{"id":634141,"date":"2023-04-22T09:56:01","date_gmt":"2023-04-22T14:56:01","guid":{"rendered":"https:\/\/news.sellorbuyhomefast.com\/index.php\/2023\/04\/22\/why-everyone-is-talking-about-generative-ai-not-just-the-experts\/"},"modified":"2023-04-22T09:56:01","modified_gmt":"2023-04-22T14:56:01","slug":"why-everyone-is-talking-about-generative-ai-not-just-the-experts","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2023\/04\/22\/why-everyone-is-talking-about-generative-ai-not-just-the-experts\/","title":{"rendered":"Why everyone is talking about generative AI, not just the experts"},"content":{"rendered":"
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Improvements over the last decade in machines\u2019 ability to generate images and text have been staggering. As is often the case with innovation, progress is not linear, but comes in leaps and bounds, which surprises and delights researchers and users alike. 2022 was a banner year for innovation in generative AI<\/a>, built on the advent of diffusion methods for image generation and of increasingly large-scale transformers for text generation.<\/p>\n

And while it provided a major leap forward for the entire natural language processing (NLP)<\/a> industry, there are three reasons why generative AI models were the first to stir the public\u2019s excitement, and why they\u2019ll still be the main points of entry into what language AI can do for the time being.<\/p>\n

What\u2019s behind the generative AI excitement?<\/h2>\n

The most obvious reason is that they fall into a very intuitive class of AI<\/a> systems. These models aren\u2019t used to create a high dimensional vector or some uninterpretable code, but rather natural-looking images, or fluent and coherent text \u2014 something that anyone can see and understand. People outside of machine learning do not need specific expertise to judge how natural or fluent the system is, which makes this part of AI research seem much more approachable than other (perhaps equally important) areas.<\/p>\n

Second, there is a direct connection between generation and how we evaluate intelligence: When examining students in school, we value the ability to generate<\/em> answers over the ability to discriminate<\/em> answers by selecting the right answer. We believe that having students explain things in their own words helps show a better grasp of the topic \u2014\u00a0ruling out the chance that they\u2019ve simply guessed the right answer or memorized it.\u00a0<\/p>\n

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So when artificial systems produce natural images or coherent prose, we feel compelled<\/em> to compare that to similar knowledge or understanding in humans, although whether this is overly generous to the actual abilities of artificial systems is an open question in the research community. What is clear from a technical perspective is that the ability of models to produce novel but plausible images and text shows that rich internal representations of the underlying domain (e.g., the task at hand, the sort of things the images or text are \u201cabout\u201d) are contained in these models.<\/p>\n

Furthermore, these representations are useful across a wider range of domains than just generation for generation\u2019s sake. In short, while generative models were the first models to grasp the public\u2019s attention, there will be many more valuable use cases to come.<\/p>\n

One thing from another<\/h2>\n

Third, the latest generative models show an ability to conditionally generate. Instead of sampling existing images or snippets of text, they have the ability to create text, video, images or other modalities which are conditioned <\/em>on something else \u2014 like partial text or imagery.<\/p>\n

To see why this is important, one needs to look no further than most human activities, which involve generating something depending on something else. To give some examples:\u00a0<\/p>\n