3 ms·
My opinion is most companies that have built in-house "AI" or machine learning teams, or hired consultants to custom build AI "use cases" for them have made the
by version_five 4y ago
My opinion is most companies that have built in-house "AI" or machine learning teams, or hired consultants to custom build AI "use cases" for them have made the wrong decision and are coming around to it. This is an area where buying a product is going to win, because most companies don't have the capability of successfully managing in-house ML teams (I've seen line departments trying to hire a PhD ML scientist) and because a company with a product has done the work to figure out a proper value proposition, as opposed to just trying to ham fist AI somewhere into operations.
- cdolan 4y agoHow can you buy a product in the AI/ML space when the data needed to generate the model (and the value) is proprietary? You may be correct that buying is better than building in a lot of these areas, but I don’t think its practical given IP concerns
- version_five 4y agoGood question. I think the "data" aspect is largely a misconception. Any proprietary data might have some value in model fine tuning that would come with product setup, but almost no businesses actually have the big diverse datasets that support a nontrivial AI model. Look at the hyped up current AI language and image generation models. They're not based on proprietary data. Nor were the past generation. So I'd argue that while proprietary data has a role in what is effectively the setup of a product, it's not the defining factor. And if anyone thinks that ML is going to come in and do something amazing on the strength of their own data, they're probably setting themselves up for disappointment. In fact, I think it's that misconception that has led companies to (imo foolishly) invest so much in in-house AI