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We’re relying on a wide set of actions and tools to help us deal with the current pandemic. The BBC is playing its part to inform, educate and entertain. And for us and others, digital technologies are playing a key role. In this blogpost, I discuss the BBC’s approach to one of most important set of digital tools: 'machine learning'.

The term machine learning (ML) covers a range of computer systems which learn from experience. With Covid-19, we know ML techniques are being used for contact mapping and predicting the effectiveness of drugs.

One reason ML is being deployed here is that it is being deployed everywhere. Tools that can be trained on vast data sets and learn and improve as a result are behind social media feeds, computer vision and robotics, financial and weather models, and of course the improved machine translation and voice recognition systems that many of us use every day.

Many of these areas are directly relevant to the BBC and its day-to-day operations. The Design and Engineering division I lead has been looking at them closely for some time, exploring ways in which machine learning can help us to enhance what the BBC offers our audiences.

We believe that ML can help us respond to audience expectations, especially from ‘digital native’ younger audiences. A key area is content discovery and recommendations. Audiences no longer accept having to put significant effort into searching for what they want. They want a personalised offer, which feels both relevant and fresh – something ML can help us to provide.

And ML can help us innovate. There is potential to transform the ways we make programmes, the way we run as a business, and of course the ways we do our journalism. Examples include speeding up video compression or finding ways of detecting and flagging disinformation.

It's not surprising that we should be looking at ML in this way: the BBC has always worked with new technologies to offer the best user experience we can. This is why we created iPlayer and Sounds, and developed approaches like our Global Experience Language (GEL), the BBC’s shared design framework. As ML has developed, we have started to explore how to use the technologies responsibly and efficiently. We have also developed a set of principles governing our deployment of ML technologies.

I want to be clear about where our ambitions lie. We are not Microsoft, Google or Baidu. We don't have their amounts of data, money or computing power. We are not aiming to compete with them by developing our own machine learning frameworks, or performing advanced research in novel algorithms.

But the BBC is a fertile environment for applying ML techniques. We have unique types of problems to solve, and we have the ability as an organisation to draw from almost one hundred years of experience in storytelling. We are ambitious in the desire to explore the positive impact of applying ML to our operations.

What does this mean in practice?

The first thing we think through is whether a ML solution is needed. We then assess the benefits of each application to both individuals and society. An example would be designing the BBC’s content recommendation engines to broaden our audience’s horizons. This is because we think there is both individual and public value in discovering new perspectives, music or experiences – not simply finding more of the same.

We also ensure that we use our resources efficiently. ML requires a solid data platform and a consistent and modern approach to experimentation across our portfolio of products and services. It is important to maintain a central and coordinated approach so that, as an organisation, we can deploy scarce capability in the most effective way and optimise on learning quickly.

We pair our Machine Learning capabilities with human judgement and diversity of experience. This applies both from a technology development perspective - where we bring together technical experts (e.g. data scientists, UX designers, product specialists) with editorial, policy, legal and R&D colleagues, and in terms of our audience experience - where the BBC’s automated curation will sit alongside human curation.

Finally, we recognise the need for collaboration and co-operation with other industries and organisations in maturing our approach with ML. Collaborations which allow media and technology companies to bring their expertise together in the public good will create more powerful experiences than anything we can do alone.

Machine learning has enormous potential to transform not just the BBC but every other organisation. I want us to use it to connect with people more effectively, to bring out the strengths of our storytelling and to find new ways of communicating our trusted journalism. I hope the power of machines will help me and my colleagues create something new, compelling and distinctively BBC for each member of our audience.

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