teddyknox

I wonder if machine learning could be used to model the probability distributions at a greater capacity, and thus reduce backtracking.

One might also consider placing the algorithm in the context of a generative adversarial network (GAN) to adapt the tile probability modeling ML component towards a pattern that is less distinguishable from a real city.

How'd you learn that you have this condition?

Did vorinostat do anything for this? One of your comments on this 6 months ago got indexed by a search engine and I just found it.

Love the modesty implicit in the short post.

Death metal is my go-to work music for when the fight with entropy feels like a losing battle. Especially as the rest of our capitalist society tells you to be happy all the time, death metal offers you the intuitive sense that you're not alone in the pain, and some solace in the fact that some people are brave enough to lament their difficulty out loud.

I think catharsis is really important and people should teach themselves to seek it the way they seek their professional goals, food and companionship.

Why do BBC articles do so well here?

Ello.co stealing their metaphorical lunch

Sometimes I use a 26 word language and it sure seem capable of saying everything...

Conventional processors will eventually run into physical limits of heat dissipation that will impede moore's law. Reversible computers are a way of circumventing this physical limitation because they dissipate less energy as heat.

When I think exabyte scale queries on a columnar datastore I think aggregations, but then I have this question: Why do we need to do exabyte scale queries in the first place? Wouldn't statistical inference via random sampling be faster and accurate enough?

(Granted, often times aggregations are happening after some filtering, at which point the relation being aggregated might be considerably smaller than exabyte scale.)

fish

What differentiates LLVM IR from, say, JVM bytecode? I'm curious because there's a stalled out GNU project under GCC called GCJ that would compile JVM bytecode to native. I wonder if the issue became that statically linking in the JVM in the binary resulted in a lot of bloat, or something more intrinsic to the suitability of JVM bytecode as a platform-independent IR...

Lucky you didn't go blind

Scala vs. Swift vs. C#. Fight!

Don Libes' daughter went to my high school, and I was working as the "webmaster" for the high school newspaper's website at the time. He would ruthlessly criticize via email the product our self-taught and earnest 2010-era PHP web development. I am not a fan of Don Libes. For those interested, the website was/is: http://theblackandwhite.net/

This reminds me of the new Black Mirror season 3 pilot

Doing a forward pass for every sample sounds like it would be prohibitive for real-time applications.

There's a new HBO series called "The Night Of" that portrays the New York justice system this way.

Trailer: https://www.youtube.com/watch?v=556N5vojtp0

Sure. I think what the OP was playing around with was whether we could generalize from, as you said, "expert knowledge of the problem[s] and good algorithms" so that AI could replace components in the incumbent programming paradigms. Agreed, when the end problem is well-defined and you know how "do" machine learning, the world is your oyster.

Why do we need to jump to ML-based programming again? I'm confident that as we build simpler interfaces and workflows for replacing the most modular components in our programs with AI, we'll begin to see which components are the next lowest-hanging fruit and what concrete ML problems need to be solved to model them.

We imagine a future where AI becomes a dominant paradigm for "writing" software -- I think that will be the case, but not in the way everyone suspects. I think 80% of the new value of software in the future will be derived from AI components, but that 80% of the production costs will still go into the structural glue code that supports the value-powerhouse models. Thus, most of the software written in the future will look similar to most of the code written today.

I also suspect that as the complexity of AI models increases, the structural code required to support these models will keep pace in complexity. For this reason, I see a future where the unit cost of high-quality software does not see any big drop, but where the value of this software continues to increase exponentially. A corollary to this is that valuable software will be no easier to program in the future than it is today.

> The thing I would like to get working is telling the net "here is a ton of different buttons, grids, lists and a lot of other UI stuff, and there are all our products - I want to maximize revenue - do whatever you like to get there".

As you referenced in your footnote, this goal is a little like the goal of putting a man on the moon, if the year is 1900. One could the make the case that something resembling an AGI agent would be needed to design/manage a merchant website with any degree of introspection and customizability. Your machine would need to understand business objectives, UI patterns, aesthetics, etc before taking the helm, and these things take years to learn even for even humans. This is not to say that AGI isn't an admirable goal, but that it's an obvious one, just as I imagine reaching the moon was in the centuries leading up to 1969.

Scientific research is certainly a massively parallel effort, pushing the boundary of our knowledge in many different direction at once, but rest assured that there are many good men and women working in this specific direction.

(also know that I have a lot of respect for this kind of vision-oriented thinking, especially because it generally leaves the writer vulnerable to criticism like this)

RFID isn't spoofable? I thought it was.

Does anyone know which Google office the button wall in the demo video is installed?

Also useful in latency-sensitive applications, such as the drone flight demo they use in their video.

Turns out you can use the same navigational keybindings to move around nvim splits and tmux splits, so that moving between the two programs is a sinch. This is what I use.

https://robots.thoughtbot.com/seamlessly-navigate-vim-and-tm...

I don't doubt that exercise promotes a healthy brain, but I sometimes wonder whether the endorphin high of exercise is really the best preparation for the day at hand. Sure, you feel great, but are you thinking great? Or do you just think you're thinking great?

In some of the cases described in this article, it sounds like these execs are addicted to the exercise that they wake up so early for (granted not a bad habit). But addiction by its nature tends to invent a narrative for its existence and the promise of greater productivity is gold.

> I was chatting with a friend of a friend the other day who mentioned she was thinking about taking Epicodus. Out loud, I said "Oh, that's awesome! I really hope you do it." But in my head, I was worried. What if she wasn't good at programming? What if she didn't do well in the class? What if she didn't get a job afterwards?

I have that feeling whenever I commend _anyone_ for planning to learn to code. I don't know the specifics of this case, and whether the author would have been unreservedly gung ho about a guy talking about his plans to learn to code, but under the impulse to have faith in my empathetic capabilities, I generally resist the author's thought process as a kind of self-indulgent false modesty, rather than true insight. This might sound like a kind of "I'm not racist, I'm xenophobic" reasoning, but as a lot of people on this forum believe: learning to code is really hard and we shouldn't be so hard on ourselves when we are skeptical of others' abilities to learn how to do it.