graphene

Can't tell if sarcasm...? What I mean is that the globe should rotate towards the East, not towards the West. This is of course assuming a stationary camera!

on that note, are you aware that the globe on stripe.com is rotating in the wrong direction?

So cool to see you guys go from strength to strength since the old days in our EF cohort!

Onwards and upwards!

From the article:

    Where does the money go?

    The penalty is divided up between the other European
    data authorities, while the money that comes to the
    ICO goes directly to the Treasury.

That's really cool! I checked Rigetti's github but there seem to be no open source Julia projects there (although, somewhat surprisingly, lots of Common Lisp!). Are you free to say what Julia is being used for at Rigetti (and Intel), and whether there are any plans to release things as open source in the future?

FWIW, it's exactly what I've heard when talking to Uber drivers in London. One guy used to work as a minicab driver; he prefers Uber because he can choose his hours, doesn't get cut out of lucrative jobs to the benefit of the company owner's nephew, and feels safer at night due to the rating system.

It may have been the "literally scared for our lives" part.

There's deeplearningbook.org, which starts with the basic maths and then goes into considerable detail on cutting edge work.

(EU national in London) Not at all. I think the doomsday threats are overblown and the negotiating parties will reach a mutually beneficial deal at the 11th hour.

Much more concerned about the possibility of the current Labour party gaining power actually.

I do something similar, here's my Makefile -- I have scripts that build figures in a separate directory, /figures. I'm sure it could be terser, but it does the job for me.

    texfiles = acronyms.tex analytical_mecs_procedure.tex analytical_mecs.tex \
               anderson_old.tex background.tex chaincap.tex \
               conclusions.tex cvici.tex gold_chain_test.tex introduction.tex \
               main.tex mcci_manual.tex methods.tex moljunc.tex \
               tb_sum_test.tex times_procedure.tex tm_mcci_workflow.tex tmo.tex \
               vici_intro.tex
 
    # dynamically generated figures
 
 
    all: main.pdf
 
    main.pdf: $(texfiles) figures/junction_occupations.pdf figures/overlaps_barplot.pdf \
                          figures/transmission_comparison.pdf \
                          figures/wigner_distributions.pdf
        pdflatex main.tex && bibtex main && pdflatex main.tex && pdflatex main.tex
 
    figures/junction_occupations.pdf: figures/junction_occupations.hs
        ghc --make figures/junction_occupations.hs
        figures/junction_occupations -w 800 -h 400 -o figures/junction_occupations.svg
        inkscape -D -A figures/junction_occupations.pdf figures/junction_occupations.svg 
 
    figures/overlaps_barplot.pdf: figures/overlaps_barplot.py
        python figures/overlaps_barplot.py
 
    figures/transmission_comparison.pdf: figures/transmission_comparison.py
        python figures/transmission_comparison.py
 
    figures/wigner_distributions.pdf: figures/wigner_distributions.py
        python figures/transmission_comparison.py
 
    clean:
        rm *.log *.aux *.blg *.bbl *.dvi main.pdf

Honest question, how is this different from what you can do in e.g. python? The python interpreter supports reloading of modules and evaluation of expressions, is there functionality that CL has above and beyond this that makes it more powerful?

Isn't it true though that there would be an O(n^2)-type difficulty in adding extra qubits, since they all need to interact?

Or is that an oversimplified view?

Yes, according to the first law of Thermodynamics, energy is conserved; regardless of whether there is computation being done, 100W of electric power will result in 100W of heat being dissipated.

About heating with computers, There is a Dutch company doing this, https://cloud.nerdalize.com/. It's interesting to think about how their economics work, because the heaters will be switched off for large parts of the year, and for much of the day. They must be banking that computation won't get much cheaper over time (in terms of FLOPS/$) because otherwise it'd be hard to recoup their initial investment. In a sense, this almost looks like a bet against Moore's law!

They trained on a computer model of the optical circuit, and only did the feed-forward step on the real thing. The rationale for that is that real-life models spend much more time (and energy) in inference mode, so that is the step you'd most want to optimize.

I can't help but think it would be really cool to automatically produce a circuit that would output the gradient of the error of the actual NN, so you could optimize that directly.

Well, yes and no. You are absolutely right that a complete implementation of TeX would be difficult, but you could read a subset of the language that is big enough to be useful, including simple macro definitions and commonly used commands, which is exactly what pandoc's LaTeX reader already does.

Not entirely what you're describing, but pandoc goes a long way towards being a sort of LLVM for text documents. In order to do all the format conversions, it transforms inputs into a tree-based internal representation, and then translates that into the output format.

Unfortunately it doesn't have a (pure) TeX reader yet, but that could be implemented relatively easily.

yes, it would be really helpful to get some examples of the counterproductive advice.

The counterargument is that as more and more industries start to do their engineering at the nanoscale (whether coming from above, as in materials and electronics, or from below as in biochemistry and pharmaceuticals), the physics of their systems will become more similar. This will cause their design rules to also become more and more similar, so it's plausible that you could end up with a small number of players possessing engineering expertise that can be applied to a very wide variety of sectors.

There is the fact that proteins are constrained to function as part of organisms that are capable of self-replication. This constraint means that proteins (mostly) are not very stable wrt oxidisation and UV degradation, and only work properly in aqueous solution.

It's anyone's guess how significant these constraints will be from the viewpoint of developing artificial protein machines (maybe rapid (bio)degradation is a good thing!), but there's definitely large swathes of chemical design space outside of arbitrary chains of known amino acids, and we might be able to discover entire classes of molecular machines that don't have the drawbacks of bioinspired proteins.

I don't know if this is what you're referring to, but a common application for government-owned supercomputers is simulating the degredation of nuclear warheads. The degradation of the fissile material as well as its surroundings is highly critical to a nation's security, and also very hard to model well.

Of course in an ideal world those cycles would be used to help cure cancer, but given that these warheads exist, it's probably a good idea to invest resources into getting an idea of what shape they're in.

You could compare raw FLOPS (Floating point operations per second) but that would only tell part of the story. These supercomputers are highly engineered for low network latency between nodes, which is necessary for many scientific workloads. Google and other companies are generally able to express their algorithms in highly parallel ways, which means there are much reduced requirements for communication between nodes.

Therefore, even if the raw performance in terms of FLOPS sound similar, the two systems will have widely differing performance on real workloads.

Hi Christian, great to have you replying directly like this; I hope my criticism came across as constructive, since I'm super excited about and impressed with this work, as someone also chasing the dream of molecular nanotechnology.

Re: rigidity; I'm curious (apologies for not having read your papers) how you define "just enough" flexibility, and how your design tools take freely moving components into account. Would you agree with my intuitive feeling that there's a tradeoff between designability and functionality, and that your spiroligomer work sits between rationally designed protein structures (very hard problem) and Drexlerian molecular-scale gears and ratchets (similar, determinsitic design rules as in macroscopic systems)? Or, do you feel that anything protein "machines" can do, spiroligomer machines can do too?

I recently started a startup that has molecular nanotechnology as the end goal, and my thinking has been that the flexibility of proteins is an essential element in achieving the capability to design and manufacture with atomic precision, and that the concomitant complexity of the large numbers of degrees of freedom can be tamed with a data-driven approach leveraging machine learning algorithms. I'd love to hear if you have any thoughts on this, and how it relates to the spiroligomer approach.

Super interesting.

If you read the Feynman speech that he references at the beginning, he actually mentions that as you scale machines down, things like mechanical rigidity will degrade and you will need to change your design rules accordingly. I always assumed that when you reach the molecular level, thermal motion and the constant bombardment by water molecules would mean that the only viable option is to use proteins, just like nature does, so it's very interesting to see that this guy is aiming to use more rigid structures at the molecular level. I guess this is a way to reduce the complexity (degrees of freedom) compared to designing protein tertiary structure. I wonder if this is too constraining though, he admits he has yet to figure out how to build mechanical machines using this approach, and intuitively I'd expect that to be very difficult with this degree of rigidity. You might need the additional flexibility of peptide chains to do many of the interesting things that are possible.

He does point out the advantage of durability, but this raises the obvious issue that one of the questioners alluded to, namely toxicity/pollution risk. I'd think degredation by biological or other means would be a feature, not a bug, since as he points out, even conventional plastics are a huge pollution problem.

Fascinating stuff nonetheless.

They fund individuals for a fixed, three month period, during which you are free to work on whatever and with whomever you want.

After three months, they invest £10000 (in exchange for 8% equity) in all teams that are settled on an idea and seem promising.

There are no fees of any kind.

(disclosure: I'm on the current cohort)

It's completely the latter.

(disclaimer: also in the current cohort)

On your second point, no, a 1000W heater will emit the same amount of heat (neglecting visible light losses, which, as other commenters point out, will be converted into heat later on) as a 1000W computer. This is the first law of thermodynamics, and the fact that the computer is doing computation makes no difference.

Maybe change the 'Net metering' rules to buy the solar power from customers at a lower price, and let the supply-demand work itself out.

It won't be that simple, since even this (allowing customers to contribute power back to the grid at scale) requires expensive infrastructure upgrades.

Keep in mind that converting solar energy to chemical energy (what plants do, and what these researchers have done) is a much harder problem than converting solar energy to electrical energy directly.

Also, the main result of this work seems to be not energetic efficiency, but the production of flexible precursor chemicals.

Thanks for your comment. As I said, it's understandable for your company to keep its software proprietary, after all Google for example, while a supporter of and contributor to open source, does not open source critical components of its business either.

On the wetware side, there is of course a similar incentive to keep the genetic sequences that result from your work private, in addition to perhaps ethical concerns regarding the dissemination of arbitrary DNA.

As a fellow software guy considering going into drug development research though, I do wonder: Given your mentioned open source credentials, it's probably plausible that you are at least sceptical about software patents? If so, what is your opinion on patents on drug molecules and DNA sequences? I'm struggling a bit with this since the only way to a big exit seems to involve protecting any findings as IP (because of the costs of the regulatory process), but I'm not sure I'd want my work to be patent-walled.

Do you know what you would do if you are one day faced with the choice of whether or not to patent one of your molecules or sequences, knowing that not doing so would have significant adverse financial effects on your company?

(This all may sound somewhat critical, but I'm really just curious. My email is in my profile if you prefer to respond privately.)

Do you do visa sponsorship? (long shot I know)