FFS, if you're not willing to read a paper with BASIC CALCULUS (it's HS/college, not advanced like fractional), then I'm not sure machine learning is the right place for you.

Math is good but my time won't be best used if I have to learn calculus all over again just to begin understanding the linguo. Rather have a generic model of what to use and when, hire those that have that capacity to dive deep when needed and implement the expected business outcome.

To me, your comment reads similarly to "Much like most business people don't spend their time reading code to do the job, I question why such academic rigor is to be demanded by someone who is translating business requirements into database operations".

You don't need to be an oil rig worker to understand where the opportunity lies is what I'm saying. It's possible to understand systems from a blackbox point of view and still be able to exploit them. Afterall, this is what we do whenever we hire a new developer, it's impossible to know what they know so I leave that up to smarter and talented people while I allocate the capitals and set a vision for what's required to capitalize on the business opportunity that AI presents with it's superior performance, scalability and economies to scale.

You outsource what you don't know or can't do and if you can't afford it you do end up doing it yourself. This is why capitalism works. It's not necessary for you to dwindle with all the underlying complexities when you model the business opportunity in terms of basic expenditure and investment returns.

Dude, I'm not even asking you for anything that difficult here, how would you like it if I walked in and said "well, I don't need to learn any programing, my time is better spent magically making it work".

You don't need to know programming if you understand your workers and they understand it enough to convey to you the time & resource constraints.

I have a million dollars for this project. What are the features and functions that will maximize my returns? Hire a couple of smart engineers who already holds the technical knowledge, able to communicate when asked all the pitfalls, potential roadblocks and unknowns so that their productivity is maximized. Set expectations on how they will be able to trade their time for money. At the end of the day, it's a naked call option with limited risk and unlimited reward by utilizing the correct knowledge workers.

Why spend time learning the reference when you can look it up or hire somebody who already has a distilled version in their head which you can utilize?

It's, like, inadvisable, to leave yourself vulnerable like that.

In an orchestra, the conductor is not replaceable but the instrument players are. Similarly, someone who allocates his or other's capital cannot be easily replaced very much like the worker's.

Your engineer leaves there are 100 others ready to take the place. Business owner leaves you'd have to find a buyer to keep everyone going.

This is different from a CEO funded by VC's where you are still replaceable but nevertheless a lot harder to replace than the cost intensive worker.

Being on the profit generating part of the business and cost intensive part of the business helped me understand why such discrepancy exists-those who are able to read and perform in chaotic and uncertain environments will always be valued than those who directly affect the margin's as a result of the monetary figure attached to their time which is always far more costlier because cost centres do not generate new revenues. Your top sales guy makes $9 for every $1 spent on him (+$8) where as your top engineer costs $3 for every $0 he generates (-$3).

tl;dr: revenue generators are king while cost generators are easily swappable due to the high supply of it as a result of it being a far safer and financially stable perception.

In this case you should immediately fire all your engineers, your CFO, and your COO. Because why would you want to employ a team of engineers who are costing you money but bringing you no value?

You should also fire your cleaning staff, who have never closed a deal in their lives, plus the plumber who most days dosen't even come in.

Granted, engineers are required to create products and maintain it. You need product to sell.

But at the marginal level for every dollar your sales person makes your engineer is not. You might argue but the product is generating revenues but that's not what drives a sale. A sale is a function of value derived from the product and the price paid for it. Engineers aren't driving the sale unless the product itself is a developer tool. But even in that case the top level business controllers will always have the final say. In the end, buying such developer centric tool is about minimizing cost expenditure.

There is critical proprietary knowledge and experience that is formed from a sales person that is driving revenues. Businesses do not want to lose the hen that lays the golden egg as they are hard and expensive to replace.

Engineers on the other hand are easily replaceable and are expensive because they cannot generate revenues. They are not efficient when pulled away to a sales meeting from the work they are doing. Their knowledge and skill costs businesses time and money with no way to get it back directly. It must be sold, money collected and redistributed to all the payrolls. That's the main risk a business owner or a CEO is dealing with where as the engineer collects a cheque at the end of the month with little to no concern or exposure to that risk.

Society rewards risk takers disproportionately at the corporate level. Even if it's tough to measure to the Board and investors losing a C-level executive is always going to be more impactful than a senior engineer who has far more workers to replace him as a result of being cost intensive.

Find me an engineer that can code and sell, now that is a truly rare hybrid, a mewtwo, but it will still be more expensive than a guy who just sells (and does well).

A poor salesman understands this better than anyone, his weight is worth the revenues he generates. For engineers it's the efficiency / dollar or output / dollar that they are competing against which is always headed towards commoditization and any business would replace them with an AI that can code if they could if it cost less.

A typical Econ 101 textbook says:

    higher demand --> higher price
    higher price --> higher supply
    higher supply --> lower price
    lower price --> higher demand

But even if we believed your demand causes supply causes zero price model, why is that unique to engineering and not business acumen? And why do programmers get paid anything, don't they work for free?

Programmers have largely driven themselves to zero, just take a look at the workers on freelance websites and how much difficulty a native English speaking freelancer has against an army of commoditized labor.

There are still six digit earning engineers and always will be but you can't look at that as a metric for the massive commoditization that has occurred with generic programming in the past 15 years.

I'm a freelancer and fully booked, so maybe those low cost competitors aren't so competitive after all.

(it cuts the other way too: if an oversupply of labour in a field as difficult-to-learn as ML does arise, the demand shortage causing wages to fall is almost certainly because ML techniques aren't giving companies and traders as much of a financial edge as they hoped for...)

Added bonus for the ML experts: in a lot of the possible areas they can choose to work in, their individual marginal contribution to the company's profitability is at least as quantifiable as that of a salesperson. In many areas of finance you can quantify the impact of an individual line of code!

On the other hand, it's easier to piggy back off the hard work of highly paid experts in this space now commoditized into 1/10th of a cent per application of an ML algorithm from Microsoft or Google or IBM.

Commoditization is going to be in full force as businesses realize they can just have a junior developer to plugin IBM Watson or Azure than hire an expensive PhD student who may know everything under the sun but cannot compete against an army of highly paid experts completely under the control of a highly collusive labor market.

Silly google, they must've skipped Econ 101 since they hire tons of engineers and are doing TERRIBLY!

Why would I go to you and not a VC firm? I sure as hell don't want to work for someone like you....

(oh and there are plenty of others like Karpathy, like Facebook, Baidu, Microsoft, Uber, Amazon, a metric fuckton of startups, etc. who are being paid ridiculous amounts of money to build nets)

Guess who's getting replaced once the VC figures out who's doing the work...

Then exactly what value are you bringing to the table? Money? Sales skills? If that's the case then why focus on ML/AI technologies? Any company needs money or sales expertise.

I say this is an arbitrage opportunity because AI is essentially commoditizing human intelligence. It didn't take much brains for people to realize cars could do away with horses. You don't need to understand how a car works down to the formula. It's enough to view it as a black box and still know all the outputs and constraints associated with it in order to orchestrate capital that allows you to seize the business opportunity.

Again, the arbitrage is buying something for cheap and immediately selling it at a higher price...if you can't understand why a deep level understanding of the academia is not necessary in order to capture the opportunity then less competition for me which is great.

But let's indulge your point of view for the sake of the argument. So you've got a black box AI that does something. You know what inputs it needs and what outputs it produces. How do you arbitrage it? What creates the price discrepancy that allows you to arbitrage at all, given that your incapacity to understand the black box renders you unable to gauge its value beyond what the market is telling you?

In that case, why not trade any random commodity?

> In that case, why not trade any random commodity?

Its much easier to gain control of markets that are new as opposed to established ones.

That should be enough for you to have an idea what expertise you'd want to hire for.

Granted, it sounds you're just looking for ML scientists or ML engineers. Data Scientist might also be able to help you out, so that's what you want to hire.

"Listen I have this great idea for a web app, it's going to be like the Facebook for hardware stores [or whatever] and people can rate/vote/tag ... I got the whole idea worked out, I just need someone to program it for me" etc. Every programmer has heard these kinds of proposals many times.

One (of many) problems with this kind of proposal is that the idea-man, unable to program (and not wanting to learn) has no idea about the complexity of what they're asking. And even if they think they have the whole idea worked out, not being able to program means they probably missed a lot of shortcuts, possibilities and best practices. The worst ones hand-wave this with the possibility of a fresh outsider-look on things! Except that the whole (fully worked-out) idea probably needs to be reworked entirely before it's even feasible or competitive. The programmer would probably have been better off without the idea, cause that's where it started to go wrong.

Similarly, if you believe math isn't worth your time but instead want to hire ML experts to implement your ML-related ideas (formulated in terms of "expected business outcome"), puts you in a very similar role.

(Tongue-in-cheek) I got a really great idea, the expected business outcome is: make lots of profit. It's fool-proof. I checked with some of my people and they agreed, making profit is a solid idea. I just need some business dude to implement for me. I'm not really sure what sort of business person, my time can be spent better than learning about subtleties between sorts of business people. I just need to hire a good one. But remember, the idea was mine first.

Actually, a lot of mental work isn't based on intelligence but rather training in pattern recognition, thinking faster, translation of information between different coding systems, and developing instincts about the behavior if complex systems. This isn't very different from manual labor when you think about it.

Now it appears the essence of that is being distilled into parameters for AI algorithms which can produce superior output by teaching it with decades of professional human knowledge which maybe dumbed down with simple machine learning.

Why's this an argument against AI run hedge funds? Funds run by humans also blow up due to black swan events.

Hedge funds are already using A.I. successfully. I talked one manager who's team was trying to layer successful individual neural networks into a "bigger brain".

What I do know is that it wasn't worth it for me personally to occupy my GPUs. By contrast, larger trading firms would probably like it.

These aren't silver bullets. Both cost more, in fees, and have a habit of leaking order information across informal channels. They're also, generally, slower--there are, on average, fewer buyers and sellers for a given security in a dark pool than in the open market.

https://www.udacity.com/course/machine-learning-for-trading-...

I averaged a return of 7-13% a year. With less than 1000 in capital, it wasn't very profitable.

I don't see why it shouldn't scale.

You've got a lot of comments like "if you're not willing to read a paper with BASIC CALCULUS ... then I'm not sure machine learning is the right place for you" to this, but I second you on this. I do have a PhD in CS and not afraid of calculus in papers. I just don't think reading PhD papers is the best path to getting practical results in DL/ML. "Not right place for you" is the sign of the "exclusiveness" problem.

I'd recommend checking the following course by Jeremy Howard and Rachel Thomas:

http://course.fast.ai/

It is designed to make DL accessible and achieves its goal very well. I'm not sure if the average Joe Hacker will be able to write top-pocker-players-beating program after this course, but it definitely gives enough insight and practice to get started in DL and be able to design and code solutions in a large number of problem classess.

But for instance understanding and specially producing meaningful language, let it be natural language or programming language? I hope not so.

Because otherwise, your average joe hacker might as well shut down their IDE and say good bye.

Well reading paper is a must to get to deep learning. Those papers may not be that math heavy once you are used to it. Most of the time, it is about network architecture and loss objectives.

Knight Capital, 2012:

https://www.bloomberg.com/news/articles/2012-08-02/knight-sh...

Knight's blow-up was a process/sysops failure, not due to their strategies going wonky.

[As a side note I believe their core strategies were human-analyst designed, not ML based, but I could be wrong.]

I'd think one would make a lot of money shorting such outfits.

The other capital that blew up from the comments suggest it was more of a human error that led to deployment failures which is exactly why AI hedge funds is such a paradox. At the end of the day, it's humans that's deploying and building the model to be approximately right with high degree of accuracy but in the day trading environment it is a poor model for success-not only do you have to be right your monetary exposure must be right...."but proper money management and you will be fine" said everyone who took part in this zero sum game.

I don't know, prove me wrong, I'm sure those ivy league engineers on wall street aren't getting paid dimes for what they do.

https://en.wikipedia.org/wiki/Knight_Capital_Group#2012_stoc...

I wonder if humankind will be the creator of a new form of life. One that is not bound by chemical processes. One that moves at the speed of light and spans the whole planet. This being, or these beings, with access to billions of sensors, will know everything, see everything, hear everything. They won’t be limited to visible light, nor to the frequencies we can hear. There are no limitations except for what sensors can record. They will be able to interpret, predict, plan. They are not bound by time as they themselves don’t physically age. They will use vessels to carry out tasks and build the infrastructure they need to live. In comparison, we will look like single-celled organisms. So dumb. Until we disappear.

But, an artificial intelligence probably has different black swans than humans, as their perception is inherently different. A tweet from Trump might not surprise us (anymore) but to an AI player, it might not be evident that a small amount of text from one Human can cause an uproar on the market. And it might not even have access to the relevant data (Twitter) at all.

Renaissance Technologies already is one of the top hedge funds in the world and it's fully automated.

[1]https://en.wikipedia.org/wiki/Renaissance_Technologies