Artificial intelligence (AI) has driven many information and communication technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has expanded far beyond AI since the Turing test proposal. Critically, recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. In some cases, even works from mathematics, statistics, and engineering would be affected. Worryingly, AI misdefinitions are observed from Western societies to the Global South. In this paper, we propose a framework to score how \textit{validated as appropriately-defined for regulation} (VADER) an AI definition is. Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works. Regarding the latter, our score is based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We demonstrate our contribution by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil. Importantly, none of the proposals assessed achieve the appropriateness score, ranging from a revision need to a concrete risk to ICT systems and works from other fields.

The European Parliament agreed to close a critical contentious point of the AI Act by adopting the definition used by the Organisation for Economic Cooperation and Development (OECD). Most other definitions have also been agreed upon, with new measures like a right to explanation also on the table of EU lawmakers. Last Friday (3 March), representatives of the European Parliament's political groups working on the AI Act reached a political agreement on one of the most politically sensitive parts of the file, the very definition of Artificial Intelligence, according to two European Parliament officials. The AI Act is a flagship legislative proposal to regulate this emerging technology based on its capacity to cause harm. What is defined as Artificial Intelligence will be highly consequential as it will also define the scope of the EU's AI rulebook.

We will consider all policies of the agent and will prove that one of them is the best performing policy. While that policy is not computable, computable policies do exist in its proximity. We will define AI as a computable policy which is sufficiently proximal to the best performing policy. Before we can define the agent's best performing policy, we need a language for description of the world. We will also use this language to develop a program which satisfies the AI definition. The program will first understand the world by describing it in the selected language. The program will then use the description in order to predict the future and select the best possible move. While this program is extremely inefficient and practically unusable, it can be improved by refining both the language for description of the world and the algorithm used to predict the future. This can yield a program which is both efficient and consistent with the AI definition.

In 2017, spurred by advocacy from civil society groups, the New York City Council created a task force to address the city's growing use of artificial intelligence. But the task force quickly ran aground attempting to come to a consensus on the scope of "automated decision systems." In one hearing, a city agency argued that the task force's definition was so expansive that it might include simple calculations such as formulas in spreadsheets. By the end of its eighteen-month term, the task force's ambitions had narrowed from addressing how the city uses automated decision systems to simply defining the types of systems that should be subject to oversight. As policymakers around the world have attempted to create guidance and regulation for AI's use in settings ranging from school admissions and home loan approvals to military weapon targeting systems, they all face the same problem: AI is really challenging to define.

As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular. In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states.

SciTech Europa delves into the world of AI, defining what it means, giving examples of the real-life applications, and discussing the ethical questions it prompts. The computer scientist John McCarthy coined the term Artificial Intelligence in 1956, and defines the field of artificial intelligence as "the science and engineering of making intelligent machines." As well as the term for the scientific discipline, artificial intelligence refers to the intelligence of a machine, program, or system, in contrast to that of human intelligence. Alessandro Annoni, the head of the European Commission's Joint Research Centre, spoke at the Science Meets Parliaments conference at the European Parliament, Brussels in February 2019. He said: "Artificial intelligence should not be considered a simple technology…it is a collection of technologies. It is a new paradigm that is aiming to give more power to the machine. It's a technology that will replace humans in some cases."

Yet AI isn't here to replace humans. We're still a far way from Artificial General Intelligence, i.e. computational systems that replicate the workings of the human brain to do any intellectual activity humans can do, becoming actualized. There are however fields of AI already impacting businesses. With the immense amount of content being created and shared online, managing, organizing, moderating and understanding that content has become pivotal for almost every industry. The field of computer vision has therefore become vital as it allows computers to "see," which in turn lets companies process enormous amounts of visual content with far less human effort and time required.

We will use a test to determine what AI is. The test will produce a certain score, and we say that this is the program's IQ. Then we decide that all computer programs the IQ of which is above a certain level satisfy the AI definition. In order to explain this concept, let us make an analogy with the admission tests given to candidates who wish to become university students. The problems given at the test are selected randomly, but all candidate students receive the same problems. Withal, solving the problems should require logical thinking, because we aim to enroll students who think logically rather than the lucky ones that may hit the right answers haphazardly. The score is based on the number of problems solved by each candidate student. We cannot say how many problems should be solved, because we do not know how many candidates will show up at the test, nor do we know how well or unwell they will perform. We may say set a certain score (e.g.

AI is the compelling topic of tech conversations du jour, yet within these conversations confusion often reigns – confusion caused by loose use of AI terminology. The problem is that AI comes in a variety of forms, each one with its own distinct range of capabilities and techniques, and at its own stage of development. Some forms of AI that we frequently hear about, such as Artificial General Intelligence, the kind of AI that might someday automate all work and that we might lose control of – may never come to pass. Others are doing useful work and are driving growth in the high performance sector of the technology industry. These definitions aren't meant to be the final word on AI terminology, the industry is growing and changing so fast that terms will change and new ones will be added.

AI is the compelling topic of tech conversations du jour, yet within these conversations confusion often reigns – confusion caused by loose use of AI terminology. The problem is that AI comes in a variety of forms, each one with its own distinct range of capabilities and techniques, and at its own stage of development. Some forms of AI that we frequently hear about, such as Artificial General Intelligence, the kind of AI that might someday automate all work and that we might lose control of – may never come to pass. Others are doing useful work and are driving growth in the high performance sector of the technology industry. These definitions aren't meant to be the final word on AI terminology, the industry is growing and changing so fast that terms will change and new ones will be added.