So much is being said about the dangers of AI. I share many concerns related to autonomous cars, massive unemployment…
[NOTE: I first posted this in December 2003 and have reposted it every December since, adding slight revisions now and…
Last week, I interviewed an AI startup named AISense whose single product, Otter Voice Notes, a cloud-based platform, seems to me to have as many uses as Word does. It records conversations of almost any length between multiple people with uncanny accuracy and speed.
Start with the billion-dollar transcription industry that serves medical procedures, legal testimony and depositions, then move into all forms of education and training, and there’s the foundation for a big business opportunity. Add to that all your business meetings and Zoom video conversations. Take all the time required to turn talk into text, correct the invariable typos and homonyms, delete “um’s” and so on. Eliminate the time and expense of turning spoken words into accurate text, and I think you will see how such technology can change your life.
It turns out there are already 13 AI Speech startups: almost all have technologies in the market and such competitive markets invariably accelerate innovations while keeping prices low. AISense is the apparent leader right now and seems to be well-positioned to maintain that lead. Whether or not it does, users still win because this level of competition usually leads to rapid innovation and refinement. Otter provides users 600 free minutes a month and the platform allows users much longer recording periods, so that you can automatically record 10 hours of conference presentations if you wish.
For me, there was another revelation: Otter.ai changed the actual dynamics of my meeting with its founders. Because I trusted their tech to take notes, I could enjoy a more immersive and authentic face-to-face interaction with co-founders Sam Liang and Yun Fu and JD Lasica, my friend and fellow author, who was also present.
Otter produced an entirely more comprehensive transcript than I ever could have otherwise accomplished, and the tags produced let me find what I needed afterward with great speed. This is AI Augmentation at its best: the humans get to do humanly interactive things, while Otter tirelessly takes notes.
Liang and Fu explained how Otter’s AI can extract conversation summaries with bulleted key points and action items. It will use social graphs to see the relationships between conversation participants and can detect emotions. My talks with the Otter guys have excited me for other, more personal, reasons. They have to do with Augmenting People: Why AI Should Back Us Up, Not Push Us Out.
There are two long-standing schools for the role of Artificial Intelligence. The first is Autonomy, where AI devices and systems simply replace us. The second is Augmented, where AI serves us as a new toolset: I favor the toolset approach and am writing Augmenting People: Why AI Should Back Us Up, Not Push Us Out to explain why you should be concerned as well.
Ultimately, decisions made today between AI augmentation and autonomy will shape the future of the human race. Over the next 10-15 years, this choice will restructure our relationships with machines, each other, the planet and the universe. Right now some of the smartest technologists in the world, working in some of the best established and most promising tech companies in the world, are inventing ways for AI to do everything from nanobots that will swim through our blood zapping cancer cells to cars that are so autonomous that they are outfitted without steering wheels and with fold-out beds for a good snooze.
You’ve already read about the cars and how great they will be. In the autonomy/augmentation debate, it seems the decision has already been made, robotic cars will prevail. The government, insurance carriers, and even car-makers are just about unanimous that these vehicles are safer and far less polluting. A younger generation seems to prefer self-driving to their parents’ preference for being behind the wheel and in control.
Just about every automaker is moving toward what is called Level 6 Autonomy, which means the driving machines do it all while humans do whatever they please. Volvo is even designing a sleeping car because passengers need to do nothing but get inside the car and tell it a destination.
There are mountains of data arguing the case for automobile autonomy. Millions of miles have been logged by traditional and new automakers with very few mishaps and only two fatal accidents—both blamed on distracted humans who made fatal errors.
Cases like this sound pretty compelling. Data is the protoplasm that makes AI seem, well, intelligent. The stats argue that if we remove humans from the loop, lives will be saved, pollutions will be reduced, existing roadways can be retrofitted to enable more cars to use existing infrastructure at higher speeds and travel time will be reduced.
Johann Gutenberg had a clear winner when he invented Movable Type. His technology replaced monks with quills, enormously expanded the distribution of news and information and motivated everyday people to learn to read. It endured for well over 400 years until Ottmar Mergenthaler invented a faster, better and cheaper way to set an entire line of type, rather than just one letter at a time, as Gutenberg’s press required. Appropriately, he called it the “line-o-type.”
They still had Linotype machines in the 1970s when I got my first job for a newspaper, the Boston Herald-Traveler. But as I started my career, the Linotype machine operators were ending theirs.
This would be a trivial piece of personal information, except that now, I am researching Artificial Intelligence and the Future of Work for a new book and Linotype operators and Kurt Vonnegut keep flashing back to me from inside my analog memory bank. I’ll tell you about Vonnegut, in a few paragraphs.
It’s important to mention that the Linotype Operators had their own union at a time when unions were still strong enough to protect tradespeople. And the unions collaborated, so if they went on strike, as a member of the American News Guild, I would be obliged to go on strike, as would the Teamsters who drove delivery trucks. If the strike got long and nasty, the Teamsters could shut down the entire country.
By the time I arrived at the old Herald, Linotype machines would be destined for trash heaps, museum exhibits and little else. Personal Computers had come along and so did a new way of printing four entire pages of news at once.
Yet, as I started my career, the Herald had an entire floor dedicated to Linotype Operators sitting at or near their machines. I passed them several times daily as I took news copy from the third floor down to the first floor where the presses printed and bundle newspapers that were then loaded onto trucks for distribution and delivery.
As I passed through the Linotype section, I’d see these guys reading books, playing checkers, or cards. While not quite hostile, I found them to be generally unfriendly.
I would eventually learn what had happened and why. A few years earlier, when word processing and cold type eliminated the need for the linotype and its operators, the unions cut a deal that avoided a strike: Every linotype operator could remain employed until retirement age. Some chose to leave but others stayed, and perhaps, would remain for decades.
It was a scenario, it seemed to me where everyone lost. Perhaps the operators who stayed lost the most: while they kept getting paid, it seemed to me they lost their pride and for that their families would suffer perhaps more than if they had lost compensation.
Player Piano
Being the Herald Copy Boy was my night job. By day I was an English Journalism major at Northeastern University, where I too was learning the skills of a job whose market value would diminish as digital innovations advanced. At about this time, I was assigned to read Player Piano, written in the 1950s by Kurt Vonnegut. It is a futuristic novel taking place after some horrific war that reduces world population. To fight it, most working class job holders went off to fight and die in huge numbers. Left behind were managers who kept things running with engineers who automated the jobs formerly performed by working class people. The machines proved far more efficient than the humans had been; so the managers kept managing things that engineers kept refining and no one else was really needed to produce goods and services.
I first read John Markoff‘s Machines of Loving Grace in 2015 as I started to write The Fourth Transformation, a book about AR and AI in which I predicted that today’s smartphones would be replaced by smart glasses. Markoff’s book gave me reason to pause. Markoff’s book convinced me that the most transformative technology of this new century would be AI and not AR. It is by no coincidence, that as I start researching Augmenting People: Why AI Should Back Us Up, Not Push Us Out, that I reread Markoff’s extremely well-researched book again.