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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..

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.

opportune 5y ago
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.
- rlayton2 5y ago
  Absolutely. I often joke that my work as a data scientist is mostly creating bar graphs for people. The actual analysis is often reasonably simple, its the aggregating of the data that is hard (its messy, its not all in the one spot and there is lots of it). So start with querying your big data to say what the top three event types are. Then slowly crank up the analysis complexity, but not too much. The data engineering has lots of scope for real solid and obvious applications.
- lifewallet_dev 5y ago
  Oh, so don't do current buzzwords but past ones like Big Data are okay. And if you wanna learn about distributed systems nothing better than Bitcoin or any cryptocurrency based on a P2P protocol.
  - lcw 5y ago
    I agree that they are novel and interesting to learn, but practically speaking, the person's point, is they are over hyped, and honestly since most use cases popping up aren't decentralized or are decentralized, but being regulated by a centralized party, like a government, it seems that they are the most inefficient way to run a distributed system.
  - opportune 5y ago
    Big data tools are just one example of distributed systems. I suggested looking into them because there are a lot of open source ones you can play with, not because I think big data isn't a buzzword (though Spark is definitely used a lot in industry). Crypto is of course a distributed system too (at least, many are) but in practice it's a bit different than anything you'd see in industry because it's trustless.
cinntaile 5y ago
It's all about incentives. 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.
kkjjkgjjgg 5y ago
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.
alar44 5y ago
tyre 5y ago
A few reasons for this: + 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.
- ravi-delia 5y ago
  > 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. That's the main reason I decided against a CS major even though I love the subject. It's just disheartening listening to discount business majors butcher even simple technical topics. The pure math track actually has more than a few people in the same situation, so I wound up meeting some enthusiasts anyway.
  - uejfiweun 5y ago
    These people may be majoring in CS, but there's really no way around it in the end - to be successful in this field, you have to really love computers. Unless you're a certifiable genius, part of the challenge of this field is just spending a ton of time in front of the computer, learning all the little things. This is a lifelong journey, one that's impossible to complete, and one you can't fake or BS your way through. I don't think CS is a bad major, it's still extremely marketable, but you just have to recognize that this is just one piece of the puzzle for the top jobs, and the other piece is technical prowess, a skill you really have to grind to get good at.
lifewallet_dev 5y ago
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.
jonas21 5y ago
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.
- wsmhy2011 5y ago
  completely agree
- Sebb767 5y ago
  > Chatbots will probably get there some day - but it will take some big improvements in NLP. They write copied platitudes, they will follow their checklist no matter what, they will usually not help at all except for wasting time and they hardly speak your language. Honestly, they emulate first level support quite well already.
- smsm42 5y ago
  I don't think NLP is the problem here. Even if you crack NLP perfectly and would somehow be able to build a semantic map, or whatever your technology would use, of the user request with perfect fidelity, there's still the question of actually understanding what it's about. Perfect NLP would help in very limited number of situation where the meaning is clear - i.e. doing some pre-defined action, like closing an account or telling the balance. But somebody comes with a request like "my dog ate my credit card, so I by mistake used my debit one and it resulted in overdraft and penalty change, can I have that reversed and get a new one?" and I'm pretty sure chatbot won't be able to handle it, NLP or not.
edgyquant 5y ago
The only reason I care to have to AI or blockchain on my resume is because both are interesting to work with.
cuteboy19 5y ago
A chatbot should be a search engine in disguise, with more focus on context. Anything else is a downgrade from a normal web interface
fsloth 5y ago
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).
marcinzm 5y ago
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.
chaostheory 5y ago
> 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.
- rprospero 5y ago
  I'm curious about your response here, as I'm certain that I'm missing something. I have cerebral palsy, which comes with issues using my hands. I've also had noticeable degradation with age of my eye-sight over the past couple of years. That said, I'm not sure how a chatbot helps people with these issues. Typing multiple sentences into the chatbot interface is certainly more tiring and error prone that click a few buttons. Similarly, reading multiple lines of minuscule text in a chatbot window is more eye strain than a couple of menus with large text (the larger text being possible since less words need to fit on the screen). As a developer, I care about accessibility issues. Given your statements, there's obviously a gaping blind spot in how I'm thinking about these issues. Could you elaborate a bit more on the topic?
  - chaostheory 5y ago
    Alexa, Siri, and Google Home are essentially AI chat bots using microphones. The former two can barely process anything you ask but you can still get a lot done with them.
pyuser583 5y ago
You’re absolutely, 100% right. 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?
torbTurret 5y ago
“… 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.” That’s great but not representative of average customers at all.
WorldMaker 5y ago
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.

opportune 5y ago

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.

rlayton2 5y ago
Absolutely. I often joke that my work as a data scientist is mostly creating bar graphs for people. The actual analysis is often reasonably simple, its the aggregating of the data that is hard (its messy, its not all in the one spot and there is lots of it). So start with querying your big data to say what the top three event types are. Then slowly crank up the analysis complexity, but not too much. The data engineering has lots of scope for real solid and obvious applications.
lifewallet_dev 5y ago
Oh, so don't do current buzzwords but past ones like Big Data are okay. And if you wanna learn about distributed systems nothing better than Bitcoin or any cryptocurrency based on a P2P protocol.
- lcw 5y ago
  I agree that they are novel and interesting to learn, but practically speaking, the person's point, is they are over hyped, and honestly since most use cases popping up aren't decentralized or are decentralized, but being regulated by a centralized party, like a government, it seems that they are the most inefficient way to run a distributed system.
- opportune 5y ago
  Big data tools are just one example of distributed systems. I suggested looking into them because there are a lot of open source ones you can play with, not because I think big data isn't a buzzword (though Spark is definitely used a lot in industry). Crypto is of course a distributed system too (at least, many are) but in practice it's a bit different than anything you'd see in industry because it's trustless.

cinntaile 5y ago

It's all about incentives. 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.

kkjjkgjjgg 5y ago

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.

alar44 5y ago

tyre 5y ago

A few reasons for this: + 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.

ravi-delia 5y ago
> 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. That's the main reason I decided against a CS major even though I love the subject. It's just disheartening listening to discount business majors butcher even simple technical topics. The pure math track actually has more than a few people in the same situation, so I wound up meeting some enthusiasts anyway.
- uejfiweun 5y ago
  These people may be majoring in CS, but there's really no way around it in the end - to be successful in this field, you have to really love computers. Unless you're a certifiable genius, part of the challenge of this field is just spending a ton of time in front of the computer, learning all the little things. This is a lifelong journey, one that's impossible to complete, and one you can't fake or BS your way through. I don't think CS is a bad major, it's still extremely marketable, but you just have to recognize that this is just one piece of the puzzle for the top jobs, and the other piece is technical prowess, a skill you really have to grind to get good at.

lifewallet_dev 5y ago

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.

jonas21 5y ago

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.

wsmhy2011 5y ago
completely agree
Sebb767 5y ago
> Chatbots will probably get there some day - but it will take some big improvements in NLP. They write copied platitudes, they will follow their checklist no matter what, they will usually not help at all except for wasting time and they hardly speak your language. Honestly, they emulate first level support quite well already.
smsm42 5y ago
I don't think NLP is the problem here. Even if you crack NLP perfectly and would somehow be able to build a semantic map, or whatever your technology would use, of the user request with perfect fidelity, there's still the question of actually understanding what it's about. Perfect NLP would help in very limited number of situation where the meaning is clear - i.e. doing some pre-defined action, like closing an account or telling the balance. But somebody comes with a request like "my dog ate my credit card, so I by mistake used my debit one and it resulted in overdraft and penalty change, can I have that reversed and get a new one?" and I'm pretty sure chatbot won't be able to handle it, NLP or not.

edgyquant 5y ago

The only reason I care to have to AI or blockchain on my resume is because both are interesting to work with.

cuteboy19 5y ago

A chatbot should be a search engine in disguise, with more focus on context. Anything else is a downgrade from a normal web interface

fsloth 5y ago

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).

marcinzm 5y ago

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.

chaostheory 5y ago

> 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.

rprospero 5y ago
I'm curious about your response here, as I'm certain that I'm missing something. I have cerebral palsy, which comes with issues using my hands. I've also had noticeable degradation with age of my eye-sight over the past couple of years. That said, I'm not sure how a chatbot helps people with these issues. Typing multiple sentences into the chatbot interface is certainly more tiring and error prone that click a few buttons. Similarly, reading multiple lines of minuscule text in a chatbot window is more eye strain than a couple of menus with large text (the larger text being possible since less words need to fit on the screen). As a developer, I care about accessibility issues. Given your statements, there's obviously a gaping blind spot in how I'm thinking about these issues. Could you elaborate a bit more on the topic?
- chaostheory 5y ago
  Alexa, Siri, and Google Home are essentially AI chat bots using microphones. The former two can barely process anything you ask but you can still get a lot done with them.

pyuser583 5y ago

You’re absolutely, 100% right. 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?

torbTurret 5y ago

“… 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.” That’s great but not representative of average customers at all.

WorldMaker 5y ago

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.