To me it is still not clear how scalable the DNA computing is, but there are nice proofs of concept already [2].
[1] https://en.wikipedia.org/wiki/DNA_computing [2] https://www.nature.com/articles/s41586-018-0289-6
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To me it is still not clear how scalable the DNA computing is, but there are nice proofs of concept already [2].
[1] https://en.wikipedia.org/wiki/DNA_computing [2] https://www.nature.com/articles/s41586-018-0289-6
Earlier quoted context omitted.
Slightly off-topic but I wanted to ask why and when did you start studying CS after law. I recently graduated from law school and now am an intern at a law firm. I have a strong interest for CS, and it bothered me for a long time that I went to law school instead of CS. I'v overcome those feelings over the years and dedicated myself to become a lawyer. But your post caught my interest. I'd be glad if you could share…
I'm gonna go one step further with the off-topicness. Do you get much of an opportunity working within law to focus on technology? I'm about to go back to University to study law but I would love to be able to combine Law with technology. Seems like an interesting area.
- Working as a lawyer in a law firm and being the expert/contact person for any tech stuff.
- Working as project mananger for Legal Tech in a law firm (Magic Circle law firms are already having these jobs)
- Being a lawyer specialised in IT/tech/IP laws, which require a domain understanding.
- Working for or founding a Legal Tech start-up.
- Owning a law firm that is having an automated workflow which is specifically engineered.
Right now, there aren't too many jobs on the market. But they will become more. And I can promise you, for most of all people tech is a black box (which is also bothering people), and with it being more and more integraded in our workflows, being tech savy will become more important in virtually any job (including government etc.).
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Isn't secure MPC also called "homomorphic encryption"? Agreed, it is a very interesting area!
I think HE falls in the superset of MPC theory but not synonymous.
I'd love to be able to play with some of these ideas in my current favorite (but alas not mathematically-speedy) language (Elixir), if anyone had any sort of intro guide to HE or MPC, would love to have a look
Secure Multi-party Computation. The basic idea is developing methods for two (or more) parties with sensitive data to be able to compute some function of their data without having to reveal the data to one another. The classic example is developing an algorithm that allows two people to figure out who is paid more without either revealing what their salary is. Such algorithms get significantly more complicated if the…
Isn't secure MPC also called "homomorphic encryption"? Agreed, it is a very interesting area!
I view both of these things as methods to perform secure computation on encrypted data. sMPC has a lot of applied research behind it while FHE has been mostly theoretical up until a decade ago. I think we'll see FHE catch up with sMPC soon here as there are technical limitations to sMPC systems that FHE doesn't have.
I think most interesting computer science fields are actually application of CS in other domains. Science changed a lot in the last decades, moving from a genius in a room looking at the data and coming up with grand theory to have vast amounts of data that no single human can make sense of. The work of the computer scientist is to quickly understand problems from various fields then solve it using tailor-made algori…
Utilizing machine learning to process and analyze historical texts could shine a light on patterns that have gone unnoticed thus far.
Secure Multi-party Computation. The basic idea is developing methods for two (or more) parties with sensitive data to be able to compute some function of their data without having to reveal the data to one another. The classic example is developing an algorithm that allows two people to figure out who is paid more without either revealing what their salary is. Such algorithms get significantly more complicated if the…
AI, machine learning, and neural networks are, of courwe, booming, but I consider them to be hyped. I consider type theory and formal verification to be more promising (but more academic). Distributed systems and everything having to do with parallel and/or high-performance systems is a good midway between what the industry likes and what's interesting from an academic point of view.
Haha. Formal verification has been around for 40/50 years and we can't say it is a wide success from a industrial point of view. It has some achievements in terms of results/methods and projects checked, but on a daily basis, pretty much no one uses it. We are ages away of having every programmer understanding formal verification and having all programs verified/proved. Type theory is in a similar situation. Many iss…
Recent prog languages have added syntax to avoid issues such as "off by on error" (generator etc...), TDD is slowly becoming a standard everywhere. The next step to improve quality in software is formal verification IMO. There is quite a bit of research in that domain and even some academic program languages integrating it within their syntax.
Swarm Computing. The hardware and networking to make it practical and useful exist now, but the field is still in its infancy. There‘s some discussion about its use in autonomous driving, construction, warfare.
What makes swarm computing different from distributed computing?
Only because I don't yet see it mentioned, the one emerging field to rule them all: program synthesis. :P
Not saying that there isn't merit to the idea. Just saying that program synthesis is more or less synonymous with programming language design when you take into account the challenges involved.