A visitor looks at an AI (artificial intelligence) sign on an animated screen at the Mobile World Congress (MWC), the telecoms industry’s largest annual gathering, in Barcelona.
Josep Lago | AFP | Getty Images
ChatGPT, a viral chatbot that generates conversational responses to written input from users, has made artificial intelligence (AI) the latest buzzword in technology.
AI took center stage at Google’s annual developer conference on May 10, when the company announced it would incorporate AI into its search engine to synthesize search results for users. The company also plans to integrate AI into Gmail to help users write emails.
Following its $13 billion investment in ChatGPT developer OpenAI, Microsoft announced that its Bing search engine will use AI to “deliver better searches, more complete answers and new chat experiences.” bottom. The company has also introduced a new set of AI capabilities called “Copilot” to popular Microsoft 365 apps such as Word and Excel.
And many companies are already integrating AI into their products. In fact, according to Deloitte’s latest State of AI in the Enterprise survey, 94% of business leaders agree that AI will be critical to their enterprise’s success over the next five years.
On the investment side, Goldman Sachs is optimistic about the future of AI, believing the technology will drive productivity gains and could boost S&P 500 earnings by more than 30% over the next decade. , Goldman Sachs senior strategist Ben Snyder told CNBC in May.
But despite the hype, if you’re interested in investing in AI or anything AI-related, it’s important to understand what you’re putting your money into before you part with your cash. Here are four AI terms you should know.
Machine learning may sound new, but the term was actually coined in 1959 by AI pioneer Arthur Samuel. Samuel defined it as the ability of a computer to learn without being explicitly programmed.
To do so, a mathematical model or algorithm is fed large datasets and trained to identify patterns within each set. In theory, algorithms can apply the same pattern recognition process to new datasets.
For example, Spotify uses machine learning to analyze the music you listen to, recommend similar artists, and generate playlists.
According to Nvidia, Large Language Models (LLMs) are algorithms that learn how to recognize, summarize, and generate text and other types of content after processing huge datasets.
These models are trained using unsupervised learning. In other words, the algorithm is given a dataset, but not programmed as to what to do with it. Through this process, LLMs learn how to determine the relationship between words and the concepts behind them.
A large language model is a form of generative AI. According to Google’s AI blog, generative AI, as the name suggests, refers to artificial intelligence that can generate content such as text, video, and audio.
To accomplish this, Nvidia says, generative AI models use machine learning to process massive datasets and respond to user input with new content.
