By Matt Asay, contributing writer for Infoworld.com
By now you’ve used a generative AI (GenAI) tool like ChatGPT to build an application, author a grant proposal, or write all those employee reviews you’d been putting off. If you’ve done any of these things or simply played around with asking a large language model (LLM) questions, you’ve no doubt been impressed by just how well GenAI tools can mimic human output.
By Bryan Abney, contributing writer for Ajmc.com
Artificial intelligence (AI) and machine learning (ML) are easily confused, but it is crucial to recognize the fundamental difference between them. AI refers to prebuilt products that identify patterns based on human behavior around recognition and decision-making, then utilizes those patterns to enable AI-assisted platforms to answer questions, provide relevant information or perform requested tasks. ML, meanwhile, employs mathematical algorithms to predict activities or outcomes based on data. ML is a subset of AI, meaning that while all ML derives from AI, not all AI is based on ML.
By Ellison Anne Williams, contributing writer for Helpnetsecurity.com
In the era of data-driven decision making, businesses are harnessing the power of machine learning (ML) to unlock valuable insights, gain operational efficiencies, and solidify competitive advantage.
By Steve Nouri, contributing writer for Forbes.com
Just as ChatGPT is turning text directions into new written materials, or just as DALL-E 2 produces realistic-looking images from prompts, generative AI is now being used to turn scientists’ directions for molecules with specific characteristics into new drugs for diseases including cancer, Alzheimer’s, arthritis, fibrosis and other rare diseases.
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