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Ask Slashdot: What Types of Jobs Are Opening Up In the New Field of AI?
Qbertino writes: I'm about to move on in my career after having a "short rethink and regroup break" and was for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something. But I have the seriously growing suspicion that artificial intelligence is coming for us programmers and IT experts faster than we might want to admit. Just last weekend I heard myself saying to a friend who was a pioneer on the web, "AI is today what the web was in 1993" -- I think that to be very true. So just 20 minutes ago I started thinking and wondering about what types of jobs there are in AI. Is anything popping up in the industry from the AI hype and what are these positions called, what do they precisely do and what are the skills needed to do them? I suspect something like an "AI Architect" for planning AI setups and clearly defining the boundaries of what the AI is supposed to do and explore. Then I presume the requirements for something like an "AI Maintainer" and/or "AI Trainer," which would probably resemble something like an admin of a big data storage, looking at statistics and making educated decisions on which "AI Training Paths" the AI should continue to explore to gain the skill required and deciding when the "AI" is ready to be let go on to the task. You're seeing we -- AFAIK -- don't even have names for these positions yet, but I suspect, just as in the internet/web boom 20 years ago, that is about to change *very* fast.
And what about Tensor Flow? Should I toy around with it or are we past that stage already and will others do AI setup and installation better than me before I know how this thing really works? Because I also suspect most of the AI work for humans will closely be tied to services and providers such as Google. You know, renting "AI" as you rent webspace or subscribe to bandwidth today. Any services and industry vendors I should look into -- besides the obvious Google that is? In a nutshell, what work is there in the field of AI that can be done and how do I move into that? Like now. And what should I maybe get a degree in if I want to be on top of this AI thing? And how would you go about gaining skill and knowledge on AI today, and I mean literally, today. I know, tons of questions but insightful advice is requested from an educated slashdot crowd. And I bet I'm not the only one interested in this topic. Thanks.
And what about Tensor Flow? Should I toy around with it or are we past that stage already and will others do AI setup and installation better than me before I know how this thing really works? Because I also suspect most of the AI work for humans will closely be tied to services and providers such as Google. You know, renting "AI" as you rent webspace or subscribe to bandwidth today. Any services and industry vendors I should look into -- besides the obvious Google that is? In a nutshell, what work is there in the field of AI that can be done and how do I move into that? Like now. And what should I maybe get a degree in if I want to be on top of this AI thing? And how would you go about gaining skill and knowledge on AI today, and I mean literally, today. I know, tons of questions but insightful advice is requested from an educated slashdot crowd. And I bet I'm not the only one interested in this topic. Thanks.
But you gotta bring your own "battle-proven" rod.
There will be job designing advanced algorithms for robotics. Tasks such as felatio require experienced professionals who understand the ins and outs of the task they are designing for.
Kudos to all you dick sucking engineers
Manually cleaning self-driving cars for $3.99 an hour.
Doing AI is much harder than being an application developer. I doubt most of us would be able to switch. Good luck.
Ahhh...the great dumpster continuum. Many a free computer will be found there. -- sowth (748135)
and AI never works since anything that works is redefined as not AI. Sucks that I've worked my ass off for four different companies, and the last one is Microsoft, where what I do is considered amazing before it works then not appreciated after it works.
Currently, I think AI will only "succeed" when it can make it's own arguments as to why it works.
Come on, Beau. Certainly you aren't THAT stupid. Oh, dear.
Nothing but a marketing gimmick for nVidia to sell more GPUs.
Train to rake leaves. People will always want someone to that for them
Not much else though, hopefully the 'amused by humans' feature is deeply ingrained into the AI and not removable
... It's just more accessible these days and more practical due to the huge increase in computing power. Back in the day, it was required to reduce the data (images, etc) to a much smaller set of features that could be fed into the AI algorithms. Now there's enough computing power for the AI engines to determine good features on their own.
Even though you can train a neural net to recognize e.g. hotdogs by just feeding it a series of pictures of hotdogs, and, of course pictures of things that might be mistaken for hotdogs... I believe it's still best to have some domain expertise in what you're trying to get the computer to learn. If you want to get into AI for self driving cars, for instance, it would be good if you knew a bit about image processing.
That's my goal at least!
Fast food sounds more your level....
At best, we have rather shoddy decision trees.
You don't want to go into some nebulous bullshit that's decades, if not centuries, away. Go into big data and analysis. That's what we have today.
AI today means brute force problem solving by running non-very-smart algorithms very fast or pattern matching. It's nowhere near real intelligence nor sentience. Not even close.
So today a lot of "AI" you read about is marketing hype and bad journalism because the two often go hand in hand. Like an article I read recently about "how an artificial intelligence called 'Harrison' sits in on meetings." It's marketing bullshit regurgitated by a journalist copypasting public relations.
That's where the jobs are. Now anyway.
Not the fish but medical.
Like 'self-driving' cars AI is a scam and my eyes glaze over when I hear anyone mentioning it. It's usually a clueless investor or someone with something to peddle. All we have are systems with an ever increasing number of if statements. There is no true AI and won't be for a very long time.
neural network admininstator, and senior mesh analys
At present, developers & sysadmins with Deep Learning experience are in demand.
You do not necessarily need to know the nitty gritty details of how it's done, as much as know the tools of the trade (hardware & software) and how to interact with them. I'd focus on that for now and build experience on existing components (GPUs, TPUs, torch7, caffe, keras etc etc).
After all, if AI really works, you don't really need to know all the low level details, you just need to keep feeding that animal with its fuel!
I'm sorry, but you're too late. All of the programming jobs are already gone. :-)
It's been around for a while.
If you really want to head in the right direction, talk to IT recruiters and ask them what they see trending. Keep a regular job while you mentally re-tool. Find a job that leverages your existing knowledge that also needs someone with the skills you're looking to acquire.
Often you may find yourself picked up by people that are willing to overlook or even foster your learning curve based on demonstrating an ability to learn.
Good Luck.
~ People that think they are better than anyone else for any reason are the cause of all the strife in the world.
It's mostly hype. Yes there will definitely be new jobs created, but mostly they will go to people with a strong academic background in the field. We are pretty much at the peak of the hype cycle and it is still very competitive to find jobs in the field. Don't think every man and his dog will be doing AI / machine learning. In fact the most likely long term scenario is that as the tooling gets more advanced, there is a consolidation of roles so that fewer people will be needed. There is no harm familiarizing yourself with the ideas and tools of machine learning, but I wouldn't put all your eggs in one basic. Realistically the chances of someone teaching themselves tensor flow and doing a few coursera courses and then finding a job in the field are slim at best.
If you're asking this question, then you haven't researched your career more intent sufficiently...
Any applicable answer really depend of your educational background. If you have already received a well-rounded engineering education, AI plus IoT plus industrial networks, processes and the rest would open interesting short to medium term prospects. But it might involve moving to round the world. The publicity around the current AI technologies focuses too narrowly to few, hard applications and misses the wider world, in my opinion.
If you aim for something more general, try those computer and information engineering degrees available to become an "AI Engineer", if you have the money, time and opportunity. This way you can built or adapt a suitable solution to a particular problem, even if the libraries around a research paper aren't mature enough or ported to the environment you work in.
Like now.
Oh sorry, I missed that.
for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something
You are in for disappointment if you think that getting into a career in AI / ML is anything like a programming hobby, such as picking up NodeJS or Java over a few weekends. First, let me make clear the terminology I'm using. Artificial Intelligence is a broad field. Although the public perception of AI is of software/robots like HAL with which humans can talk or interact, the field also encompasses knowledge representation, reasoning, and learning. Machine learning is then an important subfield of AI, and it involves supervised learning, unsupervised learning, reinforcement learning, etc. Over the last several years, many people have started to conflate AI and ML, but for someone knowledgable in these fields, the distinction is clear. ML is the practical application of algorithms towards taking inputs and producing an output prediction, and it's this area that contains the vast majority of jobs in "AI". Some basic applications of ML include spam filtering, face detection and recognition, product recommendations, fraud detection, revenue forecasting, gait and step detection, voice recognition, etc. If you look at that list and think about them, you'll come to realize that you've probably been consuming ML results for the last five years or much longer. If you want to work as a "ML engineer" in this area, you'll have to be knowledgable with ML algorithms, setting up data pipelines, running experimentation, and using ML software, such as scikit-learn, R, Caffe, Tensorflow, etc.
I manage a ML team at a large company. Let me make clear: Unless you have a strong academic background in this field, no one will take you seriously. I recently applied to be a principal engineer working on an AI/ML personal assistant, and the recruiter told me straightforwardly that the hiring managers are not interviewing anyone unless they have a recent PhD related to deep learning. I'm a bit elitist about this as well: I tend to turn away candidates that don't have at least an MS or PhD in a field related to ML. Why is this so? Because you need a rigorous background to understand why and how ML works, and this involves understanding loss functions, gradients, training vs. validation error, decision boundaries, optimization, and other things. You need to understand these things because a lot of current ML involves choosing the right knob settings (hyperparameters) that make your ML work best. If your ML algorithm isn't working well, how do you fix it? That's where this rigorous background comes in handy.
Now, there are many things related to ML that you can still work on if you don't have a strong background. As opposed to a ML specialist, there are plenty of positions related to data engineering (e.g. setting up and maintaining huge data pipelines), infrastructure administration (e.g. installing and mastering all aspects of Hadoop and Spark), visualization (e.g. creating dashboards that take fresh data and display it), among many others.
Is there really anything other than Software Engineer? (for non-research roles)
As others have said, AI isn't new. But it is a fast growing field, so I'd caution against getting too specialized. In a field that is growing fast and changing quickly, it's important to be adaptable. You only tend to see highly specialized roles in established fields, and AI is not one of those.
There is no 'AI field', it's still just computers running code. Nothing to see here, move along..
I've got a degree specific to programming and AI, but companies aren't posting positions or showing interest, and haven't been for several years. Keep in mind that most AI algorithms are basically just search algorithms, so any programmer can just look them up or download some Library of them. Calling these systems AI is just trying to capture the mystique of what folks see in the movies, but that isn't what AI is today.
A well trained (e.g. neural net) AI system is effectively just a search algorithm where the weight given to options is determined by the training input. Once trained on input good enough for some company's application, they can just copy-paste the weights the AI ended up with. There doesn't need to be an AI programming team for every self-driving car company after one of them has succeeded in a good-enough implementation that they're willing to sell.
...who has to filter Trumptweets, and keeps on kill -9ing itself...
If your skill set is "web programming", stay with it. You really are unlikely to have the capacity to learn about Machine Learning approaches. Those require actual understanding of a wide variety of skills, including how to actually use malloc()/new()/delete[]/free().
I'm not trying to be mean here, in my experience, web "programmers" (including mobile developers) really do not understand programming domains which require an understanding of anything beyond Java wherein actual understanding of the implementation of underlying technologies is required. Example: a Java programmer who "understands" SQLite typically has ZEEERO idea about how to actually set up an SQLite DB, or is capable to performing the simplest DB operations.
It'd be nice to know what's available going forward, but is any of this readily available for the displaced/long-term jobless (but still have an interest in IT/CS)?
Twitter supports and protects racists - by smearing their critics with the "Hate Speech" label.
Once we stop calling it AI it will work.
"player 4 hit player 1 with 0 stroms"
New Field of AI? MIT was doing AI in 1963 and Marvin Minksy set up a dedicated AI lab in 1970. While he wrote many books on the subject, Society of Mind is a good one to start with.
AI gets to the point were it solves a set of previously unsolvable problems, the algorithms are then researched and better non-AI solutions are then used to solve the same problems. Then AI falls out of fashion for a while and computer power increases thanks to Moore's law. Then it all repeats.
Your idiot bot truly surpasses the original. One criticism: is it supposed to be so fat?
Must talk about how cloud has failed because there were it was too long of a word and so complicated that no one could understand it... AI is so simple... only two letters.... and how you knew 15 years ago that AI was where it was at and the rest of the world is just waking up to your brilliance... and what dumb asses everyone else is for not seeing it then....
This old story is such a crock.
I highly recommend the following to anyone who wants a different perspective on modern ML:
* Talking Machines: Remembering David MacKay with Philipp Hennig — 21 April 2016
* Probabilistic-Numerics.org
This is plain old numerical methods, optimization, and search viewed through a Bayesian inference filter. I would never have termed any of this "artificial intelligence".
It took the recent large advances in unsupervised learning, the kitty classifier (and progeny), and the LSTM machine translation models to finally justify rethinking academic labels. Programs like SHRDLU from 1968 were perhaps explorations in AI, if our baby-step microscope is especially well focused. But this was closer to natural philosophy than what later became physics. Even our shiny new LSTM language models remain weirdly proximal to Searle's Chinese room. What have we really learned from watching our machines learn? Not a whole damn lot.
I'd nominate a term such as I-cubed: inexplicable inductive inference, or perhaps MIII: massively inexplicable inductive inference.
Even so impeded with an appropriate name, MIII is pretty mind-blowing. But it still ain't AI. It might be a viable building block to proceed in that direction, sooner rather than later, as we begin to erect dynamical systems upon this foundation. To drive the point home, it remains way overblown to call it MIIR: massive inexplicable inductive reasoning.
An Alberta AlphaGo Pioneer Is in China to Watch the AI Wallop Human Opponents
I haven't waded through this yet, but I suspect even the vaunted AlphaGo has a backbone of techniques that I personally wouldn't have classed as "AI" (or even AI-ish) by my own standards.
For decades, the big idea in AI was supposed to be recursion. Perhaps human language is recursive in theory, but it's only barely recursive in practice (nest more than three levels, your accurately attentive audience grows thin). Winograd is not completely wrong about this, but my long suspicion is that recursion is not going to enter the AI building through the ground floor.
Lately, we really have hit home runs with distributed representation, and to a lesser degree with convolutional image recognition. These are actual AI-ish ideas. However, two solid take-home techniques do not a field make.
Here's another possible intermediate term: generalized gradient exploitation (GGE). Plus there's tons of great mathematics about overfitting and regularization. But should we really call all this math "AI"?
In practice what AI ends up meaning is "look ma, no code!" Hey, we just built an impressive system without hiring rooms full of code monkeys, so we must be doing something right.
AI is not the moving target of lore. It's mainly our long AI pretension that fits the bill.
It's not a job yet, but I started a new comic strip written by AI and online dating profiles called Robonk.
http://www.Robonk.com
I plan to be frolicking naked on the shore and having sex with babes all day.
... get a fucking life ...
If you want to be at the forefront of AI you should first invent it and to do that, you need to define intelligence and individualism. I think brain research would be your field to try.
You seem to want to go into statistical classification and database systems, neither of which are new and have well defined paths via mathematical statistics, programming and systems design. The fact that you think learning a particular language will help shows that you are missing the point of modern IT systems but if anything, if you want to go into this field and do research, that is done in R, MATLAB, some C, Python and Java.
If you want to go into commercial AI, make an app, some news releases and a site, get a bunch of investors and hope Google or Apple buys you out before the jig is up.
Custom electronics and digital signage for your business: www.evcircuits.com
First of all if you are talking about true AI and not just basically the same sort of connectionist statistical learning algorithms that have been around for many years you are perhaps better off getting into electrical engineering rather than machine learning because we don't yet really have the hardware to properly run the software on.
AI is all about research and research is still at a very early stage. When it comes to AI we are cavemen with crude stone axes. We need some fundamental breakthroughs and they are more likely to come from new hardware than new software.
Actually I suspect we may find that the best approach is by cheating with a bit of biology. We may find it easier to grow our own biological neurons that map themselves than trying to figure out what a brain does and emulate it with electronic circuits. So you may be better off getting a degree in some sort of biological science and specializing in the brain.
My suggestion is to get at least 3 PhDs. One on the software side related to machine learning / artificial neural networks, another on the electrical engineering side of things designing something like Google's TPU but even more optimized for the most effective neural network architecture of the day, and perhaps most importantly a degree in Synthetic Biology and/or Cognitive Science for the wetware aspects which I predict will be the most successful in the future.
Probably the most effective approach to creating intelligence will be with making other species smarter. Genetically engineering bonobos or parrots or corvids to give them a better cerebral cortex for instance or figuring out how to give them some of our human genetics that makes our brains so effective in general. Of course that won't really be artificial intelligence but is just as cool in some ways and will probably happen before we can figure out how to build a brain from scratch.
There is also the artificial life angle: looking at life itself as an engineering problem. Life forms are basically just electrochemical robots evolved with genetic algorithms designed at the molecular nanotech level. Figure out for instance how to make your own form of life that is not based on DNA or even carbon. Again however this would start to stretch the meaning of 'artificial'. Once you figure out this puzzle everything starts to look artificial.
Quite an experience to live in fear, isn't it? That's what it is to be a slave.
Thanks for the replys and the lively discussion this far.
Lots of sentiment here.
First of all, on a sidenote: People should drop the notion that professional web development is trivial. It isn't. Yes, there is a disproportionate amount of eternal n00bs and dolts in the field and truckloads of throw-away code straight from Nightmare on Elm Street. Which is why I'd like to leave it and will only take jobs with teams that know CI, are building a product I'm interested in or pay obscene amounts of money. But bad software design isn't limited to web stuff and I have the suspicion that the wisecracks on this matter come from people who couldn't design a useful ux/pageflow if their life depended on it. So chill. ... So much for that.
As for the AI development jobs being more rare and more difficult: Of course they are. It's AI/ ML. I guess it's not clear from the way I phrased the question that I'm aware of this.
As for AI being a fad: I said the same thing about the web in 1995, whilst I was using Fidonet and Crosspoint (still better than E-Mail and Usenet to this very day). So as an 80 computer kid who's been wrong more times than others have thought about something IT related I'm old enough to be careful jumping to conclusions too fast. Google is kicking ass with ML and Elon Musk is focusing on it and Mercedes Benz, BMW, Bosch and Co. are peeing their pants and scrambling to catch the trend. So from that alone I conclude there must be something going on here.
As for the insightful comments on academic requirements and your own experiences with AI: Thanks for those. Very useful and sometimes afirming. Keep it coming.
We suffer more in our imagination than in reality. - Seneca
If you want to be at the forefront of AI you should first invent it and to do that, you need to define intelligence and individualism. I think brain research would be your field to try.
Before you try to invent anything, you should first learn the stuff that's already out there that others are working on.
If you want to go into commercial AI, make an app
Most AI requires more computation that fits in an app.
I am also an 80's computer kid, still coding. Recently I assessed an AI startup, with a team of young ML PhD's, on behalf of a potential investor. While apparently brilliant in science, that team was surprisingly inexperienced on software development, e.g. seriously struggling on web UI's. So perhaps you should look into job opportunities in ML teams, helping them to bridge their ML magic with the outside world?
Good idea. Good idea indeed. Goes right in my list. Thanks. :-)
We suffer more in our imagination than in reality. - Seneca
Since when is AI new?
Yep, l'm an 80's computer kid too. Cut my teeth on C64, trash80's and amigas and the like. Like many of us who both were 'into it' and also 'got it' I have also watched things I never thought were that great take off, while other things that were amazing die on the vine. There was a great magazine cover from a year or so ago that showed John Glenn with the caption "You promised me Mars colonies. Instead I got Facebook." So, your comments regarding picking the wrong thing even when it seemed right ring very true for me as well. Now, all that being said, I also agree with a lot of the comments regarding AI not being there yet, and/or the definition itself not even being that useful. If you want to get into what seems to be a new and promising area, you need to find an area either under represented or make your own. The comment regarding tieing ML outputs to web tech for presentation UI purposes was good. Perhaps looking at applying some of the ML tools to some other industry might be an area to explore. I'm kind of in the same boat. I've been doing financial systems for years and to be frank, am getting bored. While I have been very successful at it( consulted for the TSX among others) I'm thinking it is time for something new. To that end, I'm trying to get interest drummed up to instantiate a Data Sciences center for a local industry that generates millions of dollars per year, but is still largely unsophisticated in how it is run and managed. The tack I am taking is to try and get political interest in establishing said center in a rural depressed area that already has a very nice facility that was closed some years ago for unfortunate circumstances. The industry in question could easily increase revenues and efficiency, particularly with some analysis and concentrated attention. So maybe there is something along those lines you could investigate. ;-)
Or if that interests you, maybe we should talk
Typed on an iPad, so all spelling and grammatical complaints will be redirected to /dev/null
maybe AI Terminator will be a thing.
If you really want to head in the right direction, talk to IT recruiters and ask them what they see trending.
They're looking for people with 10 years experience in Windows 10.
You have no clue what you're talking about. Your AI Trainer job already exists and you're already employed under it. Feel free to stick it on your business card. Your salary? As you guessed it: zero. Every time you answer a CAPTCHA you're telling the hosting company (normally Google) what that item is so it can better train it's AIs on those types of images. CAPTCHAs are picked from images the AI currently don't understand. You're telling them what they are. Other companies like Facebook pay pennies for people to help train it's AIs on detecting images and posts which should be banned. Good luck trying to live off that.
Instead of trying to jump on an early fad (AI isn't an early fad, it's been around since the dawn of computing, in fact longer than the web) in a get rich quick scheme, go into real estate investment or switch your stocks to lowest-fee index funds. Those aren't quick solutions, but can you get rich enough through them. Figure out what you like doing and go after that. If you want money, go after the money directly. Don't go after a job you think might lead you to a high salary at some point in the future. Even better, work on passive income streams instead of giving yourself more work in a job.
In terms of AI, it's almost all machine learning. That's data mining, stats, and mathematics. Sure, you can use some of the many libraries to throw together a neural network or some other random algorithm, but you won't understand what you're doing and this will waste tons and tons of time in training your AI. AI is just doing searches across a massively large search space and giving up once the results are good enough. The more efficient you are at defining what you're looking for and how to find it, the better your AI will be.
and tend to agree with your post based on my "non-expert but considerable time spent" experience.
My story:
I wanted to create intelligence via artificial life and evolution. I didn't want to create human intelligence, just tiny little creatures trying to survive type intelligence. I provided them basic sensor inputs and motor/movement raw materials but didn't program in any usage of those things, they need to figure out through evolution (how to see, move, find food, avoid getting eaten, etc.). They started with random neural nets and through generations increased in capabilities up to a plateau.
Some conclusions:
1 - There is a lot of foundational stuff I had to learn slowly and piece meal by googling etc., for example, what is a neural net doing mathematically, why/when would you use one.
2 - I thought I might figure out something interesting or clever - but the reality is that people with math backgrounds (e.g. PhD ) are the ones that are going to figure out that next clever insight. For example, someone once asked me why I was using neural nets and not support vector machines, so I read up on support vector machines and they seemed to be doing the same thing (function approximation). But I didn't have enough training to fully grasp why a support vector machine and neural are different, what are pros and cons. Reading papers online with teacher is a slow and cumbersome way to acquire knowledge in a complex area.
3 - Interesting issue not really related to the topic at hand but fun to talk about: My other conclusion was that guiding an evolving system towards intelligence is a very tricky task. My creatures hit a plateau of behavior that was at least interesting (chasing/tracking other creatures to get food, avoiding getting eaten, avoiding obstacles) , but difficult to get beyond. How would I need to change the conditions in the environment to push them beyond that into more advanced behaviors, for example hiding around a corner or hunting in packs, etc?. The initial environment needs to be favorable for guiding random brains towards some basic functionality, but then it needs to change and continue changing to keep pushing the evolution process towards more complex capabilities. That is a tricky problem, knowing which environmental conditions would reward intelligence over speed or strength or other attributes.
By the time you get that degree, the whole field could have poopped. Even if that's not the case, it's pretty likely that things have shifted within the field from one specialisation to another. On top of that, if something's hot now chances are there'll be oversupply in four years time because everybody's a genius and they all had the same idea as you.
Plus, it might have helped to mention what qualifications & experience you already have.
Confucius say, "Find worm in apple - bad. Find half a worm - worse."
No jobs are available. AI doesn't need you.
Unless you have a strong academic background in this field, no one will take you seriously
I'm sure that's true today, but how much longer can that hold? There are only so many PHD's to go around.
Just as there are a lot of programmers today that do not have CS degrees, in the future as use of machine learning stuff especially ramps up companies will rapidly start to get less picky about a piece of paper that says you know ML, and be more than OK with practical examples of what you have DONE with ML.
"There is more worth loving than we have strength to love." - Brian Jay Stanley
I seriously considered going back to school. It would have been a luxury to have someone teach me all this stuff rather than teach it to myself. Since I already had a Ph.D., though, it didn’t make sense to get another degree. I looked around, but there just wasn’t any program that seemed like a fit for someone in my situation. I can now finally consider myself to be an NLP professional. I’m working in that field, and I regularly do stuff with machine learning and other AI techniques. My current job is in the area of machine translation (e.g. French to English, or whatever language pairs the company needs). The long effort has paid off, because I’m in demand. It was an awful lot of work to get here, though.