AI Limitations in 2024: Data hungry, opaque, brittle systems
Though we preach that AI investments can transform businesses, we are also not naive in our beliefs in AI’s current capabilities. Most modern AI systems suffer from common issues highlighted by respectable publications that we will collect here:
Reliance on large volumes of data
Impacts deep learning algorithms. Sadly, even when data is available, it’s likely to suffer from bias.
Research on one shot learning is an attempt to solve this problem.
Reliance on labeled data
Limits supervised learning algorithms to relatively few problems where labeled data is either available or where the solution is so valuable that companies invest in preparing semi-manually labeled data.
As a response, unsupervised learning algorithms are being improved.
Limited ability to adapt
Small changes to the problem can stop a perfectly working system and require it to be tweaked by experts to get back to functioning again.
Transfer learning is an area of active research to counter this issue. Recently AlphaZero was able to master chess, shogi and Go in a relatively short time.
Lack of transparency
Impacts deep learning algorithms. Since deep neural networks are complex mathematical structures, their logic can not be easily summarized to humans.
Local interpretable model agnostic explanations (LIME) and attention techniques are being developed to address the lack of transparency. This McKinsey article offers some good visualizations on how these techniques work.
Lack of methods for integrating prior knowledge
Though some AI methods solely rely on encoded prior knowledge, some like deep learning have no way of taking in summarized information from experts. This is a significant limitation while building systems that work with domains where current science can explain most phenomena accurately.
Almost all of these issues were highlighted by these sources:
- Gary Marcus, a professor of cognitive psychology at NYU and briefly director of Uber’s AI lab.
And If you have a business problem that is not addressed here:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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