15 ms·

AI Expert Roadmap

195 points by simonpure 6y ago

throwaway7281 6y ago
I'm off the main hubs, SF, London, NYC, but here, in my gamma level city people talk a bit about AI but I have yet to see applications that are not vaporware. Don't get me wrong, I love ML challenges, but the real world seems to have so many other little things to solve first, before we can finally realize our ai dreams.
- lacker 6y ago
  Things like Alexa or Google Translate already very much exist, use modern AI techniques, and are not vaporware. That's why so many corporate research labs - basically all the FAAMG companies - do AI work.
DethNinja 6y ago
I think this would be better titled as machine learning expert roadmap. How come there is no mention of logic or knowledge representation?
- tnbalsam 6y ago
  Agreed. I think this is a well-intentioned effort at marketing and sharing knowledge but it seems a bit fluffy for the "AI" side of things. For ML, I think it's a pretty darn good foundation with the traditional hand-jammed/artisinally-crafted/exceedingly data-driven approaches.
- The_rationalist 6y ago
  Fascinating blind spot isn't it?
tnbalsam 6y ago
Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would be this -- pick a topic/project that interests you, follow the classic knowledge-bootstrapping process where you read through papers and (hopefully) have an expert or trained person walk you through the specifics, then get hands-on instantly. Especially with something like fast.ai that values empirical results over hard theory, something understandably popular in the field. From there I'd recommend branching out, but I'd use a JIT approach. The field isn't necessarily super well-founded at the moment, and while machine learning fundamentals are useful, ultimately it's a waste of time compared to the long-tail benefit of getting immediate empirical results and feedback hands-on. Just my 2c, YMMV, and anyone is totally welcome to disagree as they wish! :) Best of luck, T
- jorlow 6y ago
  > I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation This is the vibe I got as well. Which is fair enough (to each his/her own), but I thought I'd mention fast.ai which takes the opposite approach: > Harvard professor David Perkins, who wrote Making Learning Whole (Jossey-Bass), has much to say about teaching. The basic idea is to teach the whole game. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.
- amaigmbh 6y ago
  You are right that the "Deep Learning" section is rather shallow up to now. We are currently working on expanding it to offer a more comprehensive view of the field and expect to release this update next week. Stay tuned! :)
  - amaigmbh 6y ago
    Just as we promised, we now updated and expanded the "Deep Learning" section. Check it out!
- wunderwuzzi23 6y ago
  I agree with your suggestion, and that is what I did to get started in machine learning as well. Solving some kind of problem and doing practical projects and playing around with models is probably the most efficient way to learn. I started with the Machine Learning course on Coursera by Andrew Ng. And while going through, I started using what I learned on a small project. I did document my journey in case it's useful or interesting for others: https://embracethered.com/blog/posts/2020/machine-learning-basics/ https://embracethered.com/blog/posts/2020/machine-learning-b...
- i_love_music 6y ago
  "I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation" Yeah I get the same feeling. I am hesitant to suggest it, but gives a little bit of a gate-keeping vibe. Like only once you've payed sufficient homage to the same education path that I took, shall you be granted permission to be a 'real data scientist'. That said, I think there is value in something like this more for hiring managers than practitioners... which makes sense considering the intro comments.
light_hue_1 6y ago
This should be renamed to "Intro to AI Roadmap" What's on this roadmap will get you in the door with some of the basics. In no way will it make you an expert.
The_rationalist 6y ago
I do appreciate they mandate paperswithcode.com
lorey 6y ago
The number of highly controversial opinions on this, to me, only shows that it makes sense to try to map a potential path. While the criticism in the comments here may be valid, it is mostly unconstructive and without any proper explanation or reasoning. It may not be perfect, but it's a great overview of what to look into.
petr25102018 6y ago
Maybe a better way to attack this problem is to watch Harvard's Introduction to AI course (or similar one) and from there pick an area that piqued your interest. This is because AI is such a vast field that nobody can learn everything (on top of data processing etc.). I made some notes from the ^ course + listed relevant Python libraries to have a good place to start: https://stribny.name/blog/2020/10/artificial-intelligence-in-python/ https://stribny.name/blog/2020/10/artificial-intelligence-in...
SomaticPirate 6y ago
Surprised to not see fast.ai or some of the more popular courses on here as well to gain more experience
nairboon 6y ago
Why is "Big Data Engineer" not a child node of "Data Engineer"?
- i_love_music 6y ago
  Yeah that seems like a major flaw in this taxonomy. Like does the author really think there is significant enough difference between 'data' and 'big data' that it warrants an entirely separate track? That's silly.
- deleted 6y ago
  [deleted]
nmrcq 6y ago
With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate. We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statistics only in this map) can be called an Expert in a statistical/mathematical field, what do we even call somebody with all the same applied software engineering & exploratory analysis, and business experience, but also a PhD in theoretical topics (math, stats, etc)? A 'super expert'? What do we call Francis Chollet, or LeCun, or anybody else? What's the differentiation between an expert from the roadmap and the team of individuals deploying GPT-3 into Google Assistant? Are they the same? As a hiring manager and team lead for a large fintech firm in London, I would happily see an individual who had really mastered the above path(s) as a strong candidate for an intermediate or upper junior role in applied data science/machine learning. But ... it's not enough to be a senior, and certainly not an expert. Just my two cents.
- bdamm 6y ago
  Expertise is relative. Having compiled a few kernels in my life I wouldn't consider myself an operating systems expert, but to a career plumber, I am. Heck, a friend of mine is a PhD in physics, operates a particle accelerator for work, and to him I'm an operating systems expert despite my protests to the contrary.
  - randcraw 6y ago
    You're an expert if other experts say so. The opinions of clueless folk are just random noise. Just because the man who can see is king among blind men doesn't mean they're qualified to call him visionary.
- mdifrgechd 6y ago
  Agree 100% with you but would add that from the perspective of a lot of business people, the bar for expert is actually pretty low and however far over it you are, they dont actually get any more out or you because they don't know what to do with you. What a lot of businesses want is someone they can call an expert but that still operates within the realm and understanding of a non specialist manager.
mdifrgechd 6y ago
Nothing wrong with this roadmap but I'd suggest that this and similar ones are squarely in the "trade" category of formation, where the focus is on a large number of practical topics instead of a more solid grounding in the fundamentals, that mostly dont even concern themselves with practical dat science - what I would call the "university" approach, but maybe not in the sense of a modern university. For work as a tradesperson, it is worth only focusing on the practical application- and I'm saying this genuinely. But I think it should be clearly distinguished from the different kinds of lasting benefits that a more solid fundamental education provides, including the flexibility to adapt. I'm very biased here, having studies Electrical Eng and CS before modern ML was remotely mainstream, and comparing my conceptual understanding and what I learned in school about math and linear algebra with the way things are understood by tradespeople with far more knowledge of modern tools than me. So crotchety old person- maybe, but I'm happy I went to university.
baron_harkonnen 6y ago
I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine doing anything else (and even then I would recommend alternatives if possible). The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon. The market is currently flooded with desperate PhDs who have reduced years studying their field into a flurry of buzzwords on a resume that they don't understand. The vast majority of AI/ML/DS teams don't know what they're doing. Management and "leadership" in this industry would be laughable if it wasn't so horrific. The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand and making sure someone else is using that so they can justify their work. You could likely cut entire 100+ DS teams at most companies today and only see a positive impact on the business. There are of course really interesting niches that require these skills and skilled practitioners. But if you're reading a guide on how to be an "AI Expert", they you likely aren't one of these people. And because of the signal noise problem in this space it's hard for even really excellent people to get matched to the hard problems that genuinely need their help.
- pradn 6y ago
  I wonder what the impact of easy-to-use cloud ML tools is going to be on the industry. BigQuery already lets users train models using a simple SQL query. AutoML makes simple ML tasks as easy as run and verify accuracy and run again. Of course, a basic amount of knowledge is still need to avoid common pitfalls - unclean data, overfitting, etc. Perhaps this will just mean data scientists can produce better work with the same amount of time. It doesn't seem like a good use of everyone's time to have to know so much cutting-edge research to solve run-of-the-mill business problems. Hardly any other area of CS requires that.
  - deleted 6y ago
    [deleted]
- 6gvONxR4sf7o 6y ago
  For readers who read that and think they still want to do it: > The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand... Don't be this ^. Please. If you do want to come into the field, please first take the time to understand the tools. Learn enough to re-derive and re-implement the tools you're using. Learn the assumptions that need to hold. Don't just learn to point the tools at data and hit go. It's not like a lot of programming where it either succeeds or fails. Failures are often silent in this field. If you can't reason through how and when a given tool will succeed and, probably more importantly, reason through how and when a given tool will fail, you haven't learned enough to know what to use and when.
rexreed 6y ago
If you are looking to work in an enterprise as a data engineer, submission of research papers with code is certainly not a pre-requisite. Learning something useful like CPMAI methodology is more practical.
SpaceManNabs 6y ago
This is great in some aspects but really shallow in others. In fundamentals, I don't see a reference to geometry or calculus. Good luck understanding methods like UMap or more modern clustering techniques, optimal transport methods in AI, and more recent gradient techniques without those. Some might say this is more for the AI researcher, but people need to implement bleeding edge stuff all the time.
rsp1984 6y ago
I really appreciate the taxonomy and links given there, but whenever I see someone claiming "you can't do Z before you've mastered X and Y" it tells me that someone's afraid of others succeeding with simply skipping the masterhood of X and Y and going straight to Z, picking up just enough X and Y in the process. In fact, since the direct path is more rocky than going via X and Y, it naturally selects the most motivated people, so those succeeding in it will generally outperform those who went X-Y-Z.
YeGoblynQueenne 6y ago
At this point I feel like some crazy person talking to themselves when I say this, but "AI" is a research field that includes many more sub-fields than machine learning, let alone deep learning, and it's impossible for someone to be an "AI Expert" while ignoring this, even if they think they are because they ignore it. I appreciate that synecdoche is a thing, but to recommend a "roadmap" to becoming an "AI Expert in 2020" while ignoring most of AI is... ... well I don't know what it is, anymore. Sign of the times? Perhaps the field (AI that is) should abandon its name just to be sure that if a backlash ever comes against deep learning it won't take everyone else's reseach with it? On the other hand, I feel a bit like the horses and the cows in Animal Farm, when the pigs took over the Revolution. Not that there was any revolution, not really, but the way that the research trends have shifted lately, from what would make good science to what will make you hired by Google, is a little bit of a shock to me. And I stared my PhD just three years ago. It's come to the point where I don't want to associate my research with "machine learning" and I don't want to use the term in my thesis, for fear of the negative connotations (of sleazy practices and shoddy research) that I am concerned might be attached to it in the time to come. And it's such a shame because the people who really advanced the field, people like Joshua Bengio, Jurgen Schmidhuber, Geoff Hinton, Yan Le Cunn etc are formidable scientists, dedicated to their preferred approaches and with the patience to nurture their ideas against all opposition. The field they helped progress so much deserved better.
- Veedrac 6y ago
  GOFAI didn't work. ML is the only AI approach that ever did. Sure, your tree searches and your planners and your grammars and your knowledge graphs and your expert systems still have their places... but they're not AI, and they never will be.
  - deleted 6y ago
    [deleted]
  - YeGoblynQueenne 6y ago
    Amicably, can I ask you how you know what you say above to be true? Where does your knowledge of AI come from? Edit: I'm asking because there is evident in your comment a confusion that is all too common on the internets today, of thinking of "GOFAI" (symbolic AI) and "ML" (machine learning) as two somehow incompatible and perhaps exclusive sub-fields of AI. This is as far from the truth as it could be, for instance machine learning really took off as a subject of research in the 1980's with the work of Ryszard Michalski and others, who considered machine learning approaches for the purpose of overcoming the "knowledge acquisition bottleneck", i.e. the difficulty of hand-crafting production rules for expert systems. Indeed, most of the early machine learning systems were propositional-logic based, i.e. symbolic. And of course, one of the most widely used and well-known type of machine learning approach used to this day, decision trees, hail from that time and also learn propositional logic, symbolic models. Of course, most people today know "ML" as a byword for deep learning, or at best statistical pattern recognition (if that). It's just another aberration brought on by the sudden explosion of interest in a once small field. I refer you to Michalski's textbook, "Machine Learning: An Artificial Intelligence Approach" and Tom Mitchell's "Machine Learning" for more information on the early days of the field, i.e. until ca. 2000.
marcinzm 6y ago
There's a split happening right now in the market between Data Scientist and Machine Learning Engineer roles. The former is generally a more soft-skills heavy role with results that are meant to drive human decisions. The later is about building systems that automatically make decisions (ie: recommendation systems, etc.). Data Science is over saturated while ML Engineering is less so.
snird 6y ago
Awesome resource. A quick plug and reminder that a learning roadmap for data engineering that is detailed with resources is available here: https://awesomedataengineering.com https://awesomedataengineering.com For those who wish to focus on data engineering.
Der_Einzige 6y ago
I chuckle a bit when they put PCA as being the very last thing you learn, way after dimensionality reduction (PCA is the classic and most "simple" dimensionality reduction technique) Also, the reality is that very few full on AI experts (and yes, this means Ph.Ds with a lot of publications at top conferences who are also FAANG applied ML engineers) will have more than 70% of these skills. Luckily, this is a field which highly rewards specialization. They don't need the breadth if they have depth in their particular areas. My experience has been that in the vast majority of applied roles involving AI, there is not enough emphasis on the software engineering skills necessary for productizing the models, leading to situations like baron_harkonnens critique.
karussell 6y ago
I assumed a tech article and first read "AI extracts roadmap" like https://mapwith.ai https://mapwith.ai or https://github.com/Microsoft/Open-Maps https://github.com/Microsoft/Open-Maps ;) Are people really planning their carrier like this? Isn't it more that you either work for some company nearby, or a company you know by some luck or explore the AI/ML topics you or your professor likes and you got stuck? (or you start disliking it and try hard to find something else)?

195 points by simonpure 6y ago

I'm off the main hubs, SF, London, NYC, but here, in my gamma level city people talk a bit about AI but I have yet to see applications that are not vaporware. Don't get me wrong, I love ML challenges, but the real world seems to have so many other little things to solve first, before we can finally realize our ai dreams.

lacker 6y ago
Things like Alexa or Google Translate already very much exist, use modern AI techniques, and are not vaporware. That's why so many corporate research labs - basically all the FAAMG companies - do AI work.

DethNinja 6y ago

I think this would be better titled as machine learning expert roadmap. How come there is no mention of logic or knowledge representation?

tnbalsam 6y ago
Agreed. I think this is a well-intentioned effort at marketing and sharing knowledge but it seems a bit fluffy for the "AI" side of things. For ML, I think it's a pretty darn good foundation with the traditional hand-jammed/artisinally-crafted/exceedingly data-driven approaches.
The_rationalist 6y ago
Fascinating blind spot isn't it?

tnbalsam 6y ago

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would be this -- pick a topic/project that interests you, follow the classic knowledge-bootstrapping process where you read through papers and (hopefully) have an expert or trained person walk you through the specifics, then get hands-on instantly. Especially with something like fast.ai that values empirical results over hard theory, something understandably popular in the field. From there I'd recommend branching out, but I'd use a JIT approach. The field isn't necessarily super well-founded at the moment, and while machine learning fundamentals are useful, ultimately it's a waste of time compared to the long-tail benefit of getting immediate empirical results and feedback hands-on. Just my 2c, YMMV, and anyone is totally welcome to disagree as they wish! :) Best of luck, T

jorlow 6y ago
> I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation This is the vibe I got as well. Which is fair enough (to each his/her own), but I thought I'd mention fast.ai which takes the opposite approach: > Harvard professor David Perkins, who wrote Making Learning Whole (Jossey-Bass), has much to say about teaching. The basic idea is to teach the whole game. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.
amaigmbh 6y ago
You are right that the "Deep Learning" section is rather shallow up to now. We are currently working on expanding it to offer a more comprehensive view of the field and expect to release this update next week. Stay tuned! :)
- amaigmbh 6y ago
  Just as we promised, we now updated and expanded the "Deep Learning" section. Check it out!
wunderwuzzi23 6y ago
I agree with your suggestion, and that is what I did to get started in machine learning as well. Solving some kind of problem and doing practical projects and playing around with models is probably the most efficient way to learn. I started with the Machine Learning course on Coursera by Andrew Ng. And while going through, I started using what I learned on a small project. I did document my journey in case it's useful or interesting for others: https://embracethered.com/blog/posts/2020/machine-learning-basics/ https://embracethered.com/blog/posts/2020/machine-learning-b...
i_love_music 6y ago
"I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation" Yeah I get the same feeling. I am hesitant to suggest it, but gives a little bit of a gate-keeping vibe. Like only once you've payed sufficient homage to the same education path that I took, shall you be granted permission to be a 'real data scientist'. That said, I think there is value in something like this more for hiring managers than practitioners... which makes sense considering the intro comments.

light_hue_1 6y ago

This should be renamed to "Intro to AI Roadmap" What's on this roadmap will get you in the door with some of the basics. In no way will it make you an expert.

The_rationalist 6y ago

I do appreciate they mandate paperswithcode.com

lorey 6y ago

The number of highly controversial opinions on this, to me, only shows that it makes sense to try to map a potential path. While the criticism in the comments here may be valid, it is mostly unconstructive and without any proper explanation or reasoning. It may not be perfect, but it's a great overview of what to look into.

petr25102018 6y ago

Maybe a better way to attack this problem is to watch Harvard's Introduction to AI course (or similar one) and from there pick an area that piqued your interest. This is because AI is such a vast field that nobody can learn everything (on top of data processing etc.). I made some notes from the ^ course + listed relevant Python libraries to have a good place to start: https://stribny.name/blog/2020/10/artificial-intelligence-in-python/ https://stribny.name/blog/2020/10/artificial-intelligence-in...

SomaticPirate 6y ago

Surprised to not see fast.ai or some of the more popular courses on here as well to gain more experience

nairboon 6y ago

Why is "Big Data Engineer" not a child node of "Data Engineer"?

i_love_music 6y ago
Yeah that seems like a major flaw in this taxonomy. Like does the author really think there is significant enough difference between 'data' and 'big data' that it warrants an entirely separate track? That's silly.
deleted 6y ago
[deleted]

nmrcq 6y ago

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate. We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statistics only in this map) can be called an Expert in a statistical/mathematical field, what do we even call somebody with all the same applied software engineering & exploratory analysis, and business experience, but also a PhD in theoretical topics (math, stats, etc)? A 'super expert'? What do we call Francis Chollet, or LeCun, or anybody else? What's the differentiation between an expert from the roadmap and the team of individuals deploying GPT-3 into Google Assistant? Are they the same? As a hiring manager and team lead for a large fintech firm in London, I would happily see an individual who had really mastered the above path(s) as a strong candidate for an intermediate or upper junior role in applied data science/machine learning. But ... it's not enough to be a senior, and certainly not an expert. Just my two cents.

bdamm 6y ago
Expertise is relative. Having compiled a few kernels in my life I wouldn't consider myself an operating systems expert, but to a career plumber, I am. Heck, a friend of mine is a PhD in physics, operates a particle accelerator for work, and to him I'm an operating systems expert despite my protests to the contrary.
- randcraw 6y ago
  You're an expert if other experts say so. The opinions of clueless folk are just random noise. Just because the man who can see is king among blind men doesn't mean they're qualified to call him visionary.
mdifrgechd 6y ago
Agree 100% with you but would add that from the perspective of a lot of business people, the bar for expert is actually pretty low and however far over it you are, they dont actually get any more out or you because they don't know what to do with you. What a lot of businesses want is someone they can call an expert but that still operates within the realm and understanding of a non specialist manager.

mdifrgechd 6y ago

Nothing wrong with this roadmap but I'd suggest that this and similar ones are squarely in the "trade" category of formation, where the focus is on a large number of practical topics instead of a more solid grounding in the fundamentals, that mostly dont even concern themselves with practical dat science - what I would call the "university" approach, but maybe not in the sense of a modern university. For work as a tradesperson, it is worth only focusing on the practical application- and I'm saying this genuinely. But I think it should be clearly distinguished from the different kinds of lasting benefits that a more solid fundamental education provides, including the flexibility to adapt. I'm very biased here, having studies Electrical Eng and CS before modern ML was remotely mainstream, and comparing my conceptual understanding and what I learned in school about math and linear algebra with the way things are understood by tradespeople with far more knowledge of modern tools than me. So crotchety old person- maybe, but I'm happy I went to university.

baron_harkonnen 6y ago

I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine doing anything else (and even then I would recommend alternatives if possible). The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon. The market is currently flooded with desperate PhDs who have reduced years studying their field into a flurry of buzzwords on a resume that they don't understand. The vast majority of AI/ML/DS teams don't know what they're doing. Management and "leadership" in this industry would be laughable if it wasn't so horrific. The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand and making sure someone else is using that so they can justify their work. You could likely cut entire 100+ DS teams at most companies today and only see a positive impact on the business. There are of course really interesting niches that require these skills and skilled practitioners. But if you're reading a guide on how to be an "AI Expert", they you likely aren't one of these people. And because of the signal noise problem in this space it's hard for even really excellent people to get matched to the hard problems that genuinely need their help.

pradn 6y ago
I wonder what the impact of easy-to-use cloud ML tools is going to be on the industry. BigQuery already lets users train models using a simple SQL query. AutoML makes simple ML tasks as easy as run and verify accuracy and run again. Of course, a basic amount of knowledge is still need to avoid common pitfalls - unclean data, overfitting, etc. Perhaps this will just mean data scientists can produce better work with the same amount of time. It doesn't seem like a good use of everyone's time to have to know so much cutting-edge research to solve run-of-the-mill business problems. Hardly any other area of CS requires that.
- deleted 6y ago
  [deleted]
6gvONxR4sf7o 6y ago
For readers who read that and think they still want to do it: > The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand... Don't be this ^. Please. If you do want to come into the field, please first take the time to understand the tools. Learn enough to re-derive and re-implement the tools you're using. Learn the assumptions that need to hold. Don't just learn to point the tools at data and hit go. It's not like a lot of programming where it either succeeds or fails. Failures are often silent in this field. If you can't reason through how and when a given tool will succeed and, probably more importantly, reason through how and when a given tool will fail, you haven't learned enough to know what to use and when.

rexreed 6y ago

If you are looking to work in an enterprise as a data engineer, submission of research papers with code is certainly not a pre-requisite. Learning something useful like CPMAI methodology is more practical.

SpaceManNabs 6y ago

This is great in some aspects but really shallow in others. In fundamentals, I don't see a reference to geometry or calculus. Good luck understanding methods like UMap or more modern clustering techniques, optimal transport methods in AI, and more recent gradient techniques without those. Some might say this is more for the AI researcher, but people need to implement bleeding edge stuff all the time.

rsp1984 6y ago

I really appreciate the taxonomy and links given there, but whenever I see someone claiming "you can't do Z before you've mastered X and Y" it tells me that someone's afraid of others succeeding with simply skipping the masterhood of X and Y and going straight to Z, picking up just enough X and Y in the process. In fact, since the direct path is more rocky than going via X and Y, it naturally selects the most motivated people, so those succeeding in it will generally outperform those who went X-Y-Z.

YeGoblynQueenne 6y ago

At this point I feel like some crazy person talking to themselves when I say this, but "AI" is a research field that includes many more sub-fields than machine learning, let alone deep learning, and it's impossible for someone to be an "AI Expert" while ignoring this, even if they think they are because they ignore it. I appreciate that synecdoche is a thing, but to recommend a "roadmap" to becoming an "AI Expert in 2020" while ignoring most of AI is... ... well I don't know what it is, anymore. Sign of the times? Perhaps the field (AI that is) should abandon its name just to be sure that if a backlash ever comes against deep learning it won't take everyone else's reseach with it? On the other hand, I feel a bit like the horses and the cows in Animal Farm, when the pigs took over the Revolution. Not that there was any revolution, not really, but the way that the research trends have shifted lately, from what would make good science to what will make you hired by Google, is a little bit of a shock to me. And I stared my PhD just three years ago. It's come to the point where I don't want to associate my research with "machine learning" and I don't want to use the term in my thesis, for fear of the negative connotations (of sleazy practices and shoddy research) that I am concerned might be attached to it in the time to come. And it's such a shame because the people who really advanced the field, people like Joshua Bengio, Jurgen Schmidhuber, Geoff Hinton, Yan Le Cunn etc are formidable scientists, dedicated to their preferred approaches and with the patience to nurture their ideas against all opposition. The field they helped progress so much deserved better.

Veedrac 6y ago
GOFAI didn't work. ML is the only AI approach that ever did. Sure, your tree searches and your planners and your grammars and your knowledge graphs and your expert systems still have their places... but they're not AI, and they never will be.
- deleted 6y ago
  [deleted]
- YeGoblynQueenne 6y ago
  Amicably, can I ask you how you know what you say above to be true? Where does your knowledge of AI come from? Edit: I'm asking because there is evident in your comment a confusion that is all too common on the internets today, of thinking of "GOFAI" (symbolic AI) and "ML" (machine learning) as two somehow incompatible and perhaps exclusive sub-fields of AI. This is as far from the truth as it could be, for instance machine learning really took off as a subject of research in the 1980's with the work of Ryszard Michalski and others, who considered machine learning approaches for the purpose of overcoming the "knowledge acquisition bottleneck", i.e. the difficulty of hand-crafting production rules for expert systems. Indeed, most of the early machine learning systems were propositional-logic based, i.e. symbolic. And of course, one of the most widely used and well-known type of machine learning approach used to this day, decision trees, hail from that time and also learn propositional logic, symbolic models. Of course, most people today know "ML" as a byword for deep learning, or at best statistical pattern recognition (if that). It's just another aberration brought on by the sudden explosion of interest in a once small field. I refer you to Michalski's textbook, "Machine Learning: An Artificial Intelligence Approach" and Tom Mitchell's "Machine Learning" for more information on the early days of the field, i.e. until ca. 2000.

marcinzm 6y ago

There's a split happening right now in the market between Data Scientist and Machine Learning Engineer roles. The former is generally a more soft-skills heavy role with results that are meant to drive human decisions. The later is about building systems that automatically make decisions (ie: recommendation systems, etc.). Data Science is over saturated while ML Engineering is less so.

snird 6y ago

Awesome resource. A quick plug and reminder that a learning roadmap for data engineering that is detailed with resources is available here: https://awesomedataengineering.com https://awesomedataengineering.com For those who wish to focus on data engineering.

Der_Einzige 6y ago

I chuckle a bit when they put PCA as being the very last thing you learn, way after dimensionality reduction (PCA is the classic and most "simple" dimensionality reduction technique) Also, the reality is that very few full on AI experts (and yes, this means Ph.Ds with a lot of publications at top conferences who are also FAANG applied ML engineers) will have more than 70% of these skills. Luckily, this is a field which highly rewards specialization. They don't need the breadth if they have depth in their particular areas. My experience has been that in the vast majority of applied roles involving AI, there is not enough emphasis on the software engineering skills necessary for productizing the models, leading to situations like baron_harkonnens critique.

karussell 6y ago

I assumed a tech article and first read "AI extracts roadmap" like https://mapwith.ai https://mapwith.ai or https://github.com/Microsoft/Open-Maps https://github.com/Microsoft/Open-Maps ;) Are people really planning their carrier like this? Isn't it more that you either work for some company nearby, or a company you know by some luck or explore the AI/ML topics you or your professor likes and you got stuck? (or you start disliking it and try hard to find something else)?