What is Artificial Intelligence
The type of artificial intelligence software that we use at Top-Down Market Research, LLC is similar to what is commonly known as a neural network. However, our artificial intelligence codes have been written from scratch and include adaptive features not found in commercially available neural network software. Understandably, our artificial intelligence systems are proprietary and specific details of our models will not be revealed. However, some general background information on neural network technology is provided here.
Biological Basis for Neural Networks
Artificial intelligence was first developed as an outgrowth of the study of the human brain and nervous system. The brain and nervous system are composed of cells called neurons. Neurons do not die and replace themselves like other cells in the body, which probably explains why many of our memories are retained over long periods of time.
Estimates are that we have as many as 100 billion neurons in the human brain, each one connected to as many as 1000 neighboring neurons. Some of the electrical signals transmitted between neurons pass through signal modifiers called synapses. Learning occurs as the synapses increase or decrease the signals passed between neurons. In this way, neurons and synapses work together in groups called networks.
Neural networks are capable of making sense out of complex patterns that would otherwise be unrecognizable. A good example of how this works is human vision. The retina in each eye has approximately 120 million light collecting cells. The cells convert the light energy to electrical impulses which are carried to the brain via the optic nerve. The brain is given the task of decoding millions of electrical impulses so that they can be assembled into a picture that makes sense. Can you imagine someone giving you a jigsaw puzzle with millions of pieces and asking you to put it together in a fraction of a second?
Neural Networks Change and Learn
Neural networks learn cause-and-effect relationships. Remember the first time you tried something new? Imagine you are learning to play tennis for the first time. You have never even picked up a tennis racket before. You don’t know how to angle the racket or how hard to swing at the ball to keep it in the court. Furthermore, you don't know what the trajectory of the ball will look like as it comes towards you across the net. How will the ball bounce? Will the spin and speed of the ball effect it’s bounce? Will the wind have a significant effect on the trajectory of the ball? Where do you need to run on the court to meet up with the ball at exactly the right time? Needless to say, there are thousands of independent variables that influence the physical mechanics of the game.
So, ask yourself this question: Before you go out onto the court to hit the ball, are you going to sit down and run calculations based on the laws of physics to account for all of these factors? No, of course not!
You just do it!
You use what little information you already have about the sport and go swing at the ball! Perhaps the first time you swing at the ball, your racket is too high and you completely miss it. You realize your error and make a correction. The next time you swing a little lower and make contact, but because of the angle of the racket, the ball sails over the fence. Over time, through trial and error, you will continue to make corrections until you find that the ball starts to go where you are aiming.
Neural Networks on Computers
That's sort of the way a neural network computer program learns too. In the case of forecasting the direction of a stock, a neural network looks at a large amount of historical economic information and attempts to make a forecast as to what will happen next. It doesn't run any complex supply and demand calculations.
It just does it!
It then compares it's forecast with what really happened and makes adjustments to compensate for it's error. Essentially, the neural network “lives” through history time after time until it becomes proficient at forecasting the future. In essence, the program has learned what factors have significant effects on the future prices of stocks. Some of the factors that affect future stock prices are hidden and are not easily recognized. But they exist nonetheless. The neural network program learns what the cause and effect relationships are and is also able to quantify how much of an effect each factor will likely have on a stock's price.
For more information on why our artificial intelligent computer systems can help increase your profits, click on Our Strategy.