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Recently, as guest speaker at an event for senior lawyers, I looked at “Artificial Intelligence (AI) and the Shape of Things to come”. I started by asking who was using artificial intelligence. A few hands went up but of course, it was a trick question! I held up my mobile phone and pointed out that everyone using Google or mobile assistants, such as, Siri and Galaxy, is using some form of AI – and it neatly demonstrates that AI has arrived in all our lives in ways we have not even realised.
AI is working its way into all aspect of work, business and leisure. The likes of Amazon Echo and Alexa have brought AI to the home while businesses have started to use AI to handle some of their core functions. Examples include processing invoice payments, insurance claims and customer complaints. In the professions such as legal services, some firms have deployed AI that decides what paragraphs to include in legal contracts. On another front, AI is being used in law enforcement, helping police forces uncover fraud.
AI is here and is touching our lives one way or another, sometimes without us even knowing it. So what is it and what kind of benefits or challenges does it bring? I am not an AI scientist and in this blog, attempt to shine only some light on this vast and fast developing topic.
AI is intelligence that humans build into machines. True AI, as set out by the Turing Test, is indistinguishable from human intelligence and that is still the stuff of science fiction. Today, what we do have are developments that provide machines with intelligence in different ways for different purposes e.g. voice recognition, Natural Language Processing (NLP), computer vision and more. These fields are developing rapidly but in a disjointed way. At some point in the future, they might be successfully brought together to create true human like intelligence, but for now we apply different types of AI depending on what we want the machine to do.
The most prevalent application of AI today is in the form of Machine Learning (ML). This is the science of giving computers the ability to learn without needing to be explicitly programmed. In ML, machines build models that define what they are supposed to do, using statistical and mathematical techniques and observed data. They learn in an iterative way; the more a machine does something the more it learns and the better it gets at doing it. ML is one of the key technologies behind developments such as improved web search engines, speech recognition and autonomous cars.
Under the hood of machine intelligence, you could find a neural network. This has numerous interconnected nodes (neurons) that process information in unison for specific purposes such as data classification or pattern recognition. Designed to mimic the human brain and its learning processes, neural networks process vast amounts of information to uncover hidden patterns in data, identify objects, or provide answers to complex queries. Today, a broader set of machine learning methods, referred to as deep learning, are applied to tap into neural networks. This is typically powered by vast amounts of data. Applications of deep learning include image and speech recognition and NLP which allows machines to interact with humans using natural languages such as English. Some of the most famous AI engines such as IBM Watson and Google AlphaGo software tap into deep learning and have demonstrated spectacular successes by winning Jeopardy and Go respectively, playing against humans.
The increasing availability of AI brings with it the opportunity for more business processes and daily routines to be automated. We are already seeing increasing levels of non-intelligent automation in everyday aspects of our lives, e.g. automatic hand brake, and lights and wiper controls in cars. This means that humans have to do less and less. Translate that into the world of business and commerce and add AI with its ability to infer meaning and understand more complex business requirements, then humans will be wanted less and less as more AI-powered robots take over business processes.
You might argue that the future of jobs is bleak but many companies that have already introduced these technologies successfully into their organisations have done so to increase capacity, offer extra services or release humans to do other more interesting and higher value jobs that reduce boredom and staff attrition rates. That said, it cannot be denied that demand for people is going to be on a downward trajectory.
On the positive side, the rise of AI also brings opportunities for new products and services, e.g. new ranges of intelligent white goods and appliances, the ability to provide enhanced remote social and medical care and new models for education. The importance of AI skills and expertise to tap into these opportunities cannot be stressed enough. In the UK we already have a shortage of technology skills. Whether we like it or not, AI is here to stay. We need to up our skills to make the most of it and to get ahead of other countries in this field, to become a world leading nation for AI.