Firstly, we have to remember that we are right at the beginning of this process and that there is no perfect combination now, nor will there be in the future. We’re using artificial intelligence in many different fields and industries, so we always have to make sure we’re taking all factors into consideration. It’s very important that we emphasise the strengths of both AI and people in this collaboration. Applied AI ultimately solves problems with maths, meaning there are certain things it does well and others it can’t. That’s not going to change for the foreseeable future. Emotional components, intuition, and creativity are still human domains—and we need to employ these skills to make best use of AI.
I’ve expressed it in a slightly exaggerated way, but I am really convinced that people will become cool again, despite artificial intelligence. This is one of the biggest topics of our age. We shouldn’t leave people out of this discussion, as the dialogue we’re having keeps throwing out questions that are fundamentally human. “What makes us human?” is one example. I think that we're going to start talking again about humans, their needs, what it means to be human, and creativity.
We asked ourselves the same thing: How do the two go together? Generative AI, generative neuronal networks, have come a long way. That was the starting point. Ultimately, it’s the oldest question in the computer science book: Can machines be creative? We wanted to see what new answers we could find with the new options out there. Roman has been an established painter for decades and is also interested in new technologies. Together we decided to team up and explore this topic. So we created the “Artificial Muse” prototype. It quickly became clear that the important thing wasn’t creating AI that could do what Roman does—paint—but to provide him with a tool that could give him new perspectives on how he works and his creative process—like a type of intelligent kaleidoscope.
How do the two go together? Generative AI, generative neuronal networks, have come a long way. That was the starting point. Ultimately, it’s the oldest question in the computer science book: Can machines be creative? We wanted to see what new answers we could find with the new options out there.
Not really. If you examine the topic in depth, you won’t really be surprised at these types of predictions and results. AI can create better AI because what’s important here is concrete computing power, machine leaning, and processing huge amounts of data. Machines can do these things better than developers. It’s the same with games. They have a “simple” environment governed by clear rules and one goal—and that’s quite easy to program. We set the learning system an objective and at some point, it will be able to do that better than humans, because, simply put, it has more computing power. And this will apply to more and more fields. It’s up to us humans to align ourselves with this change. The first time a chess computer won against a human, it was a sensation. Nowadays, professional chess players work with computers to train for matches, and those who play for fun can decide to play against a friend they know they can beat or sharpen their skills against unbeatable AI.
AI is going to change the world again. Fundamentally, in some areas. So much we can predict. But this type of disruption comes time and time again. Artificial intelligence is ultimately nothing more than a new technology, a form of software. Many people are afraid of AI because for them it’s a kind of black box—they don’t understand how it works. But this was at least partially the case with software, before AI. And there are other systems where humans can no longer understand how or why things are happening—such as the financial market. But going back to AI—of course there are certain risks. It’s up to us to manage this transformation. We have to guide the process. I think that’s hugely important.
All of us. It’s a question of financial ethics. Of course, politicians have to provide a regulatory framework, but I believe that enterprises, developers, consumers, and society as whole all share the responsibility of finding a meaningful way to manage this important and current topic. We need to create a dialogue that spans the whole of society. It’s a big task because it’s a complex topic that affects many spheres of life.
Well, that depends what you expect from this technology. For me, it’s simple: we’re talking about problem solving that draws on mathematical features and is used in clear situations. And for this we work with large amounts of data and learning processes. That’s AI in a nutshell. Software and mathematics. It can do a lot, but there’s also a lot it can’t do. And I am sure, however, that AI still has many things up its sleeve that we can’t even imagine right now.
We live in a data-fixated age. We have much more of it than ever before. So we’re quite advanced. But this question again brings me back to “human intelligence”. Machines don’t know what to do with data unless we tell them. It’s quite a creative process, in part: What data, what characteristics, does AI need to solve a specific problem? At the same time, the process is accelerated by the Internet of Things, new sensors, and 5G, for example. We have to and will learn, how to use this data.
That is a question that can never have a standard answer because the different application scenarios are virtually endless. What’s important is that companies appreciate the data and recognise its value in order to start using it. Teams have to be created, organisational framework is needed, ideally with support of management, and—without a doubt—money. This is how enterprises can create the germ of a new way of thinking, identifying specific use-case scenarios. This requires workshops and creativity. Only then can the first lighthouse project be brought to life, Making these things tangible for entire companies and their employees. You can’t just assume that everyone understands what artificial intelligence is. And many people aren’t sure that we actually need a combination of AI, human intelligence, and organisational intelligence. This is one thing I keep trying to get across. It’s not enough to just buy some technology.
What’s important is that companies appreciate the data and recognise its value in order to start using it. Teams have to be created, organisational framework is needed, ideally with support of management, and—without a doubt—money. This is how enterprises can create the germ of a new way of thinking, identifying specific use-case scenarios.
Quite poorly, unfortunately. Both schools and universities don’t have the teaching staff required to provide adequate teaching on the subject of digitalisation. It will take a good while until we come up to speed on this. The topic is, however, a complex one, and very quick-paced—training people in this is not an easy task. What I keep seeing are on-line courses, where you can hear leading scientists talk on various subjects including AI, often partially for free. But they usually require you to have a good basic knowledge of the subject.
Ten years is a relatively short period for such a complex topic. We will continue along the current path. This new type of software will change our world. We will see more automation, the world of work will adapt, humans and machines will co-operate differently and more intensively. For us humans, in ten years’ time we’ll still be busy shaping the transformation and ensuring it is a healthy change. Everyday questions arise: When should we let children come into contact with smartphones? What is good, bad, too early, or too late in a changing world? I have no doubt that humanity in general is finding it difficult to cope with these accelerated changes and will continue to do so. People have been around for a while, but this form of technology has only recently come into our lives.
Florian Dohmann is the founder of Birds on Mars - a company that develops strategies, spaces and applications at the boundaries of artificial and human intelligence. He is an expert on data, artificial intelligence and digital transformation. He supports clients in many industries in building sustainable data and AI capabilities. He is also a data science professional and guest lecturer at various universities.
At the Future Design Symposium, Florian Dohmann will talk about his art project with Roman Lipski: "Artificial Muse - Unplugged". This symposium will be held from 27 to 29 September 2019 at Experimenta Heilbronn—Germany’s largest science centre, which Bechtle supports as both an IT partner and sponsor.
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Published on Sep 25, 2019.