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What is AI

What is Artificial Intelligence

When people talk about artificial intelligence (AI), their mind often goes straight to the movies. They seem to think that some company is going to create Skynet and manufacture Terminators to initiate the extinction of humanity. This is a common misperception of AI whereby movies have associated it with sci-fi and fantasy but in reality, it is quite different.

AI is the use of computer science and programming that trains machine to imitate human tasks and thought processes. It works by analysing data and surroundings to solve problems, incrementally learning for itself to continually improve. AI functions are initially based or trained using the instructions given to it by humans and when these are a bit vague or incomplete, in theory we could get Skynet type consequences (albeit not quite so extreme).

Right now, all forms of AI use some sort of human intervention. That could be loading the training data or analysing the results and perfecting them. AI is not at a point yet where it has its own conscious decision-making process and can see the world as or better than a human would. This is likely to still be quite a long way in the future and it is important not to exaggerate the capabilities.

Instead of talking about AI, discussing its applications helps to make better sense of the term and show how it is impacted many parts of everyday life.

Applications of AI

You are probably exposed to AI every day. Whether it be using Facebook Messenger, talking to Alexa, watching Netflix, listening to Spotify or searching on Google, you are using a form of AI. Most of these examples are powered by an AI application known as machine learning.

Machine learning is the use of existing data to make future decisions. Algorithms built without programming platforms are designed to enable machines to make unsupervised choices based on the data they have been supplied with. One of the best ways to explain machine learning is when comparing to how a baby learns to walk.

A baby would start by taking in the surroundings and watching other children or adults walking. Nobody explicitly tells a baby to move their left foot forward, then the right, then the left again, then the right and so on. In gathering data from the environment, a baby will learn for themselves and attempt their first steps. Initially, they might fail so next time they use a table to help them up. Over time, the baby connects all the dots provided by data and begins to walk.

A machine will learn in the same way. Let’s say you want the machine to separate pictures of cats from pictures of dogs. To start, you give it a large collection of cat photos and it looks through to find the patterns. When it is presented with a new photo, it tries to work out whether it is a cat or dog. Every time the machine fails, it learns from the mistake and becomes more accurate. It can do this against vast volumes of data. In theory, the machine should become 100% accurate with the task.

Machine learning works using data so a human never has to program it. It might even find patterns that a human never would have done. A real-world example in Hong Kong has shown that machine learning has become more accurate than doctors in cancer diagnosis through analysing i mages of patients who have symptoms.

The digital world is full of data meaning machine learning has a major part to play. One of the key applications today is in conversational chatbots which use a form of machine learning known as natural language processing. Amazon Alexa can take voice commands and analyse them against a huge knowledge base to return the most relevant response to the user. Sensors on factory machines are being used to constantly record data and predict when maintenance could be needed before any problems arise. In contract law, algorithms can review thousands of articles simultaneously and potentially solve cases in a split second that would normally take a human weeks or months.

Those are just some applications, but it exemplifies the value of treating data as a business asset. AI is more about data than it is about the fantasy we see in the movies.

Companies that use AI

AI applications are used in almost every company that we interact with. Here are a few popular examples.

  • Google – every Google search uses machine learning. It takes what the user writes or says and applies that to algorithms, returning the most relevant results. Google can even do this with video now as the AI has advanced over several iterations since 2010.

  • Netflix – the streaming service users what we would call a recommender system. Instead of users choosing a show to watch, Netflix uses data to predict what their subscribers will want to watch next. Recent statistics have suggested as much as 80% of user choices have come via the recommendations. Subscribers are even presented with different show thumbnails based upon their likely preferences. Spotify and Amazon use similar models

  • Facebook – the social network is massively based on data. Users get ads based on their preferences and it is never just a coincidence when they are relevant. Messenger is now a conversational chatbot used by major companies to complete retail actions without any human involvement. Facebook has utilised the purchase of WhatsApp to get a great understanding of how people converse.

The future of AI

At the moment, we are only really at the start of an AI revolution. Applications like machine learning are still relatively new outside the big enterprises and have only been deployed as a very light touch. However, that said, many of the applications seem so normal that we don’t even remember they exist. Talking to Alexa to turn on your lights has become standard in some households. In the future, the same will most likely happen with driverless cars and robotics but we are a little way off that yet.

What is becoming quite scary is that humans are beginning to trust AI applications more than another human. In fact, even when in a retail store, over 60% of people surveyed said they would rather use their SmartPhone to answer questions than ask a human a ssistant. As new technology like driverless cars come into play, this lack of trust could start a societal breakdown of sorts which is perhaps why we are holding back just a bit with such game changers.