What is Deep learning

Artificial Intelligence is one of the most transformed technologies of this generation. But this generation is not all about AI since researchers have made advances in other areas like Machine Learning and Deep Learning. The three are complementary. This chapter introduces the three concepts, discussing what is entailed in each and how much it has changed over time.

What is Artificial Intelligence

Also referred to as machine intelligence, Artificial Intelligence (AI) is the intelligence that machines portray. This is an area motivated by the natural intelligence of human beings (Techopedia, n.d.). With AI, machines are given the ability to learn from experience and make adjustments so as to mimic human-like behaviors.  A majority of technologies developed in modern society, from self-driven cars to playing chess with computers, are all based on natural language processing and Deep Learning. With the aid of such technologies, training of computers is executed where these computers (robots) are fitted with algorithms that process large sets of data (Techopedia, n.d.). They recognize patterns in the data and learn from it.

The term Artificial Intelligence was first used in 1956 and its popularity increased in the 21st century thanks to the large amounts of data available and the improvement in technology which presents stronger computing power and more storage capacities. This was not the case before now. AI of the1950s was more focused on symbolic methods and problem solving. The field then attracted the interest of the United States Department of Defense – DoD, in the 1960s as it sought to train computers to mimic human behavior for security reasons. The DoD was able to make major leaps in the area, managing to map projects by the 1970s and introducing the earliest intelligent personal assistants in 2003, years before Google’s Assistant, or Siri, or Alexa were introduced (DataRobot, n.d.).

The initial progress that had been made in AI research paved the way for formal reasoning and automation which have become the norm in modern computers. Today we have smart search systems and decision support systems which can be designed in such a manner that they complement human cognitive abilities.

Artificial Intelligence should not be confused with robotic automation which is hardware-driven. AI is all about learning from a set of data. The learning happens repetitively as more data is fed into the AI system. Rather than bringing automation to manual tasks, AI tackles computerized tasks that are numerous at a faster rate without getting fatigued. That’s not to say that AI will set itself up. Rather, human input is required for the whole configuration of the AI system and also for asking the right questions (DataRobot, n.d.).

Progressive learning algorithms enable AI to adapt to situations whereby the data is used to do the programming. AI assesses data and determines regularities and structure in order to help the algorithm harness this skill. In simpler terms, the algorithm is turned to a predictor or a classifier. The same way the algorithm is able to learn how to play chess, it is also to master and determine the next product you are likely to buy in an online store. As new data is fed into the models, adaptation takes place. To facilitate the adjustment process, the AI technique of back propagation is often used. This involves adding new data to the older one when the initial data is found to be incorrect.

AI researchers are often encouraged by the many applications that AI holds for different sectors of the economy including healthcare, manufacturing, and food & water. With Artificial Intelligence, we are able to achieve various services like self-driven cars, automated trading, and enhanced pacemaker. There is no doubt that there are setbacks associated with AI, but we cannot overlook the many benefits bound to be enjoyed from the same technology.

What is Machine Learning

Machine Learning (ML) is an application of Artificial Intelligence. It is focused on enabling systems to automatically draw lessons from experience without the need to be programmed. With Machine Learning, systems are developed in such a manner that they can access data and get trained by it (Rouse, n.d.). The learning process starts in a simple way, either by observation or accessing data, after which the system is given access to the data, looking for patterns before it can make better decisions. The baseline here is that the computer gets to learn automatically without the need for human input or assistance. The system is able to make adjustments based on the lessons learned. The power of Machine Learning is in the available data. The fact that we live in a generation with large amounts of data means ML benefits the most.

Research in Artificial Intelligence and Machine Learning takes place concurrently. It aims at giving computers knowledge via data, interactions and observations of the real world. With this knowledge, the computer is empowered to adjust to new settings (Rouse, n.d.).

Due to   major progress made in the field, researchers have introduced us to numerous types of Machine Learning algorithms. Most of these take four major learning methods, namely: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

  • Supervised Learning

Algorithms of this kind have the capability to apply what had been learned in the past to new sets of data with the aid of labeled examples to predict future occurrences. Supervised learning algorithms begin from a known training dataset, just the same way humans normally approach technical problems. The algorithm proceeds to produce an inferred function that it uses for predicting future events (Rouse, n.d.). With sufficient training, the system is given the power to provide new targets for new inputs. The learning algorithm also has the capability of making comparisons. It evaluates the output it gave against the expected results in order to determine errors and make any necessary modifications.

  • Unsupervised Learning

These algorithms are implemented in cases where there is no labeling or classification of the training dataset. With unsupervised learning, the focus is on the manner in which systems are able to unlabel data to infer a function that describes a hidden structure. Even though the system may not figure out the right output, it makes exploration of the data and bases on these datasets in drawing inferences for describing hidden structures.

  • Semi-supervised Learning

Semi-supervised learning algorithms could be looked at as the middle-ground between supervised learning algorithms and unsupervised learning algorithms. This is because both labeled and unlabeled data are relied on for training. In most cases large amounts of unlabeled data and a small amount of labeled data are taken into consideration. In so doing, the accuracy of learning is improved to a greater extent. Systems are fitted with semi-supervised learning in situations where labeled data needs some skilled and relevant resources for it to be used in training.

  • Reinforcement Learning

A learning method in which there is continuous interaction with the environment to produce actions, get rewards and discover errors. Delayed reward and trial and error search are the most used features of reinforcement learning. With this method, software and machines are empowered to automatically determine the most appropriate behavior in a given context.

Experts have been able to use Machine Learning to develop systems which can analyze large amounts of data. This is a progression which is essential in modern society where connected systems yield massive datasets.

What is Deep Learning

The possibilities of Machine Learning have been an interesting topic in recent years given the fact that it can be applied to homes, offices, warehouses, and factories. In as much as the technology continues to evolve at such a rapid rate, the emergence of Deep Learning is even more exciting even as it has its own setbacks.

Also referred to as hierarchical learning, or structured learning, Deep Learning is a sub-field of Machine Learning whose focus is on learning data representations as opposed to the regular algorithms that perform specific tasks (Wikipedia, n.d.).

Deep Learning in practice borrows the features of Machine Learning whereby machines are made to do things without the input of a human being. With this technology, artificial neural networks (ANNs) are trained on the basis of large amounts of data. The structure of the ANNs is inspired by the manner in which the human brain works and is structured. Just the same way we normally learn from experience, the Deep Learning algorithm carries out a specific duty repeatedly. With every repetition, the working of the algorithm is adjusted in order to be more effective at the task.

The term Deep Learning is used due to the fact that there are different layers in neural networks which make it possible for the learning to take place. The Deep Learning algorithm can be relied on in solving just about any problem which needs reflection.

Machines powered by Deep Learning have the ability to deal with complex problems even when the dataset used is very diverse, inter-connected, and unstructured. As the algorithm learns, it further improves its performance.

The recognition capability of Deep Learning is far much improved than before. With such improvement, consumer electronics have evolved to a stage where they can now meet expectations in a manner that wasn’t possible. Furthermore, the technology is vital for applications that are safety-intensive such as self-driven cars. Deep Learning has made leaps and has even been able to work much better than humans at tasks like object classification in images.

Deep Learning is more useful today despite its theory having been first introduced in the 1980s. This is because of two major reasons:

  • Large amounts of labeled data are required for Deep Learning. We live in a generation where our yield of data is staggering. Given that Deep Learning needs an enormous amount of data to learn from, the ton of data we have today is empowering for the technology. For instance, to successfully develop driverless cars, it is essential to have thousands of hours of video and millions of images which direct the car. Such images must be able to include the diversity of humanity in terms of race and color, such that the vehicle doesn’t include human biases by recognizing one race and leaving out another.
  • Computing power is also a key factor for Deep Learning. Unlike in the past when machines operated basically just like simple calculators, modern day computers are greatly empowered to do astonishing things. Parallel architecture which has become a characteristic of high-performance GPUs is essential for Deep Learning. When this is combined with cloud computing which receives vast amounts of data, you can only expect Deep Learning to progress at passionate speeds.

There are countless applications for Deep Learning including in the manufacturing sector, medical research, automated driving, aerospace, and defense. As the technology advances, more applications are bound to emerge.

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