Deliberately leaking data to AI

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Many companies are reluctant to adopt artificial intelligence because they fear that AI engines will expose their proprietary data to other companies, including competitors. At the same time, some companies want to deliberately feed their data into AI engines as part of brand building. Is this a billion-dollar opportunity or a fatal flaw in the evolution of AI?

This controversial idea arose during a panel discussion hosted by NextAccess, a consulting firm that advises clients on how to best use AI to improve their strategies for taking products to market and generating revenue. how to do

Let me start from the beginning. Simply put, an AI engine has two components. The first is a vast database of content, called a large language model (LLM), that contains all the information an AI company can find. This includes all Wikipedia, New York Times, and other publicly available content. (There is serious and growing controversy over copyright infringement, but that’s a topic for another time.)

The second component of the AI ​​engine is an algorithm that uses LLM data to generate answers to questions. If I ask an AI engine to complete the sentence, “The dog ran up the…”, the algorithm checks LLM to see how many times this fragment already exists and which words usually complete the sentence. . It then gives the user the statistically most likely next word. In this example, “plough” is a common response, while “casserole” is not.

A company looking to leverage AI can start by asking questions. For example, a clothing company might ask, “What’s the latest trend in men’s shoes?” However, just by asking this question, the AI ​​engine knows that the clothing company is considering a new product in the category, which is information that the company will want to hide from its competitors.

A very effective use of AI would be for a company to upload some of its own data – customer response or sales history – and then ask the AI ​​engine to look for patterns and compare them to any other information in its LLM. However, many AI engines incorporate uploaded corporate data into their LLM so that someone from another company can respond with an exact question that reflects that data. While most AI companies have policies and other safeguards in place to prevent this data leakage, in several recent studies, 60-75% of companies outlaw the use of AI because they are concerned that these safeguards are insufficient. are (There are many other reasons why companies are hesitant, but data privacy is consistently at the top.)

Despite these corporate restrictions, I suspect that every company in the world has at least one employee who has used an AI engine – perhaps on a personal computer with no corporate affiliation – to solve a business problem. of the.

At the NextAccess panel discussion, one participant runs a consulting company. In direct opposition to most other companies, he is actively involved in entering his company’s data into LLMs, especially if it can somehow be associated with his company’s brand name. If someone queries an AI engine where her company’s data improves the answer, she wants the queryer to see her company as a source of wisdom, hopefully leading to new client engagements. Can proceed.

Putting company wisdom and brand in front of information seekers is not a new concept. Search engine optimization (SEO) is the process of making a company’s website more available to search engines such as Google so that the company’s web link appears in more Google queries. This practice has spawned an entire industry of consulting and technology companies that can help brands design their websites for maximum visibility to Google’s scanning tools. Companies can pay Google to have their web link appear at the top of the page for relevant queries. Importantly, these “sponsored” results are clearly marked so that the Internet traveler knows which Google answers are based on organic content and which are based on corporate payments.

Google has trained us all to know that its search engine results don’t necessarily provide the right — or even the best — answer to a question. Clicking multiple links to scour source sites has become a normal, expected routine for web searchers.

Users of AI engines currently have a different expectation. They assume that the AI ​​engine is providing the best answer. Even known AI flaws like bias and hallucinations are becoming less common in newer, more powerful AI engines. User confidence in the accuracy of AI is increasing.

Will the pull of additional revenue convince AI companies to reveal some of their algorithmic secrets to create an AI engine optimization (AEO) industry, so that companies can reorganize their data in ways that are specifically designed for AI companies? Be easy for LLMs and increase the likelihood of citing company data and brand in AI answers to consumer questions? Will AI engines offer paid placement (ideally with sponsored content indicators) to brands that want to appear in AI reactions?

And how will AI users react? Would they appreciate more relevant, specific answers? Or will they question the objectivity and impartiality of the AI ​​company? These open questions demonstrate that AI is both unlike earlier technological tools and, therefore, still unsettled in the path(s) it will take. Keep watching.

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