For some a mention of artificial intelligence (AI) summons images of robots running crazy while humans bravely trying to put the genius back in the bottle. But the reality is that today’s AI – the ability of machines to learn from experience and perform human-only tasks – is already a reality and full of opportunities to enrich and improve human life.
machine Learning, one of the most important building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large datasets. Programmers have increasingly improved the ability of machines to study data to discover patterns that allow computers to then organize information, identify relationships, make predictions, and detect anomalies. Today, modern applications of AI have already provided us with self-driving cars and virtual assistants and helped us detect fraud and manage resources such as electricity more efficiently.
Today’s machines are now capable of performing narrowly defined tasks with great precision, but – and this is an important warning – this precision is only as good as the quality and in some cases the amount of data that drives the model. The current state of machine learning will, with input from carefully considered data, allow for countless improvements to existing products and eventually the development of free-standing AI, though not fully autonomous AI devices for the “running robots” kind.
But as machine learning gets deeper, we embark on the next step toward increasingly sophisticated AI: deep learning. The sophisticated analysis of deep learning are achieved through neural networks, so-called because they loosely mimic the interconnected structure of the human brain to provide a multitude of layered functionality.
These neural networks are actually so sophisticated that the path a machine takes to reach its conclusion is not yet easily understood. Deep learning uses huge, self-enhancing neural networks – only possible and more accessible due to recent advances in computing power – to achieve extremely complex pattern bleeding such as speech or image recognition.
“Deep learning is only used when it really makes sense – where it can quickly find complicated, variable conditions hidden in large amounts of data that we haven’t been able to extract in any other way yet,” explains Mary Beth Ainsworth, global product intelligence manager for artificial intelligence and text analysis at SAS. “But deep learning means that a machine can look at a problem through a completely different analytical lens than its human counterpart. It can be used to tackle all kinds of problems. The potential of all the data we collect every day is yet to be realized. ”
There is also progress in how another key building of AI, natural language processing (NLP), you have developed into natural language understanding (NLU). If NLP is the ability to translate spoken or written languages into a form an algorithm can understand and then respond with results in a spoken or written language that people can understand, NLU is more widely known: the ability to derive meaning in language and then reacted accordingly, as people do instinctively. Siri and Alexa are the first steps on a path to give AI a much simpler and more human-friendly user experience.
And contrary to the dystopian results that some might imagine, humans remain very much in the picture. Setting up and maintaining useful machine learning requires human interaction and insight, as well as validating AI conclusions by testing them against new data – an essential component of any AI installation. In addition, choosing the right algorithm for the job, configuring it for the best performance, improving the data it will work on, getting the ideal balance between machine sophistication and its ability to consume data, and interpreting the results with the understanding that prediction is not the same as causation is part science, part art and all human power.
Little wonder then as a recent one transnational study of SAS clients suggests that one of the main concerns that businesses have when introducing artificial intelligence is having the human expertise to manage it.
“Organizations may want to jump on the AI band because it’s such a hot topic, but they have to identify what they want to do with it,” advises Ainsworth. “In the same way, other people may still have a negative view of AI, from how it is often manufactured, so it can seem overwhelming. But in many cases, they already use machine learning every day when they search the Internet, upload photos to social media, or shop online at major retailers. In the end, it’s all about using the right tool for the right job. AI requires a strategy with clearly defined tactical steps to implement the larger plan. AI can provide valuable insight, but what you do with this information still requires human direction. “
It is already clear that AI is set to be revolutionary; more so, most likely, than the transition to automating repetitive manual tasks through robot hardware. AI will require some dramatic cultural shifts. But not only will it increase productivity – identify maintenance issues before they happen, for example, or enable real-time pricing online – but it will also save time. AI will work away in the background while we are freed to innovate or take care of other tasks that require human attention. Much like Amazon’s distribution centers use robotics to pick goods, but people to pack it, commerce and many other areas of life will become a matter of teamwork. Man and machine will work together.