Everybody wants in on AI — but here’s why most companies will fail

Anybody who has anything to do with the modern world has heard of artificial intelligence as the game changer. Conspiracy theorists aside, the global think tank believes that AI is the biggest, grandest, and most revolutionary ‘thing’ to have happened to the way humans leverage technology to solve problems and live better. Alas! Teams, startups, SMBs, and enterprises that actually take the leap and attempt to develop tools and solutions using artificial intelligence soon realize that all’s not fun and games. AI is tough. And for every successful AI project, there are dozens that are struggling to even define the “intelligent” aspect of their products and solutions. While some of these challenges result out from lack of understanding of AI, intense completion in almost all industries, and lack of project vision, there are others linked to more intrinsic factors. We’re going to cover the latter in this guide. Here are the key AI problems holding the technology back.

There’s not enough computing power

Did you know — AI, as a concept, has existed since the 1950s? Why, then, did it see the light of the day only now. The answer: Everything was mere theory and concept because there just didn’t used to be enough computing power available to even begin to realize AI. The truth is, AI requires an unimaginably large number of processing tasks to be carried out near instantaneously, to create the perception of “thinking algorithms.”

Data volumes continue to grow, and the pace of growth is accelerated. The current level of success achieved in the AI is a result of cloud computing and parallel processing, majorly. The next level, however, requires more data analysis, and more complex algorithms. Naturally, the computing demands are massive.

The future of AI hinges on the speed of progress in machine learning and deep learning, two approaches that have shown the most potential and promise. This calls out for even more computing power. Next-gen computing infrastructure — quantum computing, namely — can answer the questions floating around this lack of computing power required to support lofty AI goals.

There’s not enough clean data

artificial intelligence project

If there’s one rule that machine learning experts swear by, it’s that the algorithm is only as good as the data you feed into it.

Sadly, the potential of many AI applications is never achieved, because there’s not enough good data to create good outcomes. For many organizations, data governance is a new and novel idea, and they’re only starting to find their feet in this playground. Where did data come from, when did it come, who modified it, which other data streams does it link to, how to validate it — these are tough questions, but critical for success in AI projects.

A great algorithm will be brought down to its knees if it’s not fueled by great data. Even a basic algorithm could add massive value if it gets great training data to learn from and to act upon.

There’s not enough skilled workforce

There’s a clear demand-supply gap in the AI jobs market. Because of the boom that this arena of tech has witnessed, almost every industry on the face of the earth has opened its gates to AI experts. However, because AI is a multidisciplinary field (with overlaps across mathematics, data science, statistics, and programming), it’s difficult to find experienced and qualified people.

Among the solutions that promise to remedy the problem is the idea of citizen scientists. These are the employees (or enthusiasts) who don’t have formal education or training in artificial intelligence technologies but can leverage their experience and interest to showcase practical expertise at particular use cases.

Industry-academia partnerships are also a key trend here. Leading organizations understand that their AI leaders of tomorrow are currently enrolled in machine learning, data science, and AI courses in leading universities. Hence, there’s a clear focus on building conduits for the talent to land up straight on their work-floors from the university gates!

There’s a lot of skepticism

AI algorithms

We’re not referring to the destructive ideas of AI overtaking the Earth (and similar theories). There’s still a lot of healthy and attention-worthy debate about deciding the extents to which we would want to let AI make decisions for us.

Already, banks are letting their AI algorithms make decisions such as whether or not to grant a loan to a small business. The impact on human life, hence, is massive. Because end users have no way to understand how AI made a particular decision for them, they are bound to be skeptical, anxious, and even angry.

For companies, this means that they need to be circumspect as they elevate AI in manners that touch human lives in much more important ways than today. Compare an AI tool that suggests songs based on user browsing behavior (low risk) to a tool that suggests career options, stock market purchases, etc. (high risk). For companies to make fortunes, the implication of AI has to surge, and so do the risks.

There are severe regulatory risks

Extending this thought we discussed above, let’s also consider the role of governments. As AI begins to pervade all aspects of human life, it’s certain that governments will want a practical level of control. Legislation, until now, has been badly out of tune with tech innovation. In the times to come, regulators have a large ground to cover, and address questions such as does a human have the right to know how a decision was made for him by invisible AI?

Consider GDPR, and consider how unsettling it is for SMBs and startups to invest millions in ambitious AI goals, with the knowledge that one day, they could be expected to explain the algorithm, because GDPR grants end-users the right to information that impacts them. The waters, hence, are treacherous, and hard to navigate even if companies accumulate the technical firepower necessary for AI success.

AI problems: Not unsurmountable

This guide does not intend to spread skepticism around AI. This is an attempt to outline the factors that curtail the rampant growth of AI. Constructive and thoughtful solutions to these will pave the way for betterment in human lives via AI.

Featured image: Pixabay

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