How to Build Great Products in the AI World

AI ProductsWhen technology and human ingenuity gets together, everybody in society profits. If you look at graphs of GDP / capita over long time periods (850 years+), the trend is always upwards. The only things that drag this progress down are severe periods of sickness (like the black death) and widespread war (such as WW2). Overall though, technology makes everyone healthier and richer.

At this point in our society’s history – technology has never been more important to the global economy. 5 out of the top 6 companies in the world are technology companies – Apple, Alphabet, Microsoft, Amazon and Facebook – the non tech company being ExxonMobil. Only 10 years ago though, Microsoft was the only tech company in the top 6 – clearly technology is only growing in importance.

We have moved on from the age of agriculture, steam, oil or finance to the age of technology, as Azeem Azhar showed us at ProductTank London.

The most important thing in technology is AI
There are three types of AI:

ANI – Narrow AI – A system that doesn’t have to be specifically programmed for all of its outcomes and can learn from its experience. It can still only solve a defined set of problems and cases though.
AGI – Human-ish AI – What most people mean when they think about the Turing test. These types of systems can solve lots of different problems without being programmed specifically for them.
ASI – Super AI – Intelligence that can think like us and solve general problems while defining its own goals. The key characteristic of this AI is that it will work out how to make itself smarter and will do so exponentially.
In reality, ASI is a very long way off and nobody really knows what it will look like when it gets here. The most important research work is being done to build general problem solving systems that are in the AGI bracket – and even this is largely at the mathematical modelling stage.

ANI systems are in production and use right now – from working out what objects are in a picture to predicting sports scores – these are the systems that most digital professionals will end up employing very soon if not already.

[Read More from Mind The Product]

Posted March 27, 2017 by & filed under News.