“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”
By Larry Page.
Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or definitive conclusions) and self-correction. Particular applications of the AI include expert systems, speech recognition and artificial vision.
The term AI was coined by John McCarthy, an American computer scientist in 1956 during the Dartmouth Conference, where the discipline was born. Today, it is a general term that encompasses everything from automation of robotic processes to current robotics. It has gained prominence recently due, in part, to large volumes of data, or to the increased speed, size and variety of data that companies are collecting. AI can perform tasks such as identifying patterns in data more efficiently than humans, allowing companies to obtain more information about their data.
Types of artificial intelligence
AI can be categorized in any number of ways. The first classifies AI systems as weak AI or strong AI. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple’s Siri, are a weak form of AI.
Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities, so that when presented with an unknown task, it has enough intelligence to find a solution. The Turing test, developed by the mathematician Alan Turing in 1950, is a method used to determine if a computer can really think like a human.
The second example is from Arend Hintze, an assistant professor of integrative biology and engineering and computer science at Michigan State University. It categorizes the AI into four types, from the type of AI systems that exist today to the sensitive systems, which do not yet exist. Its categories are the following:
- Type 1: Reactive machines. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but has no memory and can not use past experiences to inform future ones. Analyze possible movements and choose the most strategic movement. Deep Blue and Google’s AlphaGO were designed for narrow purposes and can not be easily applied to another situation.
- Type 2: Limited memory. These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in autonomous vehicles have been designed in this way. The observations are used to inform the actions that occur in the not so distant future, such as a car that has changed lanes. These observations are not stored permanently.
- Type 3: Theory of the Mind. This is a psychological term. It refers to the understanding that others have their own beliefs, desires and intentions that affect the decisions they make. This type of AI does not exist yet.
- Type 4: Self-knowledge. In this category, AI systems have a sense of themselves, they have consciousness. Self-aware machines understand their current state and can use the information to infer what others are feeling. This type of AI does not exist yet.
Examples of AI technology
- Automation is the process of automatically creating a system or a process function. Robotic process automation (RPA), for example, can be programmed to perform high volume repeatable tasks normally performed by human beings. RPA is different from IT automation in that it can be adapted to changing circumstances.
- Machine learning is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be considered as the automation of predictive analytics. There are three types of machine learning algorithms: supervised learning, in which data sets are labeled so that patterns can be detected and used to label new data sets; unsupervised learning, in which the data sets are not labeled and classified according to similarities or differences; and reinforcement learning, in which the data sets are not labeled, but after performing an action or several actions, the AI system receives feedback.
- The vision of the machine is the science of making computers see. The vision of the machine captures and analyzes the visual information using a camera, the analog-to-digital conversion and the digital signal processing. It is often compared to human sight, but artificial vision is not linked to biology and can be programmed to see through the walls, for example. It is used in a wide range of applications, from the identification of the signature to the analysis of medical images. Computer vision, which focuses on machine image processing, is often combined with artificial vision.
- Natural language processing (NLP) is the processing of human and non-computer language by a computer program. One of the oldest and best-known examples is the detection of spam, which looks at the subject line and the text of an email and decides if it is garbage. Current approaches to NLP are based on machine learning. NLP tasks include text translation, feelings analysis and speech recognition.
- Pattern recognition is a branch of machine learning that focuses on the identification of patterns in the data. The term, today, is outdated.
- Robotics is an engineering field focused on the design and manufacture of robots. Robots are often used to perform tasks that are difficult for humans to perform or it is difficult for them to perform consistently. They are used in assembly lines for the production of cars or by NASA to move large objects in space. More recently, researchers are using machine learning to build robots that can interact in social settings.
- Healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make diagnoses better and faster than humans. One of the best-known healthcare technologies is IBM Watson. He understands natural language and is able to answer the questions that are asked. The system extracts patient data and other available data sources to form a hypothesis, which it then presents with a confidence score scheme. Other AI applications include chatbots, a computer program used online to answer questions and help clients, to help schedule follow-up appointments or help patients through the billing process, as well as virtual health assistants They provide basic medical feedback.
- Business. The automation of robotic processes is being applied to highly repetitive tasks that humans normally perform. The machine learning algorithms are being integrated into the analysis and CRM platforms to discover information on how to better serve customers. Chatbots have been incorporated into websites to offer immediate service to customers. The automation of jobs has also become a point of conversation between academics and IT consultants, such as Gartner and Forrester.
- Education. The AI can automate the rating, giving educators more time. AI can evaluate students and adapt to their needs, helping them to work at their own pace. AI tutors can provide additional support to students, ensuring that they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.
- Finance. The AI applied to personal finance applications, such as Mint or Turbo Tax, is transforming financial institutions. Applications like these could collect personal data and provide financial advice. Other programs, IBM Watson being one, have been applied to the process of buying a house. Today, software performs much of the operations on Wall Street.
- Law. The process of discovery, through the review of documents, in the law is often overwhelming for human beings. Automating this process is a better use of time and a more efficient process. The startups are also building computerized assistants with questions and answers that can sift questions programmed to answer by examining the taxonomy and ontology associated with a database.
- Manufacturing. This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform unique tasks and were separated from human workers, but as technology advances that has changed.