What is an expert? If we take the definition of the Cambridge Dictionary, an expert is “a person with a high level of knowledge or skill relating to a particular subject or activity”. No problem with this definition, right? So… a machine cannot be an expert? Well, in this post I will try to elaborate a little around these issues and how they relate to Innovation.
There is no serious (or not so serious) innovation blog without a specific post on quotes, so after passing the 20 post frontier, I believe the time to do the “quote post” has finally arrived! Anyhow, people who know me are aware that I am quite fond of some particular quotes and of using quotes in general to focus or anchor a point, so this post actually comes as quite natural to me.
There are two visions of the world. In the first one, space is limited, so any new thing needs to come at the expense of something already existing. On the other hand, there is another vision where space is unlimited and there is always room for expansion and creation of new things. Allow me to get a little philosophical today and talk about Innovation from these perspectives.
I have just ended my holidays this summer and I would like to share with you some thinking I did during those days when you are able to clear your mind from everyday issues and problems. I want to go back to basics with a new post on the Physics of Innovation, where I try to make an analogy between physical laws and Business Innovation. So let’s give some thought to Newton’s Laws…
If you ask me which is the concept I have used the most in my Innovation Management talks, I am sure it would be that of the Innovation Horizons, well, probably closely following the definition of Innovation itself. However, I have never explained in this blog my view about this concept and why I believe it is so important, so it is about time I do it.
Some time ago, I was reading an article from @aalbaperez about how Digital Transformation is an issue much more related to people that to technology. I started thinking how different principles and theories we apply to technology, or other business management aspects, could be perfectly applicable to people. With this in mind, I want to elaborate a little in this short article about the concept of S-Curves applied to a professional career. I originally published this article directly on LinkedIn, but I hope you find it interesting.
In my last Innovation Papers post, I approached the subject of Deep Learning and introduced the concept of Neural Networks, which I had began to understand through Andrew Ng’s Coursera course on the matter. We got as far as introducing these computing constructions called neural networks (due to their “shape” being similar to neuron cells), which are capable of identifying patterns in the information we input, allowing them to distinguish between different sets of data (it is or it is not the picture of a cat). That is great, and certainly most useful. However, it is not my objective to speak about the possible applications of Deep Learning using neural networks, but instead to try to explain, in simple terms, how they are able to do what they do, as well as how surprising it was for me to see its simplicity.
Artificial Intelligence is a technology more widespread everyday, and in most places is being introduced in a discreet and unnoticed manner. We are not talking about C-3PO, HAL-9000, Skynet or any other famous robot from Science Fiction films, but instead about programs running behind the scenes that recommend us what book to buy, identify our face to unlock our phone, or finish a sketch we are drawing. One the most popular techniques in AI is Deep Learning, and its application is behind many recent innovations we encounter everyday. But how does it work? Well, I have found it to be something as simple as mysterious. Allow me to take you through my first steps in the realm of neural networks.
After a number of years involved with Innovation activities, mainly at Corporate level, I can confirm the idea that one of the fundamental ingredients in Innovation’s secret recipe is “having time to think”. Friction, sharing, serendipity, resilience, etc, they are all also very important, but today I want to speak about those times you are lost in your own thoughts, allowing for unexpected connections to appear, and how we can link this to an experiment proposed by one of the best Innovation experts I know.
Mathematics, we love it or we hate it; for some it means clarity, but for others it is a complete gibberish. The language of the Universe, it has been called, but can it also be the language of Innovation? We are currently living a explosion of mathematical models and algorithm-based business which is basically modelling reality using lots of data (Big Data) and making predictions based on those models. But, can we model innovative behaviour and predict what conditions would yield more innovative results?
