As artificial intelligence develops, computers and robots are learning more and taking over jobs in several industries. Some researchers are creating robots that are almost indistinguishable from humans in terms of intelligence and the ability to express emotions. These abilities are made possible by the formation of neural networks.
What is Neural Network?
The network is loosely based on the human brain and provides a way for computers to learn from the data programmed into them. The system of neurons involving the brain is a human, or organic, neuron network.
Neurons in the brain send out sparks of electrical activity to the axon, which includes thousands of branches within neurons. When an activity excites the synapses at the end of the axon, then it creates another electrical spark. When humans learn, it changes the synapses effectiveness, changes the brain’s neurons.
An artificial neural network (ANN) attempts to mimic this activity within the brain, which allows computers to learn. There are connections, or units, within an ANN that imitate neurons, complete with synapses. An adjustable weight is associated with each connection to alter a pattern of activities into one activity that is sent out to other connections.
The steps to alter the activities of the output data is necessary to categorize the input-output function of the system and to make an ANN that can perform certain tasks, or learns.
History of ANNs
The history of ANNs began in 1943 when a neurophysiologist, Warren McCulloch, and a mathematician, Walter Pitts, decided to demonstrate how they thought that the neurons of the brain worked. They created an ANN from electrical circuits.
Early attempts to create ANNs using computers failed until 1959 when researchers from Stanford University created ANNs named Adaline and Madaline. The names were acronyms of the systems they created called Multiple Adaptive Linear Elements.
Adaline was made so it could recognize patterns that involved two numbers. To demonstrate this, the data it was fed was from telephone numbers, so if it read the numbers streaming from a phone line, it could predict the next two numbers.
The design for Madaline was the first one to have a practical application. It was able to eliminate echoing on telephone lines by using an adaptive filter. This ANN still is used commercially despite its age.
Research involving ANNs decreased in the 1970s, but a renewal of interest took place in 1982 when John Hopfield wrote a paper explaining his idea about creating bidirectional ANNs. Until then, ANNs only worked in only one direction.
In 1982, researchers were able to create a “hybrid network” that was comprised of multiple layers. Each layer used a different scheme with which to solve problems. Improvements on this type of network took place in 1986 when researchers were able to create a back-propagation network to allow ANNs to recognize several patterns instead of only two.
Within the past 10 years, researchers began using an older concept to stop relying on huge memory banks of data. Instead, they came up with a more simplistic approach that imitates the human thought process of analyzing data. This method is known as Deep Learning.
What is Deep Learning?
Deep learning helps to match inputs into a system to its outputs, or, in simpler terms, it works to find data correlations. Due to the ability of Deep Learning to match inputs to outputs, it has been called the “universal approximater.” It learns how to approximate a function, as demonstrated by the equation:
During its learning process, the ANN can find the value of for how to calculate x, so it equates to y.
What does Deep Learning Do?
Here are some of the real-world applications for Deep Learning.
Recognition and Classification
Deep Learning allows for recognizing and classifying objects, peoples’ faces, and their expressions in images. This ability has many applications, such as recognizing persons of interest for crimes in photos or surveillance recordings.
It can also recognize the voices of speakers, help translate speech into writing, and follow commands to dial a smartphone or turn on or off devices connected to a home automation network.
This type of neural network can also detect fraud in text, classify emails as spam, and is able to recognize emotions in messages. These applications have more commercial than consumer value but being able to keep spam out of emails can help everyone reduce the number of emails they receive every day.
In the very near future, self-driving cars will be on the road. To help them recognize objects in the road, know where to turn, stop at traffic lights, or at their owners’ homes, ANNs need to be fed lots of data. To help interpret the data and so the vehicles react as expected, the networks controlling driverless cars, and other vehicles, make use of equations like the Stochastic Gradient Descent to interpret data.
Remember the universal translation on Star Trek? Well, that device, or one like it, maybe soon available thanks to ANNs. Deep Learning will help devices translate one language into another through voice recognition.
There are already some translation apps and devices on the market that travelers can use when visiting countries whose language they do not know. This technology has several business applications like being able to understand the language of a client to whom you’re trying to sell products.
An app from Google, called Google Translate, supports 103 languages and it can be used to translate videos, to type and translate languages, interpret speech, and translate handwritten messages.
ANNs can be used to apply color to black and white photographic images. This process had to be performed by humans because machines were incapable of recognizing that a black and white photo needed colorization. Now, with the help of Deep Learning techniques, images can be colorized in large batches.
Predict Geological Events
Researchers at Harvard may soon be able to teach computers to solve viscoelastic equations that help predict earthquakes. These equations measure the elasticity and viscous qualities of materials, such as the Earth, when placed under stress.
Using these equations can help predict earthquakes due to seismic activities or volcanic activity. They can also help measure the stress put on the Earth due to earthquakes, which measures their size in terms of the Richter scale.
Difference Between AI and ANNs
Although they are part of the same field, there is a difference between Artificial Intelligence and ANNs. The main difference is that artificial neural networks are the first step in creating intelligent robots and machines.
While computers can be programmed to perform certain tasks, like calculating tax returns, developing AI would make them able to perform a variety of more complicated tasks, such as those below.
Many researchers use ANNs in the field of medicine to help find diseases like brain cancer, predict heart attacks or strokes, and to customize treatments for patients using their health information and predictive analytics.
They would also be able to discover and manufacture new drugs to treat diseases. Medications could be customized for patients using mechanisms for diseases that have several causes or factors. This research would involve teaching ANNs how to operate independently because it would require computers to be able to learn without supervision.
Earlier in this article, we saw how ANNs could detect and classify spam emails. AI would take this ability a step further by being able to place emails into categories. By recognizing the information in the emails, they could then put them into categories like primary, social, and promotional.
Gmail, or the Google Email application, already perform this task. However, with AI, it would go further by sending automated responses to senders. By continuing to learn from the information derived from emails, smart responses would be able to provide more detailed messages.
ANNs are already able to take care of many financial tasks, such as mobile deposits by “reading” a check when an image of it is taken and depositing the funds into your account. They can also find fraud in the deluge of daily transaction histories and make credit decisions based on people’s financial data.
Now, businesses are developing AI to give people recommendations about investments. These companies use data from experienced investors and provide financial investment recommendations to customers at a lower cost than investment managers.
By using predictive analytics, computers could customize recommendations for those wanting to invest and grow their money. While some investment businesses are using AI for this purpose now, there is no information on its long-term success. The earliest investment business using AI was founded in 2008, which is barely 10 years ago.
There are many other applications that neural networks can help with, including using AI to develop chat services on social media, create personalized shopping recommendations, and expanding the services of smart assistants.
The technology of the future will be able to do tasks that were once found only in science fiction, and that future is just around the corner.