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AI significantly affects people’s lives, and it is encountered in more and more of our daily activities. These include activities such as using smart devices, digital voice assistants, and travel aids, as well as banking online or receiving personalised recommendations about movies to rent. Meanwhile, AI is increasingly reliant on leveraging the widespread availability of (often very large) datasets that encode relationships between data inputs to decision making processes, and the outcome of these processes, so that AI applications can enhance human decision making in a wide variety of everyday tasks (e.g. recommender systems) and specialised tasks (e.g., deciding on legal litigation risks, decisions in business/logistics planning, or decisions based on interpretation of visual data such as diagnostic scans in medicine), as well as AI applications that autonomously make and implement decisions (e.g., robots that need to sense their environment and plan actions). These applications require expertise in a broad range of Artificial Intelligence areas. The MSci in Artificial Intelligence will allow you to understand the underlying principles of these areas, including: - Foundations of AI (e.g. programming, discrete mathematics, algorithm design) - Data Science and Machine Learning (e.g. natural language processing, human-AI interaction) - Knowledge Representation, Reasoning, and Interactions (e.g. data visualisation, knowledge engineering, formal verification) - Optimisation, Planning and Autonomous Agents (e.g. autonomous robot programming, logic, network optimisation) - Ethics and philosophy of AI (e.g., legal, social, ethical and professional issues in AI and in robotics system development, philosophical work on the impact and on dangers of AI) - AI Engineering (e.g. internet and web systems, cloud-based services, AI security and privacy) It will also provide you with the background knowledge and skills required to become a successful AI professional able to work in a range of exciting roles ranging from big data engineer to robotics engineer. Women in Science As part of the faculty's work on diversity & inclusion, we are actively working to support women in science, technology, engineering and mathematics in order to address the current imbalance of women working and studying in these areas. Read more about the Women in Science Initiative here: https://www.kcl.ac.uk/nmes/women-in-science.
There is a widening demand and supply gap for AI skills, with a significant increase in online AI and Data Science vacancies. Many firms that have vacancies for AI roles had at least one vacancy that was difficult to fill. An AI professional needs to exploit a broad range of skills and is likely to conduct their work according to a range of both well-established and cutting-edge roles.
Examples of such roles are data scientist, including big data engineer, legal data scientist, and business data scientist, cloud AI engineer, logistic engineer, negotiation engineer, knowledge/graph engineer, robotics engineer, information retrieval and natural language processing applied scientist, as well as algorithmic impact and responsibility scientist.
In such roles, an AI professional may manage and analyse data of different types using a range of technologies, or extract knowledge of different forms from the data. In addition, they may develop advanced control and coordination mechanisms for robots, or understand the ethical impacts of AI, mitigating risks and ethical harms arising from the deployment of AI.
Recent graduates have found employment within the following job roles and companies:
There is a widening demand and supply gap for AI skills, with a significant increase in online AI and Data Science vacancies. Many firms that have vacancies for AI roles had at least one vacancy that was difficult to fill. An AI professional needs to exploit a broad range of skills and is likely to conduct their work according to a range of both well-established and cutting-edge roles.
Examples of such roles are data scientist, including big data engineer, legal data scientist, and business data scientist, cloud AI engineer, logistic engineer, negotiation engineer, knowledge/graph engineer, robotics engineer, information retrieval and natural language processing applied scientist, as well as algorithmic impact and responsibility scientist.
In such roles, an AI professional may manage and analyse data of different types using a range of technologies, or extract knowledge of different forms from the data. In addition, they may develop advanced control and coordination mechanisms for robots, or understand the ethical impacts of AI, mitigating risks and ethical harms arising from the deployment of AI.
Recent graduates have found employment within the following job roles and companies:
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Find out the difference between our Computer Science BSc and Artificial Intelligence BSc undergraduate degrees at King's.
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