General purpose AI: powerful but not useful

Currently, no industry is growing and changing as rapidly as the AI industry. In the AI industry, a new revolutionary idea for some kind of business emerges every few weeks. Some of its innovations, like smart home technology, are creating big profits, new jobs, and global benefits, but others, like autonomous driving cars, are either bogging down or achieving much-needed success. There are some things you can't do.

It's hard to predict which new forms and applications of AI will be successful, but anyone who has watched the industry long enough can make some educated guesses. That's why many AI experts are eyeing the development of "general-purpose AI solutions" like Google Vision.

General-purpose AI systems, which are typically backed by vast amounts of technological power (which is why Google is a prime example), seem particularly promising. However, by its very nature, general-purpose AI is not "in-depth". In many cases, they are trained using a wide range of publicly available data and cannot perform very specific analyses. For example, Google Vision analyzes almost any image and says that users can select text, objects, and content that they want to exclude during search and display. However, it may be useful for users who work with millions or billions of images, but want to accurately detect the colors in images of ceiling fans, or find images of a particular brand of sneakers. For companies like this, there is no solution.

Simply put, general-purpose AI is not trained on industry-specific datasets, so it cannot provide relevant information for specific industries. These use cases are large, but very narrow in scope. Most companies don't need image detection AI that just highlights parts of the text. Companies need imaging software that can detect specific patterns, analyze technical specifications, and list brand names. General-purpose AI is powerful, but limited in application.

Why do we need industry-specific vertical AI?

The easiest way to make general purpose AI work for industry-specific (and even business-specific) use cases is to employ a “transfer learning” process. Transfer learning means training a general-purpose AI on its own dataset instead of relying on a public general-purpose dataset. This allows for one generic AI to tell a paint company to identify a specific paint name in an image, a grocery company to identify a specific beer brand in an image, and so on. A women's clothing company can be trained to identify specific fabric patterns in images.

General-purpose AI could be an industry-specific vertical solution for companies who feel the need, providing the extra-human accuracy and specificity that companies seek when investing in artificial intelligence. I can. The idea of applying transfer learning to various types of general-purpose AI leads to better things. It is a huge AI marketplace of industry-specific vertical solutions that can be directly applied by consumers. This allows data science teams and companies without huge training budgets to tap into artificial intelligence normally available only to large companies. Just as we have a vast library of smartphone apps, we can create a vast library of AI solutions.

This approach requires companies to train a general-purpose AI, but this is not too difficult as many companies already have the necessary data at their disposal. In the case of retailers and distributors, this data is often provided in the form of product catalogs. General-purpose AI in the marketplace can handle specific challenges, and can be trained to handle unique challenges.

The problem is the creation of the AI itself. Recruiting data science talent to create AI is a barrier many companies face. For companies that cannot afford to create the AI they need from scratch, who will offer general-purpose AI solutions in the proposed marketplace?

CrowdANALYTIX uses crowdsourcing of data scientists to create AI solutions for our customers. A similar approach can be applied to creating generic AI for large markets. It provides AI created by members of the CrowdANALYTIX data science community, which companies can train and deploy themselves. That way, more companies will have access to the AI they need for digital transformation and growth without spending money on in-house data science teams.

General-purpose AI like Google Vision is attractive, but such general-purpose solutions require more steps to identify problems and make them useful to the enterprise. Crowdsourced AI marketplaces may be the answer to this problem.

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Source:https://www.crowdanalytix.com/vertical-specific-ai/
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