Artificial intelligence is an umbrella term for technologies which make computer systems "intelligent", in the sense that they are able to solve problems and learn from their own experiences. Today, machine learning is one of the best known and most widely used of these technologies.
In simple terms, it is about using mathematics, statistics, rules, computer science, neurology and linguistics to collect, systematise and recognise patterns in large amounts of data. The use of rules, algorithms and patterns is becoming more efficient and advanced every day.
Machine learning (ML) – proven and valuable
Machine learning is essentially about recognising patterns in large amounts of data. It could for example be language recognition which we find in different types of "chatbots" or dialog robots, which currently appear on many websites. This variant of artificial intelligence is based on the collection and systematisation of large amounts of data using probability calculations and algorithms. The use of rules, algorithms and patterns is becoming more efficient and advanced every day.
As long ago as 1996, machine learning enabled the supercomputer Deep Blue to beat Russian chess grandmaster Garry Kasparov in a game of chess using basic pattern recognition.
At the time, the victory was a global sensation. Today, we encounter machine learning solutions based on pattern recognition every single day. For example, when the music service Spotify recommends new playlists for us based on our listening patterns, or when Amazon presents us with targeted offers based on previous purchases.
Deep learning – advanced, but relatively immature technology
Another technology is Deep learning, which involves the exchange of data in what are known as neural networks, networks which resemble the human brain and which are used to extract experiences from historical databases.
Deep learning attempts to mimic the neural structure of the human brain, to enable the computer to make independent decisions based on scenarios and situations which the computer has not previously encountered.
Today, autonomous vehicles are perhaps the best example of a practical application of Deep learning. Another example is the development of robots which are capable of performing complex surgery without any loss of concentration or shaky hands. A little further ahead into the future, we can expect to see household robots which are capable of working out for themselves how they should tidy away mum’s laptop or little brother’s toys.
Cegal and AI
At Cegal, we have many consultants who have extensive experience of a range of ML and AI solutions.
Here are some examples of assignments we have carried out for our customers:
Solutions linked to map data which analyse the impact of environmental changes, e.g. on the development of flora and the tree line.
Development of solutions for condition analyses at sea and earth cables in the power distribution grid, to make it easier to determine which cables should be replaced first.
Solution for power generators, where weather and turbine data is organised so as to enable preventive maintenance to be carried out and the data to be used as a basis for the optimisation of power generation.