Decision Making Support Systems #3: Differences between IA and AI
In previous posts, we looked at the definition of Artificial Intelligence (AI) and the definition of Intelligence Augmentation (IA). So, what are the differences between the two? Intelligence Augmentation has always been concerned with aiding human decision making and keeping humans-in-the-loop, whereas the AI endeavor seeks to “build machines that emulate and then exceed the full range of human cognition, with an eye on replacing them.
Rob Bromage puts it well in “Artificial intelligence vs intelligence augmentation”: “AI is an autonomous system that can be taught to imitate and replace human cognitive functions. To put it simply, the machine completely replaces human intervention and interaction.IA, on the other hand, plays more of an assistive role by leveraging AI toenhancehuman intelligence, rather than replace it.”
In “Augmented Intelligence, not Artificial Intelligence, is the Future” Aaron Masih writes that “While the underlying technologies powering AI and IAare the same, the goals and applications are fundamentally different: AI aims to create systems that run without humans, whereas IA aims to create systems that makehumansbetter.”
IA systems are able to exceed system boundaries (the parts of an environment not covered by an AI model’s data inputs) because of the “unsung heroes of the AI revolution” — humans! AI systems can only work with the datasets that they’re trained on and that feed them. Subject matter expert humans excel in applying context and intuition to problems. Humans can grasp and explaincausality. Alex Bates recalls from his experience running a an AI/prescriptive maintenance firm that“…what was remarkable about the human process engineers and maintenance engineers at these plants …were all the clues they incorporated into their assessments of equipment failure, allowing them to identify what was failing and what to repair. Where they struggled, and where the AI helped, was in making sense of all the massive amount of sensor data coming off theequipment.”
Another major difference?To state the obvious, one receives much more attention than the other. Even the casual industry observer of the industry thataugmenting human intelligencerepresents a tiny fraction of the total research and development devoted to Artificial Intelligence.The lack of investment in the area seems like a missed opportunity.
So the AI approach seeks to minimize or replace the role of humans, while an IA solution seeks to amplify the abilities and performance of the humans that participate in a given activity. The IA approach benefits from the human ability to think outside system boundaries. But despite the lack of investment and press attention on Intelligence Augmentation, businesses and other organizations continue to benefit from IA applications…
I asked Filip Stachura, CEO of Appsilon Data Science, who are specialists in Data Science and Machine Learning,How much of what you do as a company qualifies as Intelligence Augmentation?
FS:Most of what we do qualifies as IA.Decision support systemscan be more realistic in many business cases. Even a recommendation engine application is a sales-support decision system, not the AI that sells itself.
FS: For one firm we did the following: Prices for products that our client sells change frequently and depend heavily on negotiations. When a client requests a product, the salesperson opens the application that we built and fills in the client and product name. They then see the history of deals made with the client andsuggestedprices/discounts for the product. The prices vary depending on the segment of the business, category/size of the client and the size of the order. Ultimately, the human salespeople make the pricing decision, but the application provides the most up to date information to optimize decision-making.
