well as computer vision, speech processing and robotics.<\/p>\n

The main technique behind the current hype around deep learning<\/a> is artificial neural networks<\/a>. Inspired by models of the brain, these mathematical systems work by mapping inputs to a set of outputs based on features of the thing being examined. In computer vision, for example, a feature is a pattern of pixels that provides information about an object.<\/p>\n

\"<\/a>

In computer vision, features are the parts of an image that are used to classify an object. For example, the nose, ears and tail may be used as features to distinguish that a picture is a cat. Modified from pixabay.com<\/p><\/div>
\nMost commonly, the supervised learning approach requires the computer to \u201clearn\u201d these associations by training on big data sets labelled by humans. What began with classifying cat videos<\/a> has now extended to applications such as driving autonomous vehicles<\/a>.<\/p>\n

Step 2: Identify where AI thrives<\/strong><\/p>\n

With this knowledge, we can start to understand where AI is optimally positioned to take over. Have a look around you and take note of tasks that require huge amounts of data processing.<\/p>\n

For example, no human would or could look through everyone\u2019s click patterns on Google to figure out what someone wants.<\/p>\n

Even the more advanced capabilities that AI has demonstrated in winning AlphaGo<\/a>, video games<\/a> and, most recently, poker<\/a> rely on training on thousands and thousands of trials.<\/p>\n

Essentially, AI is particularly good at any task that requires an enormous amount of repetitive processing. If this sounds like your job, it might be time to start thinking of a survival plan.<\/p>\n

To evaluate your \u201cautomation risk\u201d, type in your job on this site<\/a> to find out what researchers have calculated for your field. Even if you\u2019re not worried, have a look. The prepared person stays ahead.<\/p>\n