by Peter High, published on Forbes
4-11-2016
Vicarious has the mission to “build the next generation of artificial intelligence algorithms.” That said, its objectives are longer-term in nature. Vicarious has assembled a who’s who of technology legends as investors, including Jeff Bezos, Elon Musk, Peter Thiel, and Mark Zuckerberg. Co-founder, Scott Phoenix is clear that the biggest value Vicarious can contribute will be in the long-term, in the form of artificial general intelligence (AGI), or human-like intelligence. There will be plenty of value created in the interim in the form of what Phoenix refers to as the “exhaust” of the process.
Phoenix is a veteran entrepreneur, having served as CEO of Frogmetrics, which was a Y Combinator company in the class of 2008. He was also the Entrepreneur-in-Residence at Founders Fund, among other roles he has played. In this interview, Phoenix describes the goals of his 30 person organization, how he weighs the risks versus the rewards of artificial general intelligence, how AI may replace more jobs than it creates, new economic and social constructs that could ease the societal shift, Vicarious’s decision to prioritize social good over investor returns, and why more companies should do the same.
(To listen to an unabridged audio version of this interview, please click this link. This is the fourth interview in my artificial intelligence series. Please visit these links to interviews with Mike Rhodin of IBM Watson, Sebastian Thrun of Udacity, and Antoine Blondeau of Sentient Technologies. To read future articles in the series, including with Greg Brockman of OpenAI, Neil Jacobstein of Singularity University, Oren Etzioni of the Allen Institute for Artificial Intelligence, and Nick Bostrom of Oxford University, please click the “Follow” link above.)
Peter High: You are the co-founder of Vicarious, a company that is within the artificial intelligence (AI) realm. I thought we could begin with a definition of AI. It is a term that is thrown around in a variety of ways and I would like to take have you unbundle it a little bit.
Scott Phoenix: Artificial intelligence is a really funny thing for a couple of reasons. One is the “moving goal posts phenomena,” which is as soon as something that was formerly called artificial intelligence is solved, it is no longer included the umbra of what is AI. Since it is such a funny term, you can apply it to almost any business or product or company that is developing anything. You could have a consumer gadget that has AI for making sure your windows are clean, or AI in your spam filter.
At Vicarious, we have a particular and specific definition of what we mean when we say AI, which is artificial general intelligence, or human-like intelligence. To put an even more specific frame around it, we say, “given the same sensory experiences that a human being has from birth to adulthood, we are trying to write a program that learns the same concepts and has the same abilities.” That is a specific thing, whereas artificial narrow intelligence (AI as it is commonly used today) can mean just a computer that does some stuff that is useful.
High: As we have machines that are able to do a lot of the processing that humans do today is there any worry that there are aspects of the way that we think or work that are going to change profoundly?
To read the full article, please visit Forbes
2-29-2016
Sentient Technologies has patented evolutionary and perceptual capabilities that provide customers with highly sophisticated solutions, powered by the largest compute grid dedicated to distributed artificial intelligence. The company also has a war chest of $143 million in venture investment, the most of any artificial intelligence company. Antoine Blondeau founded Sentient Technologies nearly nine years ago, though it was in stealth mode for the majority of that period.
After stints at Salesforce.com and Good Technology was looking for the next challenge. He had been involved in artificial intelligence for 15 years, making him an early pioneer in the field, and already had hit a home run by being involved in developing the technology that would become Siri, of iPhone fame.
Blondeau claims we are still in the very early days of artificial intelligence’s evolution, but his vision is to create technology that will mimic the human interaction. One of the first uses of the technology is in retail, replicating the experience of having a sophisticated advisory helping to curate your shopping experience. In this interview, Blondeau provides his vision for the company, his thoughts about the future of AI, the balance between AI innovation and AI safety, as well as a variety of other topics.
(To listen to an unabridged audio version of this interview, please click this link. This is the fourth article in a series on leaders in artificial intelligence, which includes interviews with Mike Rhodin of IBM Watson and Sebastian Thrun of Udacity. To read future articles in the series, please click the “Follow” link above.)
High: Artificial intelligence seems to be gaining tremendous momentum, whether it is venture capital, media coverage, or simply progress that is obvious in the world. There are clearly a couple of trends that have made this possible in recent years: the emergence of relatively low-cost available computing power and the vast, growing abundance of data that companies in every industry are collecting. I think I have heard you say that we are in the first inning here of the game, as so much innovation is ahead of us. As somebody who got into this 15 to 20 years ago, long before this boom, where do you see things now, and how do you think things are evolving?
Blondeau: You are right on the money when you talk about what has happened over the past five or seven years that is making this possible. Some of the team members and I worked on the precursor to what became Siri. At the time, we were thinking of an algorithm running on one machine or a few machines. What has happened over the past few years is that you have the data, it is broadly available, and one of the things that we foresaw was not only that data would explode but the dimensionality of data would explode. It will connect a lot of types of data that had not been connected before. That is a big help.
The second thing is that we have moved from thinking of the machine being the compute to the network being the compute, which means that we can harness an enormous amount of compute cycles. In our case, that means running our system on up to two million CPU cores. We also have a few thousand GPU cores. It is a massive system. When we thought of this company seven years ago, we had the vision forward, but could not quite imagine how we could get there. I think now we can.
The last thing is that when you begin to think about the scale, you can begin to address problems that you had not thought were solvable previously. The ambitious nature of what you do can go up significantly. You can tackle dimensionality, you can tackle complex decision making. Effectively, you are looking at comprehensively including every step of decision making in the machine, or in this giant network machine, which previously was not something thought of as possible. That is the high level.
High: I would like to dive a bit further into the details of how this becomes reality, and how that has impacted the way in which you have thought about entering different markets. I have heard you speak about the applications in some of the primary industries where there are tremendous amounts of data and where there are particularly big problems to solve, like financial services and healthcare. I found it interesting that one of your first areas to apply Sentient Technologies is in retail and online shopping. I would love to understand further how you have chosen where to focus.
Blondeau: One of the things we did was building a powerful platform, but you never succeed by building a platform. You need to apply it to know that it is working and scales to multiple industries. So, we decided to monetize it to address trading, aspects of e-commerce, and the online content discovery experience, as well as, at the research level, institutions like MIT, University of Toronto, and Oxford to work on less immediately monetizable problems, but world problems nonetheless. I am talking here about genomics and patients in an ICU context.
In each case, the common denominator is a few things. One, can you try to solve a problem that has not been solved before? The complexity of the decision making process is key here. The second thing is can you encapsulate the whole decision making process within the machine?
1-18-2016
Perhaps nothing signaled the arrival of artificial intelligence quite like January 14, 2011, when IBM Watson defeated two legendary Jeopardy! champions, Ken Jennings and Brad Rutter. At last, the computers were smarter than us. Since then, Watson has developed into a growth engine for IBM.
For just over two years, Michael Rhodin has led IBM Watson, having come to that role after a four year stint as the head of the Software Solutions Group at the company. IBM has already found applications for Watson across seventeen industries in over thirty five countries, but as Rhodin notes in this interview, the company is just getting started.
(To listen to an unabridged audio version of this interview, please click this link. This is the third article in a series on leaders in artificial intelligence. To read future articles in the series, please click the “Follow” link above.)
Peter High: Many people will remember that Watson was developed initially to compete on Jeopardy! versus two of the biggest Jeopardy!champions of all time, and it won a million-dollar prize in the process. Then a bit more than a year ago, IBM made the decision to create a business called the IBM Watson Group, which you now lead. The company invested a billion dollars (quite a bit more than the winnings from Jeopardy!) to get the business off the ground. Was there always a desire to turn Watson into a business line?
Mike Rhodin: If you look at what we were trying to do, this was an IBM Research Grand Challenge going after– from a technology viewpoint– something in the AI world that was known as the “open domain deep Q&A problem”. It was a problem that was originally postulated based on a comment by [John] von Neumann in the late 1940′s. At that point in time, after his architecture became the basis for modern computing (the binary system that all computers today are built on), he made a comment in the late ‘40s that said someday computers will be able to answer any question. For the next sixty years, computer scientists were trying to figure out how to do what he said, which is how to build a system that could answer questions on a broad base of domain knowledge. That was the real impetus for the Watson system coming out of IBM Research.
Along the way, we decided that it was going to be necessary to have a proof point that you could answer questions in an open domain. The Jeopardy!quiz show ended up being a great example of something that had a broad topic base, lots of different types of questions, tricks, use of natural language in interesting ways, and it became a great demonstration of the technology and the breakthroughs that the IBM Research team made. So that was what led up to Jeopardy! in the first place.
After the Jeopardy! match, we were thrilled with the outcome. It was not a foregone conclusion. With any of the probabilistic systems, there is a level of chance in everything, but we knew we had a system that we thought would show well in the game show. We were pleased to be able to come out with a win against two incredibly smart leading champions of the game.
We started a period of what I would consider in-market experimentation for the two years after the Jeopardy! match. We started working with a handful of companies that had approached us that wanted to start to experiment with the technology–not to play Jeopardy!, but to use the underlying technology to start to solve problems. The preponderance of them were in the healthcare field. Industry luminaries like Memorial Sloan-Kettering or MD Anderson in cancer, or in general medicine, the Cleveland Clinic. They all started partnering with us to explore how we could take what was inside the Jeopardy! system and morph it into things that could be used in the healthcare profession. We did that for a couple years, and that gave us the confidence that led to the announcement in 2014 of the commercialization project and Watson Group.
When you think about what a doctor does every day, they gather evidence about a patient, they use that evidence to build a level of confidence in a set of hypotheses that could lead to a diagnosis, and then they make a treatment based on that. They were looking for systems that could start to help them with a particular problem that has occurred in the healthcare industry, that is, the amount of information being produced: the amount of new research and publications has outstripped the ability of doctors to keep up. In 2015 alone, we will produce something around seven hundred thousand new reference documents in medicine. I am pretty sure most doctors do not have time to read all of them. They are focused on how we could start to use what we demonstrated in the Jeopardy! match in the pursuit of helping them understand all of the information that is being published in their profession, and how to do it in a way that could help them create better outcomes for their patients. That was one of the first clues that the AI world was ready to be woken up– we have been a little bit dormant for decades — and that the systems were ready to start to move into prime time.
That was how we got to the launch last year. It became a pretty big point for us because we had decided that not only could this technology be used in health care, which we still believe is a huge opportunity, but we also recognize that it had applicability across pretty much any industry we could see. The way we thought of it was that any profession within any industry where the amount of information being produced has surpassed the ability of the humans in those professions to consume it. That is a lot of professions these days with the amount of information being produced. That part became pretty clear to us.
The second thing that was a key decision about the launch of the commercial project was the creation of an open ecosystem: we would open up the APIs on platforms so that startups could get access to the technology and start to build out businesses on top of it. When we launched the Watson Group we had our early adopter customers that we had been working with, but we also had a small number of startups that we had exposed to the technology in advance of the launch so that they could be ready to stand up with us and talk about we were doing. Since then, the ecosystem project has taken off. We have hundreds of companies building on the platform, over one hundred now in-market with commercial solutions. Another four hundred or so are under development behind that, and they will be coming out over the next year or so. That part is taking off as well.
12-29-2015
There have been a lot of great stories on technology this year that track hot topics like artificial intelligence, the Internet of Things, self-driving cars, cybercrime, and the like. Each of these topics among others are covered in great depth in the stories that follow, which are my take on the top technology stories of 2015.
What Is Code? by Paul Ford in Bloomberg BusinessWeek, June 11, 2015
On June 11, 2015, Bloomberg BusinessWeek dedicated an entire issue to the topic of coding and its importance. This 38,000 word masterpiece was the most bought issue up to that point, and the online version has interactive elements so that the reader can actually code while reading the piece. Author Paul Ford is both a writer and a coder, but he has a special gift for making a seemingly esoteric topic accessible.
To hear the author and Bloomberg Businessweek editor Josh Tyrangiel talk about the article, watch this great interview by Charlie Rose.
The AI Revolution: The Road to Superintelligence and The AI Revolution: Our Immortality or Extinction by Tim Urban in Wait but Why, January 22, 2015 and January 27, 2015, respectively
Artificial Intelligence is among the most written about topics today. Perhaps no one has written as cogently about the topic in the past year than Tim Urban on the long-form article site that he started called Wait but Why. Urban employs lucid storytelling and humor to provide the most comprehensive account of what Artificial Intelligence is and why it is so important.
