I was mentioned on an article published by the Institute for Ethics and Emerging Technologies.

“In economic terms, automation in general should be seen as a leveraging factor that amplifies the output of workers,” says Rod Furlan, an AI researcher and machine-learning expert based in Vancouver. “Thanks to the availability of legal software, one lawyer can do today work that required a team of assistants 10 years ago. Ten years from now, an individual lawyer may be able to service as many cases as a small firm does today, all thanks to AI advancements. Going forward, we can expect to do less boring work and have more time for truly intellectual tasks which are less likely to be automated in the near term.”

Read the full article.

As it turns out, the market is a formidable adaptive adversary and profitable trading models tend to expire because relationships between assets are always in constant flux. Whenever you derive a predictive model for a given asset, it is basically impossible to tell if the model will remain predictive for one day, one month or one year. If you stop to think about it, it makes sense because we are always modeling the past of a dynamic non-stationary system.

My goal was to create “wide-AI” (not narrow but not strong either) within the limited domain of trading, the idea was to build a system that was not only capable of trading any asset but that was also able to decide by itself which assets to trade and which data streams to use without any human intervention.

My job as a quant was to use several statistical and machine learning tools to derive trading models. The question I wanted an answer for was: how could I build a computer program capable of replacing myself?

To accomplish this goal I built a multi-layer system where the top layers would analyze the data and generate a population of independent programs that would in turn attempt to maximize the value of a utility function. The programs that composed the populations in the bottom layers were themselves recombinant and made of several different machine learning “blocks” and data transformation pipelines.

I was eventually successful and the most important lesson I learned is that when you create a system capable of dynamically integrating different adaptive tools at runtime, you may end up with something far greater than the sum of its parts.

While remarkable, the system I built was still confined to a single domain. After reflecting over the outcome of my research, I realized that within the limited scope of the financial markets, I had built something I decided to call a “domain survivalist”.

Considering the tradable market to be its environment and the joint set of available inputs and outputs to be its embodiment, I had created an agent capable of bootstrapping itself to its “body” and “environment” in order to survive and maximize the value of an arbitrary utility function.

The same system could just as easily trade Oil, Corn, Euros or Google stocks. However, even though it was a bit more flexible than what I could have achieved with more traditional methods, its operational range was still vexingly narrow – trading was all it could ever do.

Towards the creation of a Universal Survivalist

Now let’s take the concept of a survivalist AI to the next level. If we set our hearts and minds to it, could we build such thing as a “universal survivalist”? Could we build an AI agent capable of bootstrapping itself to any arbitrary embodiment and find ways to exploit its environment in order to maximize the value a computable utility function?

If the body of the agent is defined as the set of inputs and outputs that are available as means to respectively sample and actuate over its environment, the concept of “embodiment” becomes rather flexible and can extend itself nicely to include AI agents without any sort of physical components.

While creation of such universal survivalist system would not give us HAL 9000 (because language is too complicated), it could certainly give us the algorithmic underpinnings for AI that is versatile enough to bootstrap itself and intelligently control any arbitrarily complex system, be it a plane, a car or a firewall.

The same underlying architecture could then be used to infuse different machines with varying degrees of useable intelligence. A survivalist “born and raised” inside a car could be trained to obey traffic laws the same way police dogs are trained to serve and protect. Similarly, another identical survivalist instance that was instead attached to a security system could be trained to protect a particular location, such as a bank or a hospital. Once trained, any survivalist could then be cloned into as many similar “bodies” as needed.

Indeed something to think about…

1. Emulate enough of a biological brain to instantiate a useful intelligent artifact and try to understand it later.

-or-

2. Add enough complexity and processing power to a software-based generative or adaptive system hoping that intelligence will emerge “naturally” after a certain threshold is crossed.

The latter is what I call the miracle happens scenario because while conceivable, we have little evidence indicating that it is a sensible approach.

There is also the issue of energy consumption, while a human brain runs on about 20 watts, a traditional supercomputer capable of rivaling it would need its own power plant to run. From the energy standpoint there is absolutely no doubt we are doing it wrong.

The brain is a complex adaptive system that computes with nature itself, every atom and electron in the brain is doing what it ought to do naturally – that is, to seek stable energy configurations. Cognition happens as a mostly fortunate side effect of that. In contrast, our conventional digital processors use huge amounts of energy to coerce electrons into the unnatural configurations necessary to do our computations. Here is a good article on physical computing to elaborate on this particular point: http://bit.ly/dqu3kK

And here is the take home message: It is unlikely that a Von Neumann machine will ever deliver human level artificial cognition with low energy requirements.

The sessions of the ASIM workshop will run after the Singularity Summit workshop on Monday and Tuesday, as a satellite event to the main Singularity Summit (August 14-15). The Singularity Summit workshop finishes at 5pm on both days, so there will be time to find some dinner before joining us for our evening sessions.If you are interested in attending and would like more information, please feel free to contact the organizers.

While we all long for a better tomorrow, very few of us have the courage to try to imagine what the future might actually look like. Bound by conventions and by fear of ridicule, most of us dare not to dream or speak about the deep future, instead we as a society choose to focus on the short-term future, which is safe and generally agreeable.

Futurists everywhere, I applaud your courage. Even when you are wrong, you contribute more to the future of our species than your critics ever will.

Even though collectively we choose poverty of imagination as the default mode of thinking about the future, here we stand on the verge of profound societal changes that cannot be stopped and cannot be reasoned with. We are witnessing the dawn of an age of technological wonders, of technology so advanced that it is itself indistinguishable from magic.

Take a minute to admire the monitor in which you are reading these words. Maybe you are using a modern LCD flat panel or maybe you are using an old CRT tube. Either way, old or new, appreciate its beautiful complexity with millions of connected parts that are able to convert a symphony of electrons, bits and bytes into the perfectly weaved tapestry of light required to carry my words to you.

Now consider for a moment the most complex devices we possessed a mere 200 years ago. How does your monitor measure up to it? Do you even know how your monitor really works? What about your computer? Your cell phone? Would you be able to design any of these devices from scratch? Do you know anyone who could?

We came a long way in a very short period. Now try to imagine what miracles of science we will witness in the course of the next 200 years. No matter what you think you know about the future, I assure you that if we don’t destroy ourselves, the best is yet to come.

Like Martin Luther King, I too have a dream.

I dream of a world where people are once again thrilled about the future.

I dream that one day curing death, understanding the human brain and traveling to the stars will be seen as urgent challenges that must be conquered at all costs.

I dream that one day scientists will be considered celebrities and that each of us will be measured not by how much capital we have accumulated but by how much we have contributed to the future of our species.

I dream that one day all nations will unite in the war against ignorance and superstition, the true enemies of all sentient beings.

I dream of the day humanity finally grows up.