Technology Untangled

You've heard it from us before on this podcast, but we'll say it again. AI is transforming our world.

Depending on which market research you look at, AI in healthcare is already somewhere from a 14-21 billion dollar industry in 2023, which is almost double what it was worth just two years ago. By 2028, it's set to be a 100 billion dollar global industry, growing some 40% year-on-year. That's astonishing, even in the already skyrocketing AI sphere. In this episode, we'll be looking at a wide spectrum of expertise to get a sense of where the field is right now, what the future looks like, and some of the cool technologies which might fill it. We’ll be looking at the ways in which AI is making healthcare more efficient, and overcoming roadblocks, as well as examining the ethics of letting algorithms influence human outcomes.

We’re joined in this episode by Mike Woodacre, Chief Technologist at Hewlett Packard Enterprise. He starts by spelling out the origins of this explosive growth during the COVID Pandemic, which ushered in a new world of collaboration and inter-disciplinary use of AI and High Performance Computing to look at new vaccine options, as well as examine scientific research looking for patterns. He urges caution, though, in relying on AI solutions which haven’t been adequately trained in the locales they are being used in and so may not account for regional factors such as more or less common versions of a disease.

That’s something Andy Cachaldora, General Manager for Northern Europe at GE Healthcare, agrees with. They’ve seen an incredible expansion of AI tools not just in diagnostic machines, but also in making sure that every second of a healthcare professional’s time is being used wisely. For him, AI is about taking out the grunt work and uncertainty from running clinics, giving better outcomes all round. Again, however, he urges caution in the way AIs are trained and implemented, with poor data collection and poor planning a route to disaster.

The idea of good, global data sources to train AI is something that has inspired Joachim Schultze, professor of systems medicine from the German Centre for Neurodegenerative Medicine. In collaboration with HPE, he’s been working on a blockchain-based system of machine learning tools to analyse Leukaemia scans, which keeps the data in-hospital to ensure data protection, but sends the insights of the scans to dozens of other institutes worldwide to train their own machine learning algorithms. That’s ensuring that everyone’s AI is collaboratively being trained on the widest, global dataset possible, with no risk to patient privacy.
But where’s the human in all this? Well, right at the centre of it all. After all, any AI requires training, and the training in most cases is still provided by human medical experts, for use by their peers down the line.

And a fascinating new piece of research suggests that the reason AI imaging works so well is that the expertise of a dozen doctors looking at cases together – in clinic or when training Ais, are better than one. A kind of swarm intelligence or swarm learning experience. Rutwik Shah worked on the research at the Center for Intelligent Imaging, which found that by training with swarms of doctors, not only could inexperienced groups of junior doctors analyse scans more reliably than the best AI, they were as effective as groups of doctors with decades of experience. It’s fascinating work, which could revolutionise the way AIs are trained and behave, as well as changing the way scans are analysed.

It's a fascinating world. Come with us on the journey.

Citations:
00:57: https://www.apa.org/monitor/2022/01/special-burnout-stress

01:55: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html?gclid=CjwKCAjw5_GmBhBIEiwA5QSMxOR1CRaYz_g_dcLRAd1aJwxb3tbPBUYcQ0l9mrrtZsRv93yUzWkJTRoCruUQAvD_BwE
https://www.statista.com/statistics/1334826/ai-in-healthcare-market-size-worldwide/

06:19: https://www.bma.org.uk/advice-and-support/nhs-delivery-and-workforce/pressures/nhs-diagnostics-data-analysis#:~:text=There%20is%20not%20enough%20diagnostic%20staff%20in%20the%20NHS&text=According%20to%20the%20Royal%20College,11%2C370%20additional%20s...

The dawn of the exascale computer has arrived. In May 2022, a computer named Frontier was switched on at the Oak Ridge National Laboratory in the USA. At well over twice the computing power of the previous world record holder, it has ushered in a new era of supercomputers, with at least two more to follow in the coming months and years.

In this episode, we’ll be looking at why this undeniably impressive milestone actually means, and more importantly, why it matters. We’ll also be looking at some of the challenges remaining as we enter the exascale era – namely, how do we actually use computers at this scale?

We’re joined in this episode by Mike Woodacre, Chief Technologist at HPE. He starts by spelling out some of the core statistics underpinning the Frontier exascale computer and its 60 million parts, as well as some of the challenges endemic to computing at the cutting edge of technology.

We also meet Doug Kothe, former Director of the Exascale Computing Project at Oak Ridge National Laboratory. He’s hugely excited about the possibilities of exascale as a source of incredible compute in-depth with the ability to return answers to complex questions and simulations in almost real-time. At the same time, he’s also keen to use Frontier as a gateway to open up HPC and supercomputing to more and more organizations, via an ‘app store’ which allows potentially thousands of users simultaneous access to Frontier for their own needs.

For different reasons, Professor Rick Stevens is also excited to be entering the exascale age. He’s Argonne National Laboratory’s Associate Laboratory Director for Computing, Environment and Life Sciences. He’s keen to put their upcoming Aurora exascale computer to work on projects to revolutionise cancer treatments, from diagnostics to drug discovery, through his CANDLE program. Rick’s also cautious, though. Whilst he appreciates the promise that exascale offers, he knows that it’s not an end-goal, but a stepping stone to the next generation and new technological advances.

That’s a sentiment shared by our final guest, Cristin Merritt. She’s the Chief Marketing Officer at Alces Flight, an HPC solutions provider. She’s keenly across worldwide demand for supercomputing power, and sees an evolving landscape of commercial demand and supply growing out of the innovations that exascale offers. She’s cautious, though – right now, exascale is too experimental and non-standard to be commercially mass-market. With time, though, she believes that might just change.

The world is in a state of flux when it comes to energy production. Australian coal is being bought up by China as fast as it can be mined, Europe is coming to terms with Russian gas supplies being a bargaining chip in international politics, and the US is grappling with how to produce more energy whilst meeting green targets and keeping people in mining areas employed.

It’s a tough balancing act. So how can countries realistically become more energy independent in a sustainable way with the tech that’s viable today? This is the first of a two part special. Next time we’ll be looking at how to make the most of the energy we already have.

We start off by meeting Doug Kothe, a Nuclear Scientist who, until recently, headed up the Exascale computing team at the Oak Ridge National Laboratory in the US. He's hugely excited by recent developments in the field, but is also a realist who understands that Fusion energy is still a way off being commercially viable and scaleable.

So what are the alternatives? Professor Patricia Thornley from Aston University is Director of the Energy & Bioproducts Research Institute. They look at the energy potential of waste biomass - sewage and agricultural by-products - to provide not only electricity, but also materials such as plastics, and fuels such as gasoline, diesel and even jet fuel and hydrogen. Their research shows enormous promise - up to 45% of the UK's energy needs could be provided in a carbon-neutral or even net negative way simply by processing agri-waste. In many parts of the world, close to 100% is achievable.

But what about countries where land is at a premium? There's alternatives here, too. Carnegie Clean Energy is an Australian-based engineering firm who are perfecting their CETO wave-generation technology. They use submerged bouys pulling on cords to generate energy in an environmentally non-destructive way. As Carnegie CEO Jonathan Fievez explains, the difference in their technology is that the generators can pull on their own cords to raise, lower or angle themselves. That lets them both generate more electricity, and protect themselves from the bad weather and turbulent seas which have traditionally made the tech difficult to implement commercially.

They do this via an ingenious AI tool called reinforcement learning, whereby an AI learns to control the bouys by being rewarded for the amount of energy they generate. Testing is currently ongoing, but early results suggest a 20-40% performance improvement with less wear and tear, which could be a lifeline for remote and island communities currently relying on diesel generators.

Driving this AI technology is Hewlett Packard Enterprise Labs, who have been working in partnership with Carnegie. Christian Temporale and Maria Ridruejo have been implementing the project for HPE, and are excited by the progress that's been made. They believe that machine learning techniques such as this could make significant improvements in other technologies, such as 'smart' wind turbines, and developing better forms of solar panels.

2022 saw 421 registered natural disasters worldwide, including floods, drought, famine and earthquakes. It also saw new or escalating conflicts in Sudan, Syria and Ukraine. Thousands of NGOs, activists and charity groups do what they can to help those in need, whilst Governments and research groups try to come up with better ways of predicting, mitigating and avoiding disasters.

But you may be surprised to know there's a whole heap of ways that tech can help with rebuilding and prevention efforts. In this episode, we look at how grassroots groups and major organizations work together to leverage lateral thinking, agile mindsets, and technological expertise to mitigate the effects of societal upheaval, and even help in rebuilding efforts.

This episode was inspired by meeting Valerie Kuzmenko, a tech executive from Donetsk, who had to flee when the area became the epicentre of the original Ukraine war in 2014. In 2022, she found herself in Kyiv at the start of the invasion, and had to flee to London with her family and nothing more than a suitcase. Since recording this episode, she's found work as the Chief Marketing Officer at ScaleLabTech.

Using tech to rebuild society is a field which draws together large and small organisations in partnership. At the larger end of the scale are organizations like Airbel labs. They are the research arm of the International Rescue Committee. Atish Gonsalves heads up their EdTech wing. Airbel partner with a number of large organizations such as Whatsapp to provide educational solutions in areas where schooling is difficult, and work hard to provide not only resources for children who would otherwise be out of education for long periods, but also to help teachers continue to operate through tough times and disaster recovery.

Likewise, Hewlett Packard Enterprise use their technological expertise to provide solutions and assistance on some of the most pressing humanitarian issues, for example working with the American Red Cross to use AI to help route and maintain supplies of donated blood. However, HPE Head of Global Social Impact and Deputy Director of the HPE Foundation Fred Tan explains, it's by helping provide solutions and partnering with smaller, grassroots organisations that can encourage new ways of thinking and problem solving which can make a truly global difference, as well as encouraging HPE to think about its own operations.

And on the ground, small organizations are doing truly remarkable work with technology. We're joined by Oksana Simnova and Vatalii Lopushanskyi of RebuildUA and UADamage respectively. These two groups grew out of very different fields - RebuildUA was in Argitech working on drone mapping Ukraine's enormous farms, and UADamage grew out of a team working on Neural Network and AI applications. They now work closely together, using drones and satellite images to map out damage to buildings in Ukraine, and then logging and assessing the damage caused and matching it against pre-war imagery to assess the need for repair. They are hopeful that their findings will help rebuild Ukraine, but also be useful in mine clearing activities in future war zones.

In this episode, we'll explore how technology is changing the way we communicate. But much more than that, we're going to be looking at how it's actually changing our relationship with language itself.

Ever since the first pictograms which date way back over 5000 years, we've been searching for technologies to communicate with each other in more widespread, more efficient ways. The printing press, the telegram, the telephone, and the internet have all evolved into what we're now using today: live streaming; chatbots; VR and AR technologies and, of course, social media. The thing is, the pace of change has grown remarkably in recent decades. We're more connected than ever.

And nothing is changing that faster than AI, in particular generative AI. It’s a core area of interest for HPE Chief Technologist, Matt Armstrong-Barnes. He can see the incredible potential for globe-spanning communication that AI brings, from instant translation to live captioning, but he’s also wary of taking the human out of the loop and losing the important context, nuance and difference that makes language so special and effective.

That’s also a challenge for Courtney Napoles. She’s Linguistic Engineering Director at Grammarly, an AI-enabled software platform which aids human writing. Despite being a tech firm with a strong machine learning underpinning, they rely on humans in the loop to ensure that communication remains effective for us, rather than trying to pull us towards homogenised, machine-learning inspired writing styles.

The rise in assistive technology is having unintended benefits, too – in particular, it’s opening up the door to greater representation in the workplace, and bridging communication barriers when it comes to accessibility needs. Rob Koch is a data engineer and principal at Slalom Build, and heads the group Deaf In The Cloud Dot Com. He’s seen a remarkable breaking down of barriers in the last few years as technology has enabled him to communicate more effectively with colleagues and customers. There’s a way to go, but he’s optimistic about the future.

And speaking of the future, where are we headed next? Leslie Shannon is Nokia’s head of trend scouting. She’s keeping a keen eye on the way we communicate and the language we use, and is seeing a stark generational shift away from text and towards video and augmented reality solutions, embracing the additional context that body language and gesture can add to traditional, ‘flat’ conversations – and changing the way we speak to our tech and the language we use in the process.

Statistics and sources cited in this episode:
Global communications market value: https://www.statista.com/outlook/tmo/communication-services/worldwide
US communcations market value: https://www.statista.com/outlook/tmo/communication-services/united-states
Size of the Digital Acessibility market: https://www.mordorintelligence.com/industry-reports/digital-accessibility-software-market