We hear more and more about artificial intelligence, as well as machine learning and deep learning, the latter sometimes being misused as a synonym for the former. The term "artificial intelligence" (AI) first appeared in the 1950s and refers to all computers capable of performing tasks related to human intelligence. Machine learning is just one way to achieve artificial intelligence. Deep learning, on the other hand, is one of many methods related to machine learning.
Artificial intelligence (AI) encompasses all operations related to human intelligence and performed by computers. These include planning, language comprehension, object and sound recognition, learning, and problem-solving. The relationship between AI and the Internet of Things (IoT) is truly fascinating because it's analogous to the relationship between the brain and the human body. Through various sensory inputs such as vision and touch, our bodies are able to recognize a situation and perform corresponding actions. Our brain makes decisions based on sensory input and sends signals to the body to control its movements. The IoT is simply a set of connected sensors that, with the help of AI, can understand all the acquired data and, through the heart of the system or circuitry—our CPU—can make decisions and operate actuators to control various movements (robotic arms). Machine learning is essentially the path to achieving AI; a subgroup of AI focused on the ability of machines to receive a set of data and learn for themselves, adjusting algorithms as they gain more information about what they are processing. Often, the terms AI and machine learning (ML) are used interchangeably, especially in the field of big data. The term "machine learning," coined after AI, describes "the ability of a machine to learn without explicit programming." Therefore, machine learning is a method of "educating" an algorithm so that it can learn from a variety of environmental situations. Education, or better yet, training,
Machine learning uses neural network methods and statistical models to automatically build analytical models to find hidden information in data. Neural networks are inspired by the functions of the human brain, which consist of interconnected units (such as neurons) that process information in response to external input, thereby transmitting relevant information between different units. A typical example of machine learning is artificial vision systems, or computational systems, that can recognize objects digitally acquired by image sensors. The algorithms used in these cases must recognize certain objects, distinguish between animals, things, and people, while learning from context.
Deep learning is a method of machine learning that draws inspiration from the structure of the brain or the interconnections of various neurons. Other methods include inductive logic programming, clustering, and Bayesian networks. The latter is based on a DAG (Directed Acyclic Graph) model, which consists of a set of variables and their conditional dependencies. This model can represent the probabilistic relationship between diseases and symptoms: given a symptom as input, the probability of a given disease can be estimated.
Deep learning uses massive neural network models with various processing units; it learns complex models from vast amounts of data using computational advancements and training techniques. Common applications include imaging and speech recognition. Due to its multi-layered nature, the concept of deep learning is sometimes simply referred to as "deep neural networks." In recent years, both machine learning and deep learning have made significant strides in artificial intelligence. Both require massive amounts of data collected by countless sensors, which continue to feed the Internet of Things (IoT) ecosystem, thus enhancing AI. Improving the IoT ecosystem will guide AI, thereby guiding the methods for successful implementation. From an industrial perspective, AI can be used to predict when machines need maintenance or analyze production processes to achieve significant efficiency gains, potentially saving millions of euros. Consumers will have the opportunity to manage their time and conditions in the best possible way.
Advances in electronics continue to drive the symbiosis of artificial intelligence and the Internet of Things. Developments in computer processing and data storage have made it possible to integrate and analyze even more data. Reducing the number of computer chips and improving manufacturing techniques mean cheaper and more powerful sensors. Wireless connectivity provides vast amounts of data at very low cost and allows all these sensors to send data to the cloud. The advent of the cloud has also made this data virtually infinitely storable and possesses considerable computing power. All these advancements bring artificial intelligence closer to its ultimate goal of creating increasingly intelligent machines that permeate our daily lives.
For artificial intelligence and machine learning to continue to advance, the data driving algorithms and related decisions must be high-quality data to be properly interpreted. With the proliferation of IoT devices connecting to the internet and interconnecting, the amount of data collected, stored, and processed is increasing daily, presenting new challenges to big data security and privacy. The potential for destructive attacks by hackers is growing daily. Preventing these risks requires any organization to control the production, storage, and communication of data. Hackers could potentially gain control of critical systems and do whatever they want at any time.
