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Artificial Intelligence

SAS

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  • Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks
  • The term artificial intelligence was coined in 1956
  • Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defence took interest in this type of work and began training computers to mimic basic human reasoning
  • DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names.
  • AI automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerised tasks. And it does so reliably and without fatigue. Of course, humans are still essential to set up the system and ask the right questions
  • AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that algorithms can acquire skills
  • AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data
  • Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research, and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude
  • A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data
  • Computer vision relies on pattern recognition and deep learning to recognise what’s in a picture or video. When machines can process, analyse and understand images, they can capture images or videos in real time and interpret their surroundings
  • Natural language processing (NLP) is the ability of computers to analyse, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks
  • Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power
  • The Internet of Things generates massive amounts of data from connected devices, most of it unanalysed. Automating models with AI will allow us to use more of it
  • APIs, or application programming interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages.