The idea of a Universal robot has been around for at least 10,000 years where legend has it that a Chinese inventor created the worlds first walking talking humanoid.
Leonardo di Vinci even got into the act imagining and prototyping a robotic soldier. The dream of a servant with the intelligence of a human being, but where the owner has no moral obligation to the creature, is appealing. Being born and raised in the US, I was not raised with servants around the house, picking up clothes after me, preparing my meals and so on. If I had the means to hire such a servant, I would not feel comfortable.
I would accept a robotic servant, if it could do the things I needed and not get in the way.
To build such a servant there is a key conundrum that must be addressed. It is not technical. It is a business problem. I call this conundrum the Cost-Function roadmap.
What is the idea behind this road map? I can say with 90% certainty that we could build a universal robot for a billion dollars or so. A billion dollars is a government scale project or at least that of a company the size of a small government.
It is obvious that if we did have such a robot, we could improve productivity in America dramatically. The impact on productivity in the workspace and at home would be measured not in the billions of dollars but in the trillions of dollars in the US alone.
It is well worth doing. People feel it in the bones, but so far investors and the government are only willing to throw some loose change in the direction of small project of this sort. No one is ready for the moon shot.
That is the reality that we live in today. To harmonize this dream with the needs of the stake holders (i.e. investors and the eventual end users) we need to develop a road map. This road map would establish a string of products that would be organized as a ladder of capability. At each rung of the ladder, the product must have a value that exceeds it cost to the consumer. So far, with a nod to iRobot (who's director of communication Mathew Lloyd recently gave me a left handed compliment about my newest work... but hey, I have a thick skin).... I think we have not reached even rung one of that ladder in the consumer space.
What? ! What about the millions of robotic vacuum cleaners and RoboSapiens that have been sold?! In a previous post, I talked about "crossing the chasm" or moving from early adopters of technology to the early majority... that is building a product that a practical mom or dad would buy, not just something of interest to gadget freaks or robot lovers. Or divorced dads wanting to buy the next cool toy for their kid that they see every other week.
No, I am talking about devices that outcompete the competitors for the big markets. Consumer robotics is not there yet.
The hardest nut to crack in robotics is not the technology itself. It is to identify and build that ladder of technology rung by rung until we reach the Universal Robot. Obama is not going to fund a billion dollar project to get there.... at least not in the consumer space.
My hope is that companies like RethinkRobotics which is focusing on the manufacturing space, may be developing some super secret technology that will spill over into the consumer space.
I also think that as the era of the PC is ending we may start to ascend the ladder. Who wants to sit in front of a computer ? The computer is used at home primarily for social purposes not work. It no longer needs to be a high tech imitation of a typewriter. The computer is an aberrant and unnatural form for social communication. New technology will replace it. The telephone has already morphed into an instrument of social media, the smart phone. Obviously the TV is next. After that the kitchen (the soul of any home) will transform into a social center. And the computer will fad away.
As the era of the PC ends, I predict that we will finally begin to climb the ladder to the universal robot.
Friday, August 17, 2012
Saturday, July 21, 2012
Crossing the Chasm: will consumer robotics ever do it in my lifetime?
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| When will a consumer robot cross the chasm? |
What will be the first consumer robot application to bridge the chasm between early adopter and the early majority?
This, I believe, is the central question this consumer robotics must answer. Crossing the chasm means selling a product not just to enthusiasts, but to the early majority. I submit for your consideration that consumer robotics has NOT been able to jump the chasm yet, and the whole industry is stuck, and is of no real interest to major companies like Google, MS, Apple etc... those with companies with huge stacks of money and who have to power to transform this market. While MS has shown leadership in this area, it is the sole standout in the crowd. See the article by BG : A robot in every home.
What could that first product be that jumps the chasm? There are so many possible areas where consumer robotics can grow. Obviously any new field must be technically in reach within say, several years, it must address a pressing need. So, will it be Telepresence? Security? Intelligent toys? Elder care? Maybe even a nannybot? Or the famous "get me a beer robot" (and is the 'get me the beer robot' an expression of complete lack of imagination in the publics mind as to what robots can do for us).
Military robotics, lead mostly by iRobot, has been able to bridge the chasm, and now we see the widespread use of robots in the military and police forces. Lego Mindstorms, may have done it (or is in the process of doing it) with education/hobbyist robots. Intuitive surgical has transformed surgery. What is wrong with consumer robotics? Why should anyone be enthusiastic about it as a business opportunity?
Here is some background to the crossing the chasm problem: "Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers", Geoffrey A. Moore
What could that first product be that jumps the chasm? There are so many possible areas where consumer robotics can grow. Obviously any new field must be technically in reach within say, several years, it must address a pressing need. So, will it be Telepresence? Security? Intelligent toys? Elder care? Maybe even a nannybot? Or the famous "get me a beer robot" (and is the 'get me the beer robot' an expression of complete lack of imagination in the publics mind as to what robots can do for us).
Military robotics, lead mostly by iRobot, has been able to bridge the chasm, and now we see the widespread use of robots in the military and police forces. Lego Mindstorms, may have done it (or is in the process of doing it) with education/hobbyist robots. Intuitive surgical has transformed surgery. What is wrong with consumer robotics? Why should anyone be enthusiastic about it as a business opportunity?
Here is some background to the crossing the chasm problem: "Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers", Geoffrey A. Moore
Here is what I think is standing in the way. We are stuck with the notion that consumers will not pay more than X dollars for a robot. People are focused on that X number because there is a lot of data to support the idea that $100.00 for an entertainment robot is the max and around $300.00 for a utility robot is a maximum people will pay.
Yet, people also have cars, which cost a lot more. Why? Because they deliver something of unique value to the consumer. People will also pay a few hundred a month for cable. Why? Because there is a constantly renewing source of entertainment. TV is part of our culture.
What we need to think about is delivering VALUE to the consumer and let the price fall where it may. What is the pain that consumers undergo that a consumer robot can alleviate?
The first step, I think, in performing a methodical analysis of this question is compile a pain-list of consumer activities.
Then, the second step is to determine the technical readiness of robotic technology to address each of these pains.
It is only through a methodical analysis of this problem are we going to get anywhere. Building stuff and seeing what sticks has not gotten us very fare. Just my 2 cents.
Tuesday, July 17, 2012
The case for neuromorphic engineering
Neuromorphic engineering... trying to build computers that are more brain like... has become mainstream. Companies like Qualcomm are hiring neuromorphic engineers. Major companies like IBM and HP have DARPA funding to build more brain like computers.
But, the question is, is there really any benefit to using neural style computing versus good-old-fashioned CPUs which have gotten us oh so very far.
For neuromorphic engineering to advance further, this question has to be answered crisply and definitively.
Before we can answer that question, we have to ask: what do we want to use the computer for? I can buy a calculator that costs less than $10.00 at my local drug store that can add, subtract, multiply and divide far faster than I can in my head, and with much greater precision! I feel thick witted when compared to the simplest pocket calculator.
And there are a lot of places where being able to "crunch numbers" quickly is very important. For example, financial calculation, rendering graphics, and designing complex machines. So, lets not dismiss the obvious benefits of these machines.
However, that is not all we want computer to do. As we begin to attach computer to the real world by adding sensors and actuators-- essentially making them the computational core of robot-like machines, our computational needs are changing.
Here is where our world of computation turns upside. Humans excel at interpreting large volumes of data, ignoring what is not important, and emphasizing what is import. We attend to that which is relevant. We have also come to realize from neuroscience that the world we live in is mostly in our heads, in the form of models which we can use to reason with, but that are grounded in the real world.
Now, the interesting thing is that as our algorithms begin to resemble, more brainlike computation, we find that these algorithms cannot run efficiently on CPUs designed for computing balances on checking accounts. A new kind, of find grain parallelism is needed to handle the fire-hose of data that is flowing into these systems. As we drill down, we find that with this fine grain parallelism, in order to create efficient machines, we need to colocate computation with memory. If memory and processing are kept separate we need to have lots of long connections with use power and generate heat. Adding the local ability to adapt helps things as well. A global adaptation scheme just can't send enough signals to enough processor to keep up. This leads to extreme decentralization of computing. In the end, we end up with a collection of highly parallel, computational elements with local learning, and local data storage. We end up brainlike.
And here is the real kicker. The reason why we end up designing computers like this is to save power. Now this gives us the idea that when we look at real brains, we should also be considering how limited power and cooling capabilities shaped the creation of brains. How efficiency may have given rise to the partitioning of the brain into distinct functional regions.
So, the case for neuromorphic engineering really comes down to not necessarily computation, but the practical issue of how to host that computation most efficiently on a physical substrate. Since both brains and silicon inhabit a world with real consequences for their organization, the strongest case for neuromorphics is going to be made on the basis of power.
But, the question is, is there really any benefit to using neural style computing versus good-old-fashioned CPUs which have gotten us oh so very far.
For neuromorphic engineering to advance further, this question has to be answered crisply and definitively.
Before we can answer that question, we have to ask: what do we want to use the computer for? I can buy a calculator that costs less than $10.00 at my local drug store that can add, subtract, multiply and divide far faster than I can in my head, and with much greater precision! I feel thick witted when compared to the simplest pocket calculator.
And there are a lot of places where being able to "crunch numbers" quickly is very important. For example, financial calculation, rendering graphics, and designing complex machines. So, lets not dismiss the obvious benefits of these machines.
However, that is not all we want computer to do. As we begin to attach computer to the real world by adding sensors and actuators-- essentially making them the computational core of robot-like machines, our computational needs are changing.
Here is where our world of computation turns upside. Humans excel at interpreting large volumes of data, ignoring what is not important, and emphasizing what is import. We attend to that which is relevant. We have also come to realize from neuroscience that the world we live in is mostly in our heads, in the form of models which we can use to reason with, but that are grounded in the real world.
Now, the interesting thing is that as our algorithms begin to resemble, more brainlike computation, we find that these algorithms cannot run efficiently on CPUs designed for computing balances on checking accounts. A new kind, of find grain parallelism is needed to handle the fire-hose of data that is flowing into these systems. As we drill down, we find that with this fine grain parallelism, in order to create efficient machines, we need to colocate computation with memory. If memory and processing are kept separate we need to have lots of long connections with use power and generate heat. Adding the local ability to adapt helps things as well. A global adaptation scheme just can't send enough signals to enough processor to keep up. This leads to extreme decentralization of computing. In the end, we end up with a collection of highly parallel, computational elements with local learning, and local data storage. We end up brainlike.
And here is the real kicker. The reason why we end up designing computers like this is to save power. Now this gives us the idea that when we look at real brains, we should also be considering how limited power and cooling capabilities shaped the creation of brains. How efficiency may have given rise to the partitioning of the brain into distinct functional regions.
So, the case for neuromorphic engineering really comes down to not necessarily computation, but the practical issue of how to host that computation most efficiently on a physical substrate. Since both brains and silicon inhabit a world with real consequences for their organization, the strongest case for neuromorphics is going to be made on the basis of power.
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