Future of Artificial Intelligence - Q4 2020 by Mediaplanet UK&IE

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Q4 / 2020

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FUTURE OF ARTIFICIAL INTELLIGENCE

“AI will allow companies to be more productive with their manufacturing systems.”

“AI will help bring benefits to both patients and health systems.”

Sebastien Louradour, Fellow, Artificial Intelligence and Machine Learning, World Economic Forum

James Selka, CEO, Manufacturing Technologies Association

Andrew Davies, Digital Health Lead, Association of British HealthTech Industries (ABHI)

“A technology like facial recognition carries the potential for strengthening trust on the internet.”

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IN THIS ISSUE

When innovation is exponential, digital ethics is essential In 2020, debates about the ethical use of digital technology have been front page news.

“Brands that made investments in AI before the pandemic are reaping the benefits.” Jenalea Howell, AI Market Lead, AI Summit Series

06 “Artificial intelligence (AI) has the potential to transform many aspects of how healthcare is currently delivered.” Dr Indra Joshi, Director of AI, NHSX

07 “AI will bring a big step forward in personalised care.” Professor Metin Avkiran, Associate Medical Director, British Heart Foundation Project Manager: Chris Greenwell chris.greenwell@ mediaplanet.com Business Development Manager: Ross Bannatyne Managing Director: Alex Williams Head of Business Development: Ellie McGregor Head of Production: Kirsty Elliott Designer: Thomas Kent Digital Manager: Harvey O’Donnell Paid Media Manager: Ella Wiseman Production Assistant: Henry Phillips Mediaplanet contact information: P:+44 (0) 203 642 0737 E: uk.info@mediaplanet.com All images supplied by Gettyimages, unless otherwise specified

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WRITTEN BY

Antony Walker Deputy CEO, techUK

rom COVID-19 and Black Lives Matter to exam results and the US election result, some of the most controversial moments of 2020 have come down to questions about how, why, when and where we should use powerful new digital technologies like AI. Building ethical frameworks and guidelines But these were not new questions: Over the last five years a growing community of technologists, policy makers, academics and civil society advocates around the world have been working to build frameworks and guidelines to help us navigate the most complex questions about how digital technology should be used. When push came to shove how useful were these ethical frameworks when we had to apply them in high profile, high pressure, environments like the race to develop the contact-tracing app or the challenge of countering online disinformation? Some argue that digital ethics has failed the test of 2020. Yet, if we look at the example of contact tracing, there was actually a very informed public debate on ethics that had a direct impact on the development of apps around the world. And throughout 2020 we have seen many examples of businesses acting upon ethical concerns, ranging from the risk of ethnic bias to the challenge of climate change. But is it now time for ‘soft’ ethics to make way for ‘hard’ regulation? The importance of law and ethics in digital innovation This may feel like a binary choice, but it is not. Good law needs to be built on good ethics. To be effective, new

regulation will need to be rooted in the knowledge and understanding that has been built through the deliberation on ethics. It is also clear that regulation will always struggle to keep pace in a world of exponential change. In a world where innovators are ahead of regulators the first line of defence from doing the wrong thing is to embed a deep understanding of how to do the right thing. This is why embedding digital ethics remains vital for responsible innovation. Institutions are providing new guidance Without doubt there is a huge amount to learn from 2020. But there is also cause for optimism. There is now far greater awareness and understanding across the public and private sector of both the need to handle powerful technologies with care and how to do that in practice. We now have established ethical frameworks to enable us to ask the right questions and inform the right procedures. We have a set of institutions, such as the Ada Lovelace Institute and the Centre for Data Ethics and Innovation (CDEI) that can provide guidance on how to balance the risks and opportunities of AI. We have an active community of informed law makers, such as the All-Party Parliamentary Group on AI and civil society groups that are determined to hold both businesses and governments to account. As we have seen in just in the last week with the example of DeepMind’s protein folding discovery, AI can enable us to make huge scientific breakthroughs. The potential societal benefit of AI is huge. But so is our responsibility to use it with care. Sound digital ethics, along with good law, will be essential to getting this right.

techUK will host the fourth annual Digital Ethics Summit on the 9-10 December techuk.org/digital-ethicssummit-2020.html

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AI on the move: Mobile robots are powering the future

important aspect of this: it is secure and due to their multiple IOT sensors, they can collect far more information than an individual could do at the same time. They use machinelearning powered computer vision, the capability to extract meaning from digital images, for example highlighting different segments of a pipeline to detect leakages or abnormal temperatures.

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Navigating their way ahead, mobile robots are the latest breakthroughs in the industry. They can go where humans cannot and work in conditions that would be otherwise impossible.

he robotics industry is huge: according to Mordor Intelligence, it was valued at $39.72 billion in 2019 and is expected to register a compound annual growth rate of 25.38% between 2020 to 2025. One of the most important areas in that industry is mobile robotics. Autonomous Mobile Robots (AMR) are becoming increasingly popular in the area in comparison with older technology such as Autonomous Ground Vehicles (AGV). Fully free navigation robots are not wired and use a series of different sensor systems. This means they are able to give huge flexibility to industries across the board, without heavy upfront costs.

Monitor your operating infrastructure. You can simply use mobile robots which will fit and adapt to any environment without extra cost.

They are agile – they can go up and down stairs – and can move on terrain not traditionally designed for robots. Agility is one of the major reasons they are important, as well as the fact that it is possible to develop their intelligence without creating a new platform. They do not take jobs away from individuals by, for example, filming damage on an industrial site. Instead, they supplement the individual’s work and can also, detect damage by using other Internet of Things (IoT) sensors. Robots can be used in hazardous environments The applications for these robots are limitless, especially in fields such as the oil and gas industry. They can be used in extremely hazardous and toxic environments, which could be too dangerous or too loud for individuals to enter. Mobile robots can be used in places where there is not enough space for bigger infrastructures or people, for example in canal inspection. The collection of information data is a very

Versatile application of robotic technology Mobile robots can be used in many fields, including education and real estate, but their greatest use comes in industries that use production lines and construction sites, such as oil, gas and automotive. Today companies build costly static infrastructure and installations to automatically scan vehicles and analyse the level of damages. Tomorrow the same companies will use mobile robots which can be moved anywhere to get a digital twin of a single car. In the oil and gas sectors, robots can inspect the inside of engine rooms, where there might be huge levels of noise, very high temperatures and a toxic atmosphere but that still need regular inspections. They can inspect big plants and pipelines, where they can check on potential oil leaks. You don’t have to build and maintain the additional IT infrastructure (like cameras and static IoT sensors) to monitor your operating infrastructure. You can simply use mobile robots which will fit and adapt to any environment without extra cost. ECE group, which manages shopping centres, have used mobile robots for specific damage detection. They have a big car park on different levels and a robot regularly checks for hole damage. This is cost effective and picks up damage before it becomes too serious.

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Marek Matuszewski Manager, Cluster Reply

Emanuele Gherardini Partner, Sprint Reply

Written by: Virginia Blackburn

They can be used in extremely hazardous and toxic environments, which could be too dangerous or too loud for individuals to enter. Mobile robotics are becoming more accessible When a robot is screening an area on a construction site, it can build a digital twin. That means that every day the site owner can check with the contractor if something is missing and it can also help when invoicing with a subcontractor. AI is being used in conjunction with robots as existing solutions are being prepared for different scenarios. It will help develop new models to learn. In the future we will see more records. Technology is commoditising and prices are dropping, driven by the industrialisation of components. More AI applications are being shared and the broader the audience, the broader the use of mobile robotics.

The next-gen robots are here and Reply, with its expertise and turnkey solutions, can add value to your business. reply.com/en/ Contact us at

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It’s now time to regulate facial recognition

AI initiatives: time for positive results Brands that made investments in AI before the pandemic are reaping the benefits of those decisions, and most will not slow their adoption because of the COVID-19 crisis.

Bans on facial recognition may not be the right approach to effectively mitigate the risks of an already ubiquitous technology. A risk-based approach could be the answer.

espite the pandemic, 71% of businesses now say they are confident that their AI initiatives will deliver positive results in the next two years, according to a study by Omdia. Based on responses from 365 companies across several industry verticals, nearly half (42%) of companies reported to be running pilot or live AI projects, with ownership split evenly among CEOs, CTOs, research and development, and individual business units.

F WRITTEN BY

Sebastien Louradour Fellow, Artifical Intelligence and Machine Learning, World Economic Forum

Early adopters of AI Early adopters – those who made investments in AI before the pandemic - are reaping the benefits of those decisions, and most will not slow their adoption because of the COVID-19 crisis. Such companies are confident about positive results within the next 12 to 24 months, with nearly a third (31%) very confident, 40% confident, 23% somewhat confident, and just 3% not confident. Companies that did not invest in AI before the pandemic are likely to delay their investments until better economic conditions are in place, creating the potential for a significant competitive advantage for the early adopter companies. Enterprises are expressing interest in a wide range of AI business cases. There are 17% piloting AI in at least one function or business unit, 40% are investigating technologies and use cases, 17% have a production AI deployment in at least one function or business unit, 13% have identified at least one-use case and are developing a pilot, and 7% are scaling AI deployments across multiple business functions or units.

acial Recognition Technology (FRT) has come under increasing scrutiny for potentially undermining our privacy, misidentifying people, perpetuating systemic racism, and contributing to surveillance infrastructure. So far, the response from policymakers has predominantly focused on banning the use of FRT to prevent any harm. In the US, San Francisco and Boston have set the tone for others to follow.

A technology like facial recognition carries the potential for strengthening trust on the internet. Given the current shortcomings of the technology, this response may seem persuasive for high-risk situations. But most use cases don’t carry the same level of risks. A tailored regulation - rather than bans - should be considered as a viable alternative.

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Risks vary depending on the context Each scenario carries a different risk level; for example, using FRT in a criminal investigation has an additional weight than streamlining an airplane boarding process. Tailored regulation is essential to resolving the significant concerns involved with

the technology. Considering the context in which FRT is used can help accurately characterise risks. Once these risks are identified, organisations can then set internal processes to mitigate them by training their staff to recognise potential biases and identify risks that could emerge. These “risk-mitigation processes” are similar to the already ubiquitous in the “privacy-by-design” process. A robust digital identity tool to harness Our dependency on the digital world is growing at an unprecedented pace. A technology like facial recognition carries the potential for strengthening trust on the internet. It can help protect our digital identities by replacing logins and passwords with the simple use of our face. We find ways to chart the path to continue to improve this technology and ultimately leverage its full potential, mitigating risks along the way rather than obliterating it. Self-assessment and certification To gain trust from end-users and citizens, tech companies, and FTR users (like airports or law enforcement agencies) need to take further steps such as setting up internal self-assessments and external certification processes. Certification bodies provide labels of compliance in many areas, such as personal data protection. In many industries, policymakers and technology users already use these mechanisms to build trust and transparency. Facial recognition should follow this path and not be used as a one-size-fits-all solution anymore.

Acknowledging potential issues However, the study also found that data privacy and accountability issues could significantly slow market adoption for AI-powered solutions, with 42% of businesses reporting that privacy considerations are slowing down their AI initiatives, 18% saying they are significantly delaying initiatives. Moving into 2021 and as companies begin to plan a recovery from COVID-19 in a new world of data needs, business must seek solutions across the AI industry ecosystem to find the answers for today’s security issues and tomorrow’s ethical challenges. WRITTEN BY

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Jenalea Howell AI Market Lead, AI Summit Series

It can help protect our digital identities by replacing logins and passwords with the simple use of our face.

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Increase productivity using AI in manufacturing As advanced manufacturing and engineering continues to develop, Artificial intelligence (AI), now plays a key role in the development of the sector.

WRITTEN BY

James Selka CEO, Manufacturing Technologies Association

t is easy to assume that when you hear about artificial intelligence (AI), that it is nothing to do with you, but rather is something from a Sci-fi movie or for some big tech company. However, from digital voice assistants to sending and receiving email, we all encounter with some form of AI in our lives. The benefits of AI in consumer products are obvious; it helps us find the things we want, block what we don’t want and makes navigating the digital world easier. AI and automation As AI has continued to develop it has moved rapidly from the consumer to business environment. A recent poll of Manufacturing Technologies Association (MTA) members highlighted that AI and automation were the two most important technologies in the development of the manufacturing sector. However, what are the benefits of adopting AI within a manufacturing context, and how will it change how a manufacturer operates?

Customised mass production Firstly, AI will allow for an increased personalisation of products. Consumers will be able to order what they want and how they want it. This will allow businesses to offer a diverse range of products and solutions without having to lay down multiple production lines – essentially a company will have a form of customised mass production. Skills and labour AI will also address some of the labour and skills shortage within manufacturing. Machines and manufacturing systems currently require a highly trained operator to function. However, it will free up the operator to work on other tasks or allow them to control multiple systems simultaneously – therefore making a company more productive. Process optimisation Finally, AI will be able to monitor and optimise production processes. Optimised processes will lead to an

How automating research accelerates drug discovery

AI will allow for an increased personalisation of products. Consumers will be able to order what they want and how they want it. increased productivity, a reduction in waste, reduced system maintenance, and a more sustainable manufacturing process. In short, AI will allow companies to be more productive with their manufacturing systems, more resilient through product diversification and reduce costs and waste through optimised processes. Further development is required before these benefits are realised. But, with the current rate of change don’t expect to be waiting too long before you see AI designing and manufacturing some of the products you use on a daily basis.

To see live demonstrations of all the latest Manufacturing Technologies you can visit MACH 2022, and keep an eye out for our digital offering in early 2021. Full previews available on the MACH 2022 website.

Drug discovery starts in the lab, yet is slow and error prone. Combining robotics and artificial intelligence (AI) is the key to enable faster progress and bring new drugs to patients sooner.

WRITTEN BY

Dr Martin-Immanuel Bittner Chief Executive Officer, Arctoris Ltd

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ll industries have been transformed by the introduction of electronics, computers, automation, machine-to-machine communication and the cloud. However, there is one area that has not kept pace with innovation: not much has changed in biomedical laboratories since the days of Alexander Fleming and his discovery of penicillin. Researchers still hunch over lab benches carefully pipetting reagents by hand, recording data and taking notes on paper. Lab work, the key to discovering potential new drugs, is trapped in the past. The consequence: R&D productivity in this area has decreased 15-fold in the past five decades. Bringing a new drug from the lab bench to patients now costs $2.6bn and often takes up to 15 years. The COVID-19 pandemic has made it clear that we don’t have this kind of time to spare. To switch drug development R&D productivity from reverse gear into full speed we have to completely

rethink the way we discover new drugs and embrace the benefits of automation and AI. The rise of AI The application of AI in drug discovery has already shown great promise. Last year, Hong Kong-based Insilico Medicine sped up the process of drug design using its AI approach, shortening this critical step from the pharma average of 1.8 years to just 45 days. In April this year, UK’s BenevolentAI used machine learning to find that an approved Eli Lilly drug could be repurposed as a treatment for COVID-19. The drug went on to receive FDA approval for emergency use in COVID-19 patients in November. Robots to the rescue Yet, this is only the beginning of the AI revolution in drug discovery. Only one challenge remains in the way: the key to unlocking the true potential of AI lies in the availability of large amounts of structured, standardised, well annotated data.

This has become the bottleneck, since manually collected data is riddled with shortcomings. The proliferation of vague, unstructured data leads to inconsistencies and misleading results, estimated to cost $28bn annually in the US alone. Automation is the answer: using robotics to conduct drug discovery experiments liberates scientists from manual labour, allows them to focus on tasks requiring human creativity and ingenuity, and enables new insights to be based on rich, reliable, meaningful data. In summary, automating experiment execution and data capture generates the large, well annotated data sets that are critical for drug discovery success both for human researchers and for AI, thereby accelerating the path from the lab to the patient.

Founded in Oxford in 2016, Arctoris operates the world’s first fully automated drug discovery platform. Universities, biotech companies and pharmaceutical corporations on three continents use the Arctoris platform to remotely generate high quality dataon-demand and advance their drug discovery programmes faster. arctoris.com

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How we can shape healthcare with AI Healthcare was once considered a slow adopter of digital technologies, but a new wave of investment and advances in AI is set to change that perception.

Ensuring the UK is a destination of choice for AI development NHSX are positioning the UK to become the country where innovators choose to test their AI products, as well as putting the building blocks in place to ensure it is the best place to perfect AI so the NHS and our patients can reap the rewards.

I is assuming a more central role in countless aspects of business and society, but healthcare is an area of contradictions when it comes to AI implementation. While other sectors embraced digitisation and AI, healthcare was considered something of a laggard. However, that is now changing rapidly with significant investment being attracted into the sector. Healthcare AI “deals and dollars” saw an increase in the third quarter of 2020, with companies raising $2.1bn in equity funding across 121 deals — a quarterly growth of 37% and 38%, respectively with funding reaching a record high.

rtificial intelligence (AI) has the potential to transform healthcare, from ever more personalised treatments to earlier detection of cancer.

WRITTEN BY

Dr Indra Joshi Director of AI, NHSX

The capabilities of AI could transform patient experience, patient outcomes and system efficiency.

AI and the impact of COVID-19 During the pandemic for instance, we launched the National COVID-19 Chest Imaging Database. This library of chest images supports the development and testing of AI technologies to help patients hospitalised with COVID-19, with a number of projects already making use of it. It is a practical example of how the Lab working with partners can help create environments that foster innovation while assuring patient confidentiality and the safe deployment of AI tools into clinical practice.

Benefits to patients and health systems Driving this growth are advances in AI that have the potential to impact on the significant challenges facing major health systems: earlier disease detection, demand management and efficient delivery of services. The capabilities of AI could transform patient experience, patient outcomes and system efficiency. A recent report from Deloitte highlighted that annually, 380,000 to 403,000 lives can potentially be saved, and 1.8 billion hours freed up every year, the equivalent of having 500,000 additional full-time healthcare professionals.

Testing new technologies before scaling up The Lab’s flagship initiative, worth £140m over three years, is the AI in Health and Care Award. Round one is already supporting 42 innovations from early research to helping some products with their first real-world tests. Round two of the AI Award closed today (December 8th). And for a select group of more mature AI technologies, including: • A home test kit and mobile app by Healthy.io that could help detect early kidney disease.

Wide range of uses for AI Today, AI is primarily used to supplement the health workforce by automating repetitive, low skilled tasks, enabling the increasingly scarce, and highly trained, professionals to concentrate on the higher value tasks. However, that is likely to change in the future, with the AI becoming more autonomous in its application and having wider scope of the patient journey. This should bring significant benefits in patient access, experience and outcomes, it will also bring further challenges on the use, sharing and regulation of data and the AI enabled service. Alongside developing the technology, we also need to develop the services and governance to ensure that digital exclusion is minimised and that algorithms are free from bias. WRITTEN BY

Andrew Davies Digital Health Lead, ABHI (Association of British HealthTech Industries)

Using the NHS to help test AI products The NHS AI Lab, a specialist unit, has been created to help the NHS safely adopt AI for the good of staff and patients. We want the UK to become the country of choice for AI innovation, so the NHS and our citizens quickly get the benefits. So, we are putting the building blocks in place. The AI Lab is leading the NHS effort on all fronts, from supporting innovators to test their products to working with regulators to ensure AI can be appropriately assessed.