Slashdot Mirror
Tech Giants Are Paying Huge Salaries For Scarce AI Talent (santafenewmexican.com)
jmcbain writes: Machine learning and artificial intelligence skills are in hot demand right now, and it's driving up the already-high salaries in Silicon Valley. "Tech's biggest companies are placing huge bets on artificial intelligence (Warning: may be paywalled; alternative source)," reports the New York Times, and "typical AI specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock." The New York Times notes there are several catalysts for rocketing salaries that all come down to supply and demand. There is competition among the giant companies (e.g. Google, Facebook, and Uber) as well as the automative companies wanting help with self-driving cars. However, the biggest issue is the supply: "Most of all, there is a shortage of talent, and the big companies are trying to land as much of it as they can. Solving tough A.I. problems is not like building the flavor-of-the-month smartphone app. In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research, according to Element AI, an independent lab in Montreal."
In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research, according to Element AI, an independent lab in Montreal
In the entire world, fewer than 1000 people have the skills necessary to do unstructured tetrahedral finite element mesh generation. It is possible there are fewer than 1000 people who have the skills necessary to understand what exactly we mesh makers do. And, Surprise! there is demand for fewer than 1000 people to write unstructured tetrahedral finite element mesh generation. And far fewer than 1000 people are needed to manage them.
I am glad the periodical bubbles that infect Wall Street and venture capitalists benefits PhDs once in a while. Most of the time it benefits hedge fund monkeys or stock market cheats or lottery winners with delusions of grandeur or plain sociopaths. Happy for my grad school classmates. Enjoy the windfall while lasts, Ramachandran\s, Yang\s, Hsu\s, Gupta\s, Parpia\s and Wickramasinghe\s.
sed -e 's/Chuck Norris/Rajnikant/g' joke > fact
Greetings Professor Falken!
From my investigation of the matter it looks to be some sort of multi-variate analysis in drag. Uninteresting. Basically you get guys sitting around twiddling knobs. Finding the right parameters which works for a little bit and then you start knob twiddling again to find the next ones.
Some years back I wrote a day trading program for a friend. It dynamically changed its behavior depending on the market signals and the rules he gave it (stops, buys, shift to a different stock etc.) which he found useful. Now that was fun.
Now about fads, here's a good Reg article on the matter:
https://www.theregister.co.uk/...
putting the 'B' in LGBTQ+
Read Godel much? (Umlat the o)
putting the 'B' in LGBTQ+
I am sure "Element AI" wants to pretend there is such a thing as AI, but there isn't. Playing "Go" is not "AI" and neither is autonomous driving...
AI tends to drive a belief that modeling intelligence perfectly is a necessary requirement when it is in fact artificial. Reality dictates the bar is much lower for adoption. Good enough AI specialists will create good enough AI. Autonomous cars don't have to be perfect. They merely have to do better than humans. 40,000 vehicular deaths per year just in the US tends to set the good enough bar pretty damn low. AI will do the same.
Don't want to call it AI? OK, fine. Massive Disruption has a catchy marketing ring to it...
You should at least try to understand what you are talking about. Deep learning, aka neural networks, are not "algorithms and programs". They are part of the machine learning branch of AI. The computer is not programmed but learns by itself. People in computer science have been trying to do that for as long as computers have been around but were never quite successful until about 2012. Deep learning excels at tasks which are too complicated for humans to write code such as detecting objects in picture and analyzing recordings of voices or translating text. This is revolutionary. Even the primitive neural net technology we currently have will transform many applications in the next few years, in that they perform much better than what humans used to code and they require just a handful of AI specialists to train instead of team of 100 programmers. If the technology continues to improve, it could take over just about every field: driving, medicine, law, manufacturing, etc. But the current technology has limitations and it's not clear how much it can progress further.
Computer programmer could be one of the first job to be made obsolete by deep learning. Programmers will have retrain themselves as teachers to neural nets instead.
I just finished my PhD in neural networks and AI, and I can say that this article comes up a bit short. I actually got offered $625K (granted it's San Jose, so it's more like $150K anywhere else in the country)... I turned it down because San Jose sucks, so I took a job at a company on the east coast instead for $195K.
AI-related jobs are certainly at a premium right now as companies scramble to get rid of humans and replace them with robots.
This is a perfect example of companies (HR) searching for stuff that doesn't exist.
No doubt all of their job postings require 5 more years experience in AI than it's actually been around.
While there is undoubtedly a shortage of talent, odds are that the industry is screening out many who do have AI experience, but fail to meet the rigid and ignorant requirements HR is looking for.
When Fascism comes to America, it will call itself Anti-Fascism, and tell you to give up your guns.
Modern AI software isn't that complicated and not nearly as expensive to get people in. Look at job offers: $150k for AI research scientists in NYC. $65k in more rural areas. That's not well paid by definition at all. Sure, a pure AI scientist gets paid $500k just like a top neuroscience scientist gets paid $500k or a top biology researcher, but the majority of companies do not want to do the theoretical development of AI, any regular programmer can wrap their heads around the existing literature and build something.
Here in my area, there are a number of employers looking for AI engineers/scientists. They pay about what I make as a non-AI IT sysadmin, which is given my experience on the higher scale but by no means exceptional.
What Google and co wants is a glut of people 4-6 years from now that are "trained" in AI from college. You put out a report like this, you get massive amounts of people applying for the schools that offer programs and 5 years from now you have an over-abundance of people driving down overall wages. You also get to hire a bunch of people on H1B because the "US doesn't have the skillz" and you end up with a bunch of programmers on H1B under the guise of AI development.
Custom electronics and digital signage for your business: www.evcircuits.com
Programmers will have retrain themselves as teachers to neural nets instead.
The sky is not falling here. Writing code is the easy part. The hard part is conducting meetings among business analysts and executives and documenting specifications and iteratively building those specifications against their expectations. The magical part is knowing the difference between what is asked for and what the customer really needs - all the while layering against the business domain. An AI won't be able to do this no sooner than it could replace an intelligent person with good social skills and business acumen. The only "programmers" that will be replaced would be the kind we have already replaced with higher level programing... until the day comes when we welcome our robot overlords.
I just got mine in gender studies and I have been writing papers on how AI is the projection of masculine hegemony, domination, and misogyny into the computing sphere. And how it will add to global warming and increase the relevance of the penis social construct in masculine psychology and subjugating other genders including trans and cis genders.
It's a great job. I write gibberish, teach classes to sanctimonious well-to-do white kids, and get paid almost 6 figures - for working about 4 hours a week.
Well, at least we'll have another bubble to form and expand once the bitcoin bursts. It seems like this is the way the stock market works. The few at the top profit heavily from the bubble, the bubble bursts, and the 99% are left to deal with the aftermath.
From my investigation of the matter it looks to be some sort of multi-variate analysis in drag. Uninteresting. Basically you get guys sitting around twiddling knobs. Finding the right parameters which works for a little bit and then you start knob twiddling again to find the next ones.
Except for 1 key difference. With Deep-Neural-Nets, the knobs twiddle themselves alone.
DNN get inspiration of how some neural network work in the nature (e.g.: a column in the primary visual cortex of the brain) to design thing that you can throw at problems, and which will autonomously train themselves.
Some years back I wrote a day trading program for a friend. It dynamically changed its behavior depending on the market signals and the rules he gave it (stops, buys, shift to a different stock etc.) which he found useful. Now that was fun.
These older program require you to have precise criteria in advance.
That works perfectly well with clearly codified problem - the friend has a clear set of rules that need implementation.
That completely fails for more vague problem ("detect a face") - it would be possible in theory to design a set of rules that can detect a face - a Haar Cascade. But designing such set of rules is extremely complex and cumbersome. And each time you need something new ("detect if there's a bird"), you would need to repeat all the hard work to invent yet another set of rules.
At that point, better take an advice from how mother nature solved the problem (by using stacks of neural network in a columns) and simply throw a DNN (e.g: a Convolution Neural Net - a ConvNet) at the problem, and watch it self organize and come up with a solution to your problem.
It's the modern-day equivalent of training pigeons to peck a city images to steer a missile.
"Sufficiently advanced satire is indistinguishable from reality." - [Tips: 1DrYakQDKCQ6y52z6QbnkxHXAocMZJE61o ]
The more worrisome thing is that there probably won't be civil lawsuits because of a big disclaimer in the owners manual. People will be too stupid to think about the ramifications of allowing software to dictate whether they live or die until there is an accident that is so stupid that it is inconceivable that a human would have ever done it. It will happen eventually.
Laws are rules for the court, but merely a bottom bar to hit for life. Think beyond laws in your actions always.
"... is inconceivable that a human would have ever done it"
I think you have mis-under-estimated the ability of humans to make seriously fucked up decisions.
Nice rhetoric—factual statement masquerading as metaphor, for any reader dumb enough to go along for the ride.
The Evolution of the Flour Mill from Prehistoric Ages to Modern Times — 1905
That's about the present state of machine learning, the hand-crafting of "features" playing the role of the recently discarded flat blocks.
Wheat is an incredible dietary resource, with the starch being light enough to transport over long distances, if only one can find a way to remove it (contrast potatoes, only ever transported downhill, if at all, until the invention of steam power). Once upon a time, all food was local, as, too, was starvation (fear the blight).
A better method to mill the world's vast stores of accumulated data is a big deal, even if we remain in the relatively crude era of water-powered stone grinding wheels.
Data is a bit like wheat, it doesn't give up its curvature easily. Too much applied force creates heat and destroys the end product. The applied force must have exactly the right ratio of compressive to shear stress, which only an expert miller can judge. Deep learning is nothing more than a slightly better mill than the one we had before, and it ranks right up there beside becoming slightly better at milling wheat.
The economic value of the curvature we can now hope to unlock is quite large. And probably there's a lot of curvature yet to find that remains inaccessible to current methodology.
Data is oil. Data is also wheat.
By way of contrast, unstructured tetrahedral finite element mesh generation shaves 5% of the metal mass off a milling apparatus that already worked just fine, being just one of ten thousand noisy specializations in the great roil of small improvements where a penny shaved is a penny earned.
Nevertheless, apparently a great career option for the metaphorically challenged.
I know a guy who cut and pastes Javascript snippets to make interactive query windows for websites. His main job is as a creative director at a mid-tier advertising agency. He calls his work 'AI research'. I am not kidding - he makes over $100k per year.
The biggest problem I have found with smart people, is they don't think stupid people should be paid lots of money for work they think is simple. The more successful ones have figured out that it is much better to cash in on such situations rather than lament what the world has come too.
Establish that. There's a century of Disney movies and saturday morning cartoons that still haven't reached consensus at what "being human" means.
You might as well say "computers aren't better at Love".
This is the same reason we have pundits discussing what a driverless car does when "choosing" a human to die. Computers don't know "die", or "risk", or any mental constructs. You get it all the time with the PB&J project, people think "then spread jelly" is an instruction, but a computer has no idea what the fuck "spread" means, or what a "knife" is.
Computers don't suffer from the trolley problem, they have an "abnormal road condition" or "obstacle condition" (ie human ahead) and a routine to attempt. If it turns out the conditions become numerous, it just continues down the reaction tree. Mostly towards endpoints of "stop and demand manual control"
Anyway we have no singular definition of Love, nor Being Human, and your premise of a premise flaw is flawed.
No doubt all of their job postings require 5 more years experience in AI than it's actually been around.
That would 66 years of experience, then, right?
Well, that brings it into the same scale as the claim that the stock is worth $500k. I read "from $300,000 to $500,000 a year or more in salary and company stock" and I hear, "$32k and a box of scratch paper." But in my experience, most of these companies won't actually have $32k to pay out and will try to bid that down with more toilet paper.
Established companies whose stock has value are going to want to pay employees using cash, and they'll have no trouble finding experts for $200k because any competent software engineer can become an AI specialist in a few weeks. Most of these jobs are just standard software development using an API, after all, it isn't like all these people are building AI frameworks and need a lot of deep theory. Learning the APIs takes less time than learning the customers actual needs and the realities of their use case.
I bet you spent a good bit of time on the "testing phase" to make sure it really worked.
I only look human.
My mother is a halfling and my dad is an ogre, so that makes me an Ogreling