{"id":642548,"date":"2023-04-28T10:05:51","date_gmt":"2023-04-28T15:05:51","guid":{"rendered":"https:\/\/news.sellorbuyhomefast.com\/index.php\/2023\/04\/28\/the-future-of-generative-ai-is-niche-not-generalized\/"},"modified":"2023-04-28T10:05:51","modified_gmt":"2023-04-28T15:05:51","slug":"the-future-of-generative-ai-is-niche-not-generalized","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2023\/04\/28\/the-future-of-generative-ai-is-niche-not-generalized\/","title":{"rendered":"The future of generative AI is niche, not generalized"},"content":{"rendered":"
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The relentless hype surrounding generative AI in the past few months has been accompanied by equally loud anguish over the supposed perils \u2014 just look at the open letter calling for a pause in AI experiments. This tumult risks blinding us to more immediate risks \u2014 think sustainability and bias \u2014 and clouds our ability to appreciate the real value of these systems: not as generalist chatbots, but instead as a class of tools that can be applied to niche domains and offer novel ways of finding and exploring highly specific information.<\/p>\n

This shouldn\u2019t come as a surprise. The news that a dozen companies have developed ChatGPT plugins is a clear demonstration of the likely direction of travel. A \u201cgeneralized\u201d chatbot won\u2019t do everything for you, but if you\u2019re, say, Expedia, being able to offer customers a simple way to organize their travel plans is undeniably going to give you an edge in a marketplace where information discovery is so important.<\/p>\n<\/p><\/div>\n

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Whether or not this really amounts to an \u201ciPhone moment\u201d or a serious threat to Google search isn\u2019t obvious at present \u2014 while it will likely push a change in user behaviors and expectations, the first shift will be organizations pushing to bring tools trained on large language models (LLMs) to learn from their own data and services.<\/p>\n

And this, ultimately, is the key \u2014 the significance and value of generative AI today is not really a question of societal or industry-wide transformation. It\u2019s instead a question of how this technology can open up new ways of interacting with large and unwieldy amounts of data and information.<\/p>\n

OpenAI is clearly attuned to this fact and senses a commercial opportunity: although the list of organizations taking part in the ChatGPT plugin initiative is small, OpenAI has opened up a waiting list where companies can sign up to gain access to the plugins. In the months to come, we will no doubt see many new products and interfaces backed by OpenAI\u2019s generative AI systems.<\/p>\n

While it\u2019s easy to fall into the trap of seeing OpenAI as the sole gatekeeper of this technology \u2014 and ChatGPT<\/a> as the<\/em> go-to generative AI tool \u2014 this fortunately is far from the case. You don\u2019t need to sign up on a waiting list or have vast amounts of cash available to hand over to Sam Altman; instead, it\u2019s possible to self-host LLMs<\/a>.<\/p>\n

This is something we\u2019re starting to see at Thoughtworks. In the latest volume of the Technology Radar<\/a> \u2014 our opinionated guide to the techniques, platforms, languages and tools being used across the industry today \u2014 we\u2019ve identified a number of interrelated tools and practices that indicate the future of generative AI is niche and specialized, contrary to what much mainstream conversation would have you believe.<\/p>\n

Unfortunately, we don\u2019t think this is something many business and technology leaders have yet recognized. The industry\u2019s focus has been set on OpenAI, which means the emerging ecosystem of tools beyond it \u2014 exemplified by projects like GPT-J and GPT Neo \u2014 and the more DIY approach they can facilitate have so far been somewhat neglected. This is a shame because these options offer many benefits. For example, a self-hosted LLM sidesteps the very real privacy issues that can come from connecting data with an OpenAI product. In other words, if you want to deploy an LLM to your own enterprise data, you can do precisely that yourself; it doesn\u2019t need to go elsewhere. Given both industry and public concerns with privacy and data management, being cautious rather than being seduced by the marketing efforts of big tech is eminently sensible.<\/p>\n

A related trend we\u2019ve seen is domain-specific language models<\/a>. Although these are also only just beginning to emerge, fine-tuning publicly available, general-purpose LLMs on your own data could form a foundation for developing incredibly useful information retrieval tools. These could be used, for example, on product information, content, or internal documentation. In the months to come, we think you\u2019ll see more examples of these being used to do things like helping customer support staff and enabling content creators to experiment more freely and productively.<\/p>\n

If generative AI does become more domain-specific, the question of what this actually means for humans remains. However, I\u2019d suggest that this view of the medium-term future of AI is a lot less threatening and frightening than many of today\u2019s doom-mongering visions. By better bridging the gap between generative AI and more specific and niche datasets, over time people should build a subtly different relationship with the technology. It will lose its mystique as something that ostensibly knows everything, and it will instead become embedded in our context.<\/p>\n<\/p><\/div>\n

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Indeed, this isn\u2019t that novel. GitHub Copilot<\/a> is a great example of AI being used by software developers in very specific contexts to solve problems. Despite its being billed as \u201cyour AI pair programmer,\u201d we would not call what it does \u201cpairing\u201d \u2014 it\u2019s much better described as a supercharged, context-sensitive Stack Overflow.<\/p>\n

As an example, one of my colleagues uses Copilot not to do work but as a means of support as he explores a new programming language \u2014 it helps him to understand the syntax or structure of a language in a way that makes sense in the context of his existing knowledge and experience.<\/p>\n

We will know that generative AI is succeeding when we stop noticing it and the pronouncements about what it might do die down. In fact, we should be willing to accept that its success might actually look quite prosaic. This shouldn\u2019t matter, of course; once we\u2019ve realized it doesn\u2019t know everything \u2014 and never will \u2014 that will be when it starts to become really<\/em> useful.<\/p>\n

Provided by Thoughtworks<\/a><\/figcaption><\/figure>\n

This content was produced by Thoughtworks. It was not written by MIT Technology Review\u2019s editorial staff.<\/em><\/svg> <\/p>\n<\/div>\n

Read More<\/a>
\n Ken Mugrage<\/p>\n","protected":false},"excerpt":{"rendered":"

The relentless hype surrounding generative AI in the past few months has been accompanied by equally loud anguish over the supposed perils \u2014 just look at the open letter calling for a pause in AI experiments. This tumult risks blinding us to more immediate risks \u2014 think sustainability and bias \u2014 and clouds our ability<\/p>\n","protected":false},"author":1,"featured_media":642549,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[809,28121,46],"tags":[],"class_list":{"0":"post-642548","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-future","8":"category-generative","9":"category-technology"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/642548","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/comments?post=642548"}],"version-history":[{"count":0,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/642548\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media\/642549"}],"wp:attachment":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media?parent=642548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/categories?post=642548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/tags?post=642548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}