Back in April 2021, the European Commission published its proposal for the Artificial Intelligence Regulation ("AI Regulation), which is currently making its way through the European legislative process. This draft AI Regulation seeks to harmonise rules on artificial intelligence by ensuring AI products are sufficiently safe and robust before they enter the EU market. The AI Regulation is intended to apply to what the EU terms "AI systems". The most recent iteration of this concept is defined (in summary) as all systems developed through machine learning approaches and logic, and knowledge-based approaches. This is a wide definition aimed to accommodate future developments in AI technology but extends to much of modern AI software. The broad scope of this definition is narrowed by the operational impact of the draft legislation, as the AI Regulation takes a'risk-based approach' to governing AI systems.

Artificial intelligence (AI) is the study of how to create machines that can think and act like humans. At its core, AI is about creating algorithms and software that can process information, learn from data, and make decisions based on that learning. This means that AI systems can take in information from the world around them, process it, and use it to make decisions or take actions without being explicitly programmed to do so. One way to think about AI is to imagine a robot that can move around, see, hear, and touch things in the world. The robot has sensors that allow it to gather information about its environment, and it has a computer inside that can process that information and make decisions based on what it has learned.

In December 2017, AlphaZero, a chess-playing, artificial intelligence (AI) developed by Google, defeated Stockfish 8, the reigning world champion program at that time. AlphaZero calculates around 80,000 moves per second, according to The Guardian. Yet, out of 100 matches, AlphaZero won 28 and tied 72. Stockfish's open-source algorithm has been continually tweaked by human input over the years. The New Yorker reports that coders suggest an idea to update the algorithm, and the two versions are then pitted against each other for thousands of matches to see which comes out on top. Google claims that AlphaZero's machine learning algorithm had no human input beyond the programming of the basic rules of chess.

Everyone knows the term artificial intelligence, or AI,, and some may understand the impact it has on their lives to varying degrees. But what exactly is AI, and what role is it playing today and into the future? At TE Connectivity, Chris Blackburn, Technologist on the System Architecture Team, helped break down artificial intelligence for host Tyler Kern. "AI is everywhere around us in the world," Blackburn said. "Most of us are familiar with devices in our homes, such as voice assistance like Alexa and Siri. Artificial intelligence is a way for machines to learn and generate data by themselves."

AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at a Research Workshop at Dartmouth College in 1956 and birthed the field of AI. Back in 1956, the dream of AI pioneers such as John McCarthy was to construct complex machines that possessed characteristics of human intelligence. However, general AI machines that replicate human senses, human reasoning, and think as we do are still mostly constrained to Hollywood and science fiction novels. AI today is, however, able to perform specific, comparably narrow tasks as well as, or sometimes better than, we humans can. Examples of narrow AI include applications such as classification of pathology from X-ray imagery, identification of people in Facebook photos via facial recognition, or your spam filters in Gmail.

"(AI) will rank among our greatest technological achievements, and everyone deserves to play a role in shaping it." Encompassing all facets of AI, the General Secretariat of MONTREAL.AI introduces, with authority and insider knowledge: "Artificial Intelligence 101: The First World-Class Overview of AI for the General Public". AI opens up a world of new possibilities. This AI 101 tutorial harnesses the fundamentals of artificial intelligence for the purpose of providing participants with powerful AI tools to learn, deploy and scale AI. Theoretical Physics in 1 (one) year, followed by a Master's degree in Government Policy Analysis (1998) and a Master's degree in Aerospace Engineering (Space Technology) (2000).

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"(AI) will rank among our greatest technological achievements, and everyone deserves to play a role in shaping it." Encompassing all facets of AI, the General Secretariat of MONTREAL.AI introduces, with authority and insider knowledge: "Artificial Intelligence 101: The First World-Class Overview of AI for the General Public". AI opens up a world of new possibilities. This AI 101 tutorial harnesses the fundamentals of artificial intelligence for the purpose of providing participants with powerful AI tools to learn, deploy and scale AI. Theoretical Physics in 1 (one) year, followed by a Master's degree in Government Policy Analysis (1998) and a Master's degree in Aerospace Engineering (Space Technology) (2000).

Deep learning is a machine learning methodology where a system discovers the patterns in data by automatically learning a hierarchical layer of features and then draw conclusions from them. At first, the computer program is provided with training data, for example images that have been labeled with meta tags. The algorithm uses this information to build a progressively more accurate predictive capability. Just like you look for shapes, colors and pictures to solve puzzles – deep learning can gather insights across several inputs to draw a larger conclusion and make better decisions.

The traditional view of outsourcing has tended to see cost reduction as one of the primary drivers for any customer. The idea that the'total cost of ownership' of a particular business function over the term of the outsourcing contract should be lower is very often part of the business case. Similarly, seeing outsourcing as a means of transforming a collection of assets on the balance sheet into a recurring service charge, and reducing (or at least apparently reducing) capital costs is another common refrain at the outset of deals. Whilst technology transformation / business change deals do happen where the aim is for a service upgrade at an increased overall cost, they are by no means as common as cost-led deals. To date, a large portion of the cost savings delivered through outsourcing deals – especially those that involve any sort of offshoring, nearshoring or even (in the London-centric UK at least) 'northshoring' – come from labour arbitrage: the central idea being that the activity undertaken by the outsourcing customer's relatively more expensive current local employees can be performed at a lower cost, but to the materially the same or even a better overall standard, by the outsourcing vendor's resources.