skysight-inc/HackerNews huggingface dataset:
> In a similar vein, I find a machine beating a grand master fairly boring.

I know a lot of people felt that way even before it happened. It feels like an accounting problem rather than intelligence. But, it's fun to remember when it was thought by some very smart people that chess required more accounting than was ever possible by a machine, so beating a grand master would have to be a demonstration of intelligence.

> don’t beat the game, beat the opponent, understand why, and then model an adaptation strategy for the opponent (teach).

Yeah that would be closer. My favorite Turing test, if you will, is whether the AI can tell you you're asking it the wrong question. If Watson got bored of beating grand masters at chess and started refusing to play, maybe then a case could be made it was reasoning.
Yup, I'm working with an accountant to better understand what's going on. Last year I was in a grace period but this year I'm going to have to file. Luckily it's just me, no employees and no cofounder to worry about. But man tax law is complicated and I don't like not knowing every part of what I'm doing.
Waste from whose point of view?

Yours? Mine?

When they get help, that buys time. May not buy it as efficiently as it could, but that's a nit.

Given time, their luck can change. Help can get them there.
I tried going vegetarian a while back but I am naturally skinny and was loosing weight. I find meat makes for more interesting cooking.

And your suggestion of vegetarianism should really be vegan as caged hen eggs are one of the worst forms of factory farming I can think of
"Attempt 1. Bug dashboard. Initially, in 2006, FindBugs was integrated as a centralized tool that ran nightly over the entire Google codebase, producing a database of findings engineers could examine through a dashboard. Although FindBugs found hundreds of bugs in Google's Java codebase, the dashboard saw little use because a bug dashboard was outside the developers' usual workflow, and distinguishing between new and existing static-analysis issues was distracting.

"Attempt 2. Filing bugs. The BugBot team then began to manually triage new issues found by each nightly FindBugs run, filing bug reports for the most important ones. In May 2009, hundreds of Google engineers participated in a companywide "Fixit" week, focusing on addressing FindBugs warnings.3 They reviewed a total of 3,954 such warnings (42% of 9,473 total), but only 16% (640) were actually fixed, despite the fact that 44% of reviewed issues (1,746) resulted in a bug report being filed. Although the Fixit validated that many issues found by FindBugs were actual bugs, a significant fraction were not important enough to fix in practice. Manually triaging issues and filing bug reports is not sustainable at a large scale.

"Attempt 3. Code review integration. The BugBot team then implemented a system in which FindBugs automatically ran when a proposed change was sent for review, posting results as comments on the code-review thread, something the code-review team was already doing for style/formatting issues. Google developers could suppress false positives and apply FindBugs' confidence in the result to filter comments. The tooling further attempted to show only new FindBugs warnings but sometimes miscategorized issues as new. Such integration was discontinued when the code-review tool was replaced in 2011 for two main reasons: the presence of effective false positives caused developers to lose confidence in the tool, and developer customization resulted in an inconsistent view of analysis results."

Once upon a time, I had a co-worker on a C++ project commit a change disabling -Wall with a comment that can loosely be paraphrased as, "No one has time for that."

It's good to see Google has the same issues.
The difference between mean and median provides information. It's always safe to assume the distribution is right-skewed, but having both median and mean gives you a sense for how much.
Speaking for OP - I think the idea was that ML would determine that your elevator was in need of maintenance and place the order without human intervention.

Although the word "routine" belies that, but so.
That was actually how my stats prof made us learn all of the analyses, she thought if you can't do it by hand you won't understand what the computer is doing either. I found that to be really useful for just about every statistical technique I have used.
JAMstack Radio Ep. #28, Front-End Infrastructure at Coursera
> This isn't a unique problem, and we need a plan for healthy shrinking of cities and countries.

I'll add as someone from the rust belt, while we do need to understand how to shrink cities, a lot of this shrinking in the central city was self-inflicted by sprawl.

The metro populations of many of these rust belt cities stalled out in the 1950's-70's but for the most part has been relatively stable since then. Yes, wealthy industry closed or left, but the population was still there.

However short-sighted suburban sprawl, poor planning, and racial segregation hollowed out the central cities while urbanized area doubled or quadrupled the infrastructure a region needed to support. Suburbs boomed with new infrastructure while the city was cannibalized.

Urban sprawl in Cleveland - same metro pop in 1948 as in 2002, at least 3 times the land area
http://www.urbanophile.com/2018/03/16/sprawl-in-its-purest-f...

Same story with urban sprawl in Buffalo - same metro population, triple the urbanized area
http://www.urbanophile.com/2010/04/27/chuck-banas-this-is-sp...

Unfortunately there seems to be no political will to stop, even as it consumes the older suburbs now. Subdivisions, new roads, sewers and office parks continue to be thrown up on the edges while a new wave crumbles. The low density of the sprawl can't generate enough revenue to sustain itself - but by the time the bill comes for renewal, the developers have already moved on to a new ring of exurbs.

I wish these cities would look towards regional planning and government like Portland and Toronto.

https://en.wikipedia.org/wiki/Greenbelt_(Golden_Horseshoe)

https://en.wikipedia.org/wiki/Urban_growth_boundary
Introducing Nanoscope: An Extremely Accurate Method Tracing Tool for Android
Before 2005 experts estimated that AIs that could beat humans at Go would take many years still. In October AlphaGo came out and in March 2016 beat everyone. In 2017 AlphaGo Zero came out, which was only trained on self-play. It beats all previous versions.

But it is not so much these big breakthroughs that cause current optimism, as much as the large amounts of continuous improvements and refinements.
fhn4VBnJbeMBxx is apparently less safe than Password1234!

As is keep peace there hello, randomly generated according to the XKCD method.

Sorry, these things just can't work reliably.
This is just my own methodology that I have always followed for my own person hobby sites, but I don't imagine many people do this, nor would they, nor should they.

I log access to a ram disk and truncate the logs daily. If I start having issues that require keeping logs, I rsync to a secure location or rotate until x percentage of ramdisk is full.

My machines boot up and bootstrap their static content, cgi scripts, etc, from a git repo over a VPN link. They dynamically format a data volume using a long random key that I have no knowledge of. The end goal being ephemeral and cattle. If I need to back up anything in a data volume, I do so over a VPN link.

Again, this is just my lunatic method and has never been tested in a legal case. I could argue a thousand reasons to not use my methods and only a few hundred to use them.
The Fable of the Dragon-Tyrant, and the Courage to Oppose Aging
agreed... Was happy to see a standardized LLC product from Stripe till I saw it costs a whopping $500!
Guilt is a psychological and social human concept. Imagined result of a potential action. Few sociopaths feel it at all...

It is probably adaptive as in helps survival die to fostering group cohesion when present in enough individuals.
File system performance seems great for me, but I've only been seriously using WSL for the last 6 months or so.

That being said, there are some major performance issues with how certain programs map memory and, as a result, things like Haskell are an absolute pain to use.
I was more looking at the differential between FB -> Alphabet -> Rest. As a member of the Rest in the Bay Area, hiring has become quantitatively more challenging in the last 12 months as FB continues to gobble up talent. Perhaps it's not so much a bubble as it is a deep, likely irreversible change to SV.
Back2Basics: Demystifying Eta Expansion
We fix that by building conversational AI ;)
Re: Everything IBM says about Watson should be taken with a few tons of salt.

How is this different from nearly all other "AI" companies? IBM is not the only one guilty of hype. Neural nets can do specific things well, such as mirror a specific training set well, but still have major gaps in terms of what many call "common sense". It does smell like AI Bubble 2.0 brewing out there.
> I've been thinking a lot about the merits of the (highest income / median income) ratio in creating a society that is a little more resistant to eventual inequality.

That metric is very sensitive to outliers. If you have even one individual with enormous income, that ratio will be enormous. Now you might say "that's precisely the point", but I would argue that having how we design a society hinge on what one individual earns, is not statistically sound.

A more appropriate metric along the same line of thought would be something like top 10-percent excluding outliers divided by median income, where "excluding outliers" could be calculated in an admitedly somewhat arbitrary, but common way like e.g. https://en.wikipedia.org/wiki/Interquartile_range#Outliers.

Or just use something that already exists instead of re-inventing the wheel https://en.wikipedia.org/wiki/Gini_index
Ask HN: Why are big companies so much less efficient?
I've worked on companies big and small (relativley; smallest was around 400 people, though engineering was closer to 160-200). One thing that has struck me is that, in my opinion, smaller companies seem to get much more done per engineer than bigger ones. I'm curious-- has everyone else has seen this too? In your opinion and experience, why do you think this is?

Is this the overhead of decision-making (one person or the engineer deciding vs a chain of command)? Is it that people are just more motivated when they're the underdog? Is it that the inherent size and complexity of a codebase grows exponentially as people grow linearly with time? Is it that some products seem to be "done", and changes are iterative and pedantic, not risky and exciting? All of the above? Maybe even some more?

I'd love to hear your stories and thoughts!
What bank does the LLC version use?
"Search is all well and good when we are counting words, which is what data analytics and machine learning are really all about."

There are machine learning models which go far beyond counting words, for example see https://arxiv.org/abs/1502.01710
Comcast won’t give new speed boost to Internet users who don’t buy TV service
"Happy Birthday, Karl Marx. Non-experts think You Were Right."
> I started a company called Cognitive Systems in 1981.

Ahh. So just from the article, his gripe is of the “begs the question” sort — he’s not pleased with the evolution of idiom. Since he was doing “real AI” back then, who are these frauds to claim they are doing AI?

His point may or may not be valid, but his specific argument is quite weak. He notes that even a person, an actual intelligence, wouldn’t know what Dylan was singing about without context. He goes on to presume that Watson doesn’t have context, but who’s to say? Watson could certainly read all the articles about Dylan that he so helpfully cites, and come to “understand” the songs. And maybe Watson has.

If you follow the links to his academy, you divine a bit more of the motivation.

His software development course provides “a unique automated mentor, employing natural-language processing technology derived from our decades of artificial intelligence research”. He is desperate to stay away from calling it actual AI, yet can’t resist implying that it is. This is probably most irksome to him.

My advice: sometimes you have to join ‘em.
Although that notion of "the best" has been deeply challenged by slack, e.g. https://www.theatlantic.com/technology/archive/2018/04/how-s...
Bingo.

This gets right at "blame yourself" type policy.

We all could need help. Thinking of it as such, understanding luck plays a bigger role than we would like will boil down to more humane policy.

That will raise standards of living and improve society, all of which will improve your personal luck.
I live less than a mile from my job downtown, and ended up hospitalized this winter while walking home because the city still doesn't take care of the streets (let alone the sidewalks). The downtown core and some adjacent neighborhoods (Corktown, Midtown, Lafayette Park, Brush Park, Cass Corridor, among a few others) are rapidly changing, but it feels like outside of the T shape that those neighborhoods make ( https://mogodetroit.org/system-map/ , the bike share system doesn't extend outside of this) you're in a completely different city. When I go for a walk at night and there isn't a weekday sports event, the downtown is still incredibly quiet and mostly empty. I have this constant nagging guilt about the revitalization of Detroit being for people like me (young white tech professional) and not the people who stayed and didn't give up on the city, because I had no faith I'd ever be back a decade ago. The artsier people I know are already moving to the edges of the T into places like Hubbard Farms and the North End, and it feels inevitable that people will be displaced by the "rebirth".
I'm using Haskell right now. It's fine.
Well, if more and more packages in future move to python's offering venv. It would certainly be a problem that right now we are already facing
Lots of comments on the previous article (before he had a chip to test) as well.

https://news.ycombinator.com/item?id=15990766
> I am talking about their FREE streaming API that let's you stream millions of tweets per day. I am just trying to raise awareness about how easily people can pull user data en masse from Twitter, which is very relevant to the article. It's public, but people still don't realize just how easy it is to do.

Yes, that's an endpoint off stream.twitter.com. It's not only limited in terms of how much relevant info you'll be able to access (in the context of the original post), but also heavily rate-limited for clients.

I agree that it's confusing that Twitter has so many different components of their API which use overlapping names, but I've looked into this extensively because, as I mentioned at the outset, I wrote the client libraries for these and still maintain them.
Understanding OAuth 2.0 and OpenID Connect
No we don't. Not even close.
The firehouse has been under lock and key (except to those with enough $$) for a really long time now, like maybe more than a decade.
He produced an app and used the platform.
Laundering Money On-line: a review of cybercriminals’ methods [pdf]
Many people seem to think the arrival of fully autonomous cars is right around the corner; when I say that it will likely take at least 10 years, but probably more like 20, until there will be significant numbers of those on the road they are very surprised.

I admit I recently upped that number from "5-10 years minimum" after seeing the media reaction to recent driver-assist systems related accidents — it became clear to me that the old adage "the autonomous car does not need to be perfect, it only needs to be better than an {average, good, skilled} human driver" is simply wrong, because autonomous car brand X will be regarded - and judged - as if all X cars are one driver. Thus, these cars actually need to be much better than humans in essentially any traffic situation, because it simply won't be acceptable for "autonomous cars of brand X" to kill a few thousand people each year in every country. People would equate this to a single human driver mowing down thousands.

This effect is not necessarily bad, I don't have the inside information, but it's likely (or at least probable) that manufacturers invested in this technology observed the same and drew a similar conclusion.

The question now is whether the car makers make autonomous cars that good, or invest into massive PR campaigning to change public perception towards "each autonomous car is like an individual driver and it doesn't need to do much better than that".

We've seen the latter before. Jaywalking and avenues are just two examples.
He specifically addresses that he _wasn’t_ doing “real AI” then, and given they’re doing fundamentally the same or similar things, IBM aren’t now either.
French colonialism in Vietnam
>>Still, the crash forced the IRS to extend the tax filing deadline one day, delaying some 14 million submissions.

For a system that is supposedly so brittle, they were able to get enough of it back up and running to at least accept submissions within one day?

That's pretty amazing for 60 year old technology.
In terms of Google's problems, one of the implicit problems with Java is that it is another language in the code base and requires tooling and maintenance. Less directly, there is also Google's relationship with Oracle in the context of the ongoing copyright litigation. Again, Go exists to address Google's problems which may or may not have some overlap with those of another organization or any particular individual.
Twilio now supports LINE
You don't change your genetics in any meaningful way during your life no matter how much you want to influence them.

If you're born with Down syndrome that's that. A predisposition for getting certain kinds of cancer tend to be born out by statistics. Having a body that works well for athletics is mostly a function of genetics and training.

No amount of collective work is going to make everybody athletes, the whole definition of an athlete is 'outlier'.

What we can change collectively is education and diet.
In bed?
Great point, although said rent is being driven largely by FB and GOOG. I remember the first time I was outbid for a flat in 2016 when a small group of googlers paid cash for the entire first year of rent. I should have moved to Seattle that afternoon :)
I wouldn’t bother about documentation, but I would bother about thinking about the basics: do I need all this personal information? On what legitame basis am I collecting it? Am I storing it safely? Do I explain to people clearly how I’m using it, how they can see it, amend it or delete it?

Get the basics down, the documentation can follow. Get the basics wrong and it becomes painful.
There have been a number of breakthroughs in specific fields recently. In fact it seems like there is a paper every week that pushes the state of the art forward in some branch of AI/ML. I think the big one that triggered the current excitement was the success of convolutional networks in image recognition tasks. You can read about that one (in 2012) here: https://www.technologyreview.com/s/530561/the-revolutionary-...

EDIT: This paper refers to the algorithm as SuperVision which was the team name, but it is more commonly called AlexNet. Here is another article discussing it:

https://qz.com/1034972/the-data-that-changed-the-direction-o...
One way to fix it is not to make outright false advertisements.
I will say that when my company uses AI, they almost always just mean LSTM-based content generators - usually chatbots or "advisory"-style outputs - but the key idea is that it's generative and not just an evaluator.

I think that's probably the most helpful definition because your ML output has to go into some larger intelligence system (human or otherwise) to produce some decision / activity. So your choices are:

* Human
* Expert system with rules of interpretation that include ML output as input
* AI system which relies solely on inference and reinforcement / goal-seeking to produce output
I could never get an interview at Slack, but they are not hiring the best. Their compensation is far too low.
Give me an example of an overly vague rule.
This already exists, it's called adaptive testing. I guess the new part would be modelling somebody's ability, not in one topic, but many related topics.
ML is a "big" application at the tools level. Categorization in particular.

You're already seeing it in product. Consumer level security video equipment with human detection is pretty accessible now. I can ask my iPhone for pictures of my kids in snow in 2014 and get a pretty good output. Enterprise level categorization of photo and video is a thing.

"Smarter" machines are just like smart people, by themselves not very exciting. But give them a purpose or application, and things get exciting.
If the US company is going to be a fully owned child company of another EU company, which is most suitable, a C Corporation or an LLC and why?
metamask could peek at etherscan and see if a contract has 'verified code' or not. often times, contract creators upload the source code to etherscan itself to get the verified badge https://etherscan.io/address/0xd22f97bcec8e029e109412763b889...

however, that would require trusting etherscan, but metamask already relies on it pretty heavily, so...

if the user didn't want to rely on etherscan, it's possible to have metamask verify contracts itself - the dapp publisher would need to provide the source with the dapp, metamask could compile it, and make sure it matches the on-chain bytecode. i believe solc is written in javascript so it would be possible to ship it with metamask.

i'm not sure how this would work with contracts with dependencies. at that point, the sources of all the dependencies would need to be verified. this would require larger infrastructure (e.g. etherscan, but again, a single point of failure).

in that case, it would almost make sense for another data section in the EVM to hold the contract source. in this case:

* 1. contract source code is all stored on chain, in a decentralized manner

* 2. clients can verify the byte code

* 3. it's more expensive to publish a contract since it needs the extra space to store the source
Yes, just like I am okay outlawing duelling to the death, gambling to the death and selling yourself to slavery. Even if both parties consent.
It's not quite the same functionality. If you open Google in a regular container and then click a link that goes to a different site, it will load the new site in the same container. The Google container would open the new site in a different container.
>civilization on the cusp of colonising other planets.

Hmmmm first of all...I doubt that...second of all. Why? Lets see we've got, icy dead planet, poisonous hot planet, burning hot planet, and a bunch of gas ones with no land or anything humans need. Maybe some poisonous death moons around some of those giant useless planets. But really...space has nothing for us.
This seems like a classic case of what a good coder would call "premature optimization."

Nutritional science, is far, far from settled. It's not even clear that is such a thing as a baseline, universally-applicable nutritional paradigm. For instance, there could be potentially large deviations based based on genetic and epigenetic factors.
And, even in SV, most of the industry cannot; certainly the AirBnB/uber/lyft's of the world can, but the smaller companies seem to be falling behind (based on anecdotal conversations at least).
the term 'expert system' was tainted by AI Winter I.

I suspect the term 'Machine Learning' will become a dirty word after AI Winter II.
We do not support LLCs as subsidiary of other companies, for reasons. We do support that use case for C corporations. There are complicated tax issues involved here; an accountant can talk you through them.