They all have the potential to be great but I fell like they are all really early in their development and a lot of people are just throwing it at problems where they really don't fit.
Hello! My name is Thomas and I'm a nerd. I like tech and gadgets and speculative fiction, and playing around with programming. It's not my day job, but I'm working on making it a side gig :)
Which means it's a terrible idea for a team organisation process.
You can't base your organisation on everyone performing the process to perfection all the time, you have to account for the fact that humans are performing it.
The best process is one that always produces the desired result regardless of the proficiency with which you execute it.
But do you think such a process exists? I feel that as soon as you add the human factor you also need to have a more human approach to team organization.
Agree. I just feel that management tend to just throw it in to a project as the silver bullet and then wonder why all of these sprint planning meeting haven't gotten us to write more code.
Full-time web dev; JS lover since 2002; CSS fanatic. #CSSIsAwesome
I try to stay up with new web platform features. Web feature you don't understand? Tell me! I'll write an article!
He/him
Blockchain, sure. Scrum... arguably, I guess, though I still use it. I disagree about IoT; afaik it's still huge, and more and more smart home devices are being produced every year and seem to be doing well (though I haven't done market research or anything).
But seriously, machine learning? The biggest, most successful field of AI research and development of the last like 50 years? I can't agree there. ML is powering every major search engine, it's used for photo and video analysis for all sorts of applications from social media to law enforcement and government intelligence, it's used for every sort of mass data analysis from advertising to stock markets to demographics research, and it's invaluable to the hard sciences where quickly identifying trends in huge datasets (think about trying to manually examine astronomical datasets, the output from Large Hadron Collider experiements, or even animal migration patterns with hundreds of thousands of data points).
I'm really not trying to be a jerk and go all "someone is wrong on the internet" or anything, I'm honestly very curious: what do you see as the failures of machine learning? Sure, there have been misfires and misapplications, just like any tech, but my god, it's been absolutely exploding as a field of both CS research and practical application for literally half a century
I might interpreting over-hyped in a different way than you are then. By over-hyped I don't really mean that something is bad. Machine Learning is awesome and has solved a lot of problems that were previously, dare i say, unsolvable.
What I mean with over-hyped is that it, in many ways, have started to be used as a buzzword. It is a thing that startups instantly put in their sales pitch even though they might use it in the smallest and least significant part of their actual service. Even worse is when ML is crammed in to a project that doesn't really warrant for it. Working for an agency I have even had clients saying "We want to solve this using machine learning" when there are solutions that would have done the work better.
This of course does not mean that machine learning is bad or has failed. It just means that it is hyped and sometimes misunderstood by a lot of people working in the industry.
What I am saying is not
"over-hyped = Bad"
but rather
"over-hyped = People sometimes use it only because there is hype around it".
Oh I totally agree with the first impretation though. I think Machine Learning is absolutely terrible. Even if we solve the issues around climate change, AI research will inevitable bring the end of humanity and needs to be stopped.
Full-time web dev; JS lover since 2002; CSS fanatic. #CSSIsAwesome
I try to stay up with new web platform features. Web feature you don't understand? Tell me! I'll write an article!
He/him
Do you mean because of strong AI and the rise of the machines, or privacy concerns, or something else? I probably agree with all of your concerns at least somewhat, but even if we avoid the research heading in those directions, ML is still fundamentally important. ML is a very field that covers everything from data compression algorithms to cyber security to, as I mentioned, interpretation of scientific datasets. We would honestly never have progressed past the tech of the 70s without ML
These ones I would say:
They all have the potential to be great but I fell like they are all really early in their development and a lot of people are just throwing it at problems where they really don't fit.
Also Scrum...
Yeah scrum, the management at scale 😂
Scrum is one of them things that works amazingly... but only if done really well.
Which means it's a terrible idea for a team organisation process.
You can't base your organisation on everyone performing the process to perfection all the time, you have to account for the fact that humans are performing it.
The best process is one that always produces the desired result regardless of the proficiency with which you execute it.
But do you think such a process exists? I feel that as soon as you add the human factor you also need to have a more human approach to team organization.
That's why it's best to focus on the Agile values instead of the processes.
Agree. I just feel that management tend to just throw it in to a project as the silver bullet and then wonder why all of these sprint planning meeting haven't gotten us to write more code.
Blockchain, sure. Scrum... arguably, I guess, though I still use it. I disagree about IoT; afaik it's still huge, and more and more smart home devices are being produced every year and seem to be doing well (though I haven't done market research or anything).
But seriously, machine learning? The biggest, most successful field of AI research and development of the last like 50 years? I can't agree there. ML is powering every major search engine, it's used for photo and video analysis for all sorts of applications from social media to law enforcement and government intelligence, it's used for every sort of mass data analysis from advertising to stock markets to demographics research, and it's invaluable to the hard sciences where quickly identifying trends in huge datasets (think about trying to manually examine astronomical datasets, the output from Large Hadron Collider experiements, or even animal migration patterns with hundreds of thousands of data points).
I'm really not trying to be a jerk and go all "someone is wrong on the internet" or anything, I'm honestly very curious: what do you see as the failures of machine learning? Sure, there have been misfires and misapplications, just like any tech, but my god, it's been absolutely exploding as a field of both CS research and practical application for literally half a century
I might interpreting over-hyped in a different way than you are then. By over-hyped I don't really mean that something is bad. Machine Learning is awesome and has solved a lot of problems that were previously, dare i say, unsolvable.
What I mean with over-hyped is that it, in many ways, have started to be used as a buzzword. It is a thing that startups instantly put in their sales pitch even though they might use it in the smallest and least significant part of their actual service. Even worse is when ML is crammed in to a project that doesn't really warrant for it. Working for an agency I have even had clients saying "We want to solve this using machine learning" when there are solutions that would have done the work better.
This of course does not mean that machine learning is bad or has failed. It just means that it is hyped and sometimes misunderstood by a lot of people working in the industry.
What I am saying is not
"over-hyped = Bad"
but rather
"over-hyped = People sometimes use it only because there is hype around it".
Oh I totally agree with the first impretation though. I think Machine Learning is absolutely terrible. Even if we solve the issues around climate change, AI research will inevitable bring the end of humanity and needs to be stopped.
Do you mean because of strong AI and the rise of the machines, or privacy concerns, or something else? I probably agree with all of your concerns at least somewhat, but even if we avoid the research heading in those directions, ML is still fundamentally important. ML is a very field that covers everything from data compression algorithms to cyber security to, as I mentioned, interpretation of scientific datasets. We would honestly never have progressed past the tech of the 70s without ML
I thought scrum was an agile thing. Is it a software?
It's sort of a movement in, but not exclusive to, the software industry