Technology Review has released their list for “10 Breakthrough Technologies” for 2018. It’s hard to argue with the list having some important developments in it:
- 3-D printing with metal — this could drastically disrupt manufacturing and give rise to lighter, stronger parts;
- Artificial embryos — not exactly coming to a lab near you, but basically creating an embryo from another cell without an egg or sperm…great for research, but the ethical issues haven’t been worked out;
- Smart-design for urban settings — using sensing technology and integrating tech into high-end design has always been part of the “future” in various sci-fi movies, but Quayside in Toronto will make some of it a reality;
- Dueling neural networks — computer AI’s are bad at “creating”, but new techniques teaching them to learn off each other is creating a pseudo creativity with amazing applications for modelling, virtual entertainment, design, etc.;
- Babelfish earbuds — auto translation in an earbud is great in theory, but I’m not convinced it will move out of the tourist zone as rapidly as some claim, particularly as early designs by no less than Google have been pretty clunky;
- Zero-carbon natural gas — obviously, it’s still a non-renewable fuel, but having a clean version with no GHG emissions would be amazing, even if “Net Power’s technology won’t solve all the problems with natural gas, particularly on the extraction side. But as long as we’re using natural gas, we might as well use it as cleanly as possible.”;
- Perfect Online Privacy through zero-knowledge proof — the idea is that you can provide “proof” of something (age, financial balance) without actually providing access…not quite a simple “proxy”, more like a cryptographic tool that says “You want to check if that record over there shows the person is over 18? Let me ask it”, and rather than performing the check itself, the cryptography gets the yes/no without seeing the original data…kind of like PayPal on steroids, but that doesn’t solve all the privacy issues online, it just makes the anonymous transparency of blockchains a bit more practical;
- Genetic Fortune-Telling — the ethical issues of using DNA to predict health issues or even IQ are ridiculously bad, and based on the discrepancies in DNA testing for geneology, it can make economics look like a pure science; and,
- Quantum leaps — building quantum computers is one thing, figuring out what to do with one is another…but modelling of molecules for design seems like a great first use.
However, for me, the one “breakthrough” that I think will affect us the most is the one the magazine dubs “AI for Everybody”:
Artificial intelligence has so far been mainly the plaything of big tech companies like Amazon, Baidu, Google, and Microsoft, as well as some startups. For many other companies and parts of the economy, AI systems are too expensive and too difficult to implement fully.
Machine-learning tools based in the cloud are bringing AI to a far broader audience. So far, Amazon dominates cloud AI with its AWS subsidiary. Google is challenging that with TensorFlow, an open-source AI library that can be used to build other machine-learning software. Recently Google announced Cloud AutoML, a suite of pre-trained systems that could make AI simpler to use.
Microsoft, which has its own AI-powered cloud platform, Azure, is teaming up with Amazon to offer Gluon, an open-source deep-learning library. Gluon is supposed to make building neural nets—a key technology in AI that crudely mimics how the human brain learns—as easy as building a smartphone app.
Currently AI is used mostly in the tech industry, where it has created efficiencies and produced new products and services. But many other businesses and industries have struggled to take advantage of the advances in artificial intelligence. Sectors such as medicine, manufacturing, and energy could also be transformed if they were able to implement the technology more fully, with a huge boost to economic productivity.
Most companies, though, still don’t have enough people who know how to use cloud AI. So Amazon and Google are also setting up consultancy services. Once the cloud puts the technology within the reach of almost everyone, the real AI revolution can begin.
My only disagreement with the last one is the timing. They argue it’s available now, partly based on things like Siri and Alexa invading homes. Combined with the dueling neural networks, there are great things to be accomplished. I just don’t think they’re as close as they optimistically project they are already.