The MIT Technology Review's annual list of breakthrough technologies came out last week, and, as always, it's worth a look, both because of the general framing it offers about technology trends and because of some specific implications for insurance.
From an insurance standpoint, the two most directly relevant are what TR refers to as "the end of passwords" (yay!) and the growing availability of "synthetic" data to train AI in situations where there isn't enough real data available. But the other eight on the list of 10 are intriguing, too, especially for what they suggest about how we might be able to build on the technologies behind the mRNA vaccines against COVID to make additional, huge advances in healthcare.
The article in TR says, "New forms of authentication will finally let us get rid of [passwords] for good. Instead, we’ll use a link sent via email, a push notification, or a biometric scan.... The process is already underway. Enterprise-oriented companies like Okta and Duo, as well as personal identity providers like Google, offer ways for people to log in to apps and services without having to enter a password. Apple’s facial recognition system has taken biometric login mainstream. Most notably, Microsoft announced in March 2021 that some of its customers could go completely passwordless, and it followed up in September by telling people to delete their passwords altogether."
If you believe the analysis in "A Brief History of a Perfect Future" -- and I do, because I wrote it, along with longtime colleagues Chunka Mui and Tim Andrews -- the email links, push notifications and biometric scans are just the beginning. We'll all be able to increasingly triangulate to verify our identities based on the concept of "something you know, something you are and something you own," and the somethings you are and own will get increasingly numerous and sophisticated -- contact lenses that can identify you to your phone or computer, devices smaller than a grain of rice that you can swallow, even a DNA scan using an app on your phone.
Doing away with passwords will not only save us all time and eliminate the frustration that comes with forgetting and resetting passwords but will vastly improve cyber security. Phishing and other approaches to stealing passwords have provided hackers entree into many organizations, and those risks will diminish for individuals, companies and the insurers that cover them.
This won't happen overnight -- none of what TR promises will come to fruition this year or even next year -- but the notion of a password-free world is, like most of the promised breakthroughs, close enough to reality that we all ought to be thinking about the implications and perhaps even gaming out the effects.
"Synthetic data" for AI is actually an idea that we've occasionally published on at ITL for a couple of years now -- for instance, "How Synthetic Data Aids in Healthcare" last August, "How to Put a Stop to AI Bias" in February 2021 and "Evolving Trends in a Post-COVID-19 World" in May 2020. (Yes, an article envisioning a post-COVID world almost two years ago was more than a little optimistic.)
Basically, if you're trying to train an AI to spot fraud, for instance, but don't have the massive numbers of actual cases that machine learning requires, you have an AI create lots of "cases" by synthesizing them out of the data you do have. There's an obvious limitation: The synthetic cases are only as good as the information you feed the AI that creates them. If the examples you feed the AI don't include a certain type of fraud, well, then the synthetic data won't, either. But AI developers are still finding synthetic data useful.
As TR explains the concept of synthetic data: "Training AI requires vast amounts of data. Oftentimes, though, that data is messy or reflects real-world biases, or there are privacy concerns around the information included. Some companies are starting to create and sell synthetic data to avoid these problems. It’s not perfect, but it could be a better way to train AI."
Some of the breakthroughs that TR predicts could have significant implications for COVID, for future pandemics and for other diseases. For instance, TR says the massive investment in genomic sequencing that undergirded the mRNA vaccines against COVID is allowing for much better tracking of the virus and its variants.
The work has even produced "a pill for COVID." TR says, "Given to people within a few days of infection, an antiviral from Pfizer slashes the chance of hospitalization by 89%. The U.S. government has already placed orders for $10 billion worth of the new drug, called Paxlovid.... Pfizer’s drug could also be a ready defense against the next pandemic."
TR also holds out great hope for a vaccine for malaria, which kills more than 600,000 people a year, most of them younger than five. The GlaxoSmithKline vaccine, approved last October by the World Health Organization, is still in its early days. TR says, "It requires three doses in children between five months and 17 months old, and a fourth dose given 12 to 15 months after that. Given to more than 800,000 children in Kenya, Malawi, and Ghana, the vaccine had an efficacy of about 50% against severe malaria in the first year, and its effectiveness dropped dramatically over time." Still, combined with other measures, such as bed netting treated with insecticides, the vaccine "is expected to reduce malaria deaths by as much 70%, compared with the death rate in children given existing drugs," TR says. That could mean hundreds of thousands of lives saved each year, and second-generation vaccines are already on the way. The vaccine, which is the world's first for a parasitic infection, also holds out hope that others can be beaten back, too.
Citing potentially even broader implications, TR singled out the work by DeepMind that has allowed AI to predict how proteins will fold themselves, removing the need to use a complicated chemical process that could take a year and cost $120,000 just to determine the shape of a single protein. Knowing the shape, and not just the sequence of acids in the protein, is key to understanding how proteins interact with other and, perhaps, with drugs that might halt or reverse a disease. I was so intrigued when DeepMind made its initial announcement in late 2020 that I wrote about it at the time, and the subsidiary of Alphabet has made remarkable strides since then.
The shape of some 170,000 proteins had been determined chemically over many years when DeepMind made its initial announcement. TR now says, "DeepMind has... set up a public database that it’s filling with protein structures as AlphaFold2 predicts them. It currently has around 800,000 entries, and DeepMind says it will add more than 100 million—nearly every protein known to science—in the next year."
Finally, TR holds out some hope on climate change. It describes promising developments in grid-scale batteries -- the sort that will be needed to ensure smooth provision of electricity as renewables are increasingly integrated into our electricity supply. TR also describes the opening of a plant in Iceland that is the largest to date that removes carbon dioxide from the air. Again, this is just baby steps. The facility can capture 4,000 metric tons a year -- about the output of 900 cars -- while the world produces some 51 billion metric tons. And the cost per ton removed is $600 to $800, while it needs to be $100 a ton or even lower to be broadly practical. But bigger plants are on the way, and, importantly, some companies are willing to pay today's high costs as they strive to cancel out their emissions. Those companies are providing crucial revenue that will help carbon removal efforts scale. TR even touts a major improvement in the magnets that are needed to contain the super-hot plasma required for nuclear fusion, which has long been the Holy Grail because it would provide us the energy of the sun without the radioactive waste that comes from nuclear fission.
Having written a great deal about climate in "Perfect Future," too, I think these technologies will kick in further down the road -- especially in the case of fusion -- than the others, so I won't go into them at length here. But I encourage you to read the TR piece. I always find their annual list stretches my thinking and opens up possibilities.
Cheers,
Paul