Artificial Intelligence

Artificial intelligence surrounds much of our lives. The aim of this branch of computer science is to build intelligent machines that is able to operate as individuals; much like humans. I am sure most of us have watched the Terminator movie series and questioned to what extent will our own society converge to that in the movie. While that may sound preposterous, much of what automated system developers do revolves around building adaptive systems that react to changes in markets. Inspired by a course I am taking at school right now, I would like to use this post as a general fundamental intro to AI.

If you ask people what intelligence is, most will initially find it hard to wrap words around the idea. We just know what it is and our gut tells us we, humans, are the pinnacle of what defines intelligence. But fact is, intelligence encompasses so much. According to the first sentence in wikipedia, there are ten different ways to define it.

“Intelligence has been defined in many different ways including logic, abstract thought, understanding, self-awareness, communication, learning, having emotional knowledge, retaining, planning, and problem solving.” -Wikipedia

Since it encompasses so much it is not easy to define it in a single sentence. What can be said is that intelligence relates to one’s ability to problem solve, reason, perceive relationships, and learn.

Now that I’ve offered a sense of what intelligence means, what, on the other hand, is artificial intelligence? Artificial intelligence is the field of designing machines that is capable of intelligent behavior; machines that is able to reason; machines that is able to learn. More precisely, the definition of AI can be organized in to four different sections:

  • Thinking Humanly
  • Thinking Rationally
  • Acting Humanly
  • Acting Rationally

The first two relates to thoughts processes while the last two relates to behavior. Thinking humanly revolves around whether the entity in question is able think and have a mind of its own. This is essentially making decision, learning and  problem solving. Acting humanly is whether a machine is able to process and communicate language, store information or knowledge and act based on what it knows and learn to adapt based on new information. These set of required traits are formulated based on the famous Turing Test which examines if a machine is able to act like a human through answering questions asked by anther human being. The machine passes the test if the person asking the question isn’t able to determine if its a machine or human. Thinking rationally closely incorporates the study of logic and logical reasoning. It was first introduced by Aristotle who attempted to provide a systematic way of inferring a proposition based on a given premise. An famous example would be “Socrates is a man; all man are mortal; therefore, Socrates is mortal.” Lastly, acting rationally is the idea of choosing the most suitable behavior that produces the best expected outcome. Another word one is rational if given all its knowledge and experience, selects the action that maximizes their own performance measure/utility.


When studying AI, the term agent is used to represent an entity/model that interacts with the environment. More precisely, an agent perceives the environment through its sensors and employ actions through actuators. Comparing this to humans, imagine sensors as eyes/ears and actuators as arms and legs. The sensors will at each time step take inputs, called percepts, which are than processed by the agent program. The agent program then passes the inputs in to an agent function. The agent function maps inputs to correct outputs (actions) which are then sent via the agent program to the actuators. This agent based framework closely relates to automated trading systems. The environment is the market and the changing prices at each time interval. The agent program would be our trading system which takes in daily price information and pipes it into the agent function, or the logic of the trading system. For example, todays new price is updated which is passed in to the trading logic. The logic specifies that if the current price is $10, it will sell. The sell action is passed back to the environment as a sell order.

The above example is a very basic type of agent known as the simple reflex agent. This type of agent only makes decision solely on the current percept (price). It doesn’t have a memory of the previous states. A more complex agent known as the model based reflex agent is one in which it has memory of the past, known as its own percept sequence. Also this agent has an internal understanding of how the environment works which is detailed in its own model. This model of the world takes inputs and identify the state it is in. Given the state, the model forecasts what the likely environment will be like in the next time step. Proper action is then recommended and executed via actuators. (Think of markov models)  So far, the agents I’ve introduced largely reflect that of a function that take input and spits out a output. To make things more humanly, the next agent I will introduce is called a goal based agent. This is similar to how given our current circumstances, we aim to maximize out objective function. The objective function can be money or anything that makes us happy. More concretely, the goal based agent is an extension of the model based reflex agent but it assigns a score for each recommended action. The agent will choose the one that maximizes its own objective function.

The reader will most likely ask how this knowledge helps them make money in the markets. What I can say is that finance is enter a brave new world where together with technology is transforming how money is being made in the markets. Having a understanding in finance and statistics in my opinion is not enough. Those are the areas where your competitors are already fishing (mostly). Knowledge in subject areas like AI, speech recognition, natural language processing, machine learning, and computer vision (just to name a few) will allow you to be more creative in design. I urge the curious minds to explore the unexplored!


One comment

  1. Hello, this is a nice introduction to classical AI, however, distinction is normally made between human intelligence and general intelligence. Some of the most powerful artificial intelligence algorithms are inspired by collective behaviour found in swarms, and biology.

    This is collectively known as ‘Computational Intelligence’ and covers the following paradigms: neural networks, artificial immune systems, evolutionary computation, swarm intelligence, and fuzzy reasoning systems (the last of which, in my opinion, is actually more ‘classical’)

    Ant Algorithms are a particular class of algorithms which I think you might appreciate. They can be used to solve the shortest path problem in almost any dimension, as well as to perform real time clustering of data … one cool application is portfolio construction.

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