The Future of Artificial Intelligence and Machine Learning for Financial ServicesThe Future of Artificial Intelligence and Machine Learning for Financial Services

The reason for looking at these specific areas is to break some common misconceptions in order to really understand how far away we are from true AI and machine learning in business.

Where are artificial intelligence and machine learning today?

Computers complete different categories of tasks. At a high level, we have concrete tasks that computers use software to solve, such as adding two numbers together. We also have knowledge tasks that require more abstract thought, like playing the game Go. Machine learning and AI are both trying to improve computer’s’ ability to complete more and more abstract knowledge tasks.

However, a common mistake is to blur the lines between machine learning and AI. To date, machine learning has been focused on using information gained from previous results to predict future results, to gain actional insights from big data, while avoiding the underfitting or overfitting of data. These models are split between supervised and unsupervised approaches. The former are so called because the outputs are predetermined by the data scientist (e.g. a classification algorithm will learn to identify (say) animals from a dataset of images). Unsupervised approaches involve computers uncovering complex patterns without human guidance.

Using a probabilistic approach has resulted in a large amount of research with the now famous Naive Bayes approach. This involves taking sets of training data and using it to upskill the algorithm. Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience.

As we move from Machine Learning to true Artificial Intelligence, it is going to be a time of change, but an exciting one! Systems will start popping up in more and more complex roles – system-driven lawyers and doctors for example. For now, though, let’s focus on enabling capabilities that can help to remove frustration from our day-to-day lives.


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