Please refer to your selected AI system’s description:
- AlphaGo Zero: AlphaGo Zero plays the board game Go better than professional human players. The board game’s environment is virtual and player positions are constrained by the rules of the game. AlphaGo Zero uses both human-based inputs, including the rules of Go, and machine-based inputs, primarily data learned through repeated play against itself.
- Recommendation engine: assists consumers shopping online by generating personalised suggestions based on user’s browsing history and data. For instance, Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real time. This type of filtering matches each of the user’s purchased and rated items to similar items, then combines those similar items into tailored recommendations.
- Automated voice assistant: uses natural language processing (NLP) to match user text or voice input to executable commands. Many continually learn using artificial intelligence techniques including machine learning. Some of these assistants like Google Assistant(which contains Google Lens) and Samsung Bixby also have the added ability to do image processing to recognise objects in the image to help the users get better results from the clicked images
- SCORE: SCORE makes credit score recommendations to help gauge a loan applicant’s credit-worthiness. It does so by using human-based inputs (e.g. a set of rules) and data inputs (e.g. loan payments histories) to assess whether applicants are repaying loans on a regular basis.
- C-CORE: C-CORE scans satellite imagery over ocean areas to locate marine environmental structures (e.g., icebergs). The system processes satellite imagery to identify structures or objects. The system determines object type, position, and size of identified structures and automatically enters that information into a marine safety database.
- CASTER: CASTER reviews inputted molecular information of drugs for medical research purposes. The system is trained to recognize possible drug interactions and then models organic chemical reactions to predict drug-to-drug interactions, including potential harmful interactions.
- Face Image Quality: FIQ is a tool for determining the quality of a digital face image. The system reviews a digital face image and produces a face image quality score. The score is used by developers of facial recognition technologies to help determine the reliability of a face image collected through facial recognition technology.