AI creates jobs, right? Right!?
New technologies often confront people with a difficult factor of the unknown, with an inherent change around the corner. New technology has always – and always will be - around, we are too curious, ambitious and inventive not to want to tinker with stuff or trying to change processes for the better. The outcome of that tinkering around, adapting existing materials and processes, often results in something new, a new technology.
Go for the dream
Take some steps back and think of what the Wright brothers did over a century ago. They believed in a dream to be able to “transport” humans in the air – call it “flying” if you want. They used existing science, a whole lot of research and changed existing technology to move everything a huge step forward. Out of that process, the Wright brothers invented something completely new that would shake up the planet. An airplane.
That flying contraption - no offence to mr Wilbur and Orville Wright - was very dangerous in the beginning, the first step to getting a real airplane in the skies came at a huge cost. The Wright brothers weren’t the only ones trying to change the world. A lot of other people tried to build upon a dream and a belief. And paid a huge price.
But the moment eventually came when someone pulled it off; that moment was celebrated by the Wright brothers. They realised the “dream“ and translated it into a working piece of technology. They realised a vague belief and a century long dream into a complete new set of words, concepts, tangible things…. All those years of work, research, testing, failures and small successes ultimately resulted into a new -soon to be huge- industry.
Aviation has a link to freedom, a human need of being able to go anywhere anytime - to be in control of what is around. It was also a belief to do things better.
The same reasoning goes for computation devices: from Wilhelm Schickard’s calculating clock over Charles Babbage first mechanical computer: people want to master certain areas, be in control, or simplify the basics so we can use our time more efficiently and effective to go and explore new frontiers of science.
Those people making the actual change, always build on work of others, sure. But once an emerging technology has found a more common ground - I don’t want to talk about product life cycle but think of a Gauss distribution - the economy helps.
Because humans are eager to earn money.
These two activity streams of being curious and building something new, plus the willingness to make a profit sure help push change and innovation into the masses.
Think about it: 100 years later, and an airplane is now even called an ”Airbus”, it really has become a common “thing” in the last decade. You can buy a ticket for 5 euro taking you 1000 miles away.
I don’t know if that is a good thing in the long run, but the point here is that economy is a big pushing force of innovation, if not the most important force.
And how about Artificial Intelligence?
Truth be told, we live in exciting times when it comes to computational changes.
Yes, we do: The qubit has been defined some time ago, and that seems a promising basis of new computational power to Intel. And DeepMind is also moving forward, getting real results and bringing a first learning algorithm (see: AlphaZero).
So this all does feel like being in the early days, where pioneers were searching and paving a way to a new reality, the era we were “dialling-in to the internet” with a 1200 baud modem.
It is hard to predict when we will find a common working version of AI, and when and how it will be put to good use.
There are nice ‘consumer’ oriented steps forwards like what Google recently showed with their Duplex app demo.
This shows there will be a broad market for small functionalities.
And that’s what the economy really likes: mass market.
We believe there will be a huge market for AI driven applications. And huge market means investment, means ‘people’, even in an AI environment. But let’s be clear: AI still has some serious steps to make to get to a point of “Intelligence”.
We all need clever, curious people helping us pave that way: doing research, building tests, programming proofs-of-concepts and helping customers. At MultiMinds, we use AI and ML to find patterns and predictions in huge data lakes. We can't spill the beans on how exactly we do that, but in data there is a lot to go for. MultiMinds uses a lot of "power" and solutions we can find of the leading players to help customers go into detail of their massive data collections. And we add our own intellegence to that; meaning custom built stuff to find more logic, patterns...
That takes curious, savvy, hands-on and can-do people that aren't afraid of digging into the matter and being eager to find that one hidden gem that makes the difference for Multiminds' customers. Fortunately, multiminds has a great team of highly trained and skilled analysts that get this analytics work done.
Bottom line
We should not stop being curious, and keep trying to improve and make things better. That’s something we owe to our nature: make things better, every step of the way. And it starts with talented, skilled and eager-to-get-things-done people.
So: keep buggering on! We’ll get there.
