On innovative applications, a number of good ideas were pitched, in diverse business and societal domains, motivated by various challenges. Many different technologies and applications were proposed for solutions but nearly all of them had (in my opinion) a common underlying workflow and made one thing obvious: data is king.
Some examples of the applications discussed were:
- Drug testing based on simulations and 3D models of cells
- Elderly watch and assisted living via ambient intelligence
- Real-time creation of 3D maps of environment during safe and rescue operations
- Early detection of deteriorating machines in manufacturing
- Optimized and collaborative transportation of goods and services
- Prevention of currently untreatable mental diseases
- 3D digital pathology using digital replicas of human organs for monitoring and diagnosis
- Digitalized management and monitoring of electricity production/consumption based on M2M
As I see it, all the above applications aim to facilitate knowledge based decisions and actions. Almost all of them opt for real-time input-interpretation-action, meaning, they aim to respond to events as soon as they happen or even know how things will be and respond to that. This predictive approach is especially related to the “prevent over fix” of the applications such as elderly care, deteriorating machines and untreatable mental diseases. Building on the knowledge based decisions and actions different projects get more specialized to facilitate increased safety (i.e., development of drugs, elderly care) and situational awareness (i.e. safe and rescue operations) or optimized utilization of resources for decreased costs and waste (i.e., manufacturing, transportation, utilities & power).
And technically on a (quite) high level, the applications had a similar workflow:
- Gather data from multiple sources (volunteered, observed or deduced)
- Communicate the data (where semantics and integration are often challenging)
- Build models based on the data and vice versa consolidate the data into models
- Experiment on them and make sense out of them
- Plan and act based on the information
Again there are many technical and non-technical issues to be overcomed for many of the above applications, such as legislatory framework and standardization, but in general, very interesting stuff!
On networking, I believe things are simple. Be active and interested. Move, greet, smile, introduce yourself and other people. Do not spend a second not talking to someone. But be genuinely interested. These events are very good opportunities. Most people there have things to say. They are people with (more or less) common interests, most are individuals who take action and are ambitious. There are many opportunities to learn from others, don’t waste any opportunities. But it is important to really do listen to them. Sometimes it feels easy to just nod and smile when someone is talking to you. This can happen because your interests are not an exact match, or the other person is more knowledgeable on a subject, gets too deep and is difficult to keep up. It doesn’t matter, be honest. Ask explanations or to ask to “dumb down the level” a bit, but actively listen and try to understand. Any discussion can provide insights, ideas and solutions to matters that are important to you from a perspective you would never have.
Finally, a few points on presenting. The event included short presentations of R&D proposals and I attended more than 15 myself. Based on my observations of other speakers I made a few notes about presenting for myself and I will share them here with you:
- Never read from your notes, because the listeners will not be engaged. Know what you want to say
- The presentation is a discussion where you as a presenter, tell a story to the audience. It’s never a lecture, it’s a story where both speaker and audience should feel involved
- You can talk about the most complex thing in the world, and you may be the smartest person alive. Still, explain it with simple language, and do not abuse domain jargon because many from the audience may not be familiar
- Talk as clear as possible and with as good pace as possible, try to not lose your breath and keep the volume and rhythm
- Limit as much as possible the “eeem”, “hummm” and phrases such as ‘I mean like’ and “you know?” because they make what you say less interesting and more difficult to follow
That’s all for now folks! As I said it was an interesting event which provided me with insights on different aspects and I was happy to share them with you.