Abstract

Despite its transformative potential, artificial intelligence risks following a well-worn path where technological innovation fails to address society’s most pressing problems. The UK’s experience with major IT projects shows this disconnect: from the Horizon scandal’s wrongful prosecutions to the £10 billion failure of the NHS Lorenzo project. These weren’t only technical failures but a failure to bridge between needs and the provided solution, a failure to match supply and demand.

This misalignment persists in AI development: in 2017, the Royal Society’s Machine Learning Working group conducted research with Ipsos MORI to explore citizens’ aspirations for AI. It showed strong desire for AI to tackle challenges in health, education, security, and social care, while showing explicit disinterest in AI-generated art. Yet seven years later, while AI has made remarkable progress in emulating human creative tasks, the demand in these other areas remains unfulfilled.

This talk examines this persistent gap through a lens that’s inspired by innovation economics. We argue that traditional market mechanisms have failed to map macro-level interventions to the micro-level societal needs. We’ll explore why conventional approaches to technology deployment continue to fall short and propose radical changes needed to ensure that AI truly serves citizens, science, and society.

Henry Ford’s Faster Horse

Figure: A 1925 Ford Model T built at Henry Ford’s Highland Park Plant in Dearborn, Michigan. This example now resides in Australia, owned by the founder of FordModelT.net. From https://commons.wikimedia.org/wiki/File:1925_Ford_Model_T_touring.jpg

It’s said that Henry Ford’s customers wanted a “a faster horse”. If Henry Ford was selling us artificial intelligence today, what would the customer call for, “a smarter human”? That’s certainly the picture of machine intelligence we find in science fiction narratives, but the reality of what we’ve developed is much more mundane.

Car engines produce prodigious power from petrol. Machine intelligences deliver decisions derived from data. In both cases the scale of consumption enables a speed of operation that is far beyond the capabilities of their natural counterparts. Unfettered energy consumption has consequences in the form of climate change. Does unbridled data consumption also have consequences for us?

If we devolve decision making to machines, we depend on those machines to accommodate our needs. If we don’t understand how those machines operate, we lose control over our destiny. Our mistake has been to see machine intelligence as a reflection of our intelligence. We cannot understand the smarter human without understanding the human. To understand the machine, we need to better understand ourselves.

Panacea

Figure: Panacea the goddess of the universal remedy.

In Greek mythology, Panacea was the goddess of the universal remedy. One consequence of the pervasive potential of technology is that it is positioned, like Panacea, as the purveyor of a universal solution. Whether it is overcoming industry’s productivity challenges, or as a salve for strained public sector services, or a remedy for pressing global challenges in sustainable development, technology is presented as an elixir to resolve society’s problems.

In practice, translation of technology into practical benefit is not simple. Moreover, a growing body of evidence shows that risks and benefits from innovations are unevenly distributed across society.

When carelessly deployed, technology risks exacerbating existing social and economic inequalities.

Revolution

Arguably the information revolution we are experiencing is unprecedented in history. But changes in the way we share information have a long history. Over 5,000 years ago in the city of Uruk, on the banks of the Euphrates, communities which relied on the water to irrigate their corps developed an approach to recording transactions in clay. Eventually the system of recording system became sophisticated enough that their oral histories could be recorded in the form of the first epic: Gilgamesh.

See Lawrence (2024) cuneiform p. 337, 360, 390.

Figure: Chicago Stone, side 2, recording sale of a number of fields, probably from Isin, Early Dynastic Period, c. 2600 BC, black basalt

It was initially developed for people as a record of who owed what to whom, expanding individuals’ capacity to remember. But over a five hundred year period writing evolved to become a tool for literature as well. More pithily put, writing was invented by accountants not poets (see e.g. this piece by Tim Harford).

In some respects today’s revolution is different, because it involves also the creation of stories as well as their curation. But in some fundamental ways we can see what we have produced as another tool for us in the information revolution.

Coin Pusher

Disruption of society is like a coin pusher, it’s those who are already on the edge who are most likely to be effected by disruption.

Figure: A coin pusher is a game where coins are dropped into th etop of the machine, and they disrupt those on the existing steps. With any coin drop, many coins move, but it is those on the edge, who are often only indirectly effected, but also most traumatically effected by the change.

One danger of the current hype around ChatGPT is that we are overly focussing on the fact that it seems to have significant effect on professional jobs, people are naturally asking the question “what does it do for my role?”. No doubt, there will be disruption, but the coin pusher hypothesis suggests that that disruption will likely involve movement on the same step. However it is those on the edge already, who are often not working directly in the information economy, who often have less of a voice in the policy conversation who are likely to be most disrupted.

Royal Society Report

Figure: The Royal Society report on Machine Learning was released on 25th April 2017

A useful reference for state of the art in machine learning is the UK Royal Society Report, Machine Learning: Power and Promise of Computers that Learn by Example.

See Lawrence (2024) Royal Society; machine learning review and p. 25, 321, 395.

Public Research

Figure: The Royal Society comissioned public research from Mori as part of the machine learning review.

As is often the cas when members of the public are well convened, they offered sensible opinions about the utility and challenges of machine learning methods.

Figure: One of the questions focussed on machine learning applications.

A range of different scenaios were captured by this qualitative work.

Figure: The public were broadly supportive of a range of application areas.

Figure: But they failed to see the point in AI’s that could produce poetry.

The one scenario where the public were unable to perceive any advantage in machine learning was an example from art, the creation of poetry. This was felt to be a fundamentally human activity that machines could only emulate at best.

Mercutio

It’s ironic that despite the public focus on transport, health, cities, crime and their confusion as to why we might want AI to do art, one area where significant progress has been made is on AIs that can create literature.

Figure: Despite the public’s desire to see challenges in health, finance, transport, crime solved by machine learning and their skepticism around poetry, one of the problems we have solved seems to be poetry.

User: Hi ChatGPT, Could you write soliloquy that introduces what a Large Language Model is as spoken by Mercutio?

Machine: Ah, friends, lend me your ears, I prithee, For now I must utter a tale of wonder, Of a creation divine, called the Large Language Model, An oracle, a sorcerer, a wordsmith, yea verily, a master of the quill.

From the depths of this scholarly dominion, A creature of artifice did rise; Twas wrought of ones and zeroes, woven as if by Puck’s own hand, To mimic our thoughts, our voice, our very essence.

A thousand minds, nay, a millionfold, Conjoined in this digital Athenaeum, Words and phrases, like soldiers in battle, Marching to form the verses of our age.

This Large Language Model, a changeling so fair, Hath the power to bring forth wisdom, To transmute our queries, our curiosities, Into answers, clear as a midsummer’s day.

Yet, beware, my companions, for magic hath its limits, Not unlike the mortal bounds of we who dwell in flesh, The Large Language Model hath its strengths, But ’tis not an all-knowing oracle, a god it is not.

As Mercutio, your humble servant, doth stand before thee, I marvel at the art and science that hath brought forth this wondrous creation, This Large Language Model, a testament to man’s yearning, To comprehend the world and make meaning of the great tapestry of existence.

In faith, dear friends, let us embrace the magic and beauty of this digital bard, And mayhaps, by its grace, we shall glean some wisdom, As we wander together, hand in hand, Through this boundless realm of knowledge and wonder.

ChatGPT transcript, 14th April 2023.

A Question of Trust

In Baroness Onora O’Neill’s Reeith Lectures from 2002, she raises the challenge of trust. There are many aspects to her arcuments, but one of the key points she makes is that we cannot trust without the notion of duty. O’Neill is bemoaning the substitution of duty with process. The idea is that processes and transparency are supposed to hold us to account by measuring outcomes. But these processes themselves overwhelm decision makers and undermine their professional duty to deliver the right outcome.

Figure: A Question of Trust by Onora O’Neil which examines the nature of trust and its role in society.

Again Univesities are to treat each applicant fairly on the basis of ability and promise, but they are supposed also to admit a socially more representative intake.

There’s no guarantee that the process meets the target.

Onora O’Neill A Question of Trust: Called to Account Reith Lectures 2002 O’Neill (2002)]

O’Neill is speaking in 2002, in the early days of the internet and before social media. Much of her thoughts are even more relevant for today than they were when she spoke. This is because the increased availability of information and machine driven decision-making makes the mistaken premise, that process is an adequate substitute for duty, more apparently plausible. But this undermines what O’Neill calls “intelligent accountability”, which is not accounting by the numbers, but through professional education and institutional safeguards.

See Lawrence (2024) O’Neill, Baroness Onora: ‘A question of trust’ lecture series (2002) p. 352, 363.

The Great AI Fallacy

There is a lot of variation in the use of the term artificial intelligence. I’m sometimes asked to define it, but depending on whether you’re speaking to a member of the public, a fellow machine learning researcher, or someone from the business community, the sense of the term differs.

However, underlying its use I’ve detected one disturbing trend. A trend I’m beginining to think of as “The Great AI Fallacy”.

The fallacy is associated with an implicit promise that is embedded in many statements about Artificial Intelligence. Artificial Intelligence, as it currently exists, is merely a form of automated decision making. The implicit promise of Artificial Intelligence is that it will be the first wave of automation where the machine adapts to the human, rather than the human adapting to the machine.

How else can we explain the suspension of sensible business judgment that is accompanying the hype surrounding AI?

This fallacy is particularly pernicious because there are serious benefits to society in deploying this new wave of data-driven automated decision making. But the AI Fallacy is causing us to suspend our calibrated skepticism that is needed to deploy these systems safely and efficiently.

The problem is compounded because many of the techniques that we’re speaking of were originally developed in academic laboratories in isolation from real-world deployment.

Figure: We seem to have fallen for a perspective on AI that suggests it will adapt to our schedule, rather in the manner of a 1930s manservant.

NACA Langley

The feel of an aircraft is a repeated theme in the early years of flight. In response to perceived European advances in flight in the First World War, the US introduced the National Advisory Committee on Aeronautics. Under the committee a proving ground for aircraft was formed at Langley Field in Virginia. During the Second World War Bob Gilruth published a report on the flying qualities of aircraft that characterised how this feel could be translated into numbers.

See Lawrence (2024) Gilruth, Bob p. 190-192.

Figure: 1945 photo of the NACA test pilots, from left Mel Gough, Herb Hoover, Jack Reeder, Stefan Cavallo and Bill Gray (photo NASA, NACA LMAL 42612)

See Lawrence (2024) National Advisory Committee on Aeronautics (NACA) p. 163–168. One of Gilruth’s collaborators suggested that

Hawker Hurricane airplane. A heavily armed fighter airplane noted for its role in the Battle of Britain, the Hurricane’s flying qualities were found to be generally satisfactory. The most notable deficiencies were heavy aileron forces at high speeds and large friction in the controls.

W. Hewitt Phillips¹

and

Supermarine Spitfire airplane. A high-performance fighter noted for its role in the Battle of Britain and throughout WW II, the Spitfire had desirably light elevator control forces in maneuvers and near neutral longitudinal stability. Its greatest deficiency from the combat standpoint was heavy aileron forces and sluggish roll response at high speeds.

W. Hewitt Phillips²

Gilruth went beyond the reports of feel to characterise how the plane should respond to different inputs on the control stick. In other words he quantified that feel of the plane.

Gilrtuth’s work was in the spirit of Lord Kelvin’s quote on measurement

When you can measure what you are speaking about, and express it in numbers, you know something about it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarely, in your thoughts advanced to the stage of science.

From Chapter 3, pg 73 of Thomson (1889)

The aim was to convert a qualitative property of aircraft into quantitative measurement, thereby allowing their improvement.

Data Science Africa

Figure: Data Science Africa http://datascienceafrica.org is a ground up initiative for capacity building around data science, machine learning and artificial intelligence on the African continent.

Figure: Data Science Africa meetings held up to October 2021.

Data Science Africa is a bottom up initiative for capacity building in data science, machine learning and artificial intelligence on the African continent.

As of May 2023 there have been eleven workshops and schools, located in seven different countries: Nyeri, Kenya (twice); Kampala, Uganda; Arusha, Tanzania; Abuja, Nigeria; Addis Ababa, Ethiopia; Accra, Ghana; Kampala, Uganda and Kimberley, South Africa (virtual), and in Kigali, Rwanda.

The main notion is end-to-end data science. For example, going from data collection in the farmer’s field to decision making in the Ministry of Agriculture. Or going from malaria disease counts in health centers to medicine distribution.

The philosophy is laid out in (Lawrence, 2015). The key idea is that the modern information infrastructure presents new solutions to old problems. Modes of development change because less capital investment is required to take advantage of this infrastructure. The philosophy is that local capacity building is the right way to leverage these challenges in addressing data science problems in the African context.

Data Science Africa is now a non-govermental organization registered in Kenya. The organising board of the meeting is entirely made up of scientists and academics based on the African continent.

Figure: The lack of existing physical infrastructure on the African continent makes it a particularly interesting environment for deploying solutions based on the information infrastructure. The idea is explored more in this Guardian op-ed on Guardian article on How African can benefit from the data revolution.

Guardian article on Data Science Africa

Innovation Economy Challenges

Innovating to serve science and society requires a pipeline of interventions. As well as advances in the technical capabilities of AI technologies, engineering knowhow is required to safely deploy and monitor those solutions in practice. Regulatory frameworks need to adapt to ensure trustworthy use of these technologies. Aligning technology development with public interests demands effective stakeholder engagement to bring diverse voices and expertise into technology design.

Building this pipeline will take coordination across research, engineering, policy and practice. It also requires action to address the digital divides that influence who benefits from AI advances. These include digital divides within the socioeconomic strata that need to be overcome – AI must not exacerbate existing equalities or create new ones. In addressing these challenges, we can be hindered by divides that exist between traditional academic disciplines. We need to develop common understanding of the problems and a shared knowledge of possible solutions.

Supply Chain of Ideas

Model is “supply chain of ideas” framework, particularly in the context of information technology and AI solutions like machine learning and large language models. You suggest that this idea flow, from creation to application, is similar to how physical goods move through economic supply chains.

In the realm of IT solutions, there’s been an overemphasis on macro-economic “supply-side” stimulation - focusing on creating new technologies and ideas - without enough attention to the micro-economic “demand-side” - understanding and addressing real-world needs and challenges.

Imagining the supply chain rather than just the notion of the Innovation Economy allows the conceptualisation of the gaps between macro and micro economic issues, enabling a different way of thinking about process innovation.

Examples

The Horizon Scandal

In the UK we saw these effects play out in the Horizon scandal: the accounting system of the national postal service was computerized by Fujitsu and first installed in 1999, but neither the Post Office nor Fujitsu were able to control the system they had deployed. When it went wrong individual sub postmasters were blamed for the systems’ errors. Over the next two decades they were prosecuted and jailed leaving lives ruined in the wake of the machine’s mistakes.

Figure: The still is from the 2015 select committee.

See Lawrence (2024) Horizon scandal p. 371.

The Lorenzo Scandal

The Lorenzo scandal is the National Programme for IT which was intended to allow the NHS to move towards electronic health records.

Figure: The still is from the 2011 Public Accounts committee.

The oral transcript can be found at https://publications.parliament.uk/pa/cm201012/cmselect/cmpubacc/1070/11052302.htm.

One quote from 16:54:33 in the committee discussion captures the top-down nature of the project.

Q117 Austin Mitchell: You said, Sir David, the problems came from the middle range, but surely they were implicit from the start, because this project was rushed into. The Prime Minister [Tony Blair] was very keen, the delivery unit was very keen, it was very fashionable to computerise things like this. An appendix indicating the cost would be £5 billion was missed out of the original report as published, so you have a very high estimate there in the first place. Then, Richard Granger, the Director of IT, rushed through, without consulting the professions. This was a kind of computer enthusiast’s bit, was it not? The professionals who were going to have to work it were not consulted, because consultation would have made it clear that they were going to ask more from it and expect more from it, and then contracts for £1 billion were let pretty well straightaway, in May 2003. That was very quick. Now, why were the contracts let before the professionals were consulted?

An analysis of the problems was published by Justinia (2017). Based on the paper, the key challenges faced in the UK’s National Programme for IT (NPfIT) included:

1. Lack of adequate end user engagement, particularly with frontline healthcare staff and patients. The program was imposed from the top-down without securing buy-in from stakeholders.

2. Absence of a phased change management approach. The implementation was rushed without proper planning for organizational and cultural changes.

3. Underestimating the scale and complexity of the project. The centralized, large-scale approach was overambitious and difficult to manage.

4. Poor project management, including unrealistic timetables, lack of clear leadership, and no exit strategy.

5. Insufficient attention to privacy and security concerns regarding patient data.

6. Lack of local ownership. The centralized approach meant local healthcare providers felt no ownership over the systems.

7. Communication issues, including poor communication with frontline staff about the program’s benefits.

8. Technical problems, delays in delivery, and unreliable software.

9. Failure to recognize the socio-cultural challenges were as significant as the technical ones.

10. Lack of flexibility to adapt to changing requirements over the long timescale.

11. Insufficient resources and inadequate methodologies for implementation.

12. Low morale among NHS staff responsible for implementation due to uncertainties and unrealistic timetables.

13. Conflicts between political objectives and practical implementation needs.

The paper emphasizes that while technical competence is necessary, the organizational, human, and change management factors were more critical to the program’s failure than purely technological issues. The top-down, centralized approach and lack of stakeholder engagement were particularly problematic.

Reports at the Time

Computer Weekly Report

BBC Report

Report https://publications.parliament.uk/pa/cm201012/cmselect/cmpubacc/1070/1070.pdf

Commonalities

Key points: Both cases demonstrate a significant gap between high-level policy decisions (macro) and on-the-ground realities (micro).

• The failures stemmed from insufficient consideration of local needs, capabilities, and existing systems.
• There was a lack of effective feedback mechanisms from the micro to macro level.
• The implementations suffered from overly rigid, top-down approaches that didn’t allow for local adaptation.
• In both cases, there was insufficient engagement with end-users and local stakeholders.

Bridging

These examples provide valuable lessons for bridging the macro-micro gap in innovation: * The importance of stakeholder engagement at all levels. * The need for flexibility and adaptability in system design and implementation. * The crucial role of effective feedback mechanisms. * The value of phased implementation approaches. * The necessity of ensuring technical competence at both policy-making and implementation levels. * The importance of setting realistic timelines based on technical and operational realities. * The potential benefit of independent, technically competent oversight.

Making AI equitable

ai@cam

ai@cam is a new flagship University mission that seeks to address these challenges. It recognises that development of safe and effective AI-enabled innovations requires this mix of expertise from across research domains, businesses, policy-makers, civil society, and from affected communities. AI@Cam is setting out a vision for AI-enabled innovation that benefits science, citizens and society.

This vision is being achieved through leveraging the University’s vibrant interdisciplinary research community. ai@cam has formed partnerships between researchers, practitioners, and affected communities that embed equity and inclusion. It is developing new platforms for innovation and knowledge transfer. It is delivering innovative interdisciplinary teaching and learning for students, researchers, and professionals. It is building strong connections between the University and national AI priorities.

The University operates as both an engine of AI-enabled innovation and steward of those innovations.

Hygieia

Figure: Hygieia the goddess of health, cleanliness and sanitation.

Technology is not a universal remedy. It is a set of tools, techniques and practices that correctly deployed can be leveraged to deliver societal benefit and mitigate social harm.

In that sense we would be better off looking at the model given by Panacea’s elder sister Hygieia, the goddess of health. We should focus on building and maintaining the hygiene of a robust and equitable research ecosystem.

Figure: ai@cam is a Flagship Programme that supports AI research across the University.

We are working across the University to empower the diversity ofexpertise and capability we have to focus on these broad societal problems. In April 2022 we shared an ai@cam with a vision document that outlines these challenges for the University.

The initiative was funded in November 2022 where a £5M investment from the University.

Progress so far:

• Developing the vision
  • Engaged over 100 members of the University community across 30 departments/institutes, start-ups, and large businesses.
  • Supported 6 new funding bids
  • Five A-Ideas interdisciplinary projects
  • Policy Lab
  • 46 Pioneer computing projects

The University operates as both an engine of AI-enabled innovation and steward of those innovations.

AI is not a universal remedy. It is a set of tools, techniques and practices that correctly deployed can be leveraged to deliver societal benefit and mitigate social harm.

How ai@cam is Addressing Innovation Challenges

1. Bridging Macro and Micro Levels

A-Ideas Initiative - Challenge: Aligning high-level AI research with real-world needs. - ai@cam’s Approach: - Fund interdisciplinary projects addressing specific societal challenges. - Brought together 19 Departments across 6 Schools, ensuring diverse perspectives. - Examples: Projects on climate change, mental health, and language equity directly connect macro-level AI capabilities to micro-level societal needs.

Policy Lab

• Challenge: Translating academic insights into actionable policy.
• ai@cam’s Approach:
  • Connect researchers with policymakers and the public.
  • Produce policy briefs and engages in public dialogues.
  • Example: Collaboration with Bennett Institute and Minderoo Centre on AI governance policy brief.

2. Addressing Data, Compute, and Capability Gaps

Data: Data Intermediaries and maturity assessments

• Challenge: Improving liquidity of data access without compromising security.
• ai@cam’s Approach:
  • Data intermediaries for representing interests of those from whom data originates.
  • Assessment of data quality and data maturity assessments.

Personal Data Trusts

The machine learning solutions we are dependent on to drive automated decision making are dependent on data. But with regard to personal data there are important issues of privacy. Data sharing brings benefits, but also exposes our digital selves. From the use of social media data for targeted advertising to influence us, to the use of genetic data to identify criminals, or natural family members. Control of our virtual selves maps on to control of our actual selves.

The feudal system that is implied by current data protection legislation has significant power asymmetries at its heart, in that the data controller has a duty of care over the data subject, but the data subject may only discover failings in that duty of care when it’s too late. Data controllers also may have conflicting motivations, and often their primary motivation is not towards the data-subject, but that is a consideration in their wider agenda.

Personal Data Trusts (Delacroix and Lawrence, 2018; Edwards, 2004; Lawrence, 2016) are a potential solution to this problem. Inspired by land societies that formed in the 19th century to bring democratic representation to the growing middle classes. A land society was a mutual organization where resources were pooled for the common good.

A Personal Data Trust would be a legal entity where the trustees’ responsibility was entirely to the members of the trust. So the motivation of the data-controllers is aligned only with the data-subjects. How data is handled would be subject to the terms under which the trust was convened. The success of an individual trust would be contingent on it satisfying its members with appropriate balancing of individual privacy with the benefits of data sharing.

Formation of Data Trusts became the number one recommendation of the Hall-Presenti report on AI, but unfortunately, the term was confounded with more general approaches to data sharing that don’t necessarily involve fiduciary responsibilities or personal data rights. It seems clear that we need to better characterize the data sharing landscape as well as propose mechanisms for tackling specific issues in data sharing.

It feels important to have a diversity of approaches, and yet it feels important that any individual trust would be large enough to be taken seriously in representing the views of its members in wider negotiations.

Figure: For thoughts on data trusts see Guardian article on Data Trusts.

Figure: Data Trusts were the first recommendation of the Hall-Presenti Report. More recently the nature of different data intermediaries was clarified in a report on https://www.adalovelaceinstitute.org/report/legal-mechanisms-data-stewardship/ from the Ada Lovelace Institute.

See Guardian article on Digital Oligarchies and Guardian article on Information Feudalism.

Data Trusts Initiative

The Data Trusts Initiative, funded by the Patrick J. McGovern Foundation is supporting three pilot projects that consider how bottom-up empowerment can redress the imbalance associated with the digital oligarchy.

Figure: The Data Trusts Initiative (http://datatrusts.uk) hosts blog posts helping build understanding of data trusts and supports research and pilot projects.

Progress So Far

In its first 18 months of operation, the Initiative has:

• Convened over 200 leading data ethics researchers and practitioners;

• Funded 7 new research projects tackling knowledge gaps in data trust theory and practice;

• Supported 3 real-world data trust pilot projects establishing new data stewardship mechanisms.

2. Addressing data, Comptue and Capability Gaps

Compute: HPC Pioneer Project

• Challenge: Ensuring researchers have access to necessary compute resources.
• ai@cam’s Approach:
  • Provide access to the Dawn supercomputer for 46 projects across 20 departments.
  • Bridge the gap between advanced compute capabilities and diverse research needs.

Capability: Accelerate Programme for Scientific Discovery

• Challenge: Building AI skills across disciplines.
• ai@cam’s Approach:
  • Offer a range of AI training programs, from basic to advanced.
  • Equip domain experts with AI skills, bridging the capability gap.
  • Encourage peer to peer learning and sharing of materials supporting “AI Clubs”

The Accelerate Programme

Figure: The Accelerate Programme for Scientific Discovery covers research, education and training, engagement. Our aim is to bring about a step change in scientific discovery through AI. http://science.ai.cam.ac.uk

We’re now in a new phase of the development of computing, with rapid advances in machine learning. But we see some of the same issues – researchers across disciplines hope to make use of machine learning, but need access to skills and tools to do so, while the field machine learning itself will need to develop new methods to tackle some complex, ‘real world’ problems.

It is with these challenges in mind that the Computer Lab has started the Accelerate Programme for Scientific Discovery. This new Programme is seeking to support researchers across the University to develop the skills they need to be able to use machine learning and AI in their research.

To do this, the Programme is developing three areas of activity:

• Research: we’re developing a research agenda that develops and applies cutting edge machine learning methods to scientific challenges, with three Accelerate Research fellows working directly on issues relating to computational biology, psychiatry, and string theory. While we’re concentrating on STEM subjects for now, in the longer term our ambition is to build links with the social sciences and humanities.

Progress so far includes:

• Recruited a core research team working on the application of AI in mental health, bioinformatics, healthcare, string theory, and complex systems.

• Created a research agenda and roadmap for the development of AI in science.

• Funded 9 interdisciplinary projects:

  • Antimicrobial resistance in farming

  • Quantifying Design Trade-offs in Electricity-generation-focused Tokamaks using AI

  • Automated preclinical drug discovery in vivo using pose estimation

  • Causal Methods for Environmental Science Workshop

  • Automatic tree mapping in Cambridge

  • Acoustic monitoring for biodiversity conservation

  • AI, mathematics and string theory

  • Theoretical, Scientific, and Philosophical Perspectives on Biological Understanding in the age of Artificial Intelligence

  • AI in pathology: optimising a classifier for digital images of duodenal biopsies

• Teaching and learning: building on the teaching activities already delivered through University courses, we’re creating a pipeline of learning opportunities to help PhD students and postdocs better understand how to use data science and machine learning in their work.

Progress so far includes:

Teaching and learning

• Brought over 250 participants from over 30 departments through tailored data science and machine learning for science training (Data Science Residency and Machine Learning Academy);

• Convened workshops with over 80 researchers across the University on the development of data pipelines for science;

• Delivered University courses to over 100 students in Advanced Data Science and Machine Learning and the Physical World.

• Online training course in Python and Pandas accessed by over 380 researchers.

• Engagement: we hope that Accelerate will help build a community of researchers working across the University at the interface on machine learning and the sciences, helping to share best practice and new methods, and support each other in advancing their research. Over the coming years, we’ll be running a variety of events and activities in support of this.

Progress so far includes:

• Launched a Machine Learning Engineering Clinic that has supported over 40 projects across the University with MLE troubleshooting and advice;

• Hosted and participated in events reaching over 300 people in Cambridge;

• Led international workshops at Dagstuhl and Oberwolfach, convening over 60 leading researchers;

• Engaged over 70 researchers through outreach sessions and workshops with the School of Clinical Medicine, the Faculty of Education, Cambridge Digital Humanities and the School of Biological Sciences.

3. Stakeholder Engagement and Feedback Mechanisms

Hopes and Fears Labs

• Challenge: Incorporating public perspectives in AI development.
• ai@cam’s Approach:
  • Collaborate with Kavli Centre for Ethics, Science, and the Public.
  • Conduct public dialogues on AI, ensuring diverse voices are heard.
  • Data intermediaries as an approach to capturing public voices

Cross-University Workshops

• Challenge: Ensuring AI initiatives align with diverse academic needs.
• ai@cam’s Approach:
  • Convene workshops on AI teaching and learning.
  • Engage Professional Services Staff to support capability building.

4. Flexible and Adaptable Approaches

AI-deas Challenge Development

• Challenge: Avoiding rigid, top-down research agendas.
• ai@cam’s Approach:
  • Use a bottom-up approach, allowing researchers to propose challenge areas.
  • Facilitate collaborative refinement of ideas through workshops.

5. Phased Implementation and Realistic Planning

Overall Strategy

• Challenge: Balancing ambition with realistic implementation.
• ai@cam’s Approach:
  • Adopt a phased approach, starting with pilot initiatives like AI-deas and Policy Lab.
  • Plan for gradual scaling based on lessons learned.

6. Independent Oversight and Diverse Perspectives

Steering Group

• Challenge: Ensuring balanced guidance and oversight.
• ai@cam’s Approach:
  • Workign with a cross-institutional Steering Group.
  • Include diverse expertise from different academic disciplines and professional services.
  • Close interactions with other initiatives such as ELLIS and Data Science Africa.

7. Addressing the Innovation Supply Chain

Industry Engagement

• Challenge: Connecting academic innovation to industry needs.
• ai@cam’s Approach:
  • Collaborate with Strategic Partnerships Office to develop industrial partnerships.
  • Plan for an innovation initiative (sciencepreneurship at Cambridge) to bridge academic research and entrepreneurship.

Innovation Economy Conclusion

ai@cam’s approach aims to address the macro-micro disconnects in AI innovation. By fostering interdisciplinary collaboration, engaging diverse stakeholders, and providing crucial resources and training, ai@cam is working to create a more integrated and effective AI innovation ecosystem.

Our emphasis is on flexibility, phased implementation, and continuous feedback. This is to address the lessons learned from past IT implementation failures.

But: AI technologies and their applications will continue to evolve rapidly, we will need to remain agile, continuously assessing and adjusting our strategies to ensure we effectively bridge the gap between high-level AI capabilities and on-the-ground implementation challenges.

Thanks!

For more information on these subjects and more you might want to check the following resources.

• book: The Atomic Human
• twitter: @lawrennd
• podcast: The Talking Machines
• newspaper: Guardian Profile Page
• blog: http://inverseprobability.com

References