Wireless Noodle Episode 2: Why hasn't Artificial Intelligence helped us with COVID-19

Episode 2 of the podcast looks at the unavoidable COVID-19. We look at two aspects. Firstly the effect that AI has had on tackling it, and secondly the impact that it has had on IoT.

You can access it HERE or via Google or Apple.

The full transcript of the podcast is available below. 

COVID-19 defines 2020. Given the superlatives thrown at it in recent years isn’t it a bit surprising that Artificial Intelligence has been conspicuously incapable of contributing much to tackling it?

My name is Matt Hatton and this is The Wireless Noodle, your weekly guide to the impact of disruptive new technologies on business.

One of the prevailing trends over the last couple of months has been for experts on the technology sector to pitch in with their opinions on the treatment and impact of Covid-19. I don’t do that. I prefer to leave the medicine and the epidemiology to the experts. In this current crisis the engineers should focus on building ventilators and perhaps 3D printing masks. But, there are areas of our technology research at Transforma Insights where we can consider the impact of COVID-19. Today I want to look at two. How good AI has been at finding a cure for Coronavirus. And how the Internet of Things has been affected. 

Given the tremendous amount of coverage that artificial intelligence has received in the last few years it’s worth digging in to the extent to which it has been able to help with the biggest human problem for many years: COVID-19. Mastering Chess and Go are all very well, but if AI is there for anything, it’s to solve existential problems for the human race, not to be really good at board games. 

There are some high points, but largely it flags up the inherent deficiencies. 

We start naturally with the medical, and probably the #1 ultimate priority is finding a cure. This takes two forms, firstly drug repurposing. Think of the apparent success that the combination of Chloroquine and Hydroxychloroquine seems to have had. Companies such as Benevolent AI are looking at what existing drugs can do and have identified Baricitinib BA-RI-CI-TI-NIB (a drug used to treat arthritis) as a potential treatment. The company analyses scientific literature to identify links between genes, drugs, viruses and transmission vectors and therefore likely effective treatments for Covid-19. Then at the molecular level you have the likes of Insilico Medicine focused on designing molecules to halt replication. Others are using AI to develop new drugs. One process that looked really interesting was the use of reinforcement learning to break down molecules known to act as inhibitors to similar viruses into constituent elements and test against likely characteristics of something that may help against Covid-19. See HERE for more details.

The main problem is that of finding sufficient training data, which deep learning algorithms need to be effective. The problems here are manifold. There is very little data on the specifics of treatment and diagnosis. Testing is not widespread and the virus is often symptomless. There are also suspicions of improper reporting in some countries. It also doesn’t help that the virus is mutating. For deep learning to be effective requires a high degree of continuity, or a hell of a lot more data. 

Also immediately pressing (possibly more so) is the need for cheap and effective diagnosis. Typically tests such as Reverse Transcription Polymerase Chain Reaction (PT-PCR) are the standard approach, but these are slow and cumbersome. Much quicker is the use of AI to do image pattern recognition to spot tell-tale signs on scans of the lungs, as done by companies such as Infervision. While it’s not perfect, it is significantly quicker. Existing data sets from previous viruses seemingly can be used as training data as was the case with training a model at Renmin Hospital at Wuhan University. Ali Baba Group has demonstrated 96% accuracy with its AI application for analysis of Covid-19 CT scans, and was able to deal with one scan every 20 seconds versus 15 minutes for a person. There are promises also of non-invasive diagnosis, but nothing that has yet materialised.

What’s needed to support all of this is a good mechanism for sharing as much data as possible. The Covid-Net Open Access Neural Network, developed by the University of Waterloo and DarwinAI, offers one example, sharing a dataset of thousands of chest scans for anyone who wants to help in its development of an AI tool. One lesson we can take away from this outbreak is to have a better universal source of data under the auspices of the WHO or similar. 

AI may have a role to play in predicting patient outcomes, once someone is diagnosed with a severe case requiring hospitalisation. There are over 20 million diagnosed cases worldwide. In the EU 30% have been hospitalised. This means we have potentially hundreds of thousands of data points. According to our research at Transforma Insights, less than 30% of AI deployments have access to such a large training set. Of course, the dynamics of every use case are different, so one application might require a million pieces of training data, whereas another might need only a few thousand. However, there is reason to be optimistic. There is a slightly ghoulish question of what such a prediction of outcomes might be used for: is it for resource allocation, targeted pre-emptive action or triage? 

The use of AI to support epidemiology has also been attracting a lot of attention, such as through companies like Blue Dot. There are some established areas with existing data sets upon which AI might be trainable, for instance modelling the impact of warm weather on respiratory diseases, or understanding how transmission might occur within hospitals. There is a lot of hypothesis and supposition. What is for certain is that AI is not a magic wand. Especially with epidemiology, but also with the other areas identified, what’s generally needed is firstly some old-fashioned science, and more traditional epidemiological modelling. 

Moving away from the medical world. there are a whole load of existing non-medical systems upon which AI will already have been trained that might be tweaked to offer some benefit in dealing with the fallout or mitigating the effects of the virus. Complex systems such as social media, airline booking systems or population movements are well understood and could be used to track trends, for instance predicting how the virus might spread into new territories, or how well government information is being distributed. The challenge is that most of these systems are not currently working within their usual parameters due to the virus’s impact.

Might AI also be used to stabilise financial markets? After all, trading bots are widely used in equity trading. Unfortunately, it’s probably been doing quite the reverse. Throwing a curve-ball at the market the like of which it has never seen before and expecting trading bots to comprehend and act on the information is, shall we say, hopeful. Humans are inherently better placed to cope with the unknown. 

Covid-19 exposes a number of weaknesses in AI. For instance, AI does not cope very well with new things, and Covid-19 is very definitely a new thing. By the time we get sufficiently good data for AI to be broadly useful, we’ll probably be over the hump. AI also tends to lack imagination. Human intervention is definitely required to help shape AI responses to novel events. 

I suspect that we will learn more about AI from Covid-19 than vice versa. 

Turning from one incredibly sexy technology topic, AI, to another, IoT. Here it’s not so much a case of whether it can have an impact, as what impact COVID will have on the IoT. I mentioned in last week’s podcast that COVID had stimulated demand but inhibited supply. I think it’s worth looking into that a little more. 

At Transforma Insights we’ve built a really granular set of IoT market forecasts, which we comprehensively revised in the last few months. Being able to pick apart the impact on hundreds of different applications means we can see where the pain is likely to be felt, and which sectors will be relatively unscathed. 

The first thing to note is that in % terms the impact is relatively small. Our pre-outbreak forecast predicted USD524 billion in total IoT revenue for 2020, which we revised downwards by 7% to USD487 billion in our post-outbreak forecast. That’s still a growth relative to 2019’s USD465 billion, although it’s much more modest than expected previously. 

Looking at the difference between hardware and services goes a long way to explaining why. Three-quarters of the impact is on hardware. Non-recurring revenue is much more sensitive to downturn and records an overall decline compared to 2019. 

But the impact isn’t limited to the $37bn knocked off market revenue in 2020. There will be a ripple effect through the next few years meaning a total revenue reduction of USD65 billion. By 2024 things will be roughly back to previous modelling though.

The hardest hit segments are building automation (because people aren’t spending time in offices, so new schemes are being delayed), retail (because people aren’t out spending) and connected cars, the latter partly due to the drop off in sales of new vehicles and partly because people just aren’t sitting in vehicles and so don’t need their connected car services.  

The most significant contributing factors behind the early disruption have been the constraints to both supply and demand caused by the response to COVID-19. Lockdown measures introduced in many countries have rendered large amounts of commercial space unused and consequently reduced the spend from many sectors. Supply has been limited by a drop in manufacturing output, supply chain disruption and limits on the number of deployments that have been possible.

The overall economic impact is likely to be huge. High levels of unemployment are likely to cause a tightening of household budgets; over 40 million Americans have sought unemployment benefits during the pandemic.

 Government responses will vary, some countries will introduce austerity measures, while others will look to invest in public works projects. The EU has recently announced more than USD1 trillion in financial stimulus packages for member states. Similarly, China has announced USD400 billion for local governments to spend on infrastructure. These schemes will no doubt prompt investment in IoT as part of infrastructure projects, both explicitly mention investment in 5G infrastructure, accelerating the adoption of 5G in IoT devices. Undoubtedly some of this investment will make its way to expanding smart grid capabilities, for instance. Further spending is likely to make its way to smart city initiatives as well as transport infrastructure.

Behavioural changes will also play a significant role in changing IoT investment patterns in the coming years. The first half of 2020 has seen a huge increase in remote working and a vast reduction in travel. It’s unclear the extent to which it will stick, but such a shift would reduce the need for vehicles, both public and private, as well as building automation systems for office space, but would also encourage spending on home office equipment and consumer IoT as workers spend more time in their homes. 

A de-urbanisation might reduce the need for investment in public transport and smart city infrastructure and would encourage investment from network providers outside urban areas. 

The nightmare for a market forecaster is ‘the unprecedented’. And we live in the year of the unprecedented. We try to take our best guess of what will happen in the next few years, but currently there is more uncertainty than ever before.

[Earlier in the year my colleague Matt Arnott wrote a couple of blog posts about the impact of COVID-19 on our IoT forecasts which I've liberally 'borrowed' here. Check them in all their glory here: Forecasting IoT in a world turned upside down by Covid-19 and Total global IoT revenue forecast will be $65 billion lower due to COVID-19.]

The reductions we have had to make to our 2020 forecasts have been born out by the limited amount of real-world statistics that we’re able to gather. Cellular network connected devices represent only a very small fraction of world IoT devices. But the companies that sell them, AT&T, Vodafone and so forth, are pretty good at tracking shipments, some on a quarterly basis. As of today three major global operator groups have published figures for the second quarter of 2020: AT&T, Orange and Telefonica. All of them show an impact. Telefonica recorded a negative quarterly growth, which is almost unheard of. It was down 400,000 connections compared to 900k growth in Q1 and 1.4m in Q4 2019. This was almost all due to Brazil and largely payment terminals. There is very little lag there between economic downturn and reduced numbers of connections. Orange also suffered, seeing growth fall from 500k in Q1 to 200k in Q2. AT&T fell from 3.5m to 2.3m but I think for them the worst is yet to come. Fully half of AT&T’s business is for connected cars, which as I mentioned when looking at the overall forecasts, have been hit hard. But I suspect there is a lag factor which will delay the drop in shipments. 

[For more on this check out IoT connection growth at AT&T, Orange and Telefonica all take a hit due to COVID-19]

I’ll leave you with a thought. It’s a thought from Ray Kurzweil the American inventor. Very interesting guy, check him out. In 2001 he proposed that the rate of technological progress was accelerating at an exponential rate, meaning that in the 21st century we won’t see 100 years of progress, but 20,000 years. 

I’ll say that again. 20,000 years of progress. 

The world’s moving fast and it’s driven by technology. We see the impact clearly in the business world. Every company is becoming a technology company, and a company’s IT strategy is now almost indistinguishable from its broader strategy. And that was before COVID-19 hit. Whether for better or worse Covid will certainly have an impact on that prediction. 

In next week’s podcast I’ll be zooming out to a 20,000 ft view of technology, looking at the idea that true innovation depends on the separation of software and control layers from hardware. 

Links to some of the research that I’ve refered to in this week’s show, as well as a transcript of the recording, will be available on the podcast website at WirelessNoodle.com

Thank you for listening to The Wireless Noodle. If you would like to learn more about the research that I do on IoT, AI and more, you can follow me on Twitter at MattyHatton and you can check out TransformaInsights.com

Thanks for joining me. I’ve been Matt Hatton and you’ve been listening to the Wireless Noodle.