Artificial intelligence to better understand our world



While artificial intelligence has been an important tool in understanding our world better, it is imperative that we learn more, writes Paul Budde.

IN 2002, computer scientist, physicist and businessman Stephen wolfram published his book, A new kind of science.

Rather than seeing mathematics as the solution to unraveling the mysteries of the world and the universe, Wolfram looked at how systems work and concluded that they follow certain patterns to eventually create a particular outcome. There are many examples in the world around us, especially if we look at nature. We see some of the most beautiful patterns in our brains, birds, butterflies, flowers, galaxies, etc. The same goes for art and music, but we also see them in the gold number, prime numbers and digits of pi.

Wolfram worked on a cellular automaton. In a one-dimensional cellular automaton, there are two possible states (denoted 0 and 1) and the rule for determining the state of a cell in the next generation depends only on the current state of the cell and its two immediate neighbors . He understood how this could be applied using computer programming and was able to create systems that provided remarkable results that could not be formulated into an equation or an algorithm.

However, it was only in the last decade that he was able to further develop this with the help of more precise and more importantly artificial intelligence (AI) or machine learning (ML). , neural networks. These are computer systems at least vaguely inspired by biology neural networks.

While at the micro level (in our world) there is chaos, at the macro level, however, it seems that there are systems and patterns and very often they are very beautiful and show harmony. Knowing more about the underlying systems will provide us with better guidance on how best to understand the chaotic environments that are the reality we face. As a result of Wolfram’s work, it makes sense that what underlies our world, our universe, may well be based on simple systems.

It reminds me of some of the smart city works I’ve been involved with. Work with students in hacks. By collecting data from totally different systems and putting it together, we start, surprisingly, to see some very interesting models / systems of how a city works.

Another interesting example was revealed a few weeks ago. Operation Night Watch shows what can be achieved by a computer system combining imagination, resources, technical virtuosity and mastery of powerful technology.

Painted in 1642, Rembrandt’s Night Watch was cut in 1715 on all four sides to fit a new location in Amsterdam’s Town Hall (now the Royal Palace). The cut pieces have been lost. However, there was a copy of the complete painting made by Gerrit Lundens shortly after Rembrandt had finished it, but it was clear that it was not as good as that of the master himself. Nevertheless, this painting showed the missing pieces. With the help of AI, these parts were reconstructed.

The reconstruction was based on the system approach. First, AI was used to teach the computer what Lundens’ painting style was: his brushstrokes, techniques, etc. Then, thanks to AI, the system was fed with information based on Rembrandt’s techniques and his working method was programmed into the computer as the desired result. The result is simply breathtaking; Wolfram will be extremely proud of it.

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Ultimately, we are part of every (beautiful) pattern we see in the universe, so why would the subsystems be so different? I agree with Wolfram that we might need a totally different approach to understanding and replicating these systems. Maybe we could put all the pieces of the puzzle together and get a better picture of the whole underlying / global system. What we see as chaotic and unpredictable may well become clearer once we understand more and more of the underlying systems.

Now let’s move on to everyday reality – and the things that really matter here and now.

Let us stick to the known. We are solving more and more problems to better understand the environment / the world / the universe in which we live. We have already done a lot of good work here. However, we don’t act on what we learn, so we ignore the results rather than apply them for the good of all. Climate change is a classic example here.

We use ML, big data and who knows what else we are developing to create a better world. If we fail to implement the lessons we learn from using these tools, what is the point?

This is something that frustrates me greatly – we seem to lack the willpower to grab that knowledge and act on systems as we learn to understand them.

We also know that we are social beings, so we depend on each other. There are systems that underpin our communities as well – and again we know them very well – but here too we fail to apply them.

It is not difficult to see communities living in harmony and prospering; it is a good working system. What we are seeing over the past 50 years or so is that these communities / societies are disrupted and this indicates that social systems are failing. We know what is needed to make it work, but again, we fail to implement the solutions.

Developments of smart city systems based on grassroots developments are a good way to improve our communities.

Paul Budde is an independent Australian columnist and managing director of Paul Budde Consulting, an independent telecommunications research and consultancy organization. You can follow Paul on Twitter @PaulBudde.

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