AI, or more accurately advanced analytics, has commonly be referred to as one of the hottest jobs to have. And with the craze around startups which often time uses AI and the crash of crypto-currencies one must wonder is there an AI bubble. Anecdotally many professionals in adjacent industries (and sometimes not so adjacent industries but people who happen to have access to massive amounts of data) are incorporating more and more advanced analytics into their portfolio to widen their job search potential and command a higher wage. Many startups use ‘AI’ as a selling point as well to offer features previously though unattainable. These startups are said to have almost as much funding as the wellness service industry is projected to have.
Many ‘AI’ startups focus on advanced analytics proliferation , like how AWS focuses computer storage and processing, for robotics or applying ‘AI’ to industry specific problems. Since most AI features are based on neural nets (which require the data be already correctly labeled before it learns how to correctly label the data) that means these companies have to spend a lot on hiring more humans to label the data before then having their analysts train the model on the data, that might not even be tailored to the client in particulars business processes and customers. Also since its based on the human brain the technique has a ceiling on how much it can advance, after all our brains aren’t getting much more advanced anytime soon. With these caps on growth potential you have to wonder how can these companies protect themselves from ‘analytics superstores’ and horizontal mergers.
These limitations have a few errors that will cause a reduction in usage and effectiveness and a consolidation in providers. With so many companies specializing in making ‘AI’ easier to use and implement then its obvious that it’s a matter of time before a select few companies corner the market and have streamlined the offering to its most efficient and profitable state (like ford with the invention of cars, Apple and google with iphone and android for cell phones). Making it easier for their potential clients to simply have their software engineers import the latest AWS package for their needs. AWS and Google are already at it with their cloud computing having also specialized in deep learning, and so is Microsoft with it’s Azure platform.
Another issue is that many times these features are not as adaptable to changes in the business terrain since it was trained on old data. Even if it has re-enforcement learning capabilities it still requires that business have to label the new data anyway which will make the AI program less relevant (because humans will still do the work with ‘AI’) and make it more apparent it can’t address any technological problems one might have. As some problem areas, say new types of cars or fashion, will be undesirable to have humans label the data regularly without it feeling like an additional burden that could be fixed with AWS mechanical turk (a system that has humans label data en masse) and thus re-enforce the issue mentioned earlier.
Finally privacy and data print awareness is coming no matter how many decision makers in tech want to stick their heads in the sand. The metadata and aggregate information that is the primary revenue source for some companies will have to be reworked as their clients (often times other companies) will want to make sure their data isn’t being used to inform their competitors, and as governments that are realizing metadata can reveal personal data. This means the ‘AI’ startup will either have to be more selective in who it has as clients or put in additional work to de-tangle the personal information in their models for clients they are competing over with Google, Microsoft or AWS (which will also reduce the total value their end product can give to the client) .
These factors are why the ‘AI’ market has a cap (in addition to lack of training, diversity and innovation). ‘AI’ or advanced analytics, will continue to be the hammer of the 21st century in technological innovation and is important for us as a community to advance and grow it, but you do that through better math education, interdisciplinary collaboration and access to technology (not more data/spying and generic packages). Now we just have to hope that all of the investments into this industry has been as carefully weighted and analyzed as they do with everyday loan applications, rather than simply judging by charisma and buzzwords.