Addressing 4 systematic AI problems
Reality is suddenly dawning for many who believe that the current AI tools in the market are a silver bullet to solve everything from cancer to your toughest business problems. In spite of such benefits, many AI entrepreneur and business have faced significant systematic AI problems. A recent TechCrunch article citing a study from global investment bank Jefferies, points out that IBM Watson is not providing the promised business value to their clients. How could that be?
Is modern AI intelligent enough?
It might be because it, like other similar AI tools in the market today, is not so cognitive after all. Simply speaking, Watson technology is comprised of a bag of answers to a bag of questions for a particular subject, and a Machine Learning (ML) mechanism to best correlate answers to questions. Watson neither understands the meaning of the question or the meaning of the words it uses. And one can only go so far without understanding.
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Modern AI is being incorrectly equated in the industry to ML, which has caused at least four systemic problems:
1. NEED TO HAVE LOTS OF DATA
- With small amounts of data, there is no model
- If our data is biased we train biased models
- Only the companies with access to lots of data can create the best models. It is difficult for startups without access to data to create healthy competition
2. LACK OF TRANSPARENCY
We do not understand why the AI models arrive to decisions and predictions and therefore can’t be trusted
3. IT DOESN’T ALWAYS WORK
- When it doesn’t work we don’t know why or how to fix it
- Difficult to reach high levels of accuracy
- Uncertain time to train new models
4. THE FOCUS IS ON TECHNOLOGY INSTEAD OF ON SOLVING BUSINESS PROBLEMS
The current AI conversation is more about what can be solved in a company with a chatbot, or Robotic Process Automation, or what can be classified or predicted. Instead focus should be on the main pain points of the company that will bring about the most transformational value, using AI and not AI technologies, to arrive to the most simple and elegant solution.
I believe that the solution to the 4 systemic AI problems of modern AI resides in the integration of technologies. For sensor interpretation and integration, this means using Knowledge Representation technologies, including Qualitative Modeling, Ontologies, Web Semantics, and Edge Computing. And for reasoning, using inference tools, including Qualitative Reasoning, and Decision Trees.
The only way to get transparency is by going back to the basics of understanding the problem, the words used, and the operations to automate. “Knowledge is power” can be true thanks to AI. The transformation of data/information into knowledge/wisdom that holistic AI provides will allow real transformation in corporations.
Let’s focus on defining the AI journey of our companies. Solving the main current pain points with the best combination of technology will progressively transform companies to become more efficient. And let’s do it in a responsible way: paying attention to the type of solutions that we develop – only to enhance human capabilities and making sure that we find better professional alternatives for those whose job will be displaced by AI (that finally will be all of us).
Read more: Part II – The solution to the 4 systemic problems of modern AI
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