Oil never was that valuable. It was propped by a OPEC cartel.
If you go back in time and watch any press release from the saudis when asked about the idea of "peak oil" - they always said it was nonsense.
It was too easy to earn money just from the oil production capacity in place that they saw no reason to increase it.
The reason why the party was over for the OPEC countries was because of a petroleum engineer from texas who didn't even get rich or famous from completely changing the norms in international trade and global geopolitics [1] even spider man cannot pull something like that off.
The reduction in oil prices is doing wonders throughout the developing world. India for example had to spend huge amount of tax-payer's money to provide support to poor families. In a single year they saved 50 billion dollars [2] ! It might not seem that much, but a dollar in india can get you much further then a dollar anywhere else ( especially if its taken from saudi pockets ) ( Modi for example needs 100 billion dollars in investment to kickstart a process that he thinks will allow india to reach china's level of capacity in manufacturing )
Open economies is good - and tech was always the king, from the invention of the printing press. Its just investors who were dumb to think that paying for a monopoly is a good idea.
FDS and SmokeView, are the defacto fire simulator tools used by fire protection researchers/engineers around the world. Not only can you manipulate the fuel sources, you can manipulate the environments and rooms and watch the fluid and thermodynamics dynamics happen. Radiation, convection, conduction, flow gradiants, for you to see and play around with.
Is a nice gui layer for FDS allowing you to import CAD files for your simulations and
gives you some visual control rather than programming in Fortan for FDS.
Well I hope to swing back into it in a decade or 2 after learning the software buisness. The goal is to modernize the current tools and leverage advancements in machine learning to help the industry.
The interesting question to me is how does discriminating against asians help palantir's bottom line ?
Has this something to do with culture, or the fact that Palantir think that even american asians are leaking information to countries like china, india, etc.
> The interesting question to me is how does discriminating against asians help palantir's bottom line ?
It quite possibly doesn't; discrimination is often done (even when it is done deliberately and overtly) because it serves non-financial interests of the people choosing to do it, not because it is an efficient means of maximizing profits.
> here is a huge demand so prices will still rise well above those prices.
That is not supply and demand works, price will fall.
It might pick up again due to increased economical activity driven by population growth, but that is a second order concern. At some point housing itself will not be a limiting factor to the growth of the economy, however SF is far being Manhattan, London, Paris.. It might take 10 years to get there.
Once SF increases its population by 7-8x a simple investment in underground metro would undo the effect of congestion.
That is where technology is right now, Greater Tokyo supports 40 million people ! and the city is an economical powerhouse. It allows cool stuff like trains that travel at the speed of a bullet.
I should have clarified. Yes, increasing supply satisfies some demand and brings down prices. My point was that demand is so much higher than supply that the market value is still much higher than the intrinsic value. Saying you can build a home for $50K doesn't mean it will cost that.
Regulatory concerns are a large part why homes so much more expensive.
I built a 2 story, 4 room structure inside of a friend's warehouse for $3,800 total. I got a book on framing and just bought a bunch of lumber and drywall from Home Depot and built it myself with the help of a friend.
It wasn't legal of course. If I actually followed the law I couldn't have afforded a place to live and would have been homeless.
I agree that technology follows an exponential curve and will bring construction costs down, but gov't and society in general doesn't move at that speed and so I don't anticipate homes being much cheaper.
Besides NIMBYism, a drop in home equity will be extremely bad for the economy due to how much of debt is linked to mortgage debt, so I do not expect to see a drop in price anytime soon.
rent-extractors going bankrupt is a net-positive for the economy. There will be losers, sure, but we're better off overall if the Bay Area has three times as many people at the current levels of productivity/salary.
The issue that makes me not enjoy working with svg is the lack of negative scaling values. It makes it hard to do complex transformations on your vector widgets. Canvas is so much more better.
Hey! Thank you so much. I was working in Jupyter with Sympy to generate SVGs illustrating geometric relationships etc. (proofs of Pythagorean Theorem and stuff like that) and I had to subtract the y values from the height of the viewport to use the Sympy objects to generate coordinates for SVG.
How is this article on nautil.us ? Did the author just read the wikipedia.com page on Machine Learning ?
There is an entire field in ML called unsupervised learning. Labelling data that do not have labels attached to them.
Its not a Fundamental (uggh) limit, I am not sure if the author even knows what Fundamental means, its not like the halting problem, or heat death of the universe.
ML is also a very young field with poor mathematical understanding, optimism is the best way forward. Christopher Columbus didn't discover an entire NEW WORLD because he had a pesimistic attitude towards his ideas.
Its going to take time, but historially, we are rapidly learning about how the human brain and intelligence works - similar to how we rapidly learnt a lot of physics in the 20th century.
In popular discussion, Deep Learning has unfortunately become synonymous with Machine Learning, which also, as of late, has itself become synonymous with AI.
Now, this might sound facetious, but to help avert another AI winter (maybe 10 years down the road), we need to be loud and vocal, educating at the very least the investing strata of society as to hyponymy/hypernymy relationships between these terms.
It is bound to happen when researchers uses words like "Machine" and "Learning" to describe their field. Why not use words that actually describe it, like function approximation.
1. Just because someone decided to use the words "intelligence" and "neural" when describing a class of statistical clustering algorithms often based on backpropagation of errors doesn't mean these algorithms have anything to do with the brain or intelligence, and if they do, the relationship is not necessarily direct and immediate. Speaking about the two as if the connection is clear only muddles our understanding. It's important to remember that these terms are meant to capture the imagination, not to describe scientific knowledge.
2. Even if there are proven limitations to those algorithms, and even if those algorithms are related to human intelligence, so what? We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others. Obviously, the "intelligence algorithm" (whatever that means) has some serious limitations, and is not so good at some things. For example, human intelligence doesn't seem helpful in approximating solutions to computationally hard problems. It is obvious that intelligence (or any algorithm) has its limitations.
3. Understanding the limits of a field and having optimism are two separate things. A few good impossibility (or infeasibility) theorems help serve as a map, so you can be optimistic while knowing a bit more about your surroundings, rather than being optimistic while fumbling in the dark.
We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others.
Now wait a second. I would say humans are better at solving some problems directly and computers programmed by humans are better at solving other problems.
However, a computer with a single, fixed program alone will choke completely at some problems and humans are far more robust at finding a solution or at least "dealing" with any problem whatsoever one throws at them.
In the end, you're right that we don't know what intelligence is. And so just about any description is going to be somewhat tautological but "general problem solving ability" seems relatively less tautological than other concepts - ie, "general problem solving ability" seems about right for some value of "general" which we'll have to determine as we go along.
I'm not sure what you mean by a "fixed program". Is a statistical clustering algorithm not a "fixed program"? Even the human brain is running some "fixed program", as we cannot reprogram our brains to efficiently run general sorting algorithms on "bare neurons".
> "general problem solving ability" seems about right for some value of "general"
It may be more useful than nothing at all, but I don't see how it conveys too much information. Clearly, a universal Turing machine has a "general problem solving ability" for much more general values of "general". So I'm not sure how much information "general problem solving" conveys. More importantly, why do we even need a definition? Statistical learning is very effective at solving some useful problems. Why do we need to philosophize about the relationship between those algorithms and human intelligence before we have more data?
Because it would be interesting to solve a wider class of problems. And having a deeper insight helps define which data to look at.
Having said that - I'm always bemused that AI is supposed to compete with a hypothetical single human brain, and that the Turing Test is supposed to prove we have AI when we can build a brain that appears human.
Learning is a social activity, not an individual one. Individual brains are useless without social training and access to culture.
And some humans are actually quite stupid.
A machine that passes the Turning Test for a hypothetical middle-of-the-bell curve human isn't so interesting a thing.
I wonder if trying to emulate"human intelligence" is the way to go. What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems?
If I had to build an AI, I would like to "train it" by pitching it against other AIs. Imagine an open AI network where bots will learn by challenging each others.
Does such a thing exist?
> What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems?
Until we know what intelligence is, I'm not sure we could classify something as being distinct from human intelligence. If AI is any computational model that can learn (i.e., adapt its parameters as a response to inputs to solve various problems), then evolution would qualify as well (well, maybe it is).
But currently, "AI" is a marketing or science-fiction term, not anything we define even remotely rigorously. If we use the Turing test as a definition to AI (i.e., intelligence is the quality of an agent that is indistinguishable from a person through communication), we are not much closer today than we were forty years ago.
Science fiction can inspire science, but using sci-fi terms as if they were scientific terms is confusing, and I think we should similarly leave marketing terms to marketers (over the past decades, marketers have assigned the name AI to very different algorithms solving very different problems).
Many such things exist and have existed for years, just google it.
Also google AGI (artificial general intelligence) or see the videos I linked to on youtube in this thread.
Really the questions you are asking are sort of interesting but it indicates that you are completely unfamiliar with the decades of AI and AGI research that have been done.
Actually it appears that they read that supervised machine learning has limitations according to the no free lunch theorem and applied it to all of machine learning. See http://www.no-free-lunch.org/
Christopher Columbus didn't discover "an entire new world", he was just one of the first Europeans to land on a continent that had already been there, with plenty of people, for thousands of years.
Unless American natives knew of Europe, it was a trivial discovery. When one continent discovers another, the relevance is the new link between them, and interactions between the two economies.
If we discover aliens on a new planet, the fact that the aliens knew about themselves before that doesn't change much - first contact may be when they discover us too.
But let's not pretend this isn't slightly about political correctness, and sensitivity over colonialism, which ruins the objectivity over the subject.
Yeah, this article was not well-researched or useful at all. As mentioned upthread, it seems focused on a very specific type of supervised learning that has since gone through a major leap in usefulness in the last few years.
The problem posited at the beginning of the article is in fact one of the example applications in a couple of ML courses/textbooks.
I've gotta be honest, I've read about half of this so far and I'm not quite sure what I'm looking at.
Every object conforms to an interface, including some error handling functions. The interface is about composition and transformation and iteration/operation (map).
Is this about functional programming? It seems like it must, but I know very little about that and still have no real idea what the benefit of this is or even its purpose.
Edit: So I see that these are algebraic properties that are being defined, to relate it to my limited knowledge I see that Setoid define an equivalence relation interface, equals, which your objects must conform to.
My next question is what is the benefit of programming like this? Maybe I'd have to brush up on my algebra and learn how these relationships/"specifications" are useful.
I am actually kinda curious about this, but it's a little hard to penetrate.
I think one benefit is facilitating "getting to grips" with new code, much like design patterns in a way. Of course, this requires familiarity. If someone tells you something is a "monoid", you instantly know that the somethings can be combined in an associative way, and that there is a "neutral" something that can always be constructed without arguments.
Another benefit is that these abstractions appear again and again under many contexts. Recognizing them allows you to give them a common interface, instead of flatMap for lists, andThen for promises, and so on.
API in this case refers to it's original meaning, that is, the interface that is available on a specific "class" of objects. Here, the "class" of objects are mimicking haskell like typeclasses.
The chart on the fantasy-land github page demonstrates the interfaces available on each typeclass. For instance, a Maybe is a functor, so it has the map interface. It's also a Monad so it has the chain interface.
Unfortunately, API is a loaded term and I think the author doesn't do a good job differentiating from the common usage of the term.
Even in that meaning, it still doesn't make sense. Say there was an abstracted twitter standard defined in an RFC, and a library was provided for interacting with it. There is no Twitter(tm), just implementations of the twitter RFC.
Even then, I need to know what the names of the properties are that I have to provide to the fantasy-land-compliant-whatever. Being an expert in fantasy land doesn't tell me that "message" is the property that contains the body of the twitter-compliant message. I still need an api.
A programatic interface can be exposed via functions over data structures, methods on objects, or HTTP requests against a server's endpoints, to name a few examples.
I think the confusion here arises from a fairly common conflation of a web service with the interface used to interact with said service.
If you go back in time and watch any press release from the saudis when asked about the idea of "peak oil" - they always said it was nonsense.
It was too easy to earn money just from the oil production capacity in place that they saw no reason to increase it.
The reason why the party was over for the OPEC countries was because of a petroleum engineer from texas who didn't even get rich or famous from completely changing the norms in international trade and global geopolitics [1] even spider man cannot pull something like that off.
The reduction in oil prices is doing wonders throughout the developing world. India for example had to spend huge amount of tax-payer's money to provide support to poor families. In a single year they saved 50 billion dollars [2] ! It might not seem that much, but a dollar in india can get you much further then a dollar anywhere else ( especially if its taken from saudi pockets ) ( Modi for example needs 100 billion dollars in investment to kickstart a process that he thinks will allow india to reach china's level of capacity in manufacturing )
Open economies is good - and tech was always the king, from the invention of the printing press. Its just investors who were dumb to think that paying for a monopoly is a good idea.
http://www.npr.org/sections/money/2016/08/17/490375230/oil-3... [1]
http://www.news18.com/news/business/global-crude-oil-price-c... [2]