Artificial Intelligence for Everyone

Artificial Intelligence is the Only Way to Keep Pace

Do yourself (and humanity) a favor — sign up and take Stanford’s class Introduction to Artificial Intelligence (AI). It is free. Open to everyone. And online. You have no excuse. (If the prerequisites of knowing linear algebra and probability theory scare you — then overcome your fear by taking a few of the 10-minute classes offered by Salman Khan at Khan Academy (also free, open and online). I regularly use Sal’s classes to refresh my decades-old memory of many long forgotten math classes. Amazing stuff).

Stanford Professor Sebastian Thrun and Google’s Peter Norvig deserve tremendous credit for making this course available to anyone with an Internet connection. Why?  Because if you don’t understand artificial intelligence you won’t understand the future. Stanford and AAAI are showing the kind of leadership in education that  that can (and probably will) spawn a new wave of innovation that will transform our lives even more than the Internet.

This class is so important that everyone at ai-one signed up — even though we already know a bit about artificial intelligence ourselves (which is often called machine learning). My fiance, father, cousins…even my workout buddy also signed up. If nothing else, this class is taught by two of the smartest people working on how to solve problems faster and more accurately by using machines that can learn how to reason and learn patterns with ever decreasing human intervention.

Machine Learning is Very Different from Machine Programming

I often speak with prospective customer for our technology who immediately ask me what a learning machine can do differently than a computer that is programmed. The answer is simple but profound: Machines can now learn like humans — by detecting the meaning of data by detecting inherent patterns and associations of each element within a data set. This means the machine can learn the meaning of whatever data you feed to it. No, it can’t reason — that is, machine’s can’t spontaneously create new thoughts (yet). They can spontaneously detect how a word is related to other words, documents, websites, etc. This way of determining meaning through association is often called a semantic network. Although the concepts for creating a world wide web of semantically linked data has been around for a long time (notably described by Tim Berners-Lee in his famous paper The Semantic Web in 2001).

Linking data is the only to make sense out of it. Without links it is simply a sea of noise. Noise that is growing at an astonishing rate.

Evolve or Die: Why Everyone Needs to Know About Artificial Intelligence

Human currently doubles every 5 years — your cognitive capacity does not.

In fact, cognitive capacities are much the same for any individual human as they were before we learned enough to form civilizations. So we are only as smart as our capacity to learn — and that capacity has limits. One such limit is the Dunbar Number which is the theoretical limit of the number of people with whom you can maintain meaningful relationships. This is thought to be between 100 and 200 people. So even though I might have over 800 Facebook friends — most are people whom I do not have sustainable, long-term relationships. Many of these “friends” are people I knew in high school and have long since lost contact (except through Facebook). Interestingly, about 142 people made a personal effort to wish me a happy birthday (130 were on Facebook) — reflecting a value  that falls within widely accepted values for the Dunbar Number (which can be thought of as a Dunbar Limit).

The news for your brain gets worse. Knowledge is continuing to grow faster. Several leading indicators  (such as the adoption newly patented technologies) indicate that this pace will increase exponentially — as predicted by Ray Kurzweil in his now famous essay The Law of Accelerating Returns. Data grows even faster than human knowledge. Data includes both the factual information (that is useful) and all the outputs of sensing devices. Knowledge is the extraction of meaning from data.

Cisco’s Dave Evan’s estimates there are about 35 billion sensors connected to the Internet — enabling an internet of things. That works out to 7 devices for every human on the planet — and growing.

Artificial intelligence is the only way for humans to evolve as fast as our data. If only a few people know about artificial intelligence then only a few people will reap the benefits. Knowing about AI is essential for us to ensure a future filled with greater liberties and opportunities for everyone’s mutual benefit.

Example to Illustrate Difference Between Data and Knowledge

Sensors record data. To make that data useful (actionable or meaningful) we must use systems (such as software) to process the data into information. For example, my heart rate monitor records each heartbeat and my location over time (it is GPS enabled). I know the exact time and place for each contraction of my heart as indicated by an electrical signal. Each of these data points is meaningless unless I can see a pattern of how all those heartbeats fit together. My goal is to see a pattern where I run faster at a lower heart rate. My monitor is old — so it takes me about 20 minutes to download the data, look at it (using the really bad software that came with the system), then determine if how I am progressing (or not). NONE of this data links anywhere. So it is useless to my  insurance company — too bad because I’d like them to know that I am fitter than the average person so they can lower my health insurance rates.

Big Data

The explosion of data caused by all the billions of people and billions of sensors offers a tremendous opportunity to find new value — both in terms of new ways to make money and new ways to make discoveries to improve the human condition.

It is comical when business leaders complain about “big data” problems — rather than seeing big data as a massive, unprecedented opportunity to gain competitive advantage by understanding more than competitors. IDC’s 2011 Digital Universe Study provides great insights on how businesses can “extract value from chaos.”

Big data is a relative term. Thirty years ago, it was unimaginable to have a way to access a terabyte of data. Now I can access 10,000x more than that — from my cell phone. Thirty years from now, my great-nieces and nephews will scoff at our struggles to make sense of exabytes of “chaotic” data (absurd because chaos is only a matter of not seeing inherent patterns within data). The story of science is the never ending discovery of new patterns in things we considered random, chaotic (or divine), such as: weather, astronomical events, plagues, diseases, etc.

Making Sense of It All

We recently released an application program interface (API) that enables programmers to build artificial intelligence into software applications. The value of this API is that is generates a lightweight ontology that reveals all patterns and associations within a data set. Feed it data. It tells you how any one element (byte, word, document, etc.) relates to another. Here is a link to a video that describes ai-one’s machine learning technology.

Yes, you can get a no-obligation copy to try for yourself — just contact us.

Tags: , ,

Comments are closed.