Roots of #AI

The naming is unfortunate when talking about #AI. There isn’t anything about intelligence – not as we humans know of it. If we can rewind back to the 50’s we can perhaps rename it to something like Computational Intelligence, which is more accurate. And although I have outlined the difference between some of the elements of AI in the past, I wanted to get back to what the intent was and how this area started.

Can machines think? Some say, the origins of #AI go back to Turing and started with his paper “Computing machinery and intelligence” (PDF) when it was published in 1950.Whilst, Turing might have planed the seed, it was a program called Logic Theorist created Allen Newell, Cliff Shaw, and Herbert Simon which was the first #ArtificialIntelligence program. Of course it wasn’t called #AI then.

That started back in 1956 when a Logic Theorist was presented at a conference in Dartmouth College called “Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI)” (PDF). The term “#AI” was coined at the conference.

Since then, AI has had a roller coaster of a ride over the decades – from colder than hell (I presume) winters, to hotter than lava with it being everywhere. As someone said, time will heal all wounds.

#AI Timeline

Today, many of us use #AI, #DeepLearning, and, #MachineLearning interchangeably. Over the course of last couple of years, I have learned to ignore that, but fundamentally the distinction is important.

AI, we would say is more computational intelligence – allowing computers to do tasks that would be difficult for humans to do, certainly at scale. And these tasks are accomplished using different mechanisms and techniques, using “intelligent agents”.

Machine learning is a subset of AI, where the program or algorithm can learn from previous outputs, and improve based on that data – hence the “learning” part. It is akin to it learning from experience, but isn’t the same thing as we humans can comprehend and understand. Some of us think, the program is rewriting itself, which technically isn’t an accurate description.

Deep Learning is a set of techniques and algorithms of machine learning that are inspired from how the neurals in our brain connect together and work. These set of techniques are also called Neural Networks, and essentially are nothing but type of machine learning

For any of this AI “magic” to work, the one thing it needs to feed on is data. Without data, none of this would be possible. This data is classified into two categories – features and labels.

  • Features – these are aspects of whatever we are interested in. For example if we are interested in vehicles features could be the colour, make, and, model of the vehicle.
  • Labels – these are buckets of categories we put the things we are interested in. Using the same vehicles examples, we can have labels such as SUV, Sedan, Sports Car, Trucks, etc. that categorize vehicles.

One key principle to remember when it comes to #AI – all the outcomes that are described are in the terms of probabilities and not absolutes. All it suggests is the likelihood of something to happen, and most things cannot be predicted with total certainty. And this fundamental aspect one should remember when making decisions.

There isn’t a universal definition of AI, which sometimes doesn’t help. Each has their own perception. I have gotten over it to come to their terms and ensure we are talking the same lingo and meaning. It doesn’t help to get academic about it. 🙂

For example taking three leading analysts (Gartner, IDC, and Forrester) definition of AI (outlined below) is a good indicator on how this can get confusing.

  • Gartner – At its core, AI is about solving business problems in novel ways. It stretches across any organization from innovation, R&D and IT to data science.
  • IDC defines cognitive/Artificial Intelligence (AI) systems as a set of technologies that use deep natural language processing and understanding to answer questions and provide recommendations and direction. IDC’s coverage of cognitive/AI systems examines:
    • Digital assistants
    • Automated advisors
    • Artificial intelligence, deep learning and machine learning
    • Automated recommendation systems
  • Forrester defines AI as a liberatory technology at its core, and businesses that integrate it will free workers to become more innovative, creative, and adaptive than ever before. But these technologies are still in early stages.

And the field is just exploding now – not just with new research around #DeepLearning or #MachineLearning, but also net new aspects from a business perspectives; things like:

  • Digital Ethics
  • Conversational AI
  • Democratization of AI
  • Data Engineering (OK, not new, but certainly key)
  • Model Management
  • RPA (or #IntelligentAutomation)
  • AI Strategy

It is a new and exciting world that spans multiple spectrum. Don’t try and drink from the fire-hose, but take it in slowly, appreciate the nuances and what one brings value and discuss in terms of outcomes.