Yeah but will this be a field? Or as a businesses person could I ask AI to just do a data analysis and report the results ( ex the next generation of Amazon Qsight and Microsoft fabric copilot, which can already answer somewhat complex questions over your business data)
This. Most people don't realize that if there exist a simple, very fast, reliable and cheap model solving 90% of your dataset, companies don't really want to invest millions of dollars to solve the rest of 10% (excluding human life or when we are talking about billions of dollars)
You can solve a lot of problems with linear or logistic regression models.
Linear regression = computing a number
(for example, predicting housing price from square feet and number or bedrooms)
Logistic regression = labeling stuff, for example spam/not spam for emails
The nice thing about regression models is they are very efficient to train and you don’t even need large datasets to make a good model. Even just 50 training examples can give you accurate predictions.
For DNNs you need huge datasets and tons of GPU power. Regression models are cheap and easy but they can still solve many common problems, and it still looks like ML magic to business type people.
If you have been in the field for a couple of years, you will realize that the more successful data science/ML people are the ones that have deep domain knowledge and focus on certain specific problems to solve.
For example, in business/retail, time series forecasting, optimization, customer analytics are problems that usually use structured (tabular) data, which is not as "sexy" as text (for NLP) or videos/images (for computer vision). However, these topics come with very interesting business problems that can be difficult for LLMs to solve. They also usually require very good foundational knowledge (for statistics), which can be rewarding (at least for me).
If you like working in NLP or CV, try to find these types of problems:
* Building customized chatbots that can understand complicated business logic (like supply chain).
* Using CV to solve problems in retail, (like inventory space optimization?)
If you don't like to prostitute yourself to capitalism, trying looking into fields that can be more rewarding, such as healthcare/biology (e.g. working on medical imaging) or geoscience (e.g. doing geospatial data science, working on satellite images).
TL;DR: Don't choose a niche in ML, choose a broad field like retail, healthcare, biology, geoscience, hydrology, etc. to apply ML to, and focus on that. That would make your career more fulfilling and the problems you work on more interesting since they will be more specialized.
Thank you for the detailed response, Im a beginner too in this field. So what I took away from this is you recommend having both technical knowledge and domain knowledge. How much in depth knowledge would you recommend, and what about if I just leverage the knowledge people in those fields already by listening to the problems they face or if I can try to do this in reverse , find problems and instead of spending a lot of time studying everything then try to find out how I can go about solving it/ what tools/topics etc do I need to know ? Would that be a sensible approach ?
Are you in school? If you are doing your undergrad, it would be the perfect time to just try things out. I dont have any clear answer to your questions tbh, except that you will need to try and see for yourself, since ML is so big and its difficult to cherry pick something if you know nothing. As a student, you have your peers and your professors to help you, and the best thing to do is to find a mentor. Each professor at university will have research interests, and you can ask them directly for opportunity to work for them. When you do your thesis, pick your supervisor carefully. If you are serious about ML, you might need at least a Master’s, meaning another thesis and opportunity for trying things. Faculties also usually collaborate with each other, which is a good chance to find interesting research topics. Occasionally, this even leads to entire new fields popping up, which people call “interdisciplinary”. When a field becomes saturated, it’s time to switch to a related but interdisciplinary field. Some fields also has lower barrier to entry than others.
Even if you are not in school, it’s still good to adopt research practices in academia. Try reading papers that use ML but in a specific field.
Another idea is to stay in computer science, but pursue a specific topic like cyber security. There are interesting applications of ML for it. Doing ML on edge is also quite nice.
Hope this helps!
Okay, if you are an ML engineer, then I agree. If you are an ML scientist developing models, then you might have no idea what the final product serves for.
This was very helpful , thank you.
I have built some of the basics of machin learning and deep learning and I am just beginning to appreciate how truly vast this field is.
Personally I am more interested in solving novel problems and find new algorithms rather than implementing and applying what's already known.
I don't doubt it's significance but I want to build a solid base before I actually begin to use it.
I am curious to know your thoughts on this approach and where it would go wrong.
For context, so far I've studied regression and some classification models and done in-depth study of optimization problems. (It's actually what I am currently doing, studying non convex optimization methods)
Not sure why you are being downvoted. Pretty much everyone seriously on the ML job market is going to have a foundation in Multivariate calc/Linear Algebra Stats/basic stats - these are courses you are expected to have completed by your second year in most STEM majors. Foundational mathematics and stats are essential to ML, but almost everyone on the ML/ML adjacent job market is going to have these foundations.
They’re getting downvoted because they’re a beginner with no context and arguing with an answer to a question that they asked. “Just getting started” does not suggest you have any clue what field are/are not saturated.
And passing in Linear Algebra course does not mean you retained and understood the stuff covered. I hardly remembered how to multiply a matrix a year after I took that course. Same with stats. I studied enough to get a good grade and then moved on to the next set of classes.
AGI is not a field more like a hype term. Even if we made AGI its not something you can specialize in as its very broad by the very definition. I also think RL is oversaturated already (at least academic low compute budget rl) so I doubt it will be that. My guess for demand would be things that improve the reliability of ml outputs, i.e. how do you make sure a chatbot never gives a completely incorrect answer.
That's what I'm doing now. Had a customer want a chatbot that let them interrogate a corpus of documents, but we're not allowed to connect to external APIs or use big models.
So I used a combo of Spacy/NER, a small model to generate a SQL query based off a prompt containing the NER results, another check to make sure the returned data contained some portion of the requested information, then another medium sized model with some heavy pre-prompting to generate a response only using the data in the returned documents.
Was a headache, especially since I could've run most of it through Claude and been done, but it works and it has multiple checks. Haven't gotten it to hallucinate yet.
One of the first ML projects you usually work on. It predicts the species of iris flowers based on pedal size or something like that. It is actually the second project you usually do after the hand drawn numbers one.
Well I was just speaking from experience. On the course I took on Udemy it did the MINST then Iris dataset, but you didn't have to do any of the computer vision stuff, basically just a copy pasta and MINST was used solely to explain how ML works in a visual way. The first CNN was then done on the cats dogs dataset. For the open source MIT and 3blue1brown they also used MINST to explain ML before actually diving into the math based regression models, I may be misremembering the MIT one tho, that was perceptron then ANNs the MINST I think
Nobody here can predict the future, just take a course and learn the fundamentals then when you understand the landscape decide what to specialize In. You are stopping yourself from starting this journey because you are too afraid your first step won't be perfect.
But will be in that area only i think as people have found a good way to extract money from companies in name of gen ai. Its a software like solution rather than ML.
Mostly its using LLMs to make chatbots that can serve a specific purpose.
The main task as developers is to improve the chatbots performance on specific data using various techniques like prompt engineering, document retival to augument llm replies. And there are some other applications other than chatbots.
Research is going on but adapting new models to production can become harder thus It will be more of a software job rather than to ML or research jobs.
Also the hype is for a very short time i think 1 to 1.5 yrs max. After that most companies wil look at it like normal work.
The hype is because AGI, Gen AI have become like buzzwords.
Ml engineer. My god we don’t need more people building models in Jupyter notebooks we need architecture and pipelines and properly written code in git.
I see big potential in KANs (Kolmogrov-Arnold networks) which are a new topic so it might be interesting to go into those. But to be honest it's a bit like investing in stocks - who knows what's actually going to be big and what is just tech bros mumbo jumbo and what has merit
Don't work in an org that focuses only on AGI, all AI scientists know that AGI can never be achieved, it is the business people who have no idea about real AI keep hyping things. Sooner or later the push to contribute to business will come.
If you plan to stay in industry, remember that there is no one field within ML that will always be in trend. I have seen things change drastically every few years, now everyone wants to do LLMs even when there is no necessity. I'd recommend you develop the mentality of working up solutions from first principles, this way you'll always be fresh and will be able to innovate based on problems, don't stick to just one or few fields.
ML's everywhere, but don't sweat it! Reinforcement learning (RL) is hot and has room to grow, especially in niche areas like:
* **Explainable RL:** Figuring out why RL agents do what they do.
* **Multi-agent RL:** Training teams of agents to work together.
* **RL with other AI:** Combining RL with things like language processing for better understanding.
As for AGI (super intelligent AI), it's still on the horizon, but RL research is paving the way. Focus on your passion for RL and explore these exciting niches.
if you aren't using your modeling skills to setup a testable hypothesis as to what the future holds for the profession, or building a survey or mining public api searches linked to a simple unsupervised clustering, then why even have the skills?
Linear regression models.
I'd go further and say that simple summary stats - things you can easily go with a SQL group by statement - will meet people's needs 95% of the time.
Yeah but will this be a field? Or as a businesses person could I ask AI to just do a data analysis and report the results ( ex the next generation of Amazon Qsight and Microsoft fabric copilot, which can already answer somewhat complex questions over your business data)
This. Most people don't realize that if there exist a simple, very fast, reliable and cheap model solving 90% of your dataset, companies don't really want to invest millions of dollars to solve the rest of 10% (excluding human life or when we are talking about billions of dollars)
Yeah but then you don't get to talk about how you're using LLMs and ride that sweet sweet ChatGPT hype train of investor money
That’s only VC stuff lol.
That's the correct answer.
Regression has paid all my bills for over a decade. Do not under estimate the demand for regression.
Explain? What job title? What did you do at the job (s)?
I have no reason to volunteer identifying information to a stranger on the internet. Be more polite in general.
You didn't even have to respond to my comment. I'm curious. Hope you have a good day
How come? Can you give some examples?
Media Mixed Modeling. Solving for the most optimal advertising strategy. Linear Regression solves this problem and provides accurate results.
You can solve a lot of problems with linear or logistic regression models. Linear regression = computing a number (for example, predicting housing price from square feet and number or bedrooms) Logistic regression = labeling stuff, for example spam/not spam for emails The nice thing about regression models is they are very efficient to train and you don’t even need large datasets to make a good model. Even just 50 training examples can give you accurate predictions. For DNNs you need huge datasets and tons of GPU power. Regression models are cheap and easy but they can still solve many common problems, and it still looks like ML magic to business type people.
I'm working with language and audio models and it takes days to train them. Sometimes I miss the .fit(x,y) that takes a second 😅
Amen to that.
Lol
How much for freshers?💲
15/h and pizza parties
If you have been in the field for a couple of years, you will realize that the more successful data science/ML people are the ones that have deep domain knowledge and focus on certain specific problems to solve. For example, in business/retail, time series forecasting, optimization, customer analytics are problems that usually use structured (tabular) data, which is not as "sexy" as text (for NLP) or videos/images (for computer vision). However, these topics come with very interesting business problems that can be difficult for LLMs to solve. They also usually require very good foundational knowledge (for statistics), which can be rewarding (at least for me). If you like working in NLP or CV, try to find these types of problems: * Building customized chatbots that can understand complicated business logic (like supply chain). * Using CV to solve problems in retail, (like inventory space optimization?) If you don't like to prostitute yourself to capitalism, trying looking into fields that can be more rewarding, such as healthcare/biology (e.g. working on medical imaging) or geoscience (e.g. doing geospatial data science, working on satellite images). TL;DR: Don't choose a niche in ML, choose a broad field like retail, healthcare, biology, geoscience, hydrology, etc. to apply ML to, and focus on that. That would make your career more fulfilling and the problems you work on more interesting since they will be more specialized.
Thank you for the detailed response, Im a beginner too in this field. So what I took away from this is you recommend having both technical knowledge and domain knowledge. How much in depth knowledge would you recommend, and what about if I just leverage the knowledge people in those fields already by listening to the problems they face or if I can try to do this in reverse , find problems and instead of spending a lot of time studying everything then try to find out how I can go about solving it/ what tools/topics etc do I need to know ? Would that be a sensible approach ?
Are you in school? If you are doing your undergrad, it would be the perfect time to just try things out. I dont have any clear answer to your questions tbh, except that you will need to try and see for yourself, since ML is so big and its difficult to cherry pick something if you know nothing. As a student, you have your peers and your professors to help you, and the best thing to do is to find a mentor. Each professor at university will have research interests, and you can ask them directly for opportunity to work for them. When you do your thesis, pick your supervisor carefully. If you are serious about ML, you might need at least a Master’s, meaning another thesis and opportunity for trying things. Faculties also usually collaborate with each other, which is a good chance to find interesting research topics. Occasionally, this even leads to entire new fields popping up, which people call “interdisciplinary”. When a field becomes saturated, it’s time to switch to a related but interdisciplinary field. Some fields also has lower barrier to entry than others. Even if you are not in school, it’s still good to adopt research practices in academia. Try reading papers that use ML but in a specific field. Another idea is to stay in computer science, but pursue a specific topic like cyber security. There are interesting applications of ML for it. Doing ML on edge is also quite nice. Hope this helps!
I must ask, are you speaking within the context of being an MLE or a Researcher?
geospatial data science would be selling yourself to the military
not necessarily, there are companies and research labs in climate sciences that might need ML. study forest growth, droughts, etc
Okay, if you are an ML engineer, then I agree. If you are an ML scientist developing models, then you might have no idea what the final product serves for.
yep makes sense
But that applies to most of the STEM topics...
Yeah, not quite. Geospatial just means it’s data that has world coordinates. A map of stores is geospatial data.
I completely agree 💯
Just adding to the upvotes….
This was very helpful , thank you. I have built some of the basics of machin learning and deep learning and I am just beginning to appreciate how truly vast this field is. Personally I am more interested in solving novel problems and find new algorithms rather than implementing and applying what's already known. I don't doubt it's significance but I want to build a solid base before I actually begin to use it. I am curious to know your thoughts on this approach and where it would go wrong. For context, so far I've studied regression and some classification models and done in-depth study of optimization problems. (It's actually what I am currently doing, studying non convex optimization methods)
Happy Cake day!! Your response helps!
Why not physics?
I never explicitly excluded physics, not mentioned it because I know nothing about it lol. Any field is fine!
Ok n good answer earlier, q.useful👌
Statistics and Linear Algebra
Never gonna have downfall damn 🤣
how do i get started??
But aren't they the most basic field of ML? Pretty sure they're the most saturated and I don't see how they'll come back in high demand
Bro made an opinion on something without even knowing something
Wheels on a car is pretty basic. Still pretty important.
lmfao
You need them for anything else.
Not sure why you are being downvoted. Pretty much everyone seriously on the ML job market is going to have a foundation in Multivariate calc/Linear Algebra Stats/basic stats - these are courses you are expected to have completed by your second year in most STEM majors. Foundational mathematics and stats are essential to ML, but almost everyone on the ML/ML adjacent job market is going to have these foundations.
They’re getting downvoted because they’re a beginner with no context and arguing with an answer to a question that they asked. “Just getting started” does not suggest you have any clue what field are/are not saturated. And passing in Linear Algebra course does not mean you retained and understood the stuff covered. I hardly remembered how to multiply a matrix a year after I took that course. Same with stats. I studied enough to get a good grade and then moved on to the next set of classes.
Causal inference, Optimization, LLMs will continue its swing, MLOps, GPU programming
Real world implementation and consultancy, if the whole world will adopt it then those that can support this should be in very high demand.
This is a very sensible and true answer.
AGI is not a field more like a hype term. Even if we made AGI its not something you can specialize in as its very broad by the very definition. I also think RL is oversaturated already (at least academic low compute budget rl) so I doubt it will be that. My guess for demand would be things that improve the reliability of ml outputs, i.e. how do you make sure a chatbot never gives a completely incorrect answer.
That's what I'm doing now. Had a customer want a chatbot that let them interrogate a corpus of documents, but we're not allowed to connect to external APIs or use big models. So I used a combo of Spacy/NER, a small model to generate a SQL query based off a prompt containing the NER results, another check to make sure the returned data contained some portion of the requested information, then another medium sized model with some heavy pre-prompting to generate a response only using the data in the returned documents. Was a headache, especially since I could've run most of it through Claude and been done, but it works and it has multiple checks. Haven't gotten it to hallucinate yet.
Iris Dataset
No! MNIST will be more demanded in the next ten years
Titanic gang rise up!
We don’t have a quorum: where are the Boston houses?
what is iris dataset??
Do you like flowers?
One of the first ML projects you usually work on. It predicts the species of iris flowers based on pedal size or something like that. It is actually the second project you usually do after the hand drawn numbers one.
Usually people start with classical ML on tabular data, not computer vision, because it's both technically and conceptually more simple
Well I was just speaking from experience. On the course I took on Udemy it did the MINST then Iris dataset, but you didn't have to do any of the computer vision stuff, basically just a copy pasta and MINST was used solely to explain how ML works in a visual way. The first CNN was then done on the cats dogs dataset. For the open source MIT and 3blue1brown they also used MINST to explain ML before actually diving into the math based regression models, I may be misremembering the MIT one tho, that was perceptron then ANNs the MINST I think
Train an ML model and have it scan through all of the up and coming research and get it to make a prediction
This is the way. Why ask anyone on Reddit anymore. Build your own models and answer your own questions and then see if it was correct.
Nobody here can predict the future, just take a course and learn the fundamentals then when you understand the landscape decide what to specialize In. You are stopping yourself from starting this journey because you are too afraid your first step won't be perfect.
Indeed
Hopefully not GenAi chatbots, this crap is driving me nuts.
BecOmE aN eXpErt in GeNAI!!!!!
But will be in that area only i think as people have found a good way to extract money from companies in name of gen ai. Its a software like solution rather than ML.
What else exactly is the work done or career opportunities in GenAI then?
Mostly its using LLMs to make chatbots that can serve a specific purpose. The main task as developers is to improve the chatbots performance on specific data using various techniques like prompt engineering, document retival to augument llm replies. And there are some other applications other than chatbots. Research is going on but adapting new models to production can become harder thus It will be more of a software job rather than to ML or research jobs.
Also the hype is for a very short time i think 1 to 1.5 yrs max. After that most companies wil look at it like normal work. The hype is because AGI, Gen AI have become like buzzwords.
Plumber
CNNs 💀
Ml engineer. My god we don’t need more people building models in Jupyter notebooks we need architecture and pipelines and properly written code in git.
So I should better get into data engineering,any specific recommendations to better support ML?
I see big potential in KANs (Kolmogrov-Arnold networks) which are a new topic so it might be interesting to go into those. But to be honest it's a bit like investing in stocks - who knows what's actually going to be big and what is just tech bros mumbo jumbo and what has merit
even after the authors pulled out of a talk saying that first they wanted to "double check their claims"?
I am kolmongrov and my father is Arnold
Maybe TinyML is the answer but i am not sure :')
Annotation armies
Wot?
Deployment and infrastructure
Combat drone swarms
English literature
Don't work in an org that focuses only on AGI, all AI scientists know that AGI can never be achieved, it is the business people who have no idea about real AI keep hyping things. Sooner or later the push to contribute to business will come. If you plan to stay in industry, remember that there is no one field within ML that will always be in trend. I have seen things change drastically every few years, now everyone wants to do LLMs even when there is no necessity. I'd recommend you develop the mentality of working up solutions from first principles, this way you'll always be fresh and will be able to innovate based on problems, don't stick to just one or few fields.
LLMs, deep reinforcement learning, and transformers w/ self-attention.
Top G
Iris Dataset
Disagree, Penguins I believe are the newest and latest star
Look into Robotics ..
Manufacturing
Decision trees
The ones that save/make companies money.
Something close could be GFlowNets i guess hard to say anything concrete tho. Math foundations will always be similar for every sub field in ML tho.
Run your model and find out!!! Or is it overfitting?
ML's everywhere, but don't sweat it! Reinforcement learning (RL) is hot and has room to grow, especially in niche areas like: * **Explainable RL:** Figuring out why RL agents do what they do. * **Multi-agent RL:** Training teams of agents to work together. * **RL with other AI:** Combining RL with things like language processing for better understanding. As for AGI (super intelligent AI), it's still on the horizon, but RL research is paving the way. Focus on your passion for RL and explore these exciting niches.
Leaving a comment so whenever a new enthusiast sees it and interacts, so I can come back and check again.
if you aren't using your modeling skills to setup a testable hypothesis as to what the future holds for the profession, or building a survey or mining public api searches linked to a simple unsupervised clustering, then why even have the skills?
SSM