The first three of his revolutionary cycles are well established, the fourth is now arriving. Cycles one through three introduced calculation and data storage, connection, and shifting place and time.
Above all, Seth’s fourth cycle adds prediction.
“Call it AI if you want to, but to be specific, it’s a combination of analyzing information and then predicting what we would do if we knew what the computer knew.
…we’re giving those computers the ability to make predictions based on what thousands of people before us have done.
…If you’re a mediocre lawyer or doctor, your job is now in serious jeopardy. The combination of all four of these cycles means that the hive computer is going to do your job better than you can, soon.
With each cycle, the old cycles continue to increase. Better databases, better arithmetic. Better connectivity, more people submitting more data, less emphasis on where you are and more on what you’re connected to and what you’re doing.
…just as we made a massive leap in just fifteen years, the next leap will take less than ten. Because each cycle supports the next one.”
In an earlier post, I wrote about how neural networks can now quickly learn to do certain tasks better than humans with no external examples, only the rules that govern the task environment.
Seth points out that when we supply computers with the huge, rapidly growing databases of human behavior, the fourth cycle becomes even more capable.
In conclusion, Seth ends with:
“Welcome to the fourth cycle. The hive will see you now.“
Not long ago I wrote about the end of decent paid jobs and the need for basic income. A startling recent advance in machine learning has only heightened my concerns. Last month, Google’s subsidiary, DeepMind, published a paper on AlphaZero, an artificial intelligence (AI) the company designed to play games. The AI started with only game rules. Here’s what happened next:
“At first it made random moves. Then it started learning through self-play. Over the course of nine hours, the chess version of the program played forty-four million games against itself on a massive cluster of specialized Google hardware. After two hours, it began performing better than human players; after four, it was beating the best chess engine in the world.” —James Somers, New Yorker, How the Artificial-Intelligence Program AlphaZero Mastered Its Games
From “knowing” nothing about the game, in four hours the program became the strongest chess player in the world. AlphaZero also taught itself in a few hours to become the world’s best Go and shogi player.
As a schoolboy I played competitive chess for a few years. Although I haven’t played chess seriously since then, I still have a feeling for the game.
I was shocked watching AlphaZero’s tenth game with Stockfish, the strongest open-source chess engine in the world.
I’d describe AlphaZero’s play as completely solid, interspersed with incredible flashes of brilliance. Great human chess players have an uncanny ability to view a position and quickly select a few plausible moves for deeper study out of the many possible legal moves. The best grandmasters occasionally discover a brilliant and unexpected move in a game. AlphaZero found several during this game.
Having seen this game, I’d describe AlphaZero as the most creative, brilliant, and strongest chess player the world has ever seen.
From a novice to best in the world in four hours, is a level of performance that no human can match.
Now think about what would happen if this kind of performance could be achieved in human work environments such as:
medical scan diagnosis;
legal document creation;
engineering design; and
stock market trading.
These are only harder problems than playing a game because:
the problem space is larger; and
the data needed for learning can’t be self-generated by the AI itself and must be supplied by humans.
But these are not insuperable obstacles. If overcome, many high paid jobs for medical practitioners, lawyers, accountants, and financial analysts would disappear.
Are we moving towards a world where the only available work is in low paid “human service” areas where people are still cheaper than machines? Perhaps.
Until the arrival of robots capable of doing just about everything humans do. What work for humans remains then?
Tim describes using artificial intelligence matchmaking at events as a win for both exhibitors and attendees.
Let’s assume, for the moment, that the technology actually works. If so, I think suppliers will reap most of the touted benefits, quite possibly at the expense of attendees. Here’s why.
Successful matchmaking needs digital data about attendees. An AI platform cannot work without this information. Where will the data come from? Tim explains that his service builds a profile for each attendee. Sources include “LinkedIn, Google, and Facebook”, while also “scouring the web for additional information”.
Using social media platform information, even if attendee approval is requested first, creates a slippery slope, as privacy issues in meeting apps remain largely undiscussed and little considered by attendees during the rush of registration. The end result is that the AI matchmaking platform gains a rich reservoir of data about attendees that, without strong verifiable safeguards, may be sold to third parties or even given to suppliers.
In addition, let’s assume that exhibitors get great information about whom to target. The result: “high-value” attendees will be bombarded with even more meeting requests while attendees who don’t fit the platform’s predictions will be neglected.
In my opinion, the best and most likely to succeed third-party services for meetings are those that provide win-win outcomes for everyone concerned. Unfortunately, it’s common (and often self-serving) to overlook a core question about meeting objectives —whom is your event for? — and end up with a “solution” that benefits one set of stakeholders over another.
How well will artificial intelligence matchmaking at events work for attendees?
Artificial intelligence is hot these days, so it’s inevitable that event companies talk about incorporating it into their products, if only because it’s a sure-fire way to get attention from the meetings industry. I know something about AI because in the ’80s I was a professor of computer science, and the theory of artificial neural networks — the heart of modern machine learning — was thirty years old. AI had to wait, however, for the introduction of vastly more potent technology to allow practical implementation on today’s computers.
While the combination of powerful computing and well-established AI research is demonstrating incredible progress in areas such as real-time natural language processing and translation, I don’t see why sucking social media and registration data into a database and using AI to look for correlations is going to provide attendee matchmaking that is superior to what can be achieved using participant-driven and participation-rich meeting process combined with attendees’ real-time event experience. (Once again, exhibitors may see a benefit from customized target attendee lists, but I’m looking for win-win here.)
From the attendee point of view
When attendees enter a meeting room there’s a wealth of information available to help make relevant connections. Friends introduce me to people I haven’t yet met. Eavesdropping on conversations opens up more possibilities. Body language and social groupings also provide important potential matchmaking information. An AI matchmaking database includes none of these resources. All of them have led me (and just about everyone who’s ever attended meetings) to professional connections that matter.
“…it was one of the most innovative and eye-opening professional experiences I’ve had. Aside from coming back with lots of new tips and ideas, I easily established triple the number of new contacts, and formed stronger relationships with them, than at any other conference I’ve been to.”