inputcoffee

okay, that is in theory.

Here is the syllabus:

https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...

Do you think any part of that is unreasonable or putting the cart before the horse?

For those who don't want to click through all the links, here is the syllabus:

https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...

It seems reasonable to me. They are required to take 7 humanities courses. I certainly hope they are required to take a lot of other classes where they are required to read, reason, and write.

That was a response to your statement that the relevant research has been done.

I don't think it has. We still have to figure out ways to lower the cost of storage.

You're saying it is cheap enough that we can now mandate it?

It should be cheap enough when purely green energy is superior to any other source. Solar alone is cheap, but not solar + storage.

Well, storage is still too expensive.

There is a role for regulation but a carbon tax is the way to go.

If the government had mandated that everyone had to buy an ENIAC, would that have made computers smaller and faster?

Rather, the government should invest in research so solar + storage (let's call it green energy) becomes cheap enough that people want to use it.

This is not because its better that people want to do it, or because capitalism is glorious.

This is because you want the incentives in the right place. If green power were cheap enough, that would mean you solve the latency problem, the efficiency problem, the distribution problem and the waste problem.

Simply mandating rooftop solar might make some of these problems worse.

But 20-25% of the time it is more than 3x as volatile. Taleb built his career on this.

List prices are meaningless but Medicare prices are probably the most meaningful. Insurance prices are benchmarked off them.

There are a few things to watch out for:

1. Systematic trading doesn't necessarily require an algorithm. For instance this rule might work (over a 5 year horizon, don't try it monthly): "buy very large cap stocks if their P/E goes below 4 and sell if it goes over 10." But you don't need an algo.

2. The market has long bull runs. So you might have an algo that has some long bias. It will seem to perform above chance. You should compare it to just holding the market.

2b. If the market is going through a bull run and your algo has some leverage built in, it will outperform just holding the market.

3. If the market had a massive crash in the data set and your algo has a short bias, then you should check it against just shorting the market.

4. The issue of models, markets and biases mirror the same debate in science theories, data and statistics.

I am not sure I understand this.

It takes just as much skill to guess if volatility will go up or go down as it does to guess if prices will.

The best models we know of (say GARCH) will still pace the information in the option prices.

Yes, but please read the article I was responding to.

One useful test of science if the property of falsifiability: what would it take to demonstrate this theory is incorrect?

Without that attempt to prove the theory false, I don't think it should be considered scientific.

So, for instance, expose someone to a lot of advertising and if they don't seek out the thing they are exposed to, then this theory (at least on this basic level) might be false.

Well, you might say, the claim is not that the person will definitely seek out the familiar but is more likely to do so.

Then propose an experiment to test this. Now we have started a scientific discussion.

The theory is controversial, and there are many details that are argued about. I certainly don't have a view as to which is correct, but I do think the debate is complicated by the definition of "autism." I do agree with people who believe it is a cluster of underlying conditions with some overlapping symptoms that are bundled under the same umbrella.

I agree, and they explicitly state this as one of their three goals (" is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI.")

However, I think this will be even more valuable in human-computer interactions (their second goal).

Consider: if you just had a fight with a lover, and then had a bad day at work, and Alexa recommends that you watch a Black Mirror, it might be awful timing. In fact, as humans, we know if someone walks in panting, and frowning, and slams their hands on the desk, we shouldn't crack a joke. I think there was some research out of Google X about how much more useful robots seemed if they gave out signs of what they were doing (appeared frustrated if they couldn't complete a task etc).

There is some philosophical context here that perhaps everyone reading this is not aware of.

It has been argued that people have an innate Theory of Mind. They use it to theorize about perhaps the most important thing we theorize about: other people.

So if someone drinks water, we surmise that they were thirsty (desire) and had the belief that drinking water would quench the thirst.

There is one big debate on whether people have a set of rules that they look up (unconsciously), or if they have a little "human simulator" where they throw in the action and out comes a belief/desire configuration. This is characterized as the debate between the "Theory Theory vs Simulation Theory").

Some theorists believe that autism is a disorder of this mechanism. Stated another way, some theorists believe that autists don't have a Theory of Mind. Consider this: in order to follow your gaze I would have to believe that you will look in a certain direction only if you are a person who would not want to look off in some random direction, and that you would look at something that is interesting or noteworthy.

https://en.wikipedia.org/wiki/Theory_of_mind

Anyway, if these scientists can produce a successful model that doesn't rely in explicit rules or a theory -- a neural net -- then others might look for evidence of this sort of computation in the brain. Alternatively, they could demonstrate that this model essentially encodes a set of rules, or perhaps they could collapse the debate into a hybrid theory.

That is a different question about how often they should be updated, what sort of updates, when does construction happen etc.

I have a graduate degree in computer science and know some graph theory, but I really liked this. I thought it was interesting, reminded me of a lot of theory, and taught me some more.

I really appreciated the voice and tone. I am very surprised to see people who did not appreciate it and took the time to write how much they didn't like it. (Which is their right).

I am writing this only to let the original author know that there is an audience for this kind of "breezy" explanation.

You need the map because you want to know where to go, not in order to avoid debris.

Pick two points in your town: say your home and your office. Imagine a stranger had to get from one to the other, and they know how to drive. The map if for all the information that stranger does not have.

A lot of the comments here are skeptical of the claims in the article. (This is how comments should, and do, function).

However, for all the reasons that the article may be wrong

- the shortage is temporary

- the shortage is overblown

- the shortage is illusory

- the shortage doesn't apply etc

Please compare with the situation for other high earners (CEOs, Entertainers and Bankers). Can the same arguments be made for them? (Conan O'Brien is funny, but he isn't 30x funnier than the person at my local comedy club?)

The work of an AI researcher is mechanically reproduced so a 1% benefit can be enormous. It could be the case that the competition isn't for more of them, but for the best of them.

Yes, I forgot about fraud. That's the obvious one.

That is an interesting question.

Digital "currency" is unregulated so the question is which law is violated.

If, in purchasing a tether, there is some underlying contract that says the tether is backed by a dollar, then you might claim a violation of the contract. You may have to look at the fine print. Who was the counter party? What were the terms? Who is on the hook for it? Is it an entity that is outside the US?

However, if there isn't such a contract and someone makes the claim, you might claim there was an implied contract. This is harder. Not being a lawyer, I won't even venture to guess how this would play out.

In the absence of those two, I don't know if you can claim there was a violation of the law.

Edit: There is one more law you can violate, and this is the obvious one: Fraud!

In the US it is fraud if:

- somebody misrepresents a material fact in order to obtain action or forbearance by another person,

- the other person relies upon the misrepresentation, and

- the other person suffers injury as a result of the act or forbearance taken in reliance upon the misrepresentation.

So now the question is where is the injury? Especially if they are able to sell of Bitcoin and actually back up the Tether?

Now it might be a question of: the buyer took a risk they had not intended on taking.

My understanding from the article is that the problem isn't that the price was propped but that they created fake tether to do so.

By fake, I don't mean the tether wasn't real, but that it was not tied to dollars.

So they claimed tether was tied to dollars but they produced a lot of it to buy up Bitcoin and the fear is that they didn't wait for dollars to come in but just made the tether out of... well, ether ... and used that to prop up bitcoin which they converted into actual wealth.

This is true.

40% of the time, options prices move in the opposite direction of the earnings surprise.

For those who are skeptical, this is not magic. The reasons for it are all the reasons you might expect: the biggest investors may not agree with the market consensus, the analyst consensus doesn't weight analysts by credibility so the "credible" analysts may not match consensus and so on.

I wonder if the mood disorders are causal.

For instance, the largest mood disorder was anxiety. Let me recast anxiety as "overly caring". An anxious person may work 3x as hard at a presentation or an exam.

I don't want to glamorize mental disorders, but I think this might not be a coincidental connection.

I agree with your first statement to the point of disagreeing with your second. i.e. even the magic stuff is just incremental progress that people were not paying attention to. (Self driving cars have been wowing people since the 90s, object recognition just got incrementally better every year etc)

The best way to ascertain this is to look at the skills being hired for by the companies.

I use indeed, you might have another you like:

http://www.indeed.com/jobs?q=blockchain&l=

Click on a few to see the kind of skills you need. I am pretty sure this is more useful than our guesses.

Here is what IBM looks for in a block chain developer (whatever else you want to say about large companies, they put a lot of thought into the description):

3+ years of experience as a backend developer

Experience with Blockchain daemons such as Bitcoind and/or Stellar Core

Strong proficiency with Node.js, Java, and Golang ecosystems

Understanding the nature of asynchronous programming and its quirks

Understanding accessibility and security compliance

User authentication and authorization between multiple systems, servers, and environments

Integration of multiple data sources and databases into one system

Understanding fundamental design principles behind a scalable application

Understanding differences between multiple delivery platforms, such as mobile with API backends vs. desktop applications, and optimizing output to match the specific platform

Creating NoSQL database schemas that represent and support business processes

Implementing automated testing platforms and unit tests Proficient understanding of code versioning tools, such as Git

Experience with other languages such as: C++, Kotlin, and/or python

There are three issues I have with this:

1. "Quality" for management decisions are notoriously ill defined for the same reason that we never know if we did the right thing with the economy: the experiments can never be repeated.

2. The example serves to explain what a decision is and what a good decision would be, but that is not the concept that needs explanation!

3. "some seeming inefficiency can’t actually be inefficient because the market is efficient and inefficiencies will quickly be eliminated." is not the right statement of efficient markets. Rather, the act of trying to make it more efficient is exactly what makes it efficient. So if you see an opportunity, someone exploits it and thus makes it more efficient.

For those worried about the inflation risk: it depends on how the money is generated.

Theoretically, if it is created by, for instance, taxation (redistribution over people) or bonds (which is redistribution over time), you shouldn't risk too much overall inflation.

It will, however, change which goods increase in price. I suspect restaurants will become more expensive and yachts will be cheaper if it happened by taxation.

Having said that, inflation itself seems to be blind to the theory and does whatever it feels like.

For the purposes of this exercise, let's suppose I get $5k/mo and others don't. I think it depends on when I got it.

In my 20s -> Travel and grad school

In my 30s -> Supplementing my meager income to enjoy myself

In my 40s -> Investment

I suggest that the undergrads be encouraged to go to Arxiv and browse papers, try to work through them and see the range of papers.

The reason for this is that many papers aren't all that well written and well argued. Many are, to be sure, but it will get undergrads to understand what is clear, what is not.

It will also ensure that they are not intimidated by papers and the math on them. They should know they can dive into one and learn something and come out the other end.

But if you want specific recommendations, I find that the HCI (Human Computer Interaction) papers are very readable. Maybe its the people drawn to the field?

Here is just a random example:

https://arxiv.org/pdf/1711.03115.pdf

You pump the water from the sea. With free energy. I’m not the first to point this out. There are pages on it. Please google it.