For decades, scientists have been trying to find a way to mimic the behavior of the human brain. After all, if we can make a working "brain" in a computer, we can make as many copies of it as we want and we can quickly and efficiently scale up our capabilities. However, this has of course proven to be a big dream for quite some time and it still continues to be so today. So what exactly is meant by AI today?
When the term AI was first introduced seven decades ago by John McCarthy, communication and computation capacities were exponentially lower than what we have today. So, when scientists were trying to design computer models that mimic the behavior of our brains, they had to manually come up with mathematical models that worked well for particular tasks such as classification of some data points.
This trend, however, has dramatically changed over the past few years, thanks to the exponential growths in the amount of available data, speed of communication, and power of computation and parallelization. These changes have paved the way for techniques that exploit large amounts of data and the available highly parallelized computing power to significantly reduce the manual work that was previously needed.
The models that AI practitioners are working with today are called Artificial Neural Networks or ANNs for short. ANN models are attempts at mimicking the behavior of the human brain. Today's ANNs consist of many layers of artificial neurons — essentially performing multiplications and summations and some nonlinear functions — and are sometimes called Deep Neural Networks.
If we dive deep into the details of how these networks work, we will find deep relationships with probability theory and statistics. We can think of DNNs as sophisticated models that can capture the behavior of the data and have great predictive capabilities but are too complicated to be completely understood mathematically. Nevertheless, the more we know about how these systems work, the better we can improve them in the next iteration.
1. Computer Vision
Perhaps the first application of DNNs that attracted a lot of attention was the application of Convolutional Neural Networks (CNNs) for the task of object detection in an image. CNNs showed great success by repeatedly applying convolution operations over the input images. Some of the tasks successfully solved by CNN variants include:
- Object detection — find the location and label of different objects in an image.
- Face recognition — find the name of the person whose face is present in an image.
- Semantic segmentation — find the pixels of each specific object in an image.
2. Image and Video Generation
Shortly after the intensive use of CNNs, Generative Adversarial Networks (GANs) were introduced. The name GAN comes from the fact that during the training phase of these networks, two networks are pitted against each other. One network is responsible for generating the image and the other network is responsible for making sure that the output looks realistic. The Generative part of the network is capturing the probability distribution of the space of the images it is training on.
3. Control and Strategic Decision Making
The use of AI in control and decision making became mainstream when DeepMind introduced its ANN for playing Atari games, and later the AlphaGo project proved that AI can be quite efficient at solving problems that were known to be possible to solve only by humans. The method behind training these ANNs is called Reinforcement Learning (RL).
4. Natural Language Processing
One of the areas where AI has very recently shown a great deal of success is Natural Language Processing or NLP. There are numerous tasks that can be done with NLP, however, one of the main goals in NLP is to successfully predict the next word given a sequence of words. Successful recent works have been provided by companies such as Google and OpenAI.
5. Predictive Analysis
The predictive capability of AI systems is not constrained to specific tasks. Any company with data can harness its power to better understand its business and use AI to make better decisions. Many hedge funds have been using AI to make investment decisions and many more companies are expected to use AI for important decision-making processes.
6. Voice Recognition and Voice Generation
We are perhaps more familiar with these tasks thanks to Siri by Apple and Alexa by Amazon. The use of AI to significantly improve voice recognition and voice generation has led to a wide application by the industry. Furthermore, nowadays almost every major company enjoys an AI customer service that is at least partly powered by AI.
What to expect in the future?
As the volume of data increases exponentially and the power of parallelized computation is being harnessed more than ever, it is expected that all the above-mentioned tasks enjoy at least incremental improvements in the coming years. The combination of different AI tasks is perhaps what will lead to more interesting upcoming technologies such as fully Autonomous cars and more intelligent sounding assistants.
One of the upcoming challenges with AI, however, is that people are becoming more aware of their privacy. A possible upcoming change in the near future would be the massive implementation of AI solutions on the end user's devices and methods to preserve user's privacy.
Today's AI is capable of performing impressive tasks; however, true intelligence is achieved when a single AI agent is capable of doing various tasks. Today's AI is known as Weak AI; what needs to be achieved in the coming years is the so-called Strong AI or Artificial General Intelligence (AGI).
