Today I was reading some of the latest research papers on AI and all of a sudden I landed on this article From Francesco Corea who explains the topic very well by drawing a map and showing us how to read it. I have noticed that when it comes to AI, many people confuse it due to the perception that the media has built for us.
I have personally met decision-makers with a completely wrong perception of the topic. I think this article can lighten the way and show us a little more.
The only thing I would add to this is that AI is the concept of simulating human intelligence process by machines or as IBM would describe it "Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind" (What is Artificial Intelligence (AI)?, 2021). Furthermore, depending on what problem is being solved, the type of technology is chosen to form the AI.
(Credit: Francesco Corea, Decision Scientist and Data Strategist.)
AI knowledge map (Corea, 2021)
On the axes, you will find two macro-groups, i.e., the AI Paradigms and the AI Problem Domains. The AI Paradigms (X-axis) are the approaches used by AI researchers to solve specific AI-related problems (it includes up to date approaches). On the other side, the AI Problem Domains (Y-axis) are historically the type of problems AI can solve. In some sense, it also indicates the potential capabilities of an AI technology.
Hence, I have identified the following AI paradigms:
- Logic-based tools: tools that are used for knowledge representation and problem-solving
- Knowledge-based tools: tools based on ontologies and huge databases of notions, information, and rules
- Probabilistic methods: tools that allow agents to act in incomplete information scenarios
- Machine learning: tools that allow computers to learn from data
- Embodied intelligence: engineering toolbox, which assumes that a body (or at least a partial set of functions such as movement, perception, interaction, and visualization) is required for higher intelligence
- Search and optimization: tools that allow intelligent search with many possible solutions.
The vertical axis instead lays down the problems AI has been used for, and the classification here is quite standard:
- Reasoning: the capability to solve problems
- Knowledge: the ability to represent and understand the world
- Planning: the capability of setting and achieving goals
- Communication: the ability to understand language and communicate
- Perception: the ability to transform raw sensorial inputs (e.g., images, sounds, etc.) into usable information.
So how do you read and interpret the map? Let me give you two examples.. If you look at Natural Language Processing, this embeds a class of algorithms that use a combination of a knowledge-based approach, machine learning and probabilistic methods to solve problems in the domain of perception. At the same time though, if you look at the blank space at the intersection between Logic-based paradigm and Reasoning problems, you might wonder why there are not technologies there. What the map is conveying is not that a method does not categorically exist that can fill a space, but rather when people approach a reasoning problem they prefer to use Machine Learning, for instance.
Here is a list of technologies:
- Robotic Process Automation (RPA): technology that extracts the list of rules and actions to perform by watching the user doing a certain task
- Expert Systems: a computer program that has hard-coded rules to emulate the human decision-making process. Fuzzy systems are a specific example of rule-based systems that map variables into a continuum of values between 0 and 1, contrary to traditional digital logic which results in a 0/1 outcome
- Computer Vision (CV): methods to acquire and make sense of digital images (usually divided into activities recognition, images recognition, and machine vision)
- Natural Language Processing (NLP): sub-field that handles natural language data (three main blocks belong to this field, i.e., language understanding, language generation, and machine translation)
- Neural Networks (NNs or ANNs): a class of algorithms loosely modeled after the neuronal structure of the human/animal brain that improves its performance without being explicitly instructed on how to do so. The two majors and well-known sub-classes of NNs are Deep Learning (a neural net with multiple layers) and Generative Adversarial Networks (GANs — two networks that train each other)
- Autonomous Systems: sub-field that lies at the intersection between robotics and intelligent systems (e.g., intelligent perception, dexterous object manipulation, plan-based robot control, etc.)
- Distributed Artificial Intelligence (DAI): a class of technologies that solve problems by distributing them to autonomous “agents” that interact with each other. Multi-agent systems (MAS), Agent-based modeling (ABM), and Swarm Intelligence are three useful specifications of this subset, where collective behaviors emerge from the interaction of decentralized self-organized agents
- Affective Computing: a sub-field that deal with emotions recognition, interpretation, and simulation
- Evolutionary Algorithms (EA): it is a subset of a broader computer science domain called evolutionary computation that uses mechanisms inspired by biology (e.g., mutation, reproduction, etc.) to look for optimal solutions. Genetic algorithms are the most used sub-group of EAs, which are search heuristics that follow the natural selection process to choose the “fittest” candidate solution
- Inductive Logic Programming (ILP): sub-field that uses formal logic to represent a database of facts and formulate hypothesis deriving from those data
- Decision Networks: is a generalization of the most well-known Bayesian networks/inference, which represent a set of variables and their probabilistic relationships through a map (also called directed acyclic graph)
- Probabilistic Programming: a framework that does not force you to hardcode specific variable but rather works with probabilistic models. Bayesian Program Synthesis (BPS) is somehow a form of probabilistic programming, where Bayesian programs write new Bayesian programs (instead of humans do it, as in the broader probabilistic programming approach)
- Ambient Intelligence (AmI): a framework that demands physical devices into digital environments to sense, perceive, and respond with context awareness to an external stimulus (usually triggered by human action).
To summarize: AI is to use computer science to mimic human intelligence. AI is not a single method but depending on what problem you are solving and depending on the whereabouts of AI paradigms, different technologies are used.
Corea, F., 2021. AI Knowledge Map: How To Classify AI Technologies - KDnuggets. [online] KDnuggets. Available at: <https://www.kdnuggets.com/2018/08/ai-knowledge-map-classify-ai-technologies.html
Ibm.com. 2021. What is Artificial Intelligence (AI)?. [online] Available at: <https://www.ibm.com/uk-en/cloud/learn/what-is-artificial-intelligence> [Accessed 4 December 2021].
