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Statistically Speaking

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  • Statistically Speaking is the Office for National Statistics' podcast, offering in-depth interviews on the latest hot topics in the world of data, taking a peek behind the scenes of the UK’s largest independent producer of official statistics and exploring the stories behind the numbers.
    © 2024
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  • AI: The Future of Data

    AI: The Future of Data
    2024/05/20
    With the public release of large language models like Chat GPT putting Artificial Intelligence (AI) firmly on our radar, this episode explores what benefits this technology might hold for statistics and analysis, as well as policymaking and public services. Joining host, Miles Fletcher, to discuss the groundbreaking work being done in this area by the Office for National Statistics (ONS) and across the wider UK Government scene are: Osama Rahman, Director of the ONS Data Science Campus; Richard Campbell, Head of Reproducible Data Science and Analysis; and Sam Rose, Deputy Director of Advanced Analytics and Head of Data Science and AI at the Department for Transport. Transcript MILES FLETCHER Welcome again to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and, if you've been a regular listener to these podcasts, you'll have heard plenty of the natural intelligence displayed by my ONS colleagues. This time though, we're looking into the artificial stuff. We'll discuss the work being done by the ONS to take advantage of this great technological leap forward; what's going on with AI across the wider UK Government scene; and also talk about the importance of making sure every use of AI is carried out safely and responsibly. Guiding us through that are my ONS colleagues - with some of the most impressive job titles we've had to date - Osama Rahman is Director of the Data Science Campus. Richard Campbell is Head of Reproducible Data Science and Analysis. And completing our lineup, Sam Rose, Deputy Director of Advanced Analytics and head of data science and AI at the Department for Transport. Welcome to you all. Osama let's kick off then with some clarity on this AI thing. It's become the big phrase of our time now of course but when it comes to artificial intelligence and public data, what precisely are we talking about? OSAMA RAHMANSo artificial intelligence quite simply is the simulation of human intelligence processes by computing systems, and the simulation is the important bit, I think. Actually, people talk about data science, and they talk about machine learning - there's no clear-cut boundaries between these things, and there's a lot of overlap. So, you think about data science. It's the study of data to extract meaningful insights. It's multidisciplinary – maths, stats, computer programming, domain expertise, and you analyse large amounts of data to ask and answer questions. And then you think about machine learning. So that focuses on the development of computer algorithms that improve automatically through experience and by the use of data. So, in other words, machine learning enables computers to learn from data and make decisions or predictions without explicitly being programmed to do so. So, if you think about some of the stuff we do at the ONS, it's very important to be able to take a job and match it to an industrial classification - so that was a manually intensive process and now we use a lot of machine learning to guide that. So, machine learning is essentially a form of AI. MILES FLETCHERSo is it fair to say then that the reason, or one of the main reasons, people are talking so much about AI now is because of the public release of these large language models? The chat bots if you like, to simpletons like me, the ChatGPT’s and so forth. You know, they seem like glorified search engines or Oracles - you ask them a question and they tell you everything you need to know. OSAMA RAHMANSo that's a form of AI and the one everyone's interested in. But it's not the only form – like I said machine learning, some other applications in data science, where we try in government, you know, in trying to detect fraud and error. So, it's all interlinked. MILES FLETCHERWhen the ONS asked people recently for one of its own surveys, about how aware the public are about artificial intelligence, 42% of people said they used it in their home recently. What sort of things would people be using it for in the home? What are these everyday applications of AI and I mean, is this artificial intelligence strictly speaking? OSAMA RAHMANIf you use Spotify, or Amazon music or YouTube music, they get data on what music you listen to, and they match that with people who've been listening to similar music, and they make recommendations for you. And that's one of the ways people find out about new music or new movies if you use Netflix, so that's one pretty basic application, that I think a lot of people are using in the home. MILES FLETCHERAnd when asked about what areas of AI they'd like to know more about, more than four in 10 adults reported that they'd like to know better how to judge the accuracy of information. I guess this is where the ONS might come in. Rich then, if I could just ask you to explain what we've been up to, what the Data Science Campus has been up to, to actually bring the power of artificial intelligence to our ...
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    34 分
  • Communicating Uncertainty: How to better understand an estimate.

    Communicating Uncertainty: How to better understand an estimate.
    2024/03/25
    The ONS podcast returns, this time looking at the importance of communicating uncertainty in statistics. Joining host Miles Fletcher to discuss is Sir Robert Chote, Chair of the UKSA; Dr Craig McLaren, of the ONS; and Professor Mairi Spowage, director of the Fraser of Allander Institute. Transcript MILES FLETCHER Welcome back to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and to kick off this brand new season we're going to venture boldly into the world of uncertainty. Now, it is of course the case that nearly all important statistics are in fact estimates. They may be based on huge datasets calculated with the most robust methodologies, but at the end of the day they are statistical judgments subject to some degree of uncertainty. So, how should statisticians best communicate that uncertainty while still maintaining trust in the statistics themselves? It's a hot topic right now and to help us understand it, we have another cast of key players. I'm joined by the chair of the UK Statistics Authority Sir Robert Chote, Dr. Craig McLaren, head of national accounts and GDP here at the ONS, and from Scotland by Professor Mairi Spowage, director of the renowned Fraser of Allander Institute at the University of Strathclyde. Welcome to you all. Well, Sir Robert, somebody once famously said that decimal points in GDP is an economist’s way of showing they've got a sense of humour. And well, that's quite amusing - particularly if you're not an economist - there's an important truth in there isn't there? When we say GDP has gone up by 0.6%. We really mean that's our best estimate. SIR ROBERT CHOTE It is. I mean, I've come at this having been a consumer of economic statistics for 30 years in different ways. I started out as a journalist on the Independent and the Financial Times writing about the new numbers as they were published each day, and then I had 10 years using them as an economic and fiscal forecaster. So I come at this very much from the spirit of a consumer and am now obviously delighted to be working with producers as well. And you're always I think, conscious in those roles of the uncertainty that lies around particular economic estimates. Now, there are some numbers that are published, they are published once, and you are conscious that that's the number that stays there. But there is uncertainty about how accurately that is reflecting the real world position and that's naturally the case. You then have the world of in particular, the national accounts, which are numbers, where you have initial estimates that the producer returns to and updates as the information sets that you have available to draw your conclusions develops over time. And it's very important to remember on the national accounts that that's not a bug, that's a feature of the system. And what you're trying to do is to measure a very complicated set of transactions you're trying to do in three ways, measuring what the economy produces, measuring incomes, measuring expenditure. You do that in different ways with information that flows in at different times. So it's a complex task and necessarily the picture evolves. So I think from the perspective of a user, it's important to be aware of the uncertainty and it's important when you're presenting and publishing statistics to help people engage with that, because if you are making decisions based on statistics, if you're simply trying to gain an understanding of what's going on in the economy or society, generally speaking you shouldn't be betting the farm on the assumption that any particular number is, as you say, going to be right to decimal places. And the more that producers can do to help people engage with that in an informed and intelligent way, and therefore mean that decisions that people take on the basis of this more informed the better. MF So it needs to be near enough to be reliable, but at the same time we need to know about the uncertainty. So how near is the system at the moment as far as these important indicators are concerned to getting that right? SRC Well, I think there's an awful lot of effort that goes into ensuring that you are presenting on the basis of the information set that you have the best available estimates that you can, and I think there's an awful lot of effort that goes into thinking about quality, that thinks about quality assurance when these are put together, that thinks about the communication how they mesh in with the rest of the, for example, the economic picture that you have, so you can reasonably assure yourself that you're providing people with the best possible estimate that you can at any given moment. But at the same time, you want to try to guide people by saying, well, this is an estimate, there's no guarantee that this is going to exactly reflect the real world, the more that you can do to put some sort of numerical...
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    33 分
  • ONS: Year in Review 2023

    ONS: Year in Review 2023
    2023/12/21
        In this episode Miles is joined by the National Statistician, Sir Ian Diamond, to reflect on what has been a busy and transformative year at the Office for National Statistics.    Transcript    MILES FLETCHER  This is “Statistically Speaking”, the official podcast of the UK Office for National Statistics, I’m Miles Fletcher. This is our 20th episode, in fact, a milestone of sorts, though not a statistically significant one. What is significant is that we're joined, once again, to look back at the highlights from another 12 months here at the ONS by none other than the National Statistician himself, Professor Sir Ian Diamond. Ian, thanks for joining us again. The year started for you with being reappointed as the national statistician. As 2023 developed, how glad did you feel to be back?   SIR IAN DIAMOND  Of course, you know, I was hugely privileged to be invited to continue. It's one of the most exciting things you could ever do and I will continue to do everything in my power to bring great statistics to the service of our nation.   MF  To business then, and this time last year, we sat in this very room talking about the results of Census 2021, which were coming in quite fresh then. And we've seen the fastest growth of the population, you told us, since the baby boom of the early 1960s. Over the course of the year much more data has become available from that census and this time, we've been able to make it available for people in much richer ways, including interactive maps, create your own data set tools. What does that say about the population data generally and the way that people can access and use it now? How significant is that there's that sort of development?   SID  Well I think we need to recognise that the sorts of things that we can do now, with the use of brilliant technology, brilliant data science and brilliant computing is enabling us to understand our population more, to be able to make our data more accessible. 50, 60, 70 years ago, 150 years ago, we would have just produced in about six or seven years after the census, a report with many, many tables and people would have just been able to look at those tables. Now, we're able to produce data which enables people to build their own tables, to ask questions of data. It’s too easy to say, tell me something interesting, you know, the population of Dorset is this. Okay, that's fine, but actually he wants to know much more about whether that's high or low. You want to know much more about the structure of the population, what its needs for services are, I could go on and on. And each individual will have different questions to ask of the data, and enabling each individual to ask those questions which are important to them, and therefore for the census to be more used, is I think, an incredibly beautiful thing.   MF  And you can go onto the website there and create a picture...   SID  Anyone can go onto the website, anyone can start to ask whatever questions they want of the data. And to get very clearly, properly statistically disclosed answers which enable them to use those data in whatever way they wish to.   MF  And it's a demonstration of obviously the richness of data that's available now from all kinds of sources, and behind that has been a discussion of, that's gone on here in the ONS and beyond this year, about what the future holds for population statistics and how we can develop those and bring those on. There's been a big consultation going on at the moment. What's the engagement with that consultation been like?   SID  Well the engagement's been great, we’ve had around 700 responses, and it addresses some fundamental questions. So the census is a really beautiful thing. But at the same time, the census, the last one done the 21st of March 2021, was out of date by the 22nd of March 2021, and more and more out of date as you go on and many of our users say to us, that they want more timely data. Also by its very nature a census is a pretty constrained data set. We in our country have never been prepared to ask for example, income on the census yet this is one of the most demanded questions. We don't ask it because it is believed that it is too sensitive. And so there are many, many, many questions that we simply can't ask because of space. There are many more questions that we simply cannot ask in the granularity that we want to. We've been doing some work recently to reconcile the differences between estimates in the number of Welsh speakers from surveys with estimates on the number of people in the census who report they speak Welsh. Frankly, it would be better if we were able to ask them to get information in a more granular way. And so while the census is an incredibly beautiful thing, we also need to recognise that as time goes on, the technology and the availability of data allowing us to link data becomes much more of a great opportunity that we have been ...
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    27 分

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Statistically Speaking is the Office for National Statistics' podcast, offering in-depth interviews on the latest hot topics in the world of data, taking a peek behind the scenes of the UK’s largest independent producer of official statistics and exploring the stories behind the numbers.
© 2024
政治学 数学 科学
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