The Time to Create New Models for Teaching AI is Now
In October’s House of Lords Science & Technology Committee session on the ongoing inquiry on People and Skills in UK Science Technology Engineering and Mathematics (STEM), Robert West, Head of Education and Skills at the CBI stated ‘upskilling and reskilling are the biggest games in town”, citing it as one of the UK Government’s missions described in the White Paper on Levelling Up. This reflection is key to understanding how the UK (and many developed nations) will address the growing skills gaps in STEM. Anecdotally and evidentially, companies are crying out for qualified staff with skills in a range of digital skills including AI (Artificial Intelligence), high performance computing (HPC), data science and cyber security. While we undoubtedly need to encourage more students to take up STEM subjects, we also need to recognise that much more could be immediately delivered through the up-skilling and re-skilling of experienced people already in the workforce.
This topic is particularly close to the hearts of many universities delivering education in AI which in turn requires skills in computer science, HPC and other related topics. According to the OECD Policy Observatory on AI, over 60 countries have published over 800 policy instruments related to AI, illustrating the importance of the field and the need to drive capacity to deliver AI into an ever increasing number of market sectors. It is equally clear that the most significant barrier to AI adoption is the shortage of people with AI skills and the associated skills of HPC, data science and cyber security, reflecting the need to develop new mechanisms to deliver such education to a much wider set of students. According to the 2021 Ipsos MORI report for DCMS on “Understanding the UK AI labour market: 2020”, there were over 110,000 job postings in AI and data science in 2020, with over 50% requiring a degree, most commonly in STEM subjects. In their analysis, they found that 54% of company’s AI specialists had PhDs, a very high barrier to entry to an area in such high demand.
In the US, the insatiable demand for AI-related doctorates has meant that over 20% of computer science PhDs are now in AI. There are further challenges related to the dependence of AI on high performance computing, especially the architecting of massive GPU clusters. The demand for such skills has led to aggressive recruitment among companies, leading to salary inflation and high levels of staff churn. It has even led to problems in parts of academia with large numbers of academics in some universities, including Carnegie Mellon, Georgia Institute of Technology and University of Washington, choosing to make the jump to industry. Even highly successful AI research groups, producing dozens of AI PhDs every year, are barely making a dent in the demand.
One of the long-term solutions to the skills drought in AI, Data Science, HPC and related skills, is to push the education of such topics to points earlier and later in the education chain. Many universities already offer strong research degrees in AI, strong Masters courses in AI (including conversation courses for non-STEM graduates), often underpinned by knowledge of HPC, cyber and other essential skills. Some undergraduate degrees which include AI such topics, but there it stops. Introducing these complex subjects into secondary education is desirable but hard because of the complexity and breadth of topics which need to be taught. Therefore the obvious area to explore is the opportunity to provide education opportunities to people already enjoying technical careers in IT or where domain expertise in areas like healthcare, banking, energy production, etc can be augmented by the addition of these high-end skills. There are multiple challenges here, including how to compress large topics like AI into modular, flexible and immediately practical courses, ideally with some formal recognition and consistency between course providers. Online courses offer some hope but there is no substitute (yet) for face to face time with expert practitioners.
One of the critical enablers for this change is to develop stronger engagement between education providers and the companies developing and using these technologies so that courses are shaped and adapted to meet real-world needs. This is particularly important in AI because of the volume of leading-edge work originating in industry, coupled with the sheer pace and scale of commercial development in AI backed by billion-dollar budgets of the Silicon Valley supergiants. Disruptive models are starting to emerge such as Xavier Neil’s Ecole 42 teacherless peer-to-peer school for computer science, bypassing the need for academic educators completely. There is also the growing possibility of using AI to improve education delivery in these topic areas, allowing courses to be personalised and adapted according to the student’s needs, experience, learning styles, etc. The time to engage with this topic is now and the implications of getting it right (or wrong) will have significant impact on our future societies, economies and lives.
Future of Compute Week 2022
During this week we will deep-dive into a number of themes that if addressed could develop our large scale compute infrastructure to support the UK’s ambitions as a science and technology superpower. To find out more, including how to get involved, click the link below
Laura is techUK’s Head of Programme for Technology and Innovation.
She supports the application and expansion of emerging technologies, including Quantum Computing, High-Performance Computing, AR/VR/XR and Edge technologies, across the UK. As part of this, she works alongside techUK members and UK Government to champion long-term and sustainable innovation policy that will ensure the UK is a pioneer in science and technology
Before joining techUK, Laura worked internationally as a conference researcher and producer covering enterprise adoption of emerging technologies. This included being part of the strategic team at London Tech Week.
Laura has a degree in History (BA Hons) from Durham University, focussing on regional social history. Outside of work she loves reading, travelling and supporting rugby team St. Helens, where she is from.
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Chris is the Programme Manager for Cloud, Tech and Innovation
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Sue leads techUK's Technology and Innovation work.
This includes work programmes on cloud, data protection, data analytics, AI, digital ethics, Digital Identity and Internet of Things as well as emerging and transformative technologies and innovation policy. She has been recognised as one of the most influential people in UK tech by Computer Weekly's UKtech50 Longlist and in 2021 was inducted into the Computer Weekly Most Influential Women in UK Tech Hall of Fame. A key influencer in driving forward the data agenda in the UK Sue is co-chair of the UK government's National Data Strategy Forum. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries for 2020 Sue has also been shortlisted for the Milton Keynes Women Leaders Awards and was a judge for the Loebner Prize in AI. In addition to being a regular industry speaker on issues including AI ethics, data protection and cyber security, Sue was recently a judge for the UK Tech 50 and is a regular judge of the annual UK Cloud Awards.
Prior to joining techUK in January 2015 Sue was responsible for Symantec's Government Relations in the UK and Ireland. She has spoken at events including the UK-China Internet Forum in Beijing, UN IGF and European RSA on issues ranging from data usage and privacy, cloud computing and online child safety. Before joining Symantec, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Masters Degree on International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
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