Shiplap and Artificial Intelligence: Why AI is Important for Everyone, Not Just Those in Silicon Valley
Blog: The Tibco Blog
I want to discuss the urgent need to advance AI for all American business, not just Silicon Valley insiders. But first I have a confession to make. Like millions of others, I’m a fan of the HGTV show “Fixer Upper.” Don’t judge! It’s a nice way to unwind after work. Now entering its fifth and final season, fans of the show know that most episodes follow a simple format: Agreeable hosts Chip and Joanna Gaines take potential homebuyers to three ugly houses around Waco, Texas, and explain how the wrecks could be renovated into modern dream homes within the parameters of the buyer’s budget. Tours of dilapidated farmhouses and garish bachelor pads ensue. A fixer-upper is selected, redesign details are conveyed, crummy cabinetry and inconvenient walls are demolished, setbacks and obstacles are dispatched, and the episode closes with a tour of the now-stunning homestead transformed into a showpiece by JoJo’s creative vision and Chip’s physical labor. Catharsis achieved.
While this home improvement pageant may seem far removed from the technology space, I propose that the show’s formula mirrors our digital evolution. And the present burgeoning of AI capabilities and solutions form a key component in that journey from awful to awesome.
For example, the ugly homes selected for renovation on “Fixer Upper,” with their Congoleum floors and Formica countertops, were once paragons of modern convenience. Times change, as do lifestyles, and what worked architecturally in the Reagan era seems absurdly awkward now. Put in technology terms, there is very little similarity between the way you currently use your smartphone and the way your parents used their wall-mounted landline phone back in the analog day. And just try to imagine using all your now-essential mobile apps on the once vaunted BlackBerry 7230.
Conversely, old houses sometimes contain desirable and time-tested features worth saving or repurposing: apron sinks, oak floors, shiplap. The same is true in technology. Witness the recent surge in artificial intelligence development: Anyone of a certain age will recall that the kinds of artificial neural networks currently powering Siri and Alexa were also hot way back in the 1980s. They’re important again today because we’ve lately been unburdened by the kind of resource constraints that prevented AI fruition in the 20th century.
Back then, the AI dream was stymied by expensive and restricted access to compute capacity coupled with a paucity of data. That blight was largely solved by the rise of the cloud, which allowed vast numbers of researchers and innovators almost unimaginable storage for data and power for computation from pretty much anywhere.
For example, Libratus, the poker AI that recently beat some of the world’s best Texas Hold ’Em players, was built with more than 15 million core hours of computation. While that project was powered by the Pittsburgh Supercomputing Center, something like AWS gives anyone the ability to cheaply spin up the equivalent of 100 high-power machines for about 150 bucks. Or consider the pace and volume of trades on Wall Street today: such commonplace and widespread algorithmic trading would have been unimaginable even with state-of-the-art technology just a few short years ago.
An exponential leap in resource availability, coupled with open source and APIs, also enabled the mobile revolution, which now lets us interact on the go with our little hand-held extensions of more powerful systems on the cloud. Add to all that the emergence of IoT and you start to see that pretty much every “thing” you can think of can now become a point of computation.
Which is all well and good, but has resulted in new challenges to comfortable human habitation—the unbridled proliferation of applications.
Time was, the average working person would deal with a few primary software packages and become expert in those applications. Nowadays, nearly everything you do requires use of a distinct application, while the concept of mastering any one of them grows more elusive by the minute. The way we schedule, organize, travel, pay our bills, create products, purchase or provide goods and services, communicate personally or professionally, consume news or acquire new skills—everything, everywhere is prefaced by some interface to technology that requires mental investment on your part to maneuver. Everything is an app and the cognitive load is crushing us as a people.
In our technology and in our homes, what we need today is different from what we built yesterday. Our old architecture no longer suits our way of life.
Hence, the hype around AI. The investment and development and deployment of new solutions utilizing machine learning and advanced analytics and autonomous everything is driven by the need to simplify and simultaneously expand our technologically dense existence, to reduce that cognitive load, to alleviate the friction between us and our machines. In the context of “Fixer Upper,” we are at the stage where we are standing in a smelly, cramped, derelict kitchen while JoJo maps out the possible vista of bright stainless steel, subway tile, and stupendous stone-topped islands opening on a vast and welcoming living space. I can almost touch the shiplap accent wall…
The point is that we are just starting to visualize what can be done to reimagine our way of life with the expanded resources we have at our disposal, and this is an important phase in making our technology meet our new needs. We can now use natural language processing engines and machine vision and edge intelligence to ease the burden and unlock new potential. This visualization is necessary to spur us toward obtaining that “dream kitchen,” so I won’t criticize all the AI hype. It’s a necessary motivator. We have yet to deal with demolition (legacy systems). We’re still going to face foundation issues (security). We’ll still have to solve our plumbing problems (integration). But the vision of that open, frictionless existence makes all the impending labor worthwhile no matter where you live.
There will undoubtedly be things that don’t work well, which we won’t discover until after we’ve moved into our technological fixer upper and started living in it. But if it’s anything like the vision, it will be a lot better than the ramshackle wreck we’re starting with.
I also believe the Discovery Channel’s “Fast N’ Loud” served up great lessons about enterprise integration, but that’s another story.
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