...there’s no such thing as Flickr Pro, because today, with cameras as pervasive as they are, there is no such thing really as professional photographers, when there’s everything is professional photographers. Certainly there is varying levels of skills, but we didn’t want to have a Flickr Pro anymore, we wanted everyone to have professional quality photos, space, and sharing.
More and more ordinary people are thrust into a winner-take-all economy. It is a 21st century reprise of the Horatio Alger stories from the 19th century. A token few will find success on Kickstarter or YouTube, while overall wealth is ever more concentrated and social mobility rots. Social media sharers can make all the noise they want, but they forfeit the real wealth and clout needed to be politically powerful. Real wealth and clout instead concentrate ever more on the shrinking island occupied by elites who run the most powerful computers.
Once the data is gathered, statistical analysis is performed to create behavioral models. The consumer-facing giant computers like social media, search, or big online stores use models of people to optimize the options put in front of them to generate desired behaviors. The term “advertising” once meant an act of communication, the romanticizing of a product, but no more. Similarly, investing used to mean evaluating risk and reward, but now it has come to mean getting people locked into massive too-big-to-fail schemes in which only the little people absorb the risks and the best computer gathers the rewards.
Whether the activity of a giant computer is called “media,” “finance,” or something else, the end goal is to come up with schemes that transcend the usual connection between risk and reward. The operation of the best computers takes place at arm’s length, so that the owners don’t need to really understand what’s going on, and can take as little responsibility as possible. The financier, the social media site owner, or anyone else with a top computer is rarely held responsible for what goes on through that computer. All risk falls on those who are aggregated.
While we now know that Turing was too optimistic on the timeline, AI's inexorable progress over the past 50 years suggests that Herbert Simon was right when he wrote in 1956 "machines will be capable ... of doing any work a man can do." I do not expect this to happen in the very near future, but I do believe that by 2045 machines will be able to do if not any work that humans can do, then a very significant fraction of the work that humans can do. Bill Joy's question deserves therefore not to be ignored: Does the future need us? By this I mean to ask, if machines are capable of doing almost any work humans can do, what will humans do? I have been getting various answers to this question, but I find none satisfying.
A typical answer to my raising this question is to tell me that I am a Luddite. (Luddism is defined as distrust or fear of the inevitable changes brought about by new technology.) This is an ad hominem attack that does not deserve a serious answer.
We are facing the prospect of being completely out-competed by our own creations. A more thoughtful answer is that technology has been destroying jobs since the start of the Industrial Revolution, yet new jobs are continually created. The AI Revolution, however, is different than the Industrial Revolution. In the 19th century machines competed with human brawn. Now machines are competing with human brain. Robots combine brain and brawn. We are facing the prospect of being completely out-competed by our own creations. Another typical answer is that if machines will do all of our work, then we will be free to pursue leisure activities. The economist John Maynard Keynes addressed this issue already in 1930, when he wrote, "The increase of technical efficiency has been taking place faster than we can deal with the problem of labour absorption." Keynes imagined 2030 as a time in which most people worked only 15 hours a week, and would occupy themselves mostly with leisure activities.
I do not find this to be a promising future. First, if machines can do almost all of our work, then it is not clear that even 15 weekly hours of work will be required. Second, I do not find the prospect of leisure-filled life appealing. I believe that work is essential to human well-being. Third, our economic system would have to undergo a radical restructuring to enable billions of people to live lives of leisure. Unemployment rate in the US is currently under 9 percent and is considered to be a huge problem.
Finally, people tell me that my concerns apply only to a future that is so far away that we need not worry about it. I find this answer to be unacceptable. 2045 is merely a generation away from us. We cannot shirk responsibility from concerns for the welfare of the next generation.
Here’s a current example of the challenge we face... At the height of its power, the photography company Kodak employed more than 140,000 people and was worth $28 billion. They even invented the first digital camera. But today Kodak is bankrupt, and the new face of digital photography has become Instagram. When Instagram was sold to Facebook for a billion dollars in 2012, it employed only 13 people. Where did all those jobs disappear? And what happened to the wealth that all those middle-class jobs created?
The … truck driver is processing a constant stream of [visual, aural, and tactile] information from his environment. … To program this behavior we could begin with a video camera and other sensors to capture the sensory input. But executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behavior. …
Articulating [human] knowledge and embedding it in software for all but highly structured situations are at present enormously difficult tasks. … Computers cannot easily substitute for humans in [jobs like truck driving].
We start with a single clue, we analyze the clue, and then we go through a candidate generation phase, which actually runs several different primary searches, which each produce on the order of 50 search results. Then, each search result can produce several candidate answers, and so by the time we’ve generated all of our candidate answers, we might have three to five hundred candidate answers for the clue.
Now, all of these candidate answers can be processed independently and in parallel, so now they fan out to answer-scoring analytics [that] score the answers. Then, we run additional searches for the answers to gather more evidence, and then run deep analytics on each piece of evidence, so each candidate answer might go and generate 20 pieces of evidence to support that answer.
Now, all of this evidence can be analyzed independently and in parallel, so that fans out again. Now you have evidence being deeply analyzed … and then all of these analytics produce scores that ultimately get merged together, using a machine-learning framework to weight the scores and produce a final ranked order for the candidate answers, as well as a final confidence in them. Then, that’s what comes out in the end.
What comes out in the end is so fast and accurate that even the best human Jeopardy! players simply can’t keep up. In February of 2011, Watson played in a televised tournament against the two most accomplished human contestants in the show’s history. After two rounds of the game shown over three days, the computer finished with more than three times as much money as its closest flesh-and-blood competitor. One of these competitors, Ken Jennings, acknowledged that digital technologies had taken over the game of Jeopardy! Underneath his written response to the tournament’s last question, he added, “I for one welcome our new computer overlords.”
In January, for example, Blackstone Discovery of Palo Alto, Calif., helped analyze 1.5 million documents for less than $100,000. …
“From a legal staffing viewpoint, it means that a lot of people who used to be allocated to conduct document review are no longer able to be billed out,” said Bill Herr, who as a lawyer at a major chemical company used to muster auditoriums of lawyers to read documents for weeks on end. “People get bored, people get headaches. Computers don’t.”
The computers seem to be good at their new jobs. … Herr … used e-discovery software to reanalyze work his company’s lawyers did in the 1980s and ’90s. His human colleagues had been only 60 percent accurate, he found. “Think about how much money had been spent to be slightly better than a coin toss,” he said.
And for all their power and speed, today’s digital machines have shown little creative ability. They can’t compose very good songs, write great novels, or generate good ideas for new businesses. Apparent exceptions here only prove the rule. A prankster used an online generator of abstracts for computer science papers to create a submission that was accepted for a technical conference (in fact, the organizers invited the “author” to chair a panel), but the abstract was simply a series of somewhat-related technical terms strung together with a few standard verbal connectors.
Similarly, software that automatically generates summaries of baseball games works well, but this is because much sports writing is highly formulaic and thus amenable to pattern matching and simpler communication. Here’s a sample from a program called StatsMonkey:
UNIVERSITY PARK — An outstanding effort by Willie Argo carried the Illini to an 11-5 victory over the Nittany Lions on Saturday at Medlar Field.
Argo blasted two home runs for Illinois. He went 3-4 in the game with five RBIs and two runs scored.
Illini starter Will Strack struggled, allowing five runs in six innings, but the bullpen allowed only no runs and the offense banged out 17 hits to pick up the slack and secure the victory for the Illini.
We are being afflicted with a new disease of which some readers may not yet have heard the name, but of which they will hear a great deal in the years to come—namely, technological unemployment. This means unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.