It’s Time to Get Real
Manny January 17th, 2007
Imagine having an army of personal assistants (think millions) who read and observe everything you give them (think terabytes). This army notes every association amongst all the entities (people, places and things) in great detail (think millions of attributes and their co-incidences for each entity). Then best of all, it never forgets. That’s Saffron’s associative memory technology
Welcome. As seen on our new web site and now my first blog, we are coming out of stealth mode to more openly present our company as we “get real”. Saffron has solved a decade old problem of machine intelligence, has established customers and partners with real solutions, and has the executive team in place to change entire industries with a new, disruptive approach to data analysis. While many emerging companies claim to be revolutionary with patented breakthroughs, these claims are usually shallow, but we are beginning to speak-out and prove it true in our case.
Saffron is the realization of a 20 year dream for me. I’ve always been more of a biologist-psychologist than a computer scientist. Since I was in grad school and saw the rise of “neural network” computing in the 1980s, I have believed there was a more natural, biologically inspired way for machines to learn - more the way real brains work using associative memories, which can remember and recall how everything is (potentially) related to everything else and can then reason from memory. When we say that a doctor, lawyer, teacher, executive, analyst, or engineer is wise, we mean that they are experienced and draw on this experience when making new decisions. This is the way machine intelligence should work as well.
When I was an engineer at IBM, our interest was in solving very large-scale enterprise problems. It was clear that every industry was beginning to face the problem of too much data (structured and even more unstructured) and that traditional search, AI, statistical techniques, and neural network data mining were going to fail. Being involved in intelligent agents, I was also starting to hear customers ask for agents that were adaptive and agents that could learn I felt that the market was getting ready for a “new, new thing”.
However, like decades of others who believed in associative memories as the right approach but then failed to commercialize them, we continued to be faced with problems of how associative memories could scale. Remembering the connections from everything to everything else just doesn’t scale well. We believed that real neurons had solved this problem, and looked for inspiration from real systems in order to engineer smarter machines. So in 1999, Jim Fleming and I founded Saffron Technology to solve the associative memory scaling problem - or go back and get “real” jobs again if we failed!
Chalk it up to naive optimism, which is a requirement to leave a great job and start a new venture in any case, but we soon learned just how hard the problem was to solve. “You don’t know how deep the pond is until you step in it”, as they say. It took us longer than I thought it would be fun to crack this nut. It also took longer than I thought for vertical markets to become increasingly dissatisfied with traditional approaches. However, the “heavy lifting” demands of national security and personalized medicine have also grown over the years since our founding, and these are the markets which we now serve. Saffron’s engine has also powered everything from “The World’s Best Spam Blocker” to adverse event predictors in oil and gas production. While these applications have been the focus of our partners, we are focused on advancing the core technology. Working quietly over the years, today we have a mature solution to the growing scale and complexity of enterprises using memory-based representation and reasoning. Saffron now stands as the real leader of this new industry.
I am often asked for reading material on associative memories. While there are various books and articles of related approaches in the history of neurocomputing and in the psychology of learning and memory (if you are more technically-minded), I like to suggest On Intelligence by Jeff Hawkins is a very engaging and accessible read. While other true believers in associative memories have been few and far between over the last decades, Jeff has been one of them. In On Intelligence, he makes the case that “real intelligence” is not what has been seen in Artificial Intelligence or even in “neural networks”, which in my opinion have little if anything to do with real neural networks. Instead, real intelligence is memory-based, and Jeff defines it as “memory-based prediction”. On Intelligence provides common-sense examples and very readable arguments about why this is true of your own brain, and in the last chapter, he predicts an emerging industry of memory-based systems in the next few years. We agree and present Saffron Technology as the turning point, providing mature products and solutions today. Our enterprise “memory base” along with our turnkey applications has matured over many release cycles, including partner and customer feedback to prepare us for today.
My next postings will address our current applications of real intelligence to national security and then onward to personalized medicine. For now, I wish only to welcome you and ask for your comments and dialog. Using this blog as a manifesto, I intend to be controversial. Please just fire back to help me articulate this new approach with concrete realities rather than mere claims. To start this dialog, let me leave you with some doubt about the adequacy of traditional approaches from one of the great leaders of 20th Century statistics:
“For a long time I have thought that I was a statistician, interested in inferences from the particular to the general. But as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt.” John Tukey, “The Future of Data Analysis”, 1962.
Saffron provides you the ability to focus on the particular. Unlike statistical assumptions and the reductionism of fitting data into abstract models that lose too much information and fail, Saffron provides a new and unique way to address the growing problems of data complexity and scale. This is exciting stuff, and I hope you will join us in both telling and further creating this story.
