Find your way to be trained or even get certified in TMAP.
Start typing keywords to search the site. Press enter to submit.
Artificial intelligence (AI) is a sub-field of computer science aimed at the development of computers capable of performing tasks that are normally done by people, in particular tasks associated with people acting intelligently. AI is a system, built through coding, business rules, and (nowadays mostly) self-learning capabilities, that is able to supplement human cognition and activities and interacts with humans naturally, but also understands the environment, solves human problems, and performs human tasks.The term artificial intelligence was coined in 1956 at a conference at Dartmouth College. The mid-1950s ushered an era of optimism. Many of that era’s leading scientific minds attended the Dartmouth conference and contributed to the early advancement of the technology. Despite the early optimism, achieving artificially intelligent systems proved to be a challenge. Waves of enthusiasm were followed by troughs of disillusionment throughout the 1950s, 60s, 70s, and 80s.
The first test activity for AI is described by Alan Turing and has become known as the Turing test: “Can a computer communicate well enough with a human to convince the human that the computer, too, is human.”The LLMs of today in most situations do easily pass this Turing test. Experience shows that in more and more situations people are unable to distinguish between GenAI generated results and human created texts and artifacts. Moreover, in a significant number of situations the results of GenAI are perceived as more human than comparable human creations. Showing the continually increasing ability of artificial intelligence to mimic human behavior and capabilities.
All AI we use nowadays is categorized as artificial narrow intelligence (or ANI). This AI is focused on one task. It tries to execute this as well as possible. Examples are autonomous driving cars, natural language processing, or facial recognition done by a chatbot. The biggest breakthrough for ANI is neural networks. Neural networks mimic biological processes. The paths that are laid in the brains of animals serve as the basis for this technology. With neural networks, it is possible to build much more complex systems for our AI solutions.
The biggest advantage is the learning capability by feeding information (the most commonly used example is learning to recognize a specific object by feeding it large numbers of pictures and telling it when the object is in the picture). With reinforcement learning, it is possible to add a reward function. ANI is then evolving to a much smarter system and growing towards an Artificial General Intelligence (AGI) solution.The GenAI systems of today still qualify as ANI, although they perform more than simply one task. They can perform complex tasks and even series of tasks.
The GenAI Agents that are now evolving can be orchestrated to perform many different related tasks and work in the same way a team of humans would.
Artificial general intelligence (or AGI) is an intelligence that can execute all the tasks that a human could execute. The most important aspect of AGI is that it can execute different complex tasks in a sequence. The coffee test, as explained by Steve Wozniak, should not be a problem for AGI: “A machine is required to enter an average home and figure out how to make coffee:find the coffee machine, find the coffee, add water, find a mug, and brew the coffee by pushing the proper buttons.” When full AGI will arrive and what it will look like remains unclear at this point. To truly fulfill Wozniaks coffee test you would need a combination of artificial intelligence and some kind of embodiment, such as a humanoid robot.With the rapid development of both AI (especially GenAI) and robotics the moment that AGI can be achieved comes near.
How Artificial General Intelligence (AGI) and Artificial NarrowIntelligence (ANI) relate: AGI aims for human-level, general purpose intelligence, while ANI refers to today’s task-specific AI systems.
Artificial Super Intelligence (ASI) is about artificial intelligence that is smarter than any human. We wonder how this will be measured exactly. Some forms of ANI today are already much smarter than most (if not all) humans. On the other hand, people are very creative and unpredictable. For the foreseeable future, AI will not be able to surpass people in this area. However, we don’t dare to predict how long (or rather how short) it will take before AI indeed surpasses us in general. The subject of ASI raises many existential and philosophical questions.To learn more about this please refer to the books and papers of our colleagues of the Sogeti Research Institute for the Analysis of New Technology, e.g. their book Real Fake [Doorn 2021].At the moment ASI is purely hypothetical.
Machine Intelligence (MI) is a unifying term that encompasses what are commonly referred to as Machine Learning (ML) and Artificial Intelligence (AI). We found that calling it “AI” often led to debates about whether certain technologies qualified as “true AI”, while “ML” seemed too narrow, overlooking more advanced techniques like deep learning. By using “Machine Intelligence”, we aim to capture the full spectrum of intelligent systems, from traditional ML to cutting-edge AI approaches.We use “intelligent machines” as an overarching term for all kinds of artificial intelligence, including those integrated with physical embodiments such as robots.
Machine learning is one of the ways to achieve artificial intelligence. It combines different algorithms each with its own strengths and weaknesses. The last major breakthroughs in the field of AI are based on machine learning or more specifically on “deep learning”, which uses an artificial neural network.Other popular algorithms are: Bayesian networks, Decision Tree, K-Means Clustering and Support vector machines. Each has its own strengths and weaknesses. These algorithms are often grouped into three categories:
The above are important terms, but we will not elaborate on them further in this book; instead, we kindly refer readers to other specialized sources for more in-depth information, e.g.www.ibm.com/think/topics/machine-learning.
AmpQE overview
