- In 2019 we are deep into a giant AI bubble, and little that is published around AI is true.
- We cover the reasons that AI is so overrated.
In this article, we will cover the primary reasons why the AI bubble is out of control and is going to pop.
Problem #1: Lack of Understanding of AI
It is apparent from reading material from many websites and reviewing conference presentations that there is a sizable population that is writing and speaking on AI that does not know how it works, and have never done it themselves. The following is a quote from Forrester on AI.
“Those barriers describe how AI will play out in 2019, when companies will claw their way out of data debt, to some extent because of GDPR and escalating security concerns. Combined with intelligent tools that move data governance to a more ambient and contextual state, most firms will turn the corner on data governance thanks to AI. Firms will also expand RPA and proofs of concept to broaden the process, product, or experience scope and better understand the impact of AI. RPA and AI technology innovations will combine to create business value while serving as a testbed for broader implementations of AI. In addition, a fledgling supply-side market will surface for explainable AI to broker the distance between enthusiasm and complex machine learning.”
This assumes that AI works. How many people are merely making this assumption?
And this leads to a second issue.
Problem #2: The Assumption That AI Always Fixes Problems
A large segment of the population in the IT space has dropped their guard with regards to AI. AI is assumed to be effective, without evidence being requested that it is effective, or that it is worth the effort and expense. And how does everyone seem to know that AI is effective? Well, a large number of people are certainly writing about AI, nearly all of them with a financial bias.
This is not evidence that AI works or that companies are getting a return on their AI investments.
There is a fundamental misunderstanding as to what AI/ML can do. There are just too many people not testing the algorithm before making the claims. This is well explained by the facile coverage of McDonald’s announcement on AI for their menus that we cover in the article How Awful Was the Coverage of the McDonald’s AI Acquisition.
Problem #3: Allocating Any Improvement to AI Without Asking the Question of Whether AI Was the Best Approach to Use
If a minor observation is made, it is assumed that no other method by AI could have concluded. However, AI (which is mostly just ML algorithms that have been around for decades) AI is one of the highest overhead ways to arrive at an insight. These companies are going to quit AI. Its too much work and the results are spotty.
The quotation from IBM illustrates this.
On IBM AI
“Many ambitious artificial intelligence-backed projects never come to fruition due in large part to issues with data collection and cleaning, according to Arvind Krishna, PhD, IBM’s senior vice president of cloud and cognitive software.
During an interview with The Wall Street Journal earlier this month, Dr. Krishna noted that a common reason projects using IBM Watson AI often unravel is that companies are unprepared for the amount of time and money they must spend just collecting and preparing data. Those unglamorous yet crucial tasks, he said, make up approximately 80 percent of an entire project.
often unravel is that companies are unprepared for the amount of time and money they must spend just collecting and preparing data.”
Dr. Krishna goes on…
“You run out of patience along the way, because you spend your first year just collecting and cleansing the data,” he said. “And you say, ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.”
Problem #4: Used As a Justification for Big Data Investments
For years vendors and consulting companies told customers to accumulate vast amounts of data.
However, data itself has no value. One has to be able to derive insights from the data. And there has been a great overselling of the benefits of Big Data. This is made even starker in contrast when it is realized that companies have significant challenges in mastering “Small Data” such as being able to forecast from the sales history. Why did these vendors and consulting firms think the following:
- That all companies would have opportunities to improve predictability from Big Data?
- That all companies would be able to master the accumulation of large amounts of data?
- That ML algorithms would be worth the effort and would outperform “Small Data” forecasting?
- That all companies would be able to master ML algorithms?
Now, if these AI/data science projects don’t work, then what does that say about the expensive Big Data projects that companies invested all that money in? That is right. Much of it was wasted.
The industry is not asking the right questions and analyzing the lack of positive outcomes from AI projects. One showcase example of this is IBM Watson.
- IBM has been lying about Watson AI for over ten years.
- IBM has spent billions on Watson and had not only problems understanding how to train Watson to solve medical research problems, but also failed to harmonize different data sets.
- The curious thing is that IBM continues to sell AI projects. IBM claims to have 20,000 AI projects ongoing. However, these projects have been sold on false promises.
- IBM does not possess any AI capabilities that other entities in the space do not own, and the field of AI is filled with false claims.
Even if a company does employ many people who are familiar with how to run the significant AI/ML algorithms, there is little evidence that these algorithms work. There are further problems with formatting data as it turns out that data lakes are even more challenging to convert into a usable form than previously thought.
All of this occurs in an environment where far more proven methods of forecasting often languish due to a lack of funding, unable to match the promises and “sexiness” level of AI.
A Hypothesis, No AI Company, Wants To be Tested
Even significantly into the AI bubble, there is yet to be much evidence that AI meets the hype. Every time an example of AI failing is found, industry sources that make money on AI tell these observers that the failure is not relevant. For instance, with IBM Watson AI, IBM had over ten years and enormous resources and was not able to make a useful product in the AI space. Yes, the right questions have not been asked as to why IBM failed so severely at Watson, and what it means for AI generally.