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Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

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Bernard Marr ist ein weltbekannter Futurist, Influencer und Vordenker in den Bereichen Wirtschaft und Technologie mit einer Leidenschaft für den Einsatz von Technologie zum Wohle der Menschheit. Er ist Bestsellerautor von 20 Büchern, schreibt eine regelmäßige Kolumne für Forbes und berät und coacht viele der weltweit bekanntesten Organisationen. Er hat über 2 Millionen Social-Media-Follower, 1 Million Newsletter-Abonnenten und wurde von LinkedIn als einer der Top-5-Business-Influencer der Welt und von Xing als Top Mind 2021 ausgezeichnet.

Bernards neueste Bücher sind ‘Künstliche Intelligenz im Unternehmen: Innovative Anwendungen in 50 Erfolgreichen Unternehmen’

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6 Practical Examples Of How Quantum Computing Will Change Our World

2 July 2021

Computers have helped us to calculate the vastness of space and the minute details of subatomic particles. When it comes to counting and calculating, or following logical yes/no algorithms – computers outperform humans thanks to the electrons moving through their circuitry at the speed of light. But we generally don’t consider them as “intelligent” because, traditionally, computers haven’t been able to do anything themselves, without being taught (programmed) by us first.





So far, even if a computer had access to all of the information in the world it couldn’t do anything “smart” with it. It could find us a picture of a cat – but only because we had told it that certain pictures contain cats. In other words, ask it to find a picture of a cat and it will return with a picture which it has been told is of a cat.

This has several implications which limit its helpfulness – not least that a large amount of human time has to be spent telling it what every picture contains. The data (pictures) need to pass through a human bottleneck, where they are labelled, before the computer can, with lightning-quick precision, identify it as a cat picture and show it to us when we request it.

While this works well enough if we are just searching for cat pictures on Google to pass our time, if we want to do something more advanced – such as monitor a live video feed and tell us when a cat wanders in front of the camera – it’s not so great.

It is problems like this which machine learning is trying to solve. At its most simple, machine learning is about teaching computers to learn in the same way we do, by interpreting data from the world around us, classifying it and learning from its successes and failures. In fact, machine learning is a subset, or better, the leading edge of artificial intelligence.


How did machine learning come about?

Building algorithms capable of doing this, using the binary “yes” and “no” logic of computers, is the foundation of machine learning – a phrase which was probably first used during serious research by Arthur Samuel at IBM during the 1950s. Samuel’s earliest experiments involved teaching machines to learn to play checkers.

As knowledge – something to draw insight from and a basis for making decisions – is deeply integral to learning, these early computers were severely handicapped due to the lack of data at their disposal. Without all of the digital technology we have today to capture and store information from the analogue world, machines could only learn from data slowly inputted through punch cards and, later, magnetic tapes and storage.

Business Trends In Practice | Bernard Marr
Business Trends In Practice | Bernard Marr

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