Background: I was one of the original engineers on the Goal Line Technology system now used by the vast majority of professional leagues.
Heads are surprisingly ball shaped from a a lot of camera angles, and the male balding pattern conspires to give a good impression of different football graphical designs.
When we were developing the system, we thought we had ball detection pretty nailed, and we never had false positives from any of the heads of engineers on the team. Then, our boss came to test the system ... As far as our algorithms were concerned, his head was exactly a football, just as in the article! Highly embarrassing, but everyone saw the funny side. It gave us better data and inspired a few more robustness checks that ended up being crucial later on! :)
If you look at someone from eye level it is rather hard to mistake their head for a football. You need a certain amount of elevation before it actually starts to look like a sphere.
To be fair, he did wrote "ball" not "sphere". I'm pretty sure my head and a ball share a lot of roundness but my head has way more parts that are sticking out. Also has way less of pentagon patches on it.
This will happen, of course, but shouldn't last long. Also momentum of the tracked object should help.
Btw, funny that my comment above attracted so much hatred. Didn't want to sound like a know-it-all but yes, at least in the case of the referee I guess that checking for a body would have helped.
For example, in this stylised picture we can easily tell the player's head from the ball:
And it's totally okay! Because nobody died, we all get a nice laugh, and the AI will be tweaked to make it better. It's the kind of move-fast-and-break-things that I can get behind.
It's not that great. Remember that a real person used to do this work, and they've been replaced by a buggy robot in the name of cost savings. That's one fewer job on the market. That real person would also have been able to do things like direct the camera to follow whatever was most important - even if it wasn't happening next to the ball.
That's one more person freed from having to point a camera at a ball for 90 minutes a week, free to find more fulfilling work!
Perhaps the camera operator's new work could be sitting in a comfy chair, tuning the areas of interest and motion profiles of a dozen cameras around the stadium to find the best angle on the action, allowing them to track smoothly under servos, pausing for a minute to think about the pacing of the game and what plays might develop, instead of mindlessly following the ball from place to place.
I'm an automation engineer, I've worked with hundreds of machine operators. They're almost universally happier after their employer gives them a better tool with which to do the work. The jobs that automated/AI equipment is replacing are not good jobs. Performing the same task for 90 minutes, much less an 8 hour shift, is awful on the body, and even worse on the mind. Humans don't make great robots, and it's not great to ask a human do a job that should be done by a robot.
Are you serious? People are concerned about the loss of jobs due to the software our industry creates, and your response is to imagine a completely made-up scenario where that person gets a new, more fulfilling job that you literally just made up in your own head?
By the way, is that your idea of an ideal job for everyone? Sitting in a "comfy chair", rather than doing, you know, the actual job of a cameraman, a job that is arguably an art form in itself? Do you understand that people love sports, people actually enjoy the athleticism of running around with cameras documenting sports, and that what you're dismissing would be a dream job for many people?
Do you really truly think the people buying this technology are doing it so that they can also continue to pay that same person to sit in a room and control it, while working towards self-actualization??
And do you think the appropriate response to concerns over jobs being eliminated is that the newly unemployed should feel grateful for being "freed" from the work they loved and took pride in, to go and do something "more fulfilling"? And add insult to injury by telling them that the job they had was just not a good job and wasn't something humans should have been doing in the first place?
> I'm an automation engineer, I've worked with hundreds of machine operators.
You mean, the ones that are keeping their jobs? Have you spent just as much time with the ones whose jobs have been eliminated?
I mean, I can understand that you may be facing a little cognitive dissonance here due to your job, but ... try to show a tiny bit of empathy for your fellow human. You think AI won't be able to do your job someday? Are you totally comfortable being dropped onto the labor market in today's economy if your current professional skill set became entirely valueless overnight, or is that something you only expect other people to be OK with?
We are recreating and eliminating entire industries, jobs, and traditions, and the level of awareness and respect for that disruption needs to dialed up by several orders of magnitude, or we as an industry, and all of us as a society, are in for some serious trouble.
A future where people dont have to stand by a conveyor belt or follow a ball with a camera to feed their families is a better vision of the future.
While the transition also presents an immense painpoint and must be handled with the utmost care. We should find ways to keep everyone in society and with an income ASAP.
The jobs AI replaces now are the kind of jobs a few pick because they love them (hence would enjoy another job less), and most pick because they have no other choice (hence they chose the best they could and any other job is almost guaranteed to be worse). These most basic of jobs are taken by people who aren't really qualified to do a more skilled or better paying job and to get qualified they certainly need resources. Losing the job they have does not help.
Almost nobody chooses a cameraman, truck driver, or supermarket cashier jobs if they actually had a legitimate choice between that and a well paid tech job. But most people here have well paid tech jobs and a narrowed view of the issue.
Imagine taking that old food away from a poor person because they now have the possibility to go have a nice juicy steak.
> Almost nobody chooses a cameraman, truck driver, or supermarket cashier jobs if they actually had a legitimate choice between that and a well paid tech job.
The world beyond a chair in front of a computer is a lot more interesting to most people. I know more than a few in tech who dream of having a job where they get to interact with reality in a more direct way. And I guarantee you if the pay was closer, you'd see a lot more people adjusting their lifestyle and ditching the tech grind.
> I know more than a few in tech who dream [...] And I guarantee you if the pay was closer, you'd see
Indeed, dreams and ifs. It's no coincidence that the dreams that stay dreams are for the higher end jobs, and the ifs that stay ifs are for the lower end jobs.
> Almost nobody chooses a cameraman, truck driver, or supermarket cashier jobs if they actually had a legitimate choice between that and a well paid tech job
Cameramen make good middle class to upper middle class incomes, have good benefits, are often unionized, and their jobs take skill.
I think this might be an example of tech bubble myopia.
What you call myopia I call seeing the bigger picture. Offer some cameramen the tech salary for a tech job (pretend the qualifications are there) and see if most flat out refuse because you're right and they have a good salary and union, or would hypothetically accept it because I'm right and people would go for a higher salary as long as the job isn't an extreme example of unsatisfying work.
I'd never claim nobody picks a job because they love it above all else, just that the lower-end the job, the less likely that happens. On the other hand very few of the people I heard saying "not for the world" ever properly tested their resolve.
I think you choose to see the tip of the pyramid in this particular "cameraman" case. You pick the few with the better conditions, rather than the many, many with the worse ones. It's pretty much a certainty that most cameramen in the world do not have upper middle class incomes, good benefits, or unions on their side yet their job is just as targeted by automation as the ones you think of. The couple or truckers I know making $200+k a year are also the exception rather than the rule.
Look, I know camera operators who are expected to process and edit the material and get paid their weight in gold. Most others are "just" camera operators and get paid barely middle class salaries.
The jobs that get automated are these second type because it's easier to train an algorithm or another human to do them than for many other jobs. It also means there's less room to be choosy. And people also learn to love what they do.
The experience from a European logistics company is that truck drivers love their jobs - travels (different countries), adventure and childhood dreams.
We just said goodbye to a camerman with 30+ years of experience, from Diana's crash in Paris, to the fall of Ghadaffi, interviewing multiple US presidents one day, and Rohingya fleeing into Bangladesh (much of which was caused by the tech industry - specifically facebook) the next.
Everything is a choice in the end. But I also think that out of the hundreds of thousands of camera operators in the world you had to look for an exceptional one to make a point. Before you take it personal and form a general opinion based on that one anecdote sit on it for a minute and think, is this example relevant for the discussion? Is he the rule or the exception?
What about the situation from the article? The operator who's sitting in a small football stadium of a small Scottish second league team pointing the camera at what could be charitably described as an average football match for 90 minutes? How many operators filmed Diana's crash in Paris and the fall of Ghadaffi, and how many only film mundane, mediocre things their entire career?
> (much of which was caused by the tech industry - specifically facebook)
I took the tech industry just as a good example of an all round good, well paying job in general, not to pass judgement on what it brings to the world. My comment history could tell you as much. Mass media deserves its share of jabs as I'm sure you agree. But neither of the jabs make any my arguments or conclusion any less valid.
If you're a high school kid in Inverness wanting to earn a couple of bob on a Saturday afternoon, you could go stack shelves in tesco, or you could point a camera at a local football match, and start getting useful experience. Which is more fulfilling?
You might find you're not that good at it, you don't like standing out in the cold, but you might find you have a talent for it, and that leads onto a more formal training course
Why do you just think of cameraman jobs? Here, one industry (AI, object tracking) replaces another (cameraman). I would make the wild guess that this creates more jobs than it eliminates (because tech is not yet perfect or sometimes not applicable). The nature of human professions change all the time. Brickmakers or coopers also took pride in their profession. In the future, being a football match cameraman might just as well not be the best way to provide a useful service to others.
No, it's how you get to start, probably while still in education, but you learn about framing, focus, white balance, learn about direction (forget the ball -- you want to follow the ref at this point), on a larger event you'll be part of working as a team which is an essential soft skill many graduates don't have, on a smaller even you'll be learning how to interact in a professional environment on your own, you'll be meeting people who can help you, you can
A colleague of mine was on a camera at the weekend, he's not a cameraman, he's an engineer, he had to be there anyway to set up the camera, plug in the various cables, and get the pictures out. He was filming 3 people stood at lecterns and panning the camera between them as appropriate, which meant making his own decisions based on the flow of the conversation, speed of the pan (I don't think there were any zooms needed), etc.
It's hardly thrilling work, and you could probably automate it. I've seen that type of automation system used for radio visualisation -- hell I've set it up on occasion, but AI isn't as good as an engineer with a little situational awareness pointing a camera, let alone a creative picture correspondent.
> Performing the same task for 90 minutes, much less an 8 hour shift, is awful on the body, and even worse on the mind. Humans don't make great robots, and it's not great to ask a human do a job that should be done by a robot.
Then you have to take the next step and ask: why do they do it? If it’s tedious, unsatisfying, lacks prestige, and is utterly demoralizing, what possible reason could somebody have to do it? Either the worker is insane, or else...
That was the fill in the blank answer I was hinting at. They don’t have a better prospect, and that they can only choose from a weaker or more complicated/risky set of options after losing their current job
Exactly. And these people are usually automated out, not moved to some other position. And if they were working this non exiting job before, what guarantees they have thet after automation they magically will land an interesting and fulfilling role.
Well one guarantee is - according to scientific consensus - that this exact same process has been working for the last 200 years, and previously people have always gotten better jobs and a higher standard of living. You think the people working farm jobs or sweatshops had better opportunities at the time their jobs were automated away?
The difference is that 200 years ago, most work was physically challenging but intellectually simple. When some of those jobs were replaced by jobs that were easier physically but more challenging intellectually, we got different opportunities for different people.
Today would be analogical if we had a population of people with average IQ 150, locked in jobs that require IQ 100. By automating those jobs away, we would open new opportunities that would fit people better.
But instead, we have a world where the average IQ is 100, and the standard advice for people who lose their jobs is "learn to code". So we get the situation where some people are unable to find a job, and other people are pushed to work overtime... and we cannot balance it by hiring the former into job positions of the latter.
(Also, historically speaking, many people starved to death when their farms were taken away. The new jobs appeared later, but it took some time.)
> The jobs that automated/AI equipment is replacing are not good jobs.
Most jobs in the world are not good jobs, sure.
But unless you also take money from the capitalists and elite laborers enriched by automation to provide stronger social safety nets, automating away demand for low-skill crap jobs while making the fewer remaining jobs both better and higher paying means increasing the prevalence of the worst human misery. People might hate crap jobs, but not as much as they hate being qualified for no job in a capitalist society.
Yeah, that's not how it works. That's how you want to believe it works so you can sleep at night.
In reality that person probably turned to the gig economy, with no job security, benefits, or upwards mobility, and works twice as much to put less money into savings.
I used to think the same thing but I spent some time researching the impact of previous technological revolutions on jobs. In almost every case, more jobs end up being created. Sometimes, the automation allows products and services to be more widely consumed at lower prices leading to an increase in jobs. Sometimes new jobs are created that weren't possible before. Think about explaining the job of a YouTuber, Blogger or Reality Show Star to someone from 100 years ago.
Sometimes these debates on automation end up in a fight over how the economy of Star Trek works when you have a machine that can materialize almost anything. That's an interesting rabbit hole to dive into when you ask if any random person could just ask for an Enterprise type starship to be instantly generated. What are the constraints in that economy that make it not possible? Energy?
I'd argue that being a greeter at Walmart, or in general a human interaction role is exactly the role for people in an automation future. The greeter at Walmart tomorrow may also be responsible of a fleet of robots that perform cleaning duties or stock monitoring.
Home and elder care may employ a lot of people in future that are assisted by robots.
> I used to think the same thing but I spent some time researching the impact of previous technological revolutions on jobs. In almost every case, more jobs end up being created.
Historically, yes, but I think that's a fatally flawed analysis this time around.
It's important to consider the minimum skill level required by the market. Not all jobs require the same level of mental acuity. To date, automation has consistently removed lower skilled jobs and replaced them with higher skilled ones.
There are obviously physical limits to that process and I am convinced that we are rapidly approaching them. Take a look through the present day cutting edge results in machine learning, remembering that today's cutting edge is tomorrow's par for the course.
> To date, automation has consistently removed lower skilled jobs and replaced them with higher skilled ones.
I think that an equally likely outcome is that of the ATM. After the introduction of the ATM, the number of bank tellers in the US actually increased. Why? Because ATMs didn't completely eliminate the need for bank tellers, they just automated a lot of their most mundane tasks. However, ATMs did reduce the amount of tellers needed to operate a branch, which made it cheaper to open new branches. So in the end, the elimination of positions at existing branches was dwarfed by all the positions created at new bank branches that now could be operated profitably.
> After the introduction of the ATM, the number of bank tellers in the US actually increased
Weird, why that would happen?!... I don't think that's a common outcome elsewhere...
In Northern Europe, for example, there's almost no bank branches left now... they've been closing down for years and now it's actually hard to find any branch that's open to the public (this started with ATMs but now it's mostly a result of the cashless economy and online banking).
The US bank market is (was?) highly competitive, the US still uses a lot of cash and checks, you can generally only deposit at ATMs for your bank, and non-bank ATMs have very high fees. So it becomes a distinct advantage to have more locations for customers, because it makes banking with your bank more convenient.
The bank I use has very few branches, from what I can tell. They let me deposit checks by mail or with a smart phone and they refund all of my off-network ATM fees, I guess with the money they're saving by not operating physical branches.
You can do that with a check but you can't really take smartphone pictures or use mail to deposit cash. I have an online bank account but I do maintain a checking account with a traditional US bank for this reason.
This is true, and my bank does have one branch in my city I can go to if I ever had to deposit cash. In actuality, though, I rarely receive large sums of cash and any cash I do get I usually just spend (thus avoiding having even to visit an atm to get more cash as I commonly have to). The end result is that I haven't been inside a bank in at least 5 years.
> ATMs didn't completely eliminate the need for bank tellers, they just automated a lot of their most mundane tasks.
That looks suspiciously similar to the process of raising the skill floor that I referred to. The simpler parts of the job were relegated to a machine while the more complicated bits remained. My point is that there are inherent limits to such a process. What happened historically (including the ATM example) ceases to be relevant once such a limit is reached.
Relevant quote: "Instead of shooting where I was, you should have shot at where I was going to be." -Lrrr (to Fry)
(In the ATM example, it's specifically the bits that require vision, situational awareness, and human level conversational capabilities that were retained by the human. All things that cutting edge ML is making very visible progress on. I implore you to take a serious look at the state of cutting edge ML research with such tasks in mind.)
That's incorrect. The level of skill and automation potential aren't related. For instance healthcare is a hugely growing market with a lot of low skilled opportunities requiring human interaction.
In my country, except janitors and people working cafeteria, pretty much any job in healthcare requires some kind of degree (exception is taking care of old folks in private homes, but even those require a substancial amount of staff with some kind of schooling in it)
Theatre support worker would be one example of a healthcare role that doesn't require a degree. This is in the context of the UK NHS. I'm unsure if an equivalent role is common within the healthcare systems of other countries.
> The level of skill and automation potential aren't related.
I never claimed they were? I said that when a job is automated away, any that replace it inevitably require a higher skill level. Thus the skill floor rises over time.
> requiring human interaction
That's the key. That's a specific task that we can't (yet) automate.
Does it raise the skill floor? The actual job requirements of bank teller have not significantly changed; you always needed to be a basic functioning human being WRT "vision, situational awareness, and human level conversational capabilities"
The skill floor I'm referring to is across the entire market, not a single job. I didn't claim that the skill required in that particular example had changed at all. Rather, I pointed out that the underlying process taking place there appeared consistent with the broader trend of the skill floor rising.
My claim is that once we're able to automate away that job entirely, any that appear to replace it will almost invariably require a higher skill level.
I think that example just emphasises Reelin's point. Perhaps there was a temporary effect of more tellers, but now they've been entirely replaced with software - to the point there are many Internet only banks with no physical branches - and bank branches are closing down all over.
But now it seem to me, to work in a bank or any other office job, you need a degree from university. Whereas before, these jobs required a high school diploma and maybe some training.
Some of that volatility was women entering the workforce in large numbers, but when you consider every percent represents millions of people it’s shocking how long disruptions can be.
People need to realize that we're quickly heading towards a world that won't need as much human labor as we have available. And that's a good thing.
There's only so many bullshit paper-pushing jobs and useless over-budget infrastructure projects we can create to make up for the lack of work.
A better way to deal with too much labor on the market would be setting government-mandated maximum work-hours, so as to spread the amount of available work evenly among the entire populace.
Secondly - for those countries where this isn't a reality yet - school and university should be free and you should receive government aid while attending them, to allow those out of a job to learn new skills or just pursue their interests.
It's only a good thing if we can turn around our societies to handle this new reality and where we can keep our political and enconomical freedoms, including of course a chance at a decent living
Sure but you also need a different economic system to handle this sort of reality. Without a change, this will lead us to extreme wealth disparity and social unrest. It will be interesting to understand which countries will go with mitigations that reduces disparity vs. use increasing levels of force to keep the haves protected from have nots
While I don't disagree with your main point, I think climate change mitigation is going to require great than 100% employment, moving whole populations, building dams and levees, digging super-tornado bunkers. Fun times!
Fair enough. I would agree that, in general, we as a society are very bad at cost estimating large projects which tends to make them have budget shortfalls.
I think you’ll find many of those actually want to work hard will bristle at that policy. Not to mention finding that hard (and undesirable IMO) to enforce on entrepreneurs.
I want to believe in that, however I've witnessed a few times Parkinson's Law [1] taking place.
TLDR: Often after tech makes bureaucracy paperwork easier, some bureaucrats create more paperwork to compensate that, because they want more control over what is going on the office.
So you figured out what a massive mass of people able to work, with enough time on their hands and nothing to do will come to? Cause it's strange to consider that a 'good thing'.
Stalling progress in the name of "saving the jerbs" never works in the long run. Better to pull off the band-aid quickly and find solutions that don't require humans to continue doing jobs that can be done by machines.
Bear in mind that "real person", having been relieved of this burden of work, is now free to pursue a life of leisure; realising immediately the long-cherished human dream of robot serfdom, and no doubt sitting in splendour in their fully automated luxury condo exchanging witty banter with a supercilious android butler and enjoying the soccer on TV. Or, well, trying to.
Im a big soccer fan and I agree with you. In sports most of the time something unexpected happens outside of the field: fans doing something interesting, fights, goalkeepers or managers discussing, (check Bielsa) and clearly there would be no AI that can be trained for that.
If you're worried about existing jobs being lost, aren't you also worried about non-existing jobs never having been created?
Imagine we don't have parfocal lenses and you need multiple cameramen with all different fixed focus lenses filming different parts of the scene, or some other contrived example of less technology creating a need for more jobs. That would surely be a better world by your standard. Should we try to eliminate some technology to create extra jobs?
Where is the correct balance? Do we currently have not enough cameraman jobs or too many? It won't be exactly the right amount by coincidence.
I tend to see these solution used where before there wouldn't be anyone to point the camera in the first place.
That said I don't agree. If you can afford the technology for this type of camera and the connectivity to get it online, you can afford to pay someone £20 to point the camera for a couple of hours. It's a great opportunity for people still in school to start, and far more fulfilling than stacking shelves.
Did they though? I'm not a follower of second-tier Scottish football, but were their games normally televised before this? This seems to be a COVID thing, as fans can't attend matches at the moment.
I think the ML specialists often forget there's a gap between pattern matching and solving a problem
The problem here is: locating a ball and moving the camera
If you just try to find a ball on static image by static image you're going to have a bad time. Even worse if you try to find only the ball but without the play contexts (kicking, flying, being held, etc)
Balls "teleport" but not always
Yes, a ball can be on top of a player, but not all the time
So, no, if you didn't see the ball go there, it didn't suddenly appear in the middle of the field (but then again, with some exceptions).
If you just try to find a ball on static image by static image you're going to have a bad time.
This isn't the point you were making, but as an aside, I think converting video into static images is throwing away useful information.
Most video codecs work by encoding the changes between video frames which includes movement information. It seems hard, but worthwhile, to use this information for object recognition.
I just did a search and saw that these techniques go by the name "compressed domain analysis" [1].
Could have been much worse. Imagine the same camera falling in love with the white shirt of a exuberant woman in the public. Focusing and refocusing all the time. That would be glorious meme food. Go home camera, you are drunk.
Can be solved adding a "too-sexy" list. The fake ball was always in the same part of the field. When the problem starts, a human should be allowed to enter a command ordering to ignore the area around coordinates X,Y of the grid. Putting a virtual black rectangle in the area only to be seen by the camera. Coordinates could be passed as arguments. When the real ball enter this area the camera of course will lost it, but will quickly relocate it at the other side so is not much of a problem.
I’d think the camera isn’t good enough than model being wrong. Dude really look like a ball on my phone screen, it’s only contextually clear to me that he’s not it. They’d need a more high resolution or higher frame rate camera.
Or a system with some memory that knows how balls tend to move.
This system, to me, looks like it works on individual video frames (the camera seems to move away whenever the ball is even half-covered by a player’s body, or hard to see looking against the sun). A smarter system would know the ball doesn’t move 20 meters in a single frame, and tends to move faster than other objects on the field. That may not trivial to implement, but, I expect, would lead to a much more robust system.
Or they'll paint their head like a soccer ball to make sure that the updated AI works properly. These sorts of failures demand stress-tests. What if a future team has a soccer-ball-headed mascot?
We can certainly foresee a future, one the uncharitable might call dystopian, in which all the humans have been barcoded with purpose & capability metadata so that AIs can process them more efficiently.
Might be what'll happen. We could've been reaping the benefits of and/or living the dystopia of that today, or even 10 years ago, were the decision made to actually barcode everyone. But we didn't, and what's likely to happen is we'll push the biometrics recognition technologies to such high level, the machines will no longer need the barcodes. Then the dystopia will happen anyway.
Note the irony here: what people don't want is the dystopia, but what's stopping it now is that they find barcoding people creepy. They won't notice the AI tech until it's too late.
On a broader note, this pattern of rounding a square peg to fit if we can't square the round hole - it's what we do. All the time, everywhere. For instance, designing a reliable all-terrain vehicle is something that technologically escapes now even to this day, but thousands of years ago our ancestors figured out that if they flatten and pave the terrain, then a simple wheel would do.
It's ironic, especially when the obvious engineering solution would be to add tracking paint/sigils on the ball... and doubly ironic when the engineering solution would be unlikely to be accepted.
Only ironic to software developers. The engineers who produced the ball understand that changing the properties of the ball in that way would reflect in the aerodynamic properties of the ball, which gets fixed at the start of competition.
It’s not hard to fix your software that you created. It’s much harder to get global consensus on a change that wasn’t required before your buggy code was released into the wild.
They did this for Goal Line Technology (put a chip in the ball).
A few companies actually managed to get a production process that put a chip in the ball without affecting flight characteristics (there are a lot of regulations around this), and these balls were used in competition once (Club World Cup 2012).
Ironically, the most complicated bit is the sensors in the posts.
If someone's day is ruined because the AI camera was following a bald ref, they should probably take a step back and re-evaluate what's actually important in life.
"I was looking forward to watching my favorite local team as an outlet for the massive stress of living through a global pandemic, and instead it kept zooming to the ref's head" is, at the very least, reasonably a massive let down.
Is it, though? These are soccer fans we're talking about. An AI misreporting a loss for a win or a win for a loss could completely change the tone of the post-game riot.
You wouldn't say that is the camera was assisting with a serious surgery or firing a military rocket, it's not okay, we need to make sure the bar is set very high and there needs to be penalties/ fines for mistakes if we are going to jump on the fully autonomous bandwagon.
Those applications already have higher standards for general software reliability, to say nothing of AI. Obviously the bar is different for different applications.
Considering that at least a cameraman has lost his job recently, there's always going to be a social cost to moving to pure tech. It's never just one thing (ie. It's not just a soccer match)
I am not seeing an obvious scenario that makes it switch. I thought maybe it was when the ball "joined" with a player but it does it when its in motion too. That is just completely broken.
We often hear that the AI is more accurate than humans in some image matching, but it also clearly makes mistakes that no human would make. Well here is a great example of such a mistake which a human wouldn't be making.
> I am not seeing an obvious scenario that makes it switch. I thought maybe it was when the ball "joined" with a player but it does it when its in motion too. That is just completely broken.
I can see a pattern. It helps if you think strictly in terms of what visual feature a ball and a bald head have in common: a crescent of glare against the sun.
* The ball is obscured by legs, shadow, or a player's body (this appears to be majority of the occurrences to me) - this one seems to trigger even if it's only out of view for a fraction of a second
* The high kick at 2:07 is similar to the above, except it's the glare from the sun instead of shadow that obscures the ball (ambient brightness matches the crescent instead of the umbra on the ball)
* Most of the rest I can't even find the ball myself, I think it might be too small in the far end of the field to register clearly
.. to the AI. The fundamental problem is that no training set can ever be really complete. Humans have general intelligence to deal with this. ML does not.
I think in this case, the fundamental problem that other replies also stated is the lack of a generality of context. The system was trained to recognize balls, but it had no concept of a person. Train it on balls and people, and I'm sure the performance would improve because you can tell the system to not track things classified as a person. But then your system is also trying to draw boxes around people. So, now maybe your BB (bounding box) for people and ball keep overlapping.
Now, you train it on image segmentation (draw an outline around objects) as opposed to bounding box selection and the performance also improves. The downside is now you require significantly more compute power on hand/lower sampling rate, etc.
I think it is a testament to not just human complexity, but human efficiency.
Which is why some of us avoid calling these systems "AI".
Yes, the term has become accepted, but it's a horribly irresponsible label because it massively misleads laypeople about how these systems work and how trustworthy they are.
I wouldn't say "has become" since the term was already in widspread use in games by the early 90s at least if not earlier. I would guess many people are first exposed to the term in games and I would be surprised if the expectation you describe is actually widespread at all. Games or not, it seems like the first exposure anyone has to anything called AI would cure them of any such misconceptions.
Technically AI is correct, since AI doesn't necessarily refer to human level AGI. Unfortunately it's highly misleading because that distinction seems to be lost on the average person.
Machine learning doesn't seem to carry the same science fiction implications for whatever reason.
Maybe not the model they used but AI in general does. A simple model like P(ball is in position y at time t1 | ball is in position x at time t0) accounts for all of that.
Time: False. Time can be included as an input. Previous system state can also be communicated forward in various ways (ex the LSTM architecture).
Conceptual context: I'll agree, since that seems like it would require general abstract reasoning ability (ie strong AI). Similarly, robust treatment in the general case of physical continuity and cause and effect both seem like special cases of conceptual context to me.
That being said, bear in mind that we already have examples of not so robust treatment for special cases of physical continuity as well as cause and effect. (Example: https://news.developer.nvidia.com/transforming-standard-vide...) Who knows how close (or far) we might be from a general solution?
Well I guess without abstract reasoning ability (and thus no conceptual context in the human sense) "understanding" in the way the word is typically used is presumably impossible.
But I don't see any reason you couldn't train a model to incorporate the underlying assumption that the flow of time is unidirectional. I expect you would just need inputs (ie training data) reflecting that fact coupled with an appropriate loss function.
(Aren't networks that predict future physical states, such as motion, more or less doing this?)
It's crazy thinking about the amount of technology that went into the infamous "Fox Puck" for tracking and display (where they literally cut a puck in half to embed tech and coordinate with an array of receivers in the arena) to what can now be done by the cameras themselves.
It's kind of mystifying why a company would go to the trouble of having AI-based soccer camera to begin with.
I can't imagine that the training and maintenance of such a system could be cheaper than the pay of a single camera person and I don't remember ordinary camera people having much trouble following the ball in soccer.
Which is to say, while current ML/AI may work well for some things, for other things, it will be abandoned as a fad unless there's some fundamental improvement.
Many countries have restrictions on games. No fans allowed and the personal supporting a game has to be reduced to an unsustainable low minimum with a very high-effort hygienic concept.
This may sound strange for americans but is the current norm in most countries. So if you are allowed less and less people as covid-19 gets worse you are in search of solutions. And as there are many camera men to record the game from different angles why not comply with these restrictions by replacing them with ai...
I can imagine it being cheaper if there's a 3rd party who just leases it to you as a push-button system, and they service a bunch of teams. On the other hand it does seem incredibly penny wise and pound foolish of management. Broadcasting is like half of a pro sports team's revenue in a normal, non-lockdown year. Why you would entrust that to an AI just to eliminate a single, semi-skilled laborer is beyond me.
Modern sports do not have just a few cameras. With the tech there can be a hundred perspectives. Soccer used to be a few cams covering the whole field. Ai should be able to predict power and angles of the kick for the most exciting framing making for better experiences.
I thought about that but the video from the article seems to be only a single camera. If there's really only one running, then that seems like an odd choice in and off itself. Perhaps only one malfunctioned because it's closer to that particular lineman.
Reminds me of an incident I read about in Hackers by Steven Levy, where a ball-catching robot in the MIT AI Lab (in the 60s or 70s) mistook Prof. Marvin Minsky's bald head for a ping-pong ball.
All sorts of edge cases here to handle. Its not just a bald head.
What happens if there are multiple balls? What happens if the ball is redesigned with a different pattern? What happens if someone wears a shirt with a picture of a ball?
Does it make sense to center the camera on the ball? Does it make sense to also show where the ball can go?
I wonder how it's handled by people currently? I suppose it can be handled with more training data/labelling instead of custom coding.
We are looking are replacing the camera person job with AI just as we replaced other people's jobs with programming earlier and just as we replaced other people's jobs with AI more recently.
I worked at a company doing football (and footballer) tracking for ecery premier league game for a couple of years. Every other week there was a case we hadnt covered (and fixed for the next week);
- white ball swapped for orange ball
- player swapped shirt, new number. (We tracked players by number on their back)
- player swapped shirt to a number someone else had
- player swapped shirt to no number
- player shirt covered entirely in mud
- player swaps shirt where number is white instead of black
- white ball left on sideline (2 balls)
- crowd releases 50 white balloons onto the pitch
- ref swapped shirt colour
- sideline advert has a filled in white O on it (moving around)
There were loads more, but that was off the top of my head. I dont think we ever mistake a head for a ball though! We worked out likely balls from movement (syncing cameras with bounces)
I wonder what went wrong here. Perhaps it wasn't trained for these lighting conditions? I can't tell if they use multiple cameras around the stadium or not.
The thing about AI is that you never truly know what it actually looks looks for. This reminds me of the tanks neural net story: https://www.gwern.net/Tanks
It used to be that we had to chose between models that were 'powerful' (complex neural networks) and those that were 'interpretable' (linear régression, trees). In the last years a lot of research has come out.
We have since then developed ways of analysing the inner workings of neural networks.
Do we 'know' in an absolute way why a network thinks something? Not yet. But neither do we know that for humans. We just have a huge amount of experience with their failure modes and we work around them (see aviation).
That's true, I know that you can view what "excites" specific neurons. I still think the is a problem: this only shows whenenever you give an image what it uses from that image to classify the object, but when a new, unseen image is given it's really hard to tell beforehand what the network will actually look like and which pixels would be considered. Yes, you could look at the features and see which patterns appear there, but for large networks those are thousands or millions, so it's still hard to tell what the result will be, unless you actually use the new image as an input for the network.
Lighting conditions was my first thought, the camera aimed almost at the sun presents some challenging contrast that may not have been trained properly.
And I imagine one day you can rent a camera and have your kid's little league matches presented like a division 1.
(Or maybe you won't even need to rent the camera, if you use the free version supplied by Google which automatically adds personalized sponsorship visuals)
(Or the Facebook version, automatically posting 'hilarious' and embarrassing snippets designed to maximize user engagement)
Why are so cheap to replace a cameraman by an AI?
They could leave the camera control from remote to someone in a low income country, would make a better job for peanuts.
While it's hilarious, I wonder why they didn't keep a human backup available just in case. Anyway, I think the solution is clear: the ref must be replaced by AI.
The company StatSheet (now Automated Insights) has been using algorithms to produce sports journalism and business reports since 2007. It basically takes the score and background information and turns it into a paragraph or two. Earnings reports stories are similar. The results are better than you'd expect.
Those articles are so annoying to stumble on. They rank very highly in search results because the google crawlers like them but they are obviously written by some lame algorithm and are a waste of time to skim. I hate those types of article so much
Background: I was one of the original engineers on the Goal Line Technology system now used by the vast majority of professional leagues.
Heads are surprisingly ball shaped from a a lot of camera angles, and the male balding pattern conspires to give a good impression of different football graphical designs.
When we were developing the system, we thought we had ball detection pretty nailed, and we never had false positives from any of the heads of engineers on the team. Then, our boss came to test the system ... As far as our algorithms were concerned, his head was exactly a football, just as in the article! Highly embarrassing, but everyone saw the funny side. It gave us better data and inspired a few more robustness checks that ended up being crucial later on! :)