My personnal experience with data scientist and startups is that they're hired much much too early, when the product has a lot more fundamental issues to solve, and only because it looks cool to say that you're doing AI.
In practice, they're often frustrated for years by the lack of infrastructure to work on their ideas, but live with it because life is good.
I suspect AI today is like big data ten years ago : a lot of company think they need AI, but in fact what they need is a good product and a few algorithm requiring high-school level maths.
> I suspect AI today is like big data ten years ago
Exactly. Also as soon as big data came around nobody was doing just data, everyone was doing big data even if they had the same 10GB MySQL database they had from previous years.
AI is a bit the same. Doing any analytics? - Now it's AI. Opening and excel spreadsheet and doing a curve fit - I am a data scientist doing AI. Doing any actual ML - not learning anymore but super deep learning.
My job as a consultant is to tell you "your data fits in RAM" and charge you $20k. While it may seem expensive, the ROI is about 14 days, because now you don't need to hire that data scientist.
You nailed it. People don't get promoted for leading pedestrian projects, they get promoted for leading challenging, innovative, groundbreaking projects.
Sometimes tech reminds me rich, bored, stay at home SO's that are constantly redecorating their house. Not because they need it, but because they are bored and the next trendy design looks cool anyway.
It comes from both top and bottom. At the top managers want to justify their salaries to their managers so they always redesign / rebuild / reorganize, even if things work pretty well as is. At the bottom new programmers fresh out of college want to assert themselves. The best way to do is to propose that everything existing is old and shit and needs to be rewritten. So they volunteer of course.
How does someone get into a position like that? Are you an independent consultant or do you work for a company? Is this exclusively what you consult on?
His comment while accurate was probably a bit dramatized for effect.
My guess is he is a consultant hired to do a typical project (i.e., help me move X into the cloud, help me re-architect our data model for big data, help me implement AI for our dog walking app) and at that point he just shows them they aren't ready for it or flat out don't need it.
It's just my guess, but it's what consultants do. The bad ones are happy to take on your project and charge you $400 bucks an hour. The good ones unfortunately, deal with the dilemma of turning down lucrative work in the spirit of doing what's right.
My guess is that at some point you get tired of it and take solace in the fact that you're at least helping them to do it right.
After all, if they think they are going to hit big data scale and want the tools to handle it, you aren't completely a bad guy if you help them do it right, especially if you've already advised them not to do it.
I regret to inform you that my original comment was sarcasm. I'm not a consultant, but would be perfectly willing to do the job as I described. The hardest part would be keeping a straight face while demanding the $20k.
It's amazing how many startups don't re-evaluate their infrastructure requirements every couple of years and get stuck with numbers in their heads reflective of hardware prices five years ago.
No need for clever optimizations - Moore's Law will bail you out a lot of times.
Im curious. What do you mean by "actual ML"? Isn't regression always a form of curve fitting? So is actual ML = curve fitting with fancier models (like multi layer perceprtrons)?
SVM, other kernel methods, Bayesian networks, genetic algorithms, clustering etc.
> Isn't regression always a form of curve fitting?
Sure and that's been done before for many years. I was just saying that today everyone who was doing that, isn't doing curve fitting or regression analysis anymore but "AI" and "Deep Learning" (doesn't matter if there are not neurons involved).
I think it's a true scotsman fallacy. We don't have a good definition for ML. So we reject methods and problems as not "real" ML". Ive done some logistic regression in my work and hesistated to call it ML. But Ive read a survey about tools used by people who do ML and logistic regression was the top item on that list.
Neurons are just an inspiration from biology. You can call them layers of neurons or you can call them matrices and do matrix multiplication. Nothing special about neurons.
I understand your argument that people like to use buzzwords and you don't like this. But it's a genral problem which applies to everything, not just ML.
Cloud technologies were also same before. People still keep burning cash on AWS even if they don't get a single customer. Many never understand actual use case of AWS and it's various services.
>My personnal experience with data scientist and startups is that they're hired much much too early, when the product has a lot more fundamental issues to solve, and only because it looks cool to say that you're doing AI.
Not just startups, but big established companies as well. At bigger companies not only do you have data and infrastructure issues, you also have business process and political issues. I've seen more than a few cases where fixing a business process would have a much higher ROI than a model, but it's easier and cheaper to hire a data scientist and make a big noise about it than it is to admit your business process (that includes 50+ people) is a mess and do the work to fix it.
> a lot of company think they need AI, but in fact what they need is a good product and a few algorithm requiring high-school level maths
Yep, AI is the new silver bullet that will solve everyone's problems. It seems much easier to throw a million dollars at someone with the right credentials than to make hard choices and build a better product.
That's because you ultimately don't care for building the better product. You care to make an impeccable impression of doing the right thing - in the current market this will get you more capital than your customers will ever pay you.
This is my experience as well, and I'm wondering if this industry could learn something from another one: video games. In that industry, many specialized technical people work on a large project, but their goals and "infrastructure" (seem to be) aligned.
Are the issues we see in many software companies w.r.t. AI/ML/DS an effect of poor role definition/team hierarchy? It seems to me that a five-person team complete with an "engineer", scientist/researcher, a couple of devs for APIs and pretty pictures, and one other "utility" role would totally kill it and create amazing things. But, I've never worked in an environment where the ML people aren't completely segregated into their environment, so I don't know.
>Are the issues we see in many software companies w.r.t. AI/ML/DS an effect of poor role definition/team hierarchy?
I don't think so, unless they are getting AI/ML/DS people to do regular software features and bug fixes.
Front-end/Back-end dev roles merged into full-stack roles at places, but you don't see the same merging between AI/ML/DS roles and front/back/fullstack roles.
They can’t, and don’t. The secret is that experienced PhDs (mostly) dominate the high end of “AI” hiring, but don’t have much title or departmental differentiation from people who are “only” specialized software engineers in top tech companies. But this isn’t very well known, large companies want to recruit as much talent as they can regardless of role, and startups want to compete on paper - therefore, you have the following effects:
1. Startups give whatever sexy title they want to the people they can afford, which makes titles fairly useless, because they almost never can afford the talent commanding “sky-high” salaries.
2. Within large tech companies like Google and Facebook, it’s hard to immediately tell which of the many data sciencey, machine learning-y titles correspond to the truly stratospheric salaries versus the engineers that work with those roles. For some teams, like DeepMind, Google Brain or FAIR, it’s easier to tell. But for others it’s a mixed bag.
For comparison, see the fashionable term of art “quant” in the financial industry, which has similarly devolved into marketing and a bimodal distribution. As a rule of thumb, you generally can’t trust AI titles or salaries at startups unless those startups are really known for their talent; further, you can safely assume that, at companies capable of paying for top talent, the very impressive salaries belong to titles which seem the most exotic and out of reach for general engineers in the job description.
Fortunately startups don’t really need to compete for top talent as a genuine technological differentiator, they just need to engage in signaling, so this is mostly a non-problem. Startups almost never have problems usefully improved by the cutting edge of machine learning research, and can instead use off the shelf tools and existing software to accomplish the same things. Frankly, it’s exceptionally rare for a startup to even have the massive data, pipeline and munging infrastructure requisite for actual research.
This actually makes a lot of sense. I have a lot of friends employed as Data Scientist or Data Engineers who don't seem to be able to explain exactly what they do, or describe what "research" they are doing. From what I understand, it seems like they are designing pipelines that ingest data, run an off the shelf AI algorithm and display nice graphs. There is a lot of pipelines you can design for different kinds of data so I imagine they always have something to do.
But... Consider the wide variety of solutions for taking notes. It’s a trivial problem that can be addressed in any number of ways. But it’s a problem that people spend a lot of time solving.
Existing start ups can't. But if you are a well-known AI researcher, or the student of one, you start your own company with students and friends and get your team bought for the talent.
In my experience, the best scientists are not motivated by money, at least not beyond enough to provide a comfortable lifestyle. For me it's much more important to work on interesting problems in a good team. Also, startups can provide an exhilarating feeling of 'anything is possible' which you rarely get at a large corporation.
You'd be amazed how little impact spreading that wealth around in the form of salaries will actually make on an individual salary. And on just how much those offices and perks actually contribute to company image.
I strongly disagree. And given that I'm working for a company who's entire purpose is to make money, I fail to see why I should not do the same. Nothing good comes from me forgoing making all that I can make.
Agree with you absolutely. Everyone here to make money. If they don't need you or they need you and you don't work, will they give you money ? If you are good at something, never do it for ...
You'll make a lot more money just starting your own business. Like, a ridiculous amount more. Then when you start hiring, you'll understand why attitudes like yours are toxic.
Again, I'm failing to see why you're advocating for a business to make all the money it can, but why you decry the same for employees. Double standard much?
You should try to get as much as you can for your family. But the best way to do this is to move up the value chain, not keep trying to squeeze blood out of a stone. You seem dead set on getting more compensation for bringing the exact same amount of value to the table.
My solution to your "fact" is to not work for a bunch of dicks. I've never gotten fired "as soon as it makes sense" once I transitioned into development. You seem to work for a lot of dicks. Stop doing that.
No, I work for business people. The very same type of people you tell me I should gift money by leaving it on the table.
This is going to be the last reply, but you've not made your case as to why I should gift my employer free money by leaving it on the table. There is exactly zero benefit to me for not getting everything I can, and quite a bit of upside. I have also found your double standard regarding the behavior of companies and the behavior of employees, and your handwaving away of that double standard, to be quite insane.
In short, you feel free to do whatever you want, and gift your employer free money. I, on the other hand, am going to take care of myself and my family by getting the maximum value I can out of the time I have to give my employer, and get paid as much as I can.
The original employer-employee relationship was between farmers and strongman warlords. If the strongmen didn't protect the farmers then they didn't get fed and if the farmers didn't produce as much as they could then they ran the risk of getting overrun by guys they didn't have an existing working relationship with.
So yeah, I think that was an inherently civil relationship. Agrarian empires were the original forms of civilization. Just because there's a hierarchy doesn't make it not civilized. Hierarchy is instead what makes it civilized. Hierarchy means that everyone can relax and focus on what's in their wheelhouse.
This is a value for value business relationship. It’s not mercenary for “coders” to capture more of the value that they created in the first place. An employer is not entitled to get cheaper labor just because it helps them get richer.
Like I told the other commenter, if you want to capture more of the value, you need to own more of the business.
Look, if we're talking giant corporations here, I agree with you. The company isn't going to miss another $5k/year. But if you play hardball with a $3M company, then they're only going to keep you until they can outsource your job away. Their budget is what they live and die on, and surplus profit typically gets rolled back into the business, not wasted on dividends.
You're directly affecting company viability by not being willing to leave some money on the table.
I understand what you are saying especially when it comes to a "lifestyle" business that keeps an employee on when they have a bad year. The employee trades some salary for security. I'm not so sure how common that sort of business is though.
You've got the start-ups that are focused on a big exit to pay their investors and let the founders cash out. They (and their VC investors) want their employees to sacrifice/invest/commit "like a founder" - but without the founders upside.
You've also got the large businesses that will, in your own words, "keep you until they can outsource your job away" for a little more profit.
This is the reality for developers and they have slowly decided to seek their fair share to the consternation of businesses that were used to adding that value to their own bottom line.
When I hear someone saying that "coders" (or sometimes "code monkeys") shouldn't be focused on salary I hear someone who (A) doesn't respect my profession and (B) doesn't see why they shouldn't be able to exploit me.
(When it comes to my own clients, I do leave money on the the table. I could justify it by saying that I do it "in the interests of a long term relationships" but in reality I'm emotionally invested in their success and I want their projects to succeed.)
That's not a bad approach but...from my perspective, why should I work for a company that either doesn't value my contributions or can't capitalize on them? The money I leave on the table doesn't go to charity after all, it goes in someone else's pocket or is invested to the benefit of the business owners.
To paraphrase on old saying "Developers go where they are wanted and stay where they're well treated."
Look at from a developers perspective; why should they bust their hump to deliver 20% more value only to be rewarded with a 3% increase in salary? ("Gee Chris, I would love to give more but company policy...") Why shouldn't I go someplace that does value my efforts?
The only way to fix the structural inequality of the employer-employee relationship is to have your own business. It is not a business relationship. It's an evolution on the lord-serf relationship.
> Look at from a developers perspective; why should they bust their hump to deliver 20% more value only to be rewarded with a 3% increase in salary? ("Gee Chris, I would love to give more but company policy...") Why shouldn't I go someplace that does value my efforts?
You should not bust your hump. Deploy adroit political acumen to reduce your workload. I can't remember the last time I busted my hump on a development job.
But if you want more money, you absolutely should go somewhere else. What I'm saying is that expecting your existing company to be the vehicle for that advancement is naive at best.
I requested, and got, two large raises at my last job. I was still underpaid at the end of it. I'm underpaid now, even though I got another massive raise when I switched companies.
The reality is, you get a market salary from the market, not from any one company. A company is either going to be open to paying market rates or they won't be. You have to make the decision whether to accept that. Playing hardball with a company that's not prepared to pay you market just won't get you anywhere. Find a company that's prepared to pay market.
I have brought additional value to a company and had them just tell me no. No they were not going to give me a raise. I had to jump ship to get a raise. You're stance is nice from an ideal CEO perspective but it never works.
"But if you play hardball with a $3M company, then they're only going to keep you until they can outsource your job away."
They're going to do that anyway.
"You're directly affecting company viability by not being willing to leave some money on the table."
If my extra $5k is the difference between live and die, then the company was failing anyway, and I should take as much as I can before the company closes, to tide me over while I look for a new job.
Hm, I think I disagree with this. The famous statistician and scientist RA Fisher said "To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of." and less-statistically-inclined researchers in academia have often observed this to be true: when statistical/analytical/"data" related considerations are not taken into account during the early stages (design, planning) of a project, it is very difficult (and time and money consuming) to "bolt them on" after the fact. If "AI" (or whatever you want to call it) is going to be a fundamental feature of a product, data scientists (or whatever you want to call them) should be involved right from the very beginning.
In practice, they're often frustrated for years by the lack of infrastructure to work on their ideas, but live with it because life is good.
I suspect AI today is like big data ten years ago : a lot of company think they need AI, but in fact what they need is a good product and a few algorithm requiring high-school level maths.
That could explain the shortage...