With ROS and a package called “rosjava“, software developers will be able to write code in the Java programming language and control robots in a standardized way — much in the same way that programmers writing applications for Windows or the Mac can access and control computer hardware.

Google’s approach also offers compatibility with Android. Robots will be able to take advantage of the “cloud-based” (in other words, online) features used in Android phones, as well as new cloud-based capabilities specifically for robots. In essence this means that much of the intelligence that powers the robots of the future may reside on huge server farms, rather than in the robot itself. While that may sound a little “Skynet-esque,” it’s a strategy that could offer huge benefits for building advanced robots.

One of the most important cloud-based robotic capabilities is certain to be object recognition. In my book, The Lights in the Tunnel, I have a section where I talk about the difficulty of building a general-purpose housekeeping robot largely because of the object recognition challenge:

A housekeeping robot would need to be able to recognize hundreds or even thousands of objects that belong in the average home and know where they belong. In addition, it would need to figure out what to do with an almost infinite variety of new objects that might be brought in from outside.

Designing computer software capable of recognizing objects in a very complex and variable field of view and then controlling a robot arm to correctly manipulate those objects is extraordinarily difficult. The task is made even more challenging by the fact that the objects could be in many possible orientations or configurations. Consider the simple case of a pair of sunglasses sitting on a table. The sunglasses might be closed with the lenses facing down, or with the lenses up. Or perhaps the glasses are open with the lenses oriented vertically. Or maybe one side of the glasses is open and the other closed. And, of course, the glasses could be rotated in any direction. And perhaps they are touching or somehow entangled with other objects.

Building and programming a robot that is able to recognize the sunglasses in any possible configuration and then pick them up, fold them and put them back in their case is so difficult that we can probably conclude that the housekeeper’s job is relatively safe for the time being.

Cloud robotics is likely to be a powerful tool in ultimately solving that challenge.  Android phones already have a feature called “Google Goggles” that allows users to take photos of an object and then have the system identify it. As this feature gets better and faster, it’s easy to see how it could have a dramatic impact on advances in robotics. A robot in your home or in a commercial setting could take advantage of a database comprising the visual information entered by tens of millions of mobile device users all over the world. That will go a long way toward ultimately making object recognition and manipulation practical and affordable.

In general, there are some important advantages to the cloud-based approach:

• As in the object recognition example, robots will be able to take advantage of of a wide range of online data resources.
• Migrating more intelligence into the cloud will make robots more affordable, and it will be possible to upgrade their capability remotely — without any need for expensive hardware modifications.  Repair and maintenance might also be significantly easier and largely dealt with remotely.
• As noted in the video below, it will be possible to train one robot, and then have an unlimited number of other robots instantly acquire that knowledge via the cloud.  As I wrote previously, I think that machine learning is likely to be be highly disruptive to the job market at some point in the future in part because of this ability to rapidly scale what machines learn across entire organizations — potentially threatening huge numbers of jobs.

The last point cannot be emphasized enough. I think that many economists and others who dismiss the potential for robots and automation to dramatically impact the job market have not fully assimilated the implications of machine learning. Human workers need to be trained individually, and that is a very expensive, time-consuming and error-prone process. Machines are different: train just one and all the others acquire the knowledge. And as each machine improves, all the others benefit immediately.

Imagine that a company like FedEx or UPS could train ONE worker and then have its entire workforce instantly acquire those skills with perfect proficiency and consistency. That is the promise of machine learning when “workers” are no longer human. And, of course, machine learning will not be limited to just robots performing manipulative tasks — software applications employed in knowledge-based tasks are also going to get much smarter.

The bottom line is that nearly any type of work that is on some level routine in nature — regardless of the skill level or educational requirements — is likely to someday be impacted by these technologies. The only real question is how soon it will happen.

The video below is a presentation from Google’s I/O conference on Cloud Robotics. It is fairly long and very technical, but if you have a strong interest or would like to see what some actual robot programming code looks like, check it out:

ASIMO can navigate complex environments along with people, recognize and distinguish faces and voices — even when people are speaking simultaneously. And it can do a lot of other stuff.

Details are here.  Check out the video below:

.

As I wrote previously, a lot of people have jobs that are safe from automation not because they are especially advanced or creative, but because they involve skills such as dexterity and hand-eye coordination that are currently beyond the capability of machines.  Things are changing.

And, as I noted here on the subject of personal robots:

The thing is that for a robot to autonomously run around the house doing a variety of tasks requires a very sophisticated level of technology. If that technology is developed and becomes affordable then it will certainly make its way into a variety of commercial applications—in fact, it may well be deployed there first.

It seems to me that if we have affordable personal robots that are actually capable of doing anything useful, then that technology implies that millions of jobs will be at risk in areas like:

• stocking shelves in supermarkets and other retail stores
• moving materials in stores and warehouses
• providing security in a variety of settings

This is the second version of ASIMO. What will the 5th version look like? What about v. 10?

Singularity Hub also has the story.

The Economist has a column on artificial intelligence and the Luddite fallacy that mentions “The Lights in the Tunnel.” Paul Krugman comments briefly.

Tim Worstall, writing at Forbes, strongly disagrees with my concerns about automation. He is responding primarily to what I said in the television discussion I had with Robin Hanson.   Worstall says:

Ford’s basic thesis (laid out in his book “The Lights in the Tunnel”) is that machines are now getting so advanced that they’re going to be better than human beings at doing everything and thus there will be no jobs. He also thinks this is a very bad thing, a view which I think is terribly strange.

In fact, I’ve never said there will be “no jobs.” I’ve only said that technology may ultimately eliminate the bulk of routine jobs. But that will nonetheless result in major problems. History suggests that the 25% unemployment the United States experienced during the Great Depression is probably pretty close to the limit of what a democratic society can withstand.

I also have never said that advancing technology, or job automation, is “a bad thing.” In fact, I agree that it is a great thing. I just believe we need to reform our economic system so it will be a great thing for everyone — and not just a tiny elite. Suggesting such reforms was really the whole point of my book, “The Lights in the Tunnel.”  We cannot escape the fact that a great many people are best equipped to do routine things and will have great difficulty moving to non-routine/creative areas, even if those jobs are available. If we assume a bell curve distribution, then by definition, 50% of the workforce is average or below average in terms of capability. At the same time, technology is also encroaching on even the high skill jobs held by people with above average capability. Ultimately these trends will demand a response.

Later Worstall says this:

Machines are about to get so good that humans just won’t have anything to do at all. We won’t need to sow, weed or reap, sew or in fact anything. Not only will food and clothes be made by machine, the machines that make the machines that make the food and clothes (and houses and cars and computer games and….) will be made by machines.

Humans will therefore have no jobs, no jobs at all. Ford thinks this is appalling as therefore human beings will have no incomes. I think it sounds like a rather wondrous world actually, even without humans having any incomes.

The problem is that in the world as it exists today, you do not get to consume without an income. I think Tim is saying that all those machines will make production so efficient — and prices so low — that even people with very low incomes will still be able to consume. There are a couple of problems here. First if your income is ZERO, then it doesn’t matter how low prices are: you are out of luck.  And a great many people will be in that situation unless we dramatically improve our social safety net.

The second issue is that efficient machines will not drive down many of the fixed costs that take up most household budgets. Powerful robots are not going to lower the principal on your mortgage. Nor are they likely to drive down food prices much, as agriculture is already highly mechanized. We can dream that technology will dramatically lower health care costs, and maybe it will happen someday — but probably not until after you lose your job.

In general, if prices fall in one area — say food production or computers — that has historically been a good thing. But if wages and prices fall across the board then that is DEFLATION. And a big problem with deflation is that while wages,  prices and asset values may fall — debts do not.  In time, people will default or debt service will leave them with little discretionary income to spend on other things — creating the risk of a deflationary spiral. I’ve written more about this in a previous post.

Other Links:

Marshall Brain has started posting new items to his Robotic Nation Evidence Blog.

Supply Chain Digest: Automation In The Workplace Is Inevitable – The Question Is: Are We Smart Enough To Plan For It?

Occupy Silicon Valley?

CBS MoneyWatch:  Is Silicon Valley fueling unemployment?

TechCrunch:  A Tale Of Two Countries: The Growing Divide Between Silicon Valley And Unemployed America

WordPress won’t let me embed the video, but you can watch the show here.

I thought it was a very interesting discussion, and at a full half hour, was the most in-depth treatment I’ve seen so far on this issue.

Robin Hanson, who appeared on the show with me, comments on his blog.  Robin and I had an interesting mini-debate about the economic implications of intelligent machines a while back.

Libertarian economist Arnold Kling (and Robin Hanson fan) also comments over at EconLog.

Slate also published a series of articles on the subject by Farhad Manjoo.

There were two panel discussions. Here is the list of participants from the event page at the New America Foundation:

Panel 1 – Can Robots Do Nuance?

Robbie Allen
Founder and CEO, Automated Insights, Inc.

Dr. Sarah Kramer
Primary care physician, University of Washington Medicine

Michael Schmidt
President, Nutonian, Inc.

Slate Video: Robots on the Silver Screen

Panel 2 – Can the Economy Survive a Robot Uprising?

Dr. Tyler Cowen
Holbert C. Harris Chair of Economics, George Mason University
Co-author, Marginal Revolution blog

Martin Ford
Author, The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future

Michael Lind
Policy Director, Economic Growth Program
New America Foundation

A video of the discussion is here. One of the most interesting questions Farhad asked me was one that I have seen raised quite a few times:  Why do I worry that machines might outpace workers when we all know that people in the future will be enhanced by “transhumanist”  technology (brain implants, etc.)?  That’s at 1:01:20.

You can read it here

Update

Mark Lewis, a computer science professor at Trinity University, thinks I might be too conservative in my projections for Watson’s future impact on medicine.

My feeling has been that in areas like medicine and self-driving cars/trucks the technology may run ahead of social acceptance. Also there are some powerful groups that might lobby hard to slow progress (AMA, Teamsters, etc.).

How long will it be, for example, before society would trust a machine to independently prescribe drugs? (Possible future Kindle bestseller: How to Get Watson to Give You Vicoden: The Insider’s Guide). But, then again, which would be harder: gaming a smart computer to get a prescription, or just finding a doctor that will prescribe on demand?

I’ve heard from a number of people who, like Mark, think things are likely to progress faster than we might expect in these areas. Let us know what you think in comments.

This is something that I’ve been wondering about for quite a while. I’ve been writing here primarily about the impact of technology on the job market, and I think it is clearly one of the primary reasons for the ever-increasing inequality we’ve seen over the past few decades.  Although there are certainly other important factors, including the demise of private sector unions, globalization and perhaps the entry of millions of women into the workforce. 

There is also, of course, a positive feedback loop between the concentration of income and wealth, and the concentration of political influence.  Extreme income inequality allows a few wealthy members of society to effectively capture the political process and push through an agenda that is in their favor. In the U.S. this has resulted in dramatically lower marginal tax rates on the wealthy, and also an unsustainably low rate of overall taxation: The U.S. currently collects about 14% of GDP in federal taxes, as compared with a historical average of 18%.

The problem I see going forward is that there is really nothing whatsoever on the horizon to counteract the trend toward increasing inequality. The trend was reversed in the 1930s by direct government intervention.  The time when policies of that type might have been implemented seems to be past – we are now moving aggressively in the opposite direction, and austerity measures seem likely to accelerate the drive toward even more inequality.

While we can have a reasonable debate  about which forces have led to the concentration of income we now face, I would argue strongly that technology will be the primary factor going forward. I believe this because of the exponential progress of information technology.

If you get in your car and gradually double your speed, so you are travelling at 5, 10, 20, 40 and finally 80 miles per hour, that would be similar to the way computing power continues to advance. And the point is that when you are going 80 miles an hour you cover far more ground that when you were just starting out.

That’s where we find ourselves today: information technology has beeen progressing for decades and is now reaching the level where advances in areas like artificial intelligence and robotics are likely to unfold far more rapidly than most people expect. This could impact jobs at virtually all levels:  from fast food workers to professionals with college degrees. 

Corporate managers won’t hesitate to deploy these technologies throughout their organizations, and they’ll collect huge bonuses as a reward for doing so. The result may well be even more dramatic concentration of income as those who own or control large amounts of capital (CEOs, Wall Street) win big and the vast majority of people who rely on wages or salaries continue to lose out as they face higher unemployment and stagnant wages — perhaps in the face of significant food and energy inflation.

If inequality continues to increase relentlessly, it seems likely that major social disruptions are inevitable. We see this in Europe and the Middle East already. What people should keep in mind is that — despite conservative rhetoric about the welfare state – the U.S. has the weakest social safety net of any advanced country. Once you exhaust your unemployment benefits and your savings, you are in serious trouble if you can’t either find a job or get someone to take you in.  

The current recession has now been going on for so long that one has to begin to wonder how many families out there are getting close to the brink. At the same time, huge numbers of young people are unemployed and probably see little prospect of that changing anytime soon. That has been one of the primary drivers of unrest in the Middle East.

One argument against the possibility of unrest in the U.S. is that there seems to be no clear organizing mechanism. In the past, private sector unions were heavily involved in organizing people, but their influence is now greatly diminished. In the Middle East, social media has played a key role.

Another issue is that many people seem to be confused or uninformed about what policies are in their own self-interest. A large percentage of the population does not realize (or acknowledge) that it receives substantial benefits from the government. We see Tea Party supporters — many relying on Social Security and Medicare – who seem to truly believe that it would be better if the debt ceiling is not raised.

Is it possible or likely that we’ll have social unrest in the U.S?  Please leave a comment and let us know what you think.

Updates

This is also running over at Huffingtonpost, where it has over 1000 comments so far…

Maybe I should have said “United Kingdom” rather than “United States.”  But stay tuned, U.S. austerity measures have not been fully implemented yet…

I’ve set up a profile and started a discussion about the impact of technology (especially automation/robotics/AI) on the job market and economy.

You can view it here

Right now, Google+ is in test mode, so if you don’t have access and would like to participate in the discussion, click here to get an invitation to join Google+ . Once you have an account, you can invite others.

I don’t use Facebook, but one of my readers was kind enough to set up a page for my book, The Lights in the Tunnel:

http://www.facebook.com/pages/Lights-in-the-Tunnel/145229482171278

Social media has been ablaze because Netflix just raised its prices.  I do think that the issues discussed on this blog are going to be (or already are) a much bigger deal than the extra six dollars a month that Netflix is charging…

If any of my readers are active Facebook/Twitter users, I’d certainly appreciate it if you’d try to bring more attention to these issues.

People sometimes worry that we’re getting too productive, able to satisfy the demands of our economy with “too few” workers.  That’s an age-old worry, and those who want to downplay it cite the fact that, as the graph shows, there is a positive, not a negative, correlation between productivity and job growth overtime.

But look at the end of the graph.  Productivity accelerates while employment growth decelerates.  And that ain’t no blip either…it suggests the possibility of a structural change in this relationship.

Now here is the section on the relationship between workers and machines from my book The Lights in the Tunnel. Compare the graph above to the one from my book below and notice that they both have diverging lines that seem to be saying something similar:

_____________

The Average Worker and the Average Machine

Think of an average worker using an average machine somewhere in the economy. Obviously, in the real world there are millions of workers using millions of different machines. Over time, of course, those machines have gotten far more sophisticated. Imagine a typical machine that is generally representative of all machines in the economy. At one time, that machine might have been a water wheel driving a mill. Then it became something driven by a steam engine. Later, an industrial machine powered by electricity. Today, the machine is probably controlled by a computer or by embedded microprocessors.

As the average machine has gotten more sophisticated, the wages of the worker operating that machine have increased.*  As I pointed out in the previous section, more sophisticated machines also make production more efficient and that results in lower prices and, therefore, more money left in consumers’ pockets. Consumers then go out and spend that extra money, and that creates jobs for more workers who are likewise operating machines that keep getting better.

Again, the question we have to ask is: Can this process continue forever? I think the answer is no, and the very unpleasant graph below illustrates this.

The problem, of course, is that machines are going to get more autonomous. You can see this in the graph at the point where the dotted line (conventional wisdom) and the solid line diverge. As more machines begin to run themselves, the value that the average worker adds begins to decline. Remember that we are talking here about average workers. To get the graph above, you might take the distribution of incomes in the United States and then eliminate both the richest and the poorest people. Then graph the average income of the remaining “typical” people (the bulk of consumers) over time. If you were to instead graph Gross Domestic Product (GDP) per capita, you would end up with a similar graph, but the divergence between the dotted and the solid lines would occur somewhat later. This is because the wealthiest people (who own the machines or have high skill levels) would initially benefit from automation and would drag up the average. Recall that we saw this in our tunnel simulation in Chapter 1.

Once the lines diverge, things get very ugly. This is because the basic mechanism that gets purchasing power into the hands of consumers is breaking down. Eventually, unemployment, low wages—and perhaps most importantly—consumer psychology will cause a very severe downturn. As the graph shows, within the context of our current economic rules, the idea of machines being “fully autonomous” is just a theoretical point that could never actually be reached.

Some people might feel that I am being overly simplistic in equating “technological progress” with “machines getting better.” After all, technology is not just physical machines; it is also techniques, processes and distributed knowledge. The reality, however, is that the historical distinction between machines and intellectual capital is blurring. It is now very difficult to separate innovative processes from the advancing information technology that nearly always enables and underlies them. Improved inventory management systems and database marketing are examples of innovative techniques, but they rely heavily on computers. In fact, we can conceivably think of nearly any process or technique as “software”—and, therefore, part of a machine. 

If you still have trouble accepting this scenario, you might try asking yourself a couple of questions: (1) Is it possible for a machine to keep getting better forever without eventually becoming autonomous? (2) Even if it is possible, then wouldn’t the machine someday become so sophisticated that its operation would be beyond the ability of the vast majority of average people? And wouldn’t that lead right back to making the machine autonomous?

_______

* The idea that long-term economic growth is, to a large extent, the result of advancing technology was formalized by economist Robert Solow in 1956. Economists have lots of different theories about how long-term growth and prosperity come about, but nearly all of them agree that technological progress plays a significant role.