At an entry-level, Artificial Intelligence is a branch of computer science that, to quote Bill Gates’s mission when starting Microsoft Research, seeks to build computers that can see, hear, and understand humans.
Alan Turing’s work to make thinking machines, summarized in “Computer Machinery and Intelligence,” was a major milestone in AI’s history. In that 1950 paper, Turing asked, “Can machines communicate in natural language in a manner indistinguishable from that of a human being?” This is the essence of the famous Turing Test, which has become a key benchmark for generations of AI researchers in evaluating the power of their programs. There seems to be general agreement that AI became an independent branch of scientific investigation in 1956, at the Dartmouth Summer Research Project on Artificial Intelligence. For our purposes, the term AI applies to programs that simulate human cognitive abilities as well as those that process and apply the information captured. Natural language processing (NLP) and speech and image recognition applications are examples of AI. NLP seeks to understand written language texts. Speech recognition—say, turning voices or spoken language into text— is a related field. Image processing is another parallel field and is often referred to as image recognition or computer vision.
As a discipline, AI is fast-evolving and so is its definition. Applications that counted as AI just a few years ago, like optical character recognition, or OCR, for example, may no longer.
What Are Machine Learning and Deep Learning?
The quality of machine translation has improved by leaps and bounds in recent months. Recommendations from commercial services, like Amazon and Netflix, have become ubiquitous. As with AlphaGo and AlphaGo Zero, these developments have all been driven by advances in machine-learning and deep-learning technologies.
So, what is machine learning and deep learning? The term machine learning was coined in 1959 by computer scientist Arthur Samuel about “a field of study that gives computers the ability to learn without being explicitly programmed.” Machine learning applications are Artificial Intelligence programs that can develop additional programs themselves to interpret input and predict the output.
While machine learning may be a new term that many investment managers have only recently encountered, the neural network is a related concept that finance professionals, particularly quants, may be more familiar with. A neural network is a form of machine learning inspired by how human brains process information.
Deep learning is among the hottest buzz words today. Many claim deep learning’s emergence has revitalized AI research. Deep learning is the multi-layer neural networks: programs that process the initial input in multiple stages to generate the final output, at each stage taking the output of the last stage as the input. It is reminiscent of how we tend to break down complex tasks into a series of smaller steps. Deep learning is one of the many approaches to machine learning.
Together with the increase in computing power and the avalanche of data now available, advances in deep learning techniques have helped bring about the AI spring we are experiencing today.
Artificial Intelligence terminology and methods will continue evolving. For example, Yann LeCun has recently proposed that the term differential programming should replace deep learning. What matters for investment professionals are the toolkits these innovations have made available to us.
So where does the scorecard stand today? Computers are making strides and maybe outpacing us, humans.
• In the ImageNet competition of 2017, AI programs beat the best human record by an increased margin, emphasizing computers can now “see” images better than us.
• In 2017, Google and Microsoft speech recognition programs transcribed as accurately as humans, so computers can now “hear” just as well as us.
• Two AI programs have succeeded in reading better than an average adult as of January 2018, so we can say computers can now “understand” us.
• In January 2018, Google debuted the Cloud AutoML platform that puts the power of machine learning in more programmers’ hands. So more machines will be able to see, hear, and understand.
Artificial Intelligence technology has made tremendous progress in the last few years and many more tools are now at programmers’ disposal. The next big push will be applying AI across fintech industries.