Key Points
- Artificial intelligence is transforming industry thanks to a wealth of data, faster computers and deep learning algorithms
- Biotech firms are using AI to handle huge amounts of information to improve diagnostics and create new drugs
- Baillie Gifford is also experimenting with using AI to make the best use of its investment teams’ time
WATCH: Investment managers Julia Angeles (centre) and Kirsty Gibson (right) answer Malcolm Borthwick’s questions about AI
Artificial intelligence is an exponentially improving technology.
As its development continues to accelerate, what once seemed extraordinary becomes commonplace while new advances continue to astonish.
For example, in 2012, when Google created an AI that detected cats in YouTube videos, many hailed it as a historic moment. These days our smartphones automatically spot all manner of pets in their recordings. But Google’s sister company DeepMind recently achieved a fresh breakthrough by identifying the structure of nearly all proteins known to science.
As investors, what’s exciting is that businesses of all kinds are putting the technology to commercial use.
“One thing that’s really changed over the past decade is the explosion of data that’s been enabled by sensors and computing power,” said investment manager Kirsty Gibson, speaking at Baillie Gifford’s AI-focused Ideas 2022 online conference.
“That’s not just been specific to one or two industries but across the spectrum. So it’s not only ecommerce and advertising that have benefited but also, for example, manufacturers monitoring their processes and pre-empting faults before they occur.”
AI-enhanced biotech
Healthcare has been a frontrunner in using AI to spot patterns in gigantic datasets.
“Human biology is one of the most complex systems out there – I describe it as a problem of large numbers,” said Julia Angeles, an investment manager who specialises in health innovation and shared the stage with Gibson.
“In the past, the data gathered was siloed into different areas of study. For example, the human genome has three billion base pairs. We have thousands of proteins. And there are trillions of bacteria that impact our health. What AI lets us do is combine these many data points to unlock the complexity.
“One benefit is that we can start developing more precise diagnostic tools. Another is we can develop medicines from the bottom up.”
Moderna’s Covid vaccine is a high-profile example of AI being put to use.
The firm used the technology to optimise designs of its ‘novel mRNA constructs’, essentially the set of instructions that teach our bodies how to protect themselves against the virus. In addition, it used AI to automate quality control, reducing the need for manual checks when speed was of the essence.
“Moderna’s also using AI to predict how the virus will mutate over time,” added Angeles.
“So it can get in advance of the virus rather than just chasing it.”
Exscientia is another example from the biotech industry. It ‘precision engineers’ drugs using AI-designed molecules, optimising its creations for efficacy at combating disease and enhancing patient safety in parallel.
“Normally, it takes more than 10 years and billions of dollars to create a drug,” said Angeles.
“Even then, the chance of success is only about 10 per cent, often because the drug attacks other parts of the body, making it unusable.
“To compensate for all that risk, pharma companies charge high prices. But with AI, there’s potentially an opportunity to reinvent the business model completely.”
Personalised picks
AI in its existing state is sometimes described as being ‘narrow’, meaning users can only apply it to a single or restricted set of tasks. For instance, image recognition or spotting a cybersecurity attack.
Researchers eventually hope to create ‘artificial general intelligence’ (AGI), which would handle a broad range of intellectual challenges, making it more similar to humans.
In the meantime, even narrow AI can be powerful thanks to its scale and speed.
For instance, The Trade Desk uses artificial intelligence to show different adverts to different people watching the same TV programme on connected devices.
“It moves us away from asking who a show’s average viewer is to who is actually watching it, and how can I advertise to them in a relevant way,” says Gibson.
“And that’s only possible because it’s processing 12 million ads a second.”
Lemonade is another case. It’s disrupting the insurance industry by drawing on data that legacy rivals don’t have access to and then using AI to draw conclusions.
“Lemonade has an AI called Maya that typically asks customers 13 questions,” explained Gibson.
“But it gathers about 1,700 data points, including how long customers took to answer those queries and whether they read all the terms and conditions before deciding how to price the premium.
“So rather than putting you in a kind of average pool of those in the same demographic, the quote is based on you as an individual.”
Common uses of AI
- Image recognition
- Pattern spotting
- Natural language processing
- Speech synthesis
- Fraud prevention
- Personalisation
- Autonomous vehicles
- Spam filters
- Facial recognition
- Recommendation engines
- Robotics
- Non-player characters in video games
- Text-to-image modelling
- Planning and forecasting
- Surveillance
- Emotion analytics
- Image enhancement
- Coding suggestions
- Drug design
Ethical considerations
As a long-term, actively engaged asset manager, Baillie Gifford’s thinking about AI goes beyond how it might generate growth. The firm also considers potential misuses of the technology that could undermine a business’s reputation and prospects over time.
One issue we are starting to grapple with is ‘algorithmic bias’. It arises when AI unfairly favours one group over another. For example, people getting better lending rates or bigger bills because of their race or sexuality, even if those factors weren’t deliberately targeted by the programmers.
“There are huge biases in the world already, and AI can amplify them,” warned Gibson.
To help consider such challenges, Baillie Gifford’s investors and other specialists regularly speak to academics. They include Prof Shannon Vallor at The University of Edinburgh’s Futures Institute, whose chair in the ethics of data and artificial intelligence we sponsor.
“It helps us see potential blind spots,” Angeles said. “Prof Vallor is helping us understand what questions we should consider when engaging with companies.”
Investing with AI
Some teams within Baillie Gifford are also experimenting with how AI could enhance their investment decisions.
More companies exist than our investors have time to speak to, not to mention all the research journals, blogs and other sources of information competing for their attention.
“AI is a handy tool that can be a guiding compass. It can help us to focus on what matters and where we can add value,” said Angeles.
Her team is working with four data scientists on a project to explore how this might be possible.
“One exciting area is analysing networks – people on management teams and the composition of boards, to help us assess leadership quality. I could also see people-movement acting as a signal: whether a company is attracting talent or losing it.”
Some futurists have likened artificial intelligence to electricity: a revolutionary technology that will change many aspects of life and unleash countless innovations.
A degree of caution is required but, as investors, there’s good reason to be excited about its potential for change and growth.
“The opportunity is huge,” Gibson concluded.
“It’s already transforming industries, and I think there’s much more transformation to come.”
Words by Leo Kelion
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