I worked for a large e-commerce company. I wanted to investigate putting all our support data into Watson and see what sort of recommendations it could provide, maybe a sort of auto-suggestion to help our customers. Three really funny points stand out from the experience:
1) To apply for Watson access you needed to show C-level approval, so our CEO put his name and phone number on the application (trying Watson was somewhat his idea). A few months later, an IBM marketing team called HIS CELL and asked for ME. Imagine how it felt to have the CEO walk up to me, deadpan hand me his personal iphone and say "It's for you."..
2) They told me they'd help me with the support data idea, and every meeting we set up they tried to pitch "what if we put Watson on all of your customer's storefronts, we could add a 'powered by watson' banner on every page, and you give us a cut of GMV?". I pivoted them to our plugin framework and told them to build it themselves.
3) To demo the technology, the first step was to buy a $250k server from IBM. To demo it.
IBM is famous for charging people for the privilege of talking to them, even if you're trying to sell them something.
This strategy makes sense if you consider that even in it's heyday Watson was 95% data science consulting firm and 5% actual valuable technology.
I really think Watson is one of the biggest tech marketing bamboozles of the 21st century. Through Jeopardy they really had a segment of the business world and the general public convinced that they had cracked AI, but behind the scenes it was all one-off custom solutions under one trademark.
The company I worked for used some Oracle tech and I was trying to get some high level information about a product but their website kept requesting my e-mail just to show me some documentation.
Once I provided them with my e-mail, I started receiving "You must take us to your leader" messages in a tone as if I was their employee and they were commanding me to take them to my CEO. I can't imagine myself chasing the CEO in the building because some sales people in Oracle told me to do so :)
To be fair, after being in meetings with theirs sales engineers(who wore the best shirts I've ever seen) a few times I grew to respect their stubbornness and the way they structured their corporate machine. It's a valuable lesson to have an exposure to corporate dealings I believe, before that I used to do freelance stuff and had no idea how a simple webpage can cost millions and why a large corporation won't buy that easily from a small company with similar or better product at the fraction of the cost.
>1) To apply for Watson access you needed to show C-level approval, so our CEO put his name and phone number on the application (trying Watson was somewhat his idea). A few months later, an IBM marketing team called HIS CELL and asked for ME. Imagine how it felt to have the CEO walk up to me, deadpan hand me his personal iphone and say "It's for you."..
This sounds like the biggest power move you could ever pull.
Everything about this sounds like they hired inexperienced sales people and promised them huge payouts if they could close certain deals. The kinds of sales people who won’t hesitate to burn a lot of customer relationships to the ground as long as they could close a few big deals for themselves.
Wow, I can't believe how accurate this story is, same thing happened to me I think summer 2016 but I thought it was because our execs were idiots not that IBM would treat every company like that... CTO calling me to his office to talk to IBM on their personal phone, he was the only one who wanted Watson (this was a healthcare company, I was VP of Eng). And yes, they were obsessed with putting their logo everywhere, and as soon as we heard it was so expensive, we had to tell our CTO to chill, we stopped, cause you know you can hire at least 2 devs for that money.
I concur with the experience of dealing with IBM sales.
Years ago my client wanted me to checkout out IBM mobile app builder - Worklight, The pricing wasn't available on website and so I had to contact them. Soon I was reached out by their sales department and before I could get any details on the pricing I was speaking with VP of sales.
If I remember it correctly, It was priced ~>150K USD and even after repeatedly telling the VP that I was just exploring what their product was, The VP told me he was ready to fly in to my office the same week to 'talk further'. It was weird to end that conversation with them and my client had a good laugh when I shared the experience.
I don't get why IBM finds it hard to deal with Startups, All other behemoths (MS/Amazon/Google etc.) have successfully created products for Startups often by offering generous freebies and 'Pay as you go' plans . Where as IBM still thinks they can slap Fortune 500 pricing on entry-level startups.
I guess as long as those Fortune 500 companies & even Govt. fall for that Watson type products, They don't have a reason to change.
> an IBM marketing team called HIS CELL and asked for ME.
What the fuck is this? A name/email for your company when trying something out is so that you can keep track of any support requests we need, not for you to sell shit to me.
In my experience this seems to be a theme with very senior executives - they are very often interested in snake oil and can’t seem to discern snake oil from real medicine.
This reminded me of my experience with them a few years back with MQTT. They were pushing their Bluemix/cloud hard and I just wanted to test it out. Never again.
It used to be you can't get fired for hiring big blue. In the end it was always a lot of sales /pre sales folks, and a lot of substandard subcontractors milking the golden cow. I don't miss managing their implementations/deliveries at all.
I worked at IBM Watson as one of the early engineers when they first started commercializing the product. It was a fucking joke - Ginni Rometty would go up on stage and said that Watson can help diagnose cancer from CT scans and we would just look at each other and be like "Dude, Watson is just a glorified Lucene index, wtf is she talking about." They started selling Watson as the end-all for everything from cancer diagnosis to customer service chat - they even had a stupid Watson Chef thing at SXSW one year - but none of that used the original Watson codebase - it was all built from the ground up and lots of it was just simple logistic regression
As someone doing a CS degree now, I seem to be the only one who doesn't want to have anything on my resume to do with "AI", blockchain, ML, NFT, chatbots, etc... all I see is overhyped product after product, one-size-fits all solutions that frustrate customers and create problems for humans to clean up, hugely valued companies that have very little real improvement over conventional technology, etc.
An "AI chatbot" is far inferior to a real user interface. A real user interface allows discoverability (looking through menus to notice functions that may be useful later), experimentation, and puts the user in control of the program.
For example, my bank apparently only supports viewing the reason for card declines through the chatbot--something I never knew, because I took the time to go through the menus when I first got the app and learn what functions existed.
Since you're still a student, I feel like maybe I can offer some advice:
First, I think you're getting the wrong lesson from this. The key takeaway is stay to away from IBM. Almost everyone in the field has known that Watson is a bunch of marketing hype since day one. It's no surprise that Watson Health didn't work out. That doesn't mean that everything is overhyped, and it's important to develop a good sense for what is and what isn't when deciding where to work.
Second, every technology looks stupid when it's new. Airplanes, computers, the Internet, mobile phones -- they all had drawbacks that made them vastly inferior to the alternatives for most tasks for the first years/decades of their existence. It takes a lot of iteration and improvement to make something that's useful for everyone. Chatbots will probably get there some day - but it will take some big improvements in NLP. Perhaps this is the time to be working on them since we have a good idea of what we'd like them to do, and we just need to solve the challenges to get there.
Finally, realize that you're not the typical user. I doubt if very many people take the time to go through the menus like you did.
If you want to do something less buzzwordy with lots of real-life applications, look into distributed systems. Try running an Apache big data project yourself and write some programs/queries for it, try making a change to the project to do something cool. My suggestion to check out an Apache big data project is just that it gives you a good place to learn, not so you can be a "hadoop specialist" or anything like that.
There is way more real world usage of the distributed systems concepts and skills you'd learn there (especially in large tech companies) than any other flavor of the month. While ML is also commonly used in the industry, the signal:noise is really bad, because a lot of its uses are superfluous buzzword-driven development. However, many many companies rely on distributed systems to be able to operate at scale.
+ People care about what other people are talking about. They like to fit in, like they're part of the cutting-edge.
+ Less experienced people have less…experience with the downsides of what they're reading about.
+ CS is no longer mostly people who care about computer science, in the same way that economics isn't only for people who want the understand economics. Tech salaries — especially engineers' — are super high, like investment bankers. So people study the respective fields as a means to an end.
+ Twitter is driven by VCs, tech press, and people marketing themselves. They're work themselves into circular frenzies all the time. Little of it matters. Almost none of them have any record of predicting what's next and a long, long record of being wrong. This is true of most people! But these are the spaces many people look to to see what is "wanted".
You seem to have good instincts. Don't be distracted by peers who work at "hot" startups or big named companies. Find something you believe should actually exist in the world and work on that. It will give you an intrinsic reward that money can't buy and status can't fill.
Sorry but you're wrong, all those buzzwords have their merit and there are real impressive and innovative companies or projects built on those hypes, not all is "worthless" or a "scam". Don't let your ignorance blur your mind, learn about them, use them, have your own ideas cause this post sounds like you've been reading way too much HN.
> An "AI chatbot" is far inferior to a real user interface.
That's because you're lucky. You have good enough sight and you can use your hands. Unfortunately, that's not the case for millions of people especially since our populations are aging more.
Searching movies to watch with Alexa works better than with the clumsy TV keyboard. I think for some applications or some people chatbots may be better than traditional UIs. Also they should be getting better over time.
Most solutions to real-world problems offer tons of deliciously complicated CS issues to chew through.
Just find problems to solve that are interesting to you, hype is irrelevant in finding a worthwhile thing to do (i.e. that a thing is hyped does not make it worse than something else - it does not make it better, either, though).
I mean, you're making decisions based on your external perceptions of media stories which is generally not a good way to make decisions. Either for or against something.
ML and AI is used in an absurd number of places both small and large. Most aren't ones that make news stories because they just optimize an existing experience. Sometime too much but that's more of a fault of capitalism and definitely improves the profits of the parent company. Search engines including ones on websites (ie: media, ecommerce, etc.) are powered by ML at any larger company. Recommendations systems are powered by ML and exist on most media and ecommerce sites. Fraud detection of various sorts when it comes to payments and accounts. Even mundane things like internal processes within companies like predicting which columns in a data upload correspond to what.
You aren't entirely wrong. In any hype cycle a lot of money gets thrown at buzzwords, some money "smarter" than others, and a lot of people do things with that money just because money "has to" be spent.
On the cynical flipside though, you can't entirely keep "buzzwords" off your resume simply because recruiters will always squint and "find them" because there's money involved if they do. You are almost always going to get recruiters for whatever the day's buzzword is.
I have a Masters degree and Python experience, and to most AI/ML recruiters that looks like "possible AI/ML researcher". They aren't entirely wrong, I did study AI/ML in grad school. They aren't very right either, because I learned enough to be "dangerous" and came out of grad school a massive AI/ML cynic. (That the field is mostly just Sparkling Statistics and people in general are bad at statistics and easily wowed by Sparkling Statistics. That this isn't the first hype cycle in the field, and it isn't likely to be the last either; we've not really improved on the research of the AI boom in the 1960s/1970s nor the other big AI boom of the late 1980s. They had chat bots then, they had most of the algorithms figured out, including their weaknesses. This boom we've just massively increased Garbage In, Garbage Out to those algorithms and given ourselves enough blinders to pretend that everything is still under control. We haven't solved their weaknesses. We haven't actually made major conceptual leaps. We just have a lot more data and a lot more speed. Which is something the 1960s researchers both predicted and warned us about.)
So yeah, your cynicism here is very valid, at least in my opinion. There's not a lot you can do about it, especially if your aim is to outright avoid recruiters chasing you for whatever VC money is getting thrown at them. I don't have a lot of other advice here other than the only thing you can really do with such cynicism is to apply it as best as you can to improving the things that you have the power to improve.
Ironically, the main project I work on today involves a lot of AI/ML and I'm known as a bit of a wet blanket on the team trying to temper expectations and trying to keep us from doing the worst mistakes ("confidence" values do not mean what people think they mean and showing them in any UI anywhere, especially under the name "confidence" is a massive mistake; machines don't have the "ego" for "confidence", it's a poorly applied statistical term of art that people use to badly anthropomorphize the statistical models). I don't win every battle and I do my best to make it clear I come from a point of pragmatism. It's a hard balance to keep.
I really expected that we'd see a change in my lifetime, that GPs in particular would be replaced by a lower-cost Watson descendant, with there being some other role for patient interaction, wet work, and data entry (perhaps just nurses).
My mom worked for a GP for about 20 years, and it seemed to me that most of what made that guy a doctor was bedside manner + being able to remember a lot of things. But GPs often make astounding amounts of money while leaning heavily on their staff to actually handle patients and keep the business running. I thought it could help drugs get a little cheaper too, because there wouldn't be any point in the pharma companies sending out salespeople to do lunch seminars to convince the GPs to prescribe this or that drug (this still happens).
Maybe this will still happen, but it doesn't seem imminent anymore.
They are a nightmare. I was part of a huge project to replace a large part of a telecom operators infrastructure. IBM global services ran the operators IT outsourced. The project failed after a year because of them. It was the 3rd such project to fail. The company in question couldn’t bring themselves to realise it had been their outsourced operators fault once again. Even though they had again lost the bid to do said work.
PwC/Accenture were worse. Hire arts graduates because they got a degree from a good university, chuck them on a 2 month coding/consulting course. Happy days $$$
I recently left Red Hat for greener pastures. From where I sat, IBM was slowly turning toward wisdom again, having been run aground by its previous few CEOs. I was skeptical when IBM bought Red Hat, but after several years of not screwing it up, I'm pretty hopeful. Now, Krishna is working on streamlining the business and making the rest of IBM more like Red Hat. Splitting off the low performing Kyndryl, and selling Watson, are part of this by cutting obsolete sectors; focusing on getting Red Hat the resources it needs to rapidly accelerate, and on building the talent pool by hiring more junior engineers, are the positive changes working to turn IBM back into a powerhouse.
I could be mistaken because it’s been a while, but I read that Watson’s diagnostic capabilities turned out to be mostly marketing and that eventually IBM ended up hiring teams of doctors to process the diagnosis requests that were coming into Watson.
Watson became a marketing term after the company spent hundreds of millions to brand Watson to be synonymous with AI. The term Watson then got appended to existing businesses as it allowed them all to benefit from the brand equity and Watson ads. This unfortunately happened even if there wasn’t any AI capabilities, so it eventually backfired.
Watson Health seems to have been focused on selling the narrative of AI in healthcare, even though the technology wasn’t there.
The divestiture is only for IP also, and it seems most people in the group will be laid off.
Sounds like what happened at Theranos! I read the analysis Watson was generating was ultimately just ignored by doctors because it came to inaccurate conclusions, so that makes sense.
I have no idea what they got for the money they spent. Merge Healthcare was the most miserable work experience I have ever had. They had patents, I guess, but the actual technology was garbage. And the owner was…a piece of work, let's say that.
This is wholly unsurprising. IBM's big play was to integrate data science methods into the workflow. But they approached it from a "we will replace your labor costs" versus "we will augment your labor costs." Besides their AI models being fairly poor in quality, technology doesn't replace people very well where extrapolation is needed. So the quality of service Watson brought was significantly lower than what these businesses offered prior to adoption. So keeping Watson became an exercise in how well the business understands sunk costs and switching costs.
The technology they sent on Jeopard answered a question, I think, that was looking for the name of a specific king of Egypt with "What are trousers?".
Seems pretty obvious that anything that would do that is not human-like intelligence, and probably the search results should be taken with a handful of salt even if they stuck some impressive natural language generation after it.
Anecdotally, the main business value I've seen from ML/AI tech has been in cases where
1. A basic solution shipped and made a ton of money e.g. Ads, Search, recommendations etc.
2. It is financially feasible to have a dedicated team(s) make small incremental progress on these solutions. Even very small gains are beneficial.
3. The business perceives a threat if they fall behind in this area.
The thing is that the gains on the basic solution (heuristics, off the shelf pre-trained CV model, open voice recognition) are pretty small, and if the threat of others making progress goes away - the inferred value of further investment will probably vanish as well.
Other applications which put the AI in the driver's seat (sometimes literally) seem far from production - or if they do work, then they work reasonably well using an alternate approach from what you might expect.
Watson was mostly data science powered consulting pretending to have/be a product. They played heavily on the Jeopardy thing from a marking standpoint but what they were actually trying to sell was a hot mess.
I do consider this a good milestone in getting past the latest “AI” hype cycle and focusing on what actually works in that space. Sat through too many meetings with non-technical execs saying “what if we apply Watson here?”. The likes of McKinsey were pushing this stuff hard in what they were whispering into executives ears.
There should be a way to bet (short) against “projects” or products, not the whole company. When they hyped about Watson Health, I “knew” it will fail.
It will be interesting to see if self driving cars and the way they've been rushed to market with the same brute force marketing will meet a similar fate.
A friend of mine wanted to show off his Tesla by making it come to the front of the restaurant from where he parked it. Like he hit a button and it was to drive up. It got stuck somehow and was diagonal in the row. He was like “ehh sometimes it doesn’t work.”
AI in general is very over stated. When it works it’s great, when it doesn’t (which is often) then you lose all trust in it.
This is kind of funny to see after reading the Tech Review's piece on Watson Health from 4 years ago (https://www.technologyreview.com/2017/06/27/4462/a-reality-c...). They were wrong on the outcome but right on the diagnosis - that the marketing got way ahead of the engineering.
It was acquired by IBM for use in Watson back in 2015. Blekko was an interesting attempt at addressing search engine problems using a thing called "slashtags" to better categorize searches.
Many people shame the startups for fake it until you make it, but IBM with Watson and Watson Health did exactly that for years and 'serious' analysts were predicting how their healthcare AI efforts will increase their revenue.
[+] [-] tekstar|4 years ago|reply
1) To apply for Watson access you needed to show C-level approval, so our CEO put his name and phone number on the application (trying Watson was somewhat his idea). A few months later, an IBM marketing team called HIS CELL and asked for ME. Imagine how it felt to have the CEO walk up to me, deadpan hand me his personal iphone and say "It's for you."..
2) They told me they'd help me with the support data idea, and every meeting we set up they tried to pitch "what if we put Watson on all of your customer's storefronts, we could add a 'powered by watson' banner on every page, and you give us a cut of GMV?". I pivoted them to our plugin framework and told them to build it themselves.
3) To demo the technology, the first step was to buy a $250k server from IBM. To demo it.
Big LOLs all around, never trust big blue.
[+] [-] stathibus|4 years ago|reply
This strategy makes sense if you consider that even in it's heyday Watson was 95% data science consulting firm and 5% actual valuable technology.
I really think Watson is one of the biggest tech marketing bamboozles of the 21st century. Through Jeopardy they really had a segment of the business world and the general public convinced that they had cracked AI, but behind the scenes it was all one-off custom solutions under one trademark.
[+] [-] mrtksn|4 years ago|reply
Once I provided them with my e-mail, I started receiving "You must take us to your leader" messages in a tone as if I was their employee and they were commanding me to take them to my CEO. I can't imagine myself chasing the CEO in the building because some sales people in Oracle told me to do so :)
To be fair, after being in meetings with theirs sales engineers(who wore the best shirts I've ever seen) a few times I grew to respect their stubbornness and the way they structured their corporate machine. It's a valuable lesson to have an exposure to corporate dealings I believe, before that I used to do freelance stuff and had no idea how a simple webpage can cost millions and why a large corporation won't buy that easily from a small company with similar or better product at the fraction of the cost.
[+] [-] Traster|4 years ago|reply
This sounds like the biggest power move you could ever pull.
[+] [-] PragmaticPulp|4 years ago|reply
[+] [-] lifewallet_dev|4 years ago|reply
[+] [-] Abishek_Muthian|4 years ago|reply
Years ago my client wanted me to checkout out IBM mobile app builder - Worklight, The pricing wasn't available on website and so I had to contact them. Soon I was reached out by their sales department and before I could get any details on the pricing I was speaking with VP of sales.
If I remember it correctly, It was priced ~>150K USD and even after repeatedly telling the VP that I was just exploring what their product was, The VP told me he was ready to fly in to my office the same week to 'talk further'. It was weird to end that conversation with them and my client had a good laugh when I shared the experience.
I don't get why IBM finds it hard to deal with Startups, All other behemoths (MS/Amazon/Google etc.) have successfully created products for Startups often by offering generous freebies and 'Pay as you go' plans . Where as IBM still thinks they can slap Fortune 500 pricing on entry-level startups.
I guess as long as those Fortune 500 companies & even Govt. fall for that Watson type products, They don't have a reason to change.
[+] [-] WrtCdEvrydy|4 years ago|reply
What the fuck is this? A name/email for your company when trying something out is so that you can keep track of any support requests we need, not for you to sell shit to me.
[+] [-] rubyfan|4 years ago|reply
[+] [-] lvl100|4 years ago|reply
[+] [-] vvram|4 years ago|reply
[+] [-] pettycashstash2|4 years ago|reply
[+] [-] formeribmer|4 years ago|reply
[+] [-] morpheuskafka|4 years ago|reply
An "AI chatbot" is far inferior to a real user interface. A real user interface allows discoverability (looking through menus to notice functions that may be useful later), experimentation, and puts the user in control of the program.
For example, my bank apparently only supports viewing the reason for card declines through the chatbot--something I never knew, because I took the time to go through the menus when I first got the app and learn what functions existed.
[+] [-] jonas21|4 years ago|reply
First, I think you're getting the wrong lesson from this. The key takeaway is stay to away from IBM. Almost everyone in the field has known that Watson is a bunch of marketing hype since day one. It's no surprise that Watson Health didn't work out. That doesn't mean that everything is overhyped, and it's important to develop a good sense for what is and what isn't when deciding where to work.
Second, every technology looks stupid when it's new. Airplanes, computers, the Internet, mobile phones -- they all had drawbacks that made them vastly inferior to the alternatives for most tasks for the first years/decades of their existence. It takes a lot of iteration and improvement to make something that's useful for everyone. Chatbots will probably get there some day - but it will take some big improvements in NLP. Perhaps this is the time to be working on them since we have a good idea of what we'd like them to do, and we just need to solve the challenges to get there.
Finally, realize that you're not the typical user. I doubt if very many people take the time to go through the menus like you did.
[+] [-] opportune|4 years ago|reply
There is way more real world usage of the distributed systems concepts and skills you'd learn there (especially in large tech companies) than any other flavor of the month. While ML is also commonly used in the industry, the signal:noise is really bad, because a lot of its uses are superfluous buzzword-driven development. However, many many companies rely on distributed systems to be able to operate at scale.
[+] [-] tyre|4 years ago|reply
+ People care about what other people are talking about. They like to fit in, like they're part of the cutting-edge.
+ Less experienced people have less…experience with the downsides of what they're reading about.
+ CS is no longer mostly people who care about computer science, in the same way that economics isn't only for people who want the understand economics. Tech salaries — especially engineers' — are super high, like investment bankers. So people study the respective fields as a means to an end.
+ Twitter is driven by VCs, tech press, and people marketing themselves. They're work themselves into circular frenzies all the time. Little of it matters. Almost none of them have any record of predicting what's next and a long, long record of being wrong. This is true of most people! But these are the spaces many people look to to see what is "wanted".
You seem to have good instincts. Don't be distracted by peers who work at "hot" startups or big named companies. Find something you believe should actually exist in the world and work on that. It will give you an intrinsic reward that money can't buy and status can't fill.
[+] [-] cinntaile|4 years ago|reply
Joining the hypetrain is a great way to get a bigger budget to play with.
AI chatbots are all about saving money and hiding the real customer service as much as possible, it's not about creating a nice experience.
[+] [-] lifewallet_dev|4 years ago|reply
[+] [-] chaostheory|4 years ago|reply
That's because you're lucky. You have good enough sight and you can use your hands. Unfortunately, that's not the case for millions of people especially since our populations are aging more.
[+] [-] pyuser583|4 years ago|reply
I once worked for a company an “AI” company that was business logic, with humans handling the hard cases. They screamed “AI! AI!”
They had a really really really good product. They’re quite successful and good at what they do. The customers are happy. They’re makings money.
So why bragging about non-existent AI?
[+] [-] kkjjkgjjgg|4 years ago|reply
[+] [-] fsloth|4 years ago|reply
Just find problems to solve that are interesting to you, hype is irrelevant in finding a worthwhile thing to do (i.e. that a thing is hyped does not make it worse than something else - it does not make it better, either, though).
[+] [-] edgyquant|4 years ago|reply
[+] [-] torbTurret|4 years ago|reply
That’s great but not representative of average customers at all.
[+] [-] marcinzm|4 years ago|reply
ML and AI is used in an absurd number of places both small and large. Most aren't ones that make news stories because they just optimize an existing experience. Sometime too much but that's more of a fault of capitalism and definitely improves the profits of the parent company. Search engines including ones on websites (ie: media, ecommerce, etc.) are powered by ML at any larger company. Recommendations systems are powered by ML and exist on most media and ecommerce sites. Fraud detection of various sorts when it comes to payments and accounts. Even mundane things like internal processes within companies like predicting which columns in a data upload correspond to what.
[+] [-] cuteboy19|4 years ago|reply
[+] [-] WorldMaker|4 years ago|reply
On the cynical flipside though, you can't entirely keep "buzzwords" off your resume simply because recruiters will always squint and "find them" because there's money involved if they do. You are almost always going to get recruiters for whatever the day's buzzword is.
I have a Masters degree and Python experience, and to most AI/ML recruiters that looks like "possible AI/ML researcher". They aren't entirely wrong, I did study AI/ML in grad school. They aren't very right either, because I learned enough to be "dangerous" and came out of grad school a massive AI/ML cynic. (That the field is mostly just Sparkling Statistics and people in general are bad at statistics and easily wowed by Sparkling Statistics. That this isn't the first hype cycle in the field, and it isn't likely to be the last either; we've not really improved on the research of the AI boom in the 1960s/1970s nor the other big AI boom of the late 1980s. They had chat bots then, they had most of the algorithms figured out, including their weaknesses. This boom we've just massively increased Garbage In, Garbage Out to those algorithms and given ourselves enough blinders to pretend that everything is still under control. We haven't solved their weaknesses. We haven't actually made major conceptual leaps. We just have a lot more data and a lot more speed. Which is something the 1960s researchers both predicted and warned us about.)
So yeah, your cynicism here is very valid, at least in my opinion. There's not a lot you can do about it, especially if your aim is to outright avoid recruiters chasing you for whatever VC money is getting thrown at them. I don't have a lot of other advice here other than the only thing you can really do with such cynicism is to apply it as best as you can to improving the things that you have the power to improve.
Ironically, the main project I work on today involves a lot of AI/ML and I'm known as a bit of a wet blanket on the team trying to temper expectations and trying to keep us from doing the worst mistakes ("confidence" values do not mean what people think they mean and showing them in any UI anywhere, especially under the name "confidence" is a massive mistake; machines don't have the "ego" for "confidence", it's a poorly applied statistical term of art that people use to badly anthropomorphize the statistical models). I don't win every battle and I do my best to make it clear I come from a point of pragmatism. It's a hard balance to keep.
[+] [-] alar44|4 years ago|reply
[deleted]
[+] [-] saxonww|4 years ago|reply
My mom worked for a GP for about 20 years, and it seemed to me that most of what made that guy a doctor was bedside manner + being able to remember a lot of things. But GPs often make astounding amounts of money while leaning heavily on their staff to actually handle patients and keep the business running. I thought it could help drugs get a little cheaper too, because there wouldn't be any point in the pharma companies sending out salespeople to do lunch seminars to convince the GPs to prescribe this or that drug (this still happens).
Maybe this will still happen, but it doesn't seem imminent anymore.
[+] [-] trollied|4 years ago|reply
PwC/Accenture were worse. Hire arts graduates because they got a degree from a good university, chuck them on a 2 month coding/consulting course. Happy days $$$
[+] [-] cantrememberpw8|4 years ago|reply
I recently left Red Hat for greener pastures. From where I sat, IBM was slowly turning toward wisdom again, having been run aground by its previous few CEOs. I was skeptical when IBM bought Red Hat, but after several years of not screwing it up, I'm pretty hopeful. Now, Krishna is working on streamlining the business and making the rest of IBM more like Red Hat. Splitting off the low performing Kyndryl, and selling Watson, are part of this by cutting obsolete sectors; focusing on getting Red Hat the resources it needs to rapidly accelerate, and on building the talent pool by hiring more junior engineers, are the positive changes working to turn IBM back into a powerhouse.
[+] [-] user3939382|4 years ago|reply
[+] [-] absoluteharam|4 years ago|reply
Watson Health seems to have been focused on selling the narrative of AI in healthcare, even though the technology wasn’t there.
The divestiture is only for IP also, and it seems most people in the group will be laid off.
[+] [-] pram|4 years ago|reply
[+] [-] perardi|4 years ago|reply
https://www.reuters.com/article/us-merge-healthcare-m-a-ibm/...
I have no idea what they got for the money they spent. Merge Healthcare was the most miserable work experience I have ever had. They had patents, I guess, but the actual technology was garbage. And the owner was…a piece of work, let's say that.
https://www.npr.org/2018/12/12/675961765/tribune-tronc-and-b...
[+] [-] lvl100|4 years ago|reply
[+] [-] jackcosgrove|4 years ago|reply
Was it the orange ties?
[+] [-] tomrod|4 years ago|reply
[+] [-] sklargh|4 years ago|reply
[+] [-] rvense|4 years ago|reply
Seems pretty obvious that anything that would do that is not human-like intelligence, and probably the search results should be taken with a handful of salt even if they stuck some impressive natural language generation after it.
[+] [-] civilized|4 years ago|reply
IBM created a machine that could win at Jeopardy, not a universal expert or problem solver.
Say what you want about Google, but they didn't claim to solve any practical problems by creating AlphaZero.
[+] [-] lumost|4 years ago|reply
1. A basic solution shipped and made a ton of money e.g. Ads, Search, recommendations etc.
2. It is financially feasible to have a dedicated team(s) make small incremental progress on these solutions. Even very small gains are beneficial.
3. The business perceives a threat if they fall behind in this area.
The thing is that the gains on the basic solution (heuristics, off the shelf pre-trained CV model, open voice recognition) are pretty small, and if the threat of others making progress goes away - the inferred value of further investment will probably vanish as well.
Other applications which put the AI in the driver's seat (sometimes literally) seem far from production - or if they do work, then they work reasonably well using an alternate approach from what you might expect.
[+] [-] JCM9|4 years ago|reply
I do consider this a good milestone in getting past the latest “AI” hype cycle and focusing on what actually works in that space. Sat through too many meetings with non-technical execs saying “what if we apply Watson here?”. The likes of McKinsey were pushing this stuff hard in what they were whispering into executives ears.
[+] [-] mromanuk|4 years ago|reply
[+] [-] cube00|4 years ago|reply
[+] [-] seibelj|4 years ago|reply
AI in general is very over stated. When it works it’s great, when it doesn’t (which is often) then you lose all trust in it.
[+] [-] daniel-thompson|4 years ago|reply
[+] [-] crispyambulance|4 years ago|reply
It was acquired by IBM for use in Watson back in 2015. Blekko was an interesting attempt at addressing search engine problems using a thing called "slashtags" to better categorize searches.
[+] [-] avrionov|4 years ago|reply
Compare their results with Tesla.