It will come as absolutely no surprise that we at ENO8 are bullish on artificial intelligence (obviously). Integrating next-generation innovations into custom digital solutions is literally our bread and butter. So you’d be forgiven if you may be a little skeptical when we sing the praises of A.I. month in and month out as a present and future boon for enterprises (we don’t really say all that, but our general stance on A.I. is that it is transformational as well as integral to businesses-of-the-future’s digital operations, and we write to that effect pretty consistently). That said, we’re not unabashedly rosy about A.I. either — using one A.I. flourish does not make you a native A.I. company, and there are very real ethical and technical issues on the immediate and eventual horizons both. The other thing we want to warn against today?
Thinking something is A.I. when in point of actual fact, it isn’t. Let me explain.
As A.I. has come to dominate the technical consciousness of innovators and middle-stage adopters alike, the terminology has gotten a little loose around what ‘true’ A.I. really is. And because companies and individuals alike want to seem like they’re on the cutting edge, the term has gotten thrown around more than it might warrant. Which is why the fine folks at MIT’s Technology Review put together a fun but generally accurate, back-of-the-napkin flow chart for “is it AI or not”, embedded here:
It starts with general senses, like can the item/function “see”, “hear”, “read”, “move” and/or “reason.” From there, it proceeds through the varying stages of complexity for each of those ‘tasks’ to ascertain whether the object of investigation really is using AI, or simply a facsimile thereof.
According to the flowchart’s creator, her rubric for building the rough drawing starts “[i]n the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can.”
Because A.I. is constantly evolving and improving, things that were once considered A.I. might not meet our modern definition of A.I. And in a lot of ways, artificial intelligence, such as it were, is aspirational by nature. We’re trying to build something that can take in huge amounts of data and stimuli, generate a rational, predictive conclusion in real time and then act intelligently on that data. And what we think meets that criteria today can fall short of future understandings of A.I. So yeah, it’s always a bit aspirational until we reach the singularity, and that aspirationality (not a real word, but it’s more fun that way) makes defining the term correctly a bit sticky.
Because of that changing nature of A.I., according to Karen Hao at MIT TR, “its definition is constantly evolving. What would have been considered AI in the past may not be considered AI today. Because of this, the boundaries of AI can get really confusing, and the term often gets mangled to include any kind of algorithm or computer program.”
As A.I. becomes a larger and larger part of digital operations for firms across the world, correctly defining it has a huge impact on working relationships between technologists and the companies they work for/with. It’s all well and good to sell a client or boss on your tech savvy and futurist bona fides by insisting the new feature you’ve included is “A.I.” But really, it could just be another algorithm dressed up to seem more fancy than it actually is.
By promoting simple but instructive flow charts like this, we hope to help un-muddy the A.I. waters so you can tell for real who’s integrating A.I. in a meaningful way, and who’s just pretending at it.
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