Everything is Artificial Intelligence
If your only tool is a hammer, then every problem looks like a nail. This overly-user cliché seems very apropos when thinking about the rush to suggest artificial intelligence for practically every system and flaunt machine learning as a silver bullet to solve whatever value the new product or service is purported to provide.
Indeed, AI technology can be very powerful. Recent advances in machine learning, aided by ubiquitous connectivity and cloud computing, demonstrate how potent this technology is and promise much more to come. But AI-based systems can also be difficult to build and even more so to perfect and scale and deploy.
- Artificial intelligence can be very impressive under limited controlled testing. But many underestimate the effort to generalize and validate them for real-world situations.
- Certain types of machine learning algorithms are highly influenced by latent biases in the data used to train and validate them. These systems will repeat and amplify the same biases unbeknown to their users.
- The opaque nature of certain AI algorithms and software vendors that consider their algorithms proprietary and aren’t willing to discloses how they operate result in “unexplainable AI”: systems whose output cannot be reversed-engineers and explained. Errors, inaccuracies and biases are practically impossible to detect in advances and may be discovered only while the system is deployed or never.
AI-based systems are rapidly becoming more powerful but could also be imperceptibly biased, hard to validate and control, and arguably unethical, threaten privacy and outright dangerous. No wonder the public opinion on AI is quite lukewarm. And similar concerns are often raised by professionals and in business circles.
But is AI really a silver bullet? Is it the only, or even the best technology to solve every problem and build every new product?
Or is AI becoming this proverbial hammer that we use indiscriminately just because it’s in vogue?
Are there other approaches that have been around long enough and can accomplish the task at lower cost and risk? For instance, can clustering and regression analysis be used in place of a neural network to formulate the relationships among data variables? Can statistical process control (SPC) and control chart provide the same level of monitoring and improve overall equipment effectiveness (OEE) as promised by machine learning systems? (In fact, some predictive maintenance systems touting machine learning are nothing more than old-fashioned SPC systems with fancy user interfaces…)
Even when AI tools do offer superior classification, prediction and control policy planning, there may be numerous instances in which AI technologies can be used for experimentation and prototyping, but then implemented using established methods that can be validated and scaled more easily and safely.
“When you are fundraising, it’s AI;
When you are recruiting, it’s machine learning;
When you are implementing, it’s linear regression.”
Choose your AI hammers prudently!