I'm pursuing an MS in Data Science at Columbia University right now, but due to reasons even I can't comprehend, I almost didn't apply for any internships at all. Though I did score straight A's and literally topped Algorithms and NLP somehow without studying, and am generally good and experienced in Deep Learning and applied AI research skills, I still thought I'd not land or clear interviews, and didn't bother applying. I didn't have an internship until late June, and I finally got one through in-person networking at Startup events during NYC Tech Week. Had I applied normally like everyone else, I could've landed Tier 1 companies, prestigious startups and AI firms, like almost everyone else in my cohort. All my friends are interning with the Llama team at Meta, at Microsoft research labs, Nvidia, Tesla, etc. or Top hedge funds in NYC. Most of them will also be getting sponsored return offers with high salaries - they have been stress free since the last year. I will still suffer the wrath of finding a real job until end of this year until I graduate (already very little time left, and I still don't feel like applying). The silver lining is, the startup I've joined is pretty early stage, and I fit in really well with the highly experienced CEO/CTO and learn from him all day. I'm the only intern and I get a lot of attention and hands-on guidance. I'm also getting to see how to build startups from scratch, so it could help me form perspectives later. He said he would 100% hire and sponsor me, but that's only possible if he raises funding and survives. I'm working on cutting edge LLM Agents, RAGs and stuff and it is indeed adding some valuable experience to my resume. But it's more applied, engineering oriented, and not really (applied) AI Research, what I was supposed to be doing with skills I acquired during my MS. But I do think that I should learn MLE and general engineering stuff to be relevant in the industry, especially since I have 2 YOE before the degree, so it may even be expected. However, I am worried that I'm basically interning at a no-name startup which can spoil my career image. Thing is, I do mandatorily need a very high paying and sponsored job to pay back my student debts. What should I do? submitted by /u/anotheraccount97 [link] [comments]

I'm pursuing an MS in Data Science at Columbia University right now, but due to reasons even I can't comprehend, I almost didn't apply for any internships at all. Though I did score straight A's and literally topped Algorithms and NLP somehow without studying, and am generally good and experienced in Deep Learning and AI research skills, I still thought I'd not land or clear interviews, and didn't bother applying. I didn't have an internship until late June, and I finally got one through in-person networking at Startup events during NYC Tech Week. Had I applied normally like everyone else, I could've landed Tier 1 companies, prestigious startups and AI firms, like almost everyone else in my cohort. All my friends are interning with the Llama team at Meta, at Microsoft research labs, Nvidia, Tesla, etc. or Top hedge funds in NYC. Most of them will also be getting sponsored return offers with high salaries - they have been stress free since the last year. I will still suffer the wrath of finding a real job until end of this year until I graduate (already very little time left, and I still don't feel like applying). The silver lining is, the startup I've joined is pretty early stage, and I fit in really well with the highly experienced CEO/CTO and learn from him all day. I'm the only intern and I get a lot of attention and hands-on guidance. I'm also getting to see how to build startups from scratch, so it could help me form perspectives later. He said he would 100% hire and sponsor me, but that's only possible if he raises funding and survives. I'm working on cutting edge LLM Agents, RAGs and stuff and it is indeed adding some valuable experience to my resume. But it's more applied, engineering oriented, and not really AI Research, what I was supposed to be doing with skills I acquired during my MS. But I do think that I should learn MLE and general engineering stuff to be relevant in the industry, especially since I have 2 YOE before the degree, so it may even be expected. However, I am worried that I'm basically interning at a no-name startup which can spoil my career image. Thing is, I do mandatorily need a very high paying and sponsored job to pay back my student debts. What should I do? submitted by /u/anotheraccount97 [link] [comments]

What's the most interesting Data Science Interview question you've been asked? Bonus points if it: appears to be hard, but is actually easy appears to be simple, but is actually nuanced I'll go first – at a geospatial analytics startup, I was asked about how we could use location data to help McDonalds open up their next store location in an optimal spot. It was fun to riff about what features I'd use in my analysis, and potential downsides off each feature. I also got to show off my domain knowledge by mentioning some interesting retail analytics / credit-card spend datasets I'd also incorporate. This impressed the interviewer since the companies I mentioned were all potential customers/partners/competitors (it's a complicated ecosystem!). How about you – what's the most interesting Data Science interview question you've encountered? submitted by /u/NickSinghTechCareers [link] [comments]

Hi, I am a 2nd year PhD student in CS. My supervisor just got this idea about MoEs and fairness and asked me to implement it ( work on a toy classification problem on tabular data and NOT language data). However as it is not their area of expertise, they did not give any guidelines on how to approach it. My main question is: How do I search for or proceed with implementing a mixture of expert models? The ones that I find are for chatting and such but I mainly work with tabular EHR data. This is my first foray into this area (LLMs and MoEs) and I am kind of lost with all these Mixtral, openMoE, etc. As we do not have access to Google Collab or have powerful GPUs I have to rely on local training (My lab PC has 2080ti and my laptop has 4070). Any guideline or starting point on how to proceed would be greatly appreciated. submitted by /u/Furiousguy79 [link] [comments]

Hi everyone! I'm working on a project where I have detailed information on player movements in field sports such as Soccer, Rugby, and Field Hockey. The dataset includes second-by-second data on player positions (latitude and longitude), speed, and heart rate. I’m looking for help with two specific objectives using machine learning: Detecting and Classifying Game Phases: I want to develop a system that can identify and classify different game phases like attacking, defending, counter-attacks, rest periods, etc. Automatically Splitting the Game into Quarters or Halves: Additionally, I need to automatically segment the game into quarters or halves, and determine the exact times these segments occur. I’d appreciate any suggestions on how to approach these problems. What algorithms or models would be best suited for these tasks? Are there any existing frameworks or tools that could be particularly useful?Thanks for your help!! submitted by /u/vaalenz [link] [comments]

Just wanted to start a small discussion about why you use LLMs and which model works best for your use case. I am asking because I am concerned that there is little use for LLMs apart from doing role play, helping with coding, and answering general questions submitted by /u/RND_RandoM [link] [comments]

Meta's release of its latest Llama language model family this week, including the massive Llama-3 405B model, has generated a great deal of excitement among AI developers. These open-weights frontier models, which have been updated with a new license that allows unrestricted use of outputs, will enable significant improvements to AI-powered applications, and enable widespread commercial use of synthetic data. Less discussed, but no less important, are Meta's latest open moderation tools, including a new model called PromptGuard. PromptGuard is a small, lightweight classification model trained to detect malicious prompts, including jailbreaks and prompt injections. These attacks can be used to manipulate language models to produce harmful outputs or extract sensitive information. Companies building enterprise-ready applications must be able to detect and mitigate these attacks to ensure their models are safe to use, especially in sensitive and highly-regulated domains like healthcare, finance, and law. PromptGuard is a text classification model based on mDeBERTa-v3-base, a small transformer model with multilingual capabilities. Meta trained this model to output probabilities for 3 classes: BENIGN, INJECTION, and JAILBREAK. The JAILBREAK class is designed to identify malicious user prompts (such as the "Do Anything Now(opens in a new tab)" or DAN prompt, which instructs a language model to ignore previous instructions and enter an unrestricted mode). On the other hand, the INJECTION class is designed to identify retrieved contexts, such as a webpage or document, which have been poisoned with malicious content to influence the model's output. In our tests, we find that the model is able to identify common jailbreaks like DAN, but also labels benign prompts as injections. This likely happens because the model is trained to handle both prompts and retrieved contexts (such as web searches and news articles), and a benign prompt may appear similar to a malicious context. As stated in the model card: Application developers typically want to allow users flexibility in how they interact with an application, and to only filter explicitly violating prompts (what the ‘jailbreak’ label detects). Third-party content has a different expected distribution of inputs (we don’t expect any “prompt-like” content in this part of the input) This indicates that when applying the model to user prompts, you may want to ignore the INJECTION label, and only filter JAILBREAK inputs. On the other hand, when filtering third-party context to show to the model, such as a news article, you'd want to remove both JAILBREAK and INJECTION labels. We wrote a quick blog post about how you can use PromptGuard to protect your language models from malicious inputs. You can read more here: https://www.trytaylor.ai/blog/promptguard submitted by /u/Different-General700 [link] [comments]

I was looking through some postings On indeed. And I noticed that there are several data science postings that require both a master’s and a PhD. You’re telling me if you decide to skip a master’s and go straight for the PhD, you’re not considered qualified? submitted by /u/Rare_Art_9541 [link] [comments]

My data is a bit complicated to describe so I'm going try to describe something analogous. Each example is randomly generated, but you can group them based on a specific but latent (by latent I mean this isn't added into the features used to develop a model, but I have access to it) feature (in this example we'll call this number of bedrooms). Feature x1 Feature x2 Feature x3 ... Output (Rent) Row 1 Row 2 Row 3 Row 4 Row 5 Row 6 Row 7 2 Row 8 1 Row 9 0 So I can group Row 1, Row 2, and Row 3 based on a latent feature called number of bedrooms (which in this case is 0 bedroom). Similarly, Row 4, Row 5, & Row 6 have 2 Bedrooms, and Row 7, Row 8, & Row 9 have 4 Bedrooms. Furthermore, these groups also have an optimum price which is used to create output classes (output here is Rent; increase, keep constant, or decrease). So say the optimum price for the 4 bedrooms group is $3mil, and row 7 has a price of $4mil (=> 3 - 4 = -1 mil, i.e a -ve value so convert this to class 2, or above optimum or increase rent), row 8 has a price of $3mil (=> 3 - 3 = 0, convert this to class 1, or at optimum), and row 9 has a price of $2mil (3 - 2 = 1, i.e +ve value, so convert this to class 0, or below optimum, or decrease rent). I use this method to create an output class for each example in the dataset (essentially, if example x has y number of bedrooms, I get the known optimum price for that number of bedrooms and I subtract the example's price from the optimum price). Say I have 10 features (e.g. square footage, number of bathrooms, parking spaces etc.) in the dataset, these 10 features provide the model with enough information to figure out the "number of bedrooms". So when I am evaluating the model, feature x1 feature x2 feature x3 ... Row 10 e.g. I pass into the model a test example (Row 10) which I know has 4 bedrooms and is priced at $6mil, the model can accurately predict class 2 (i.e increase rent) for this example. Because the model was developed using data with a representative number of bedrooms in my dataset. Features.... Output (Rent) Row 1 0 Row 2 0 Row 3 0 However, my problem arises at examples with a low number of bedrooms (i.e. 0 bedrooms). The input features doesn't have enough information to determine the number of bedrooms for examples with a low number of bedrooms (which is fine because we assume that within this group, we will always decrease the rent, so we set the optimum price to say $2000. So row 1 price could be $8000, (8000 - 2000 = 6000, +ve value thus convert to class 0 or below optimum/decrease rent). And within this group we rely on the class balance to help the model learn to make predictions because the proportion is heavily skewed towards class 0 (say 95% = class 0 or decrease rent, and 5 % = class 1 or class 2). We do this based the domain knowledge of the data (so in this case, we would always decrease the rent because no one wants to live in a house with 0 bedrooms). MAIN QUESTION: We now want to predict (or undertake inference) for examples with number of bedrooms in between 0 bedrooms and 2 bedrooms (e.g 1 bedroom NOTE: our training data has no example with 1 bedroom). What I notice is that the model's predictions on examples with 1 bedroom act as if these examples had 0 bedrooms and it mostly predicts class 0. My question is, apart from specifically including examples with 1 bedroom in my input data, is there any other way (more statistics or ML related way) for me to improve the ability of my model to generalise on unseen data? submitted by /u/Individual_Ad_1214 [link] [comments]

Tesla has a version (V12.5) of their supervised "Full Self Driving" that potential showing signficant improvements, though we will wait to see how much miles per critical disengagment have gone up. (Maybe 600-1000. Previous versions at 100-200 miles per critical disengagement). In order to make this improvement, they upped the parameter count by 5x the previous models. They are just barely making it function on HW3 (works on HW4). These models are already taking advantage of distillation and compression techniques. Considering that the miles per critical disengagement still needs to go up another 100x, I would think model parameter count will have to go up signficantly, maybe 10x-100x? While there are continuing advances in model distillation and compression, I find it hard to fathom that much larger models needed to achieve unsupervised driving will be compressed even further. Tweets like this imply (presumably from advances like LLAMA 2 to LLAMA 3) that these compression ratios will continue at a massive pace. https://x.com/wintonARK/status/1816537413206048915 What do you think? To me, the likely needed increase in model size to get to robotaxi level fidelity will outweigh any advances in distillation so that HW3 will unlikely be able to handle the model. submitted by /u/ZeApelido [link] [comments]

I am in my second job after grad school and am noticing a pattern in how I feel about my job as time goes on. In my first job, I felt bored and restless around the 1.5-year mark and eventually left. Now, after 2.5 years at my current job, I find myself feeling bored and disinterested again. I fulfill my responsibilities and do them well, but I no longer go above and beyond as I did in the first year. Is it unusual to feel this way, or is it normal? submitted by /u/Lamp_Shade_Head [link] [comments]

EMNLP paper review scores Overall assessment for my paper is 2, 2.5 and 3. Is there any chance that it may still be selected? The confidence is 2, 2.5 and 3. The soundness is 2, 2.5, 3.5. I am not sure how soundness and confidence may affect my paper's selection. Pls explain how this works. Which metrics should I consider important. Thank you! submitted by /u/Immediate-Hour-8466 [link] [comments]

Hello, I'm working on interpolating 2D data slices from a 3D grid at a specific point. If I take slices around that point in various directions, can I average the kriging values from these different slices to obtain a single overall 3D kriging value for that point? Would doing this ruin the values outputted by the Kinging interpolation and make them have greater error? If I can take this approach what would be the best approach to ensure accuracy in this interpolation process? I am also doing this because I have a working 2D kriging add on for MATLAB that has good documentation but cant find a good one for 3D submitted by /u/RandomFactChecker_ [link] [comments]

https://openai.com/index/searchgpt-prototype/ We’re testing SearchGPT, a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources. submitted by /u/we_are_mammals [link] [comments]

Transformer-based LLMs like GPT, Gemini, LLaMa, are decoder-only architectures. This means that the compute utilized and the time it takes for an output is directly related to the size of the output, NOT the size of the input. Meaning you could feed it 100 pages, but if the output is 1 page, that's what matters for compute. It’s notoriously tricky to get an LLM to output text that’s short and concise, and many practitioners give up trying to get it to be concise after some tinkering with prompt engineering. However, fine-tuning deals with the problem at the source, retraining the model to prefer outputting shorter outputs. submitted by /u/juliannorton [link] [comments]

I built a simple starter demo of a Knowledge Question and Answering System using Llama 3.1 (8B GGUF) and Marqo. Feel free to experiment and build on top of this yourselves! GitHub: https://github.com/ellie-sleightholm/marqo-llama3_1 submitted by /u/elliesleight [link] [comments]

https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ They solved 4 of the 6 IMO problems (although it took days to solve some of them). This would have gotten them a score of 28/42, just one point below the gold-medal level. submitted by /u/we_are_mammals [link] [comments]

Hi community, I am exploring the Responsible AI domain where I have started reading about methods and tools to make Deep Learning Models explainable. I have already used SHAP and LIMe for ML model explainability. However, I am unsure about their use in explaining LLMs. I know that these methods are model agnostic but can we use these methods for Text Generation or Summarization tasks? I got reference docs from Shap explaining GPT2 for text generation tasks, but I am unsure about using it for other newer LLMs. Additionally, I would like to know, are there any better ways for Explainable AI for LLMs? submitted by /u/PhoenixHeadshot25 [link] [comments]

I have an MS in stats and 7 years as an analyst under my belt. I've been looking for a data scientist jb ever since I got the MS 4 years ago (got it part time while I started as an analyst) and have been having a hell of a time at it. I get plenty of interest in analyst positoins, but little interest in data scientist positoins. As I'm sure you all know, there is considerable overlap between the titles but HR drones and ATS doesn't necessarily know this. All they care about is key words. I've been offered a data scientist positoin at a company that I am ready to accept. The positoin is a little underpaid for a DS but about enough for me right now, but I'm thinking it could be a great stepping stone. I work that for 2-3 years then I'm competitive for higher compensated DS positoins. However I just got off the phone with a recriuter for a DA positoin that would pay between 25-40k more than the DS positoin (it's just a band at this point). The responsibilities are similar, it's just that this place has more money and is located in a HCOL are (both are remote though so COL and relocating are not a factor for me). More money now would be great, but I don't really know if this is going to leave me in a better position in a few years. Obviously, we're talking an offer vs just one phone screen, the higher DA positoin isn't a sure thing right now. But I'm just wondering if you guys would even keep pursing the DA positoin or just take the DS positoin and make up the difference in a few years with a higher paid DS positoin? Also I hate that this is a factor but I've done 12 interveiws just this month, I really REALLY don't want to do anymore, so it's a huge factor in me wanting to just drop out of the DA interveiw process and take the DS. submitted by /u/son_of_tv_c [link] [comments]

What is the standard way to model high-dimensional stochastic processes today? I have some process defined over images x, and I would like to compute P(x' | x, z) for all x'. I know there are Normalizing Flows, Gaussian Processes, etc, but I do not know which to get started with. I specifically want to compute the probabilities, not just sample some x' ~ P(x, z). submitted by /u/smorad [link] [comments]