Deep learning is an imitation of actual human brain neurons and its functions.

Unlike any other time, the past decade has seen unprecedented development in the field of Artificial Intelligence (AI). There are a lot of talks on machine learning doing things humans currently do in our workplace. Deep learning is leading in some of the fronts of machine learning making practical changes.

Deep learning is an artificial intelligence function that imitates the working of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural network (ANN). It has networks capable of learning unsupervised or unstructured data. Deep learning is often known as deep neural learning or deep neural network.

Deep learning is often compared with the actual human brain functions. The human brain can recognise a friend’s face or his/her voice even after a long gap. We can find our mother among so many people in a crowded marketplace. The human brain has learned to execute complex day-to-day activities. The functioning system behind the mechanism is 100 billion cells called neurons. The neurons build massive parallel and distributed networks, through which humans learn to carry out complex activities. The deep learning system is an inspiration of a biological neural system. Scientists and researchers started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans.

There are two types of neural network models used in deep learning,

• Convolutional Neural Network (CNN)- used in image-related applications like autonomous driving and robot vision.
• Recurrent Neural Network (RNN)- used in most of the Natural Language Processing (NLP) based text or voice applications such as chatbots, virtual assistants.

Functions of deep learning

Deep learning brings about an explosion of data in all forms and from across the globe. This large set of data, called big data, is collected from users interface in social media, internet search engines, e-commerce platforms, etc. This enormous data is considered as a data asset when it holds the details of an organisation or a company. Big data can be shared through applications like cloud computing.

Big data is mostly unstructured and contains files from diverse kind of sources like video, images and documents. It is so vast that it could take decades for humans to comprehend it and extract relevant information. Using AI and its applications, organisations make use of the data to increase their revenue and better the working system. Here are some of the use cases of deep learning at work.

Self-driving technology: Self-driving technology is one of the most important prospects that researchers are trying to unravel in the upcoming years. Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In order to decrease accidents, deep learning helps detect pedestrians in the road.

Aerospace and defence: Defence needs constant navigation. It will be very good if the navigation system is able to detect safe and unsafe zones from a long distance. Deep learning installed in satellites helps identify objects and locate areas of interest.

Medical Research: Deep learning is a major component in detecting cancer cells. Cancer researchers at UCLA have built an advanced microscope that yields a high-dimensional at a set used to train a deep learning application to accurately identify cancer cells.

Industry automation: Deep learning in industries is used to automatically find unsafe machines and alarms people to get away from the location. It ensures the security of workers in heavily machinery surroundings.

Required tools 

Deep learning mandates a lot of sophisticated tools in which some are free like TensorFlow, PyTorch and Keras. Whereas, some other tools are highly expensive. Data learning deals with enormous data and complex algorithms that needs luxurious hardware infrastructure to handle. The deep learning tools are referred to as Machine Learning as a Service (MLaaS) solutions. Amazon AWS, Microsoft Azure and Google Cloud are some of the platforms that provide deep learning tools.

Advantages of deep learning

• In deep learning, neurons are being trained to perform conceptual tasks such as finding edges in a photo or facial features within the face.
• Deep learning over most of the other machine learning approaches keeps away the worry about trimming down the number of features used.

Disadvantages of deep learning

• Deep learning networks may require hundreds of thousands of millions of hand-labelled examples.
• In deep learning, it is very expensive to train in fast timeframes as fast players need commercial-grade GPUs.

Sometimes deep learning is taken for a ‘black box’ for its complex and extremely difficult to understand the working model.

The post WHAT IS DEEP LEARNING? A SIMPLE GUIDE WITH EXAMPLES appeared first on Artificial Intelligence.

Today, artificial intelligence is dominating most of the games — from board games to interactive fiction games. They are providing complex, decision-making environments for AI to experiment with. The ability of games to provide interesting and complex problems, offering creativity and expression, has made them one of the most popular and meaningful domain for AI researchers.

Games offer one of the most meaningful domains that can process, interpret and stimulate human behaviour. The current gaming industry is not only deploying better graphics but is also exploring the area of virtual gameplay. The two-way relationship of gaming and AI has begun to tread a new road and it can be said that the gaming industry is largely revolutionising the way AI works.

AI In Gaming Industry

Application of AI to the gaming industry can be dated back to 1956 by Arthur Samuel’s checkers program. Since its first application which could beat professional players to the present day’s AlphaGo, AI in gaming has come a long way.

Today we see an enormous upsurge of AI in game. First Encounter Assault Recon, popularly known as F.E.A.R. and The Last Of Us are some of the most popular games that give a very realistic experience with the use of AI.

How Does Gaming Aid AI?

Games are difficult because of the complexity and the skill that demands of them to play. This complexity of games makes it very desirable for AI to work on. A typical game has about 101685 possible states, whereas the number of protons in the observable universe are just of the order of 1080. This can tell about the degree to which the gaming industry is complicated and rich with data. And where there is plenty of data, AI is always a privilege. With larger sets of training data, AI would have the ability to be less predictable and more spontaneous, thereby making the gameinfinitely interesting and impulsive.

Interaction:

As every game involves players, the interaction of the player with the game is advantageous to AI, as it gives access to the algorithm to study the player experience an emotional behaviour. The study of this game and human interaction proves a key to not only study the human behaviour, but it also makes a way for AI to build a better human-computer interaction system. It further pushes the AI boundaries to study and understand the human-computer interaction systems and address the challenges faced by its applications in the real world.

Decision-Making:

This is the main crux of AI. AI must be able to make decisions by looking at the opponent’s action. There are various models used for decision-making in the game. Markov model is the most popular model. Fine State Machine (FSM) is one of the many AI methods used for decision-making.

Prediction Ability:

Prediction involves anticipating the next move of the player, so that decision-making can be done based on it. This is done using methods like past-pattern recognition and random guess. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Tree Learning are the algorithms used for prediction. Regression algorithms are used for predicting player behaviour. This process includes situations like predicting times when the player is expected to be in the particular level of the game, what item will the player pick next, when will he move to the other lane, are made. Experimenting with this is virtual games, we implement these algorithms and models in the real world as well.

Intelligence:

Social intelligence and human-computer interaction are the most supreme objectives of AI. These two things are taken into consideration by games and that way they help in AI development. Virtual characters exhibiting human behaviour as well as intelligence.

AI had learnt about the intelligence of computers the most from games, than from any other application, because they provide a virtual platform to test every kind of algorithm. Moreover, they also provide complicated mathematical problems to deal with, so the AI learning is not just restricted to the gaming world.

The success of deep Q-learning in learning to play arcade games with a human-level performance by just looking at and processing the pixels on the screen, is an example of intelligence. The study of intelligence within games not only lets us know more about human intelligence but also about AI intelligence.

The recent Dota2 tournament, ‘The International’, had bots competing with professional players. Although they couldn’t win the match, it must be noted that the ability that AI can be bestowed with, to play games as complicated as Dota2, is remarkable. Another example into the future of AI in games is at the Michigan State University, where a group of researchers have deployed AI to learn a game by learning from every player’s behaviour. It will adapt to individual player’s behaviour and play the next move.

Games offer both entertainment and interaction, in turn having a very high realisation of the affective loop which is very important in gaming. They provide a multitude of fancy features at once — visual art, sound design, graphic design, beautification, are narrative, virtual cinematography, all in one single software. Games are perfect testbeds for AI because they act as the best application of computer creativity. As a result, with the use of computational creativity in the gaming industry, provides a way to advance AI. It not only challenges computer creativity but also advances it.

The post The Gaming Industry Is Revolutionising Artificial Intelligence, One Win At A Time appeared first on Artificial Intelligence.