I am working on a project regarding marketing of AI powered products in Retail stores. I am trying to find some products that market ‘AI’ as the forefront feature, eg Samsung’s BeSpoke AI series, Bmw’s AI automated driving etc. Need them to be physical products so I can go to stores and do research and survey. Any kind of help is appreciated. submitted by /u/Complete-Holiday-610 [link] [comments]

Latest releases of models such as GPT-4 and Claude have a significant jump in the maximum context length (4/8k -> 128k+). The progress in terms of number of tokens that can be processed by these models sound remarkable in % terms. What has led to this? Is this something that's happened purely because of increased compute becoming available during training? Are there algorithmic advances that have led to this? submitted by /u/analyticalmonk [link] [comments]

Used to receive invitation by this time of the year. Maybe I am forgotten. submitted by /u/noname139713 [link] [comments]

I'm a recent engineering graduate who's switching roles from traditional software engineering ones to ML/AI focused ones. I've gone through an introductory probability course in my undergrad, but the recent developments such as diffusion models, or even some relatively older ones like VAEs or GANs require an advanced understanding of probability theory. I'm finding the math/concepts related to probability hard to follow when I read up on these models. Any suggestions on how to bridge the knowledge gap? submitted by /u/AffectionateCoyote86 [link] [comments]

Say I have 1000 PDF docs that I use as input to a RAG Pipeline. I want to to evaluate different steps of the RAG pipeline so that I can measure: - Which embedding models work better for my data? - Which rerankers work and are they required? - Which LLMs give the most factual and coherent answers? How do I evaluate these steps of the pipeline? Based on my research, I found that most frameworks require labels for both retrieval and generation evaluation. How do I go about creating this data using a LLM? Are there any other techniques? Some things I found: For retrieval: Use a LLM to generate synthetic ranked labels for retrieval. Which LLM should I use? What best practices should I follow? Any code that I can look at for this? For Generated Text: - Generate Synthetic labels like the above for each generation. - Use a LLM as a judge to Rate each generation based on the context it got and the question asked. Which LLMs would you recommend? What techniques worked for you guys? submitted by /u/awinml1 [link] [comments]

Hi everyone. I want to build this idea of mine for a class project, and I wanted some input from others. I want to build an AI algorithm that can play the game Drift Hunters (https://drift-hunters.co/drift-hunters-games). I imagine I have to build some reinforcement learning program, though I'm not sure exactly how to organize state representations and input data. I also imagine that I'd need my screen to be recorded for a continuous period of time to collect data. I chose this game since it's got three very basic commands(turn left, turn right, and drive forward) and the purpose of the game(which never ends) is to maximize drift score. Any ideas are much appreciated. lmk if u still need more info. Thanks everyone. submitted by /u/Valuable-Wishbone276 [link] [comments]

PDF: https://arxiv.org/abs/2404.11457 GitHub: https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey ​ Abstract: With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces emerging challenges, particularly in terms of biases and unfairness, which may threaten the information ecosystem. In this paper, we present a comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of LLMs. We first unify bias and unfairness issues as distribution mismatch problems, providing a groundwork for categorizing various mitigation strategies through distribution alignment. Subsequently, we systematically delve into the specific bias and unfairness issues arising from three critical stages of LLMs integration into IR systems: data collection, model development, and result evaluation. In doing so, we meticulously review and analyze recent literature, focusing on the definitions, characteristics, and corresponding mitigation strategies associated with these issues. Finally, we identify and highlight some open problems and challenges for future work, aiming to inspire researchers and stakeholders in the IR field and beyond to better understand and mitigate bias and unfairness issues of IR in this LLM era. ​ https://preview.redd.it/3glvv92v6dvc1.png?width=2331&format=png&auto=webp&s=af66f2bf082620882f09ea744eda88cf06c67112 ​ https://preview.redd.it/d48pt3sw6dvc1.png?width=1126&format=png&auto=webp&s=2343460399473bde3f5e37c0bbcfdc88ffc81efb submitted by /u/KID_2_2 [link] [comments]

I’m going to be starting my Masters Thesis in my stats program. I have narrowed down two projects with different faculty members. One of them is entirely self guided, and that advisor doesn’t really have any expertise in this area and the other one is basically with another faculty member but the research topic is within his domain, so he can help me, but the application strays too far from what industry I want to be in. Project 1: Causal Machine Learning, and nonparametric estimation for identifying heterogenous treatment effects in advertising/marketing data. There’s a dataset by a company in the marketing/adtech space that they have made public, and is essentially an open dataset related to shopping transactions, and demographics of shoppers, and information on marketing campaigns that were run by the company. Ideally this would be a causal inference project, where I get to actually learn causal inference and causal (double) machine learning on my own, and apply it to this dataset. No new methods being created, just a standalone causal inference analysis and basically I walk away with learning a new area of statistics and a way to solve causal problems. My advisors background is nonparametric regression so he would be able to give me advice on the estimators being used (some of the methods use random forests, tree based methods, splines, kernel methods etc.) so he could be of help in that sense. But he knows nothing about causal inference. So I’d be on my own on this one. I want to take on this project despite it being quite isolated because I want to signal to my future employer that I can work on data science problems involving causal inference. I only have 1 year, so id have to self learn causal inference + do the analysis + the paper. Project 2: Bayesian Dimension Reduction in gene expression data Another advisor works in the area of dimension reduction, and broadly works in genomics. The project he was proposing for me was an actual opportunity to create some new methods. So much more on the research side than doing an analysis like project 1. It involves looking at Bayesian approaches to doing common dimension reduction techniques like PCA, Factor Analysis etc. From this paper I’d walk away with a good opporuntity to dive deeper into Bayesian inference, and work on methods research. I’ve never done a bayes project before, despite just taking a class on it. The cons to this is I don’t really have any interest working in bioinformatics, but this research is within the realm of my advisor so he can actually help me. The other con, is I don’t want my future employer to think I’m not qualified for tasks involving causal inference because I worked on dimension reduction, and with applications to genetic data, which is not really the industry I want to be in. Does anyone have any input on this? Does choosing a topic not related to causal inference lessen my chances of getting a job involving that? Do people care what my thesis topic is? Or does the MS in Stats signal that I could work on causal inference? submitted by /u/Direct-Touch469 [link] [comments]

Trying to implement a recommender model for an equivalent of a dating app, where I don't want to recommend someone if they have already been seen by the current user. For the embeddings ANN search, I've looked into Pinecone, VertexAI and Qdrant, but it seems that the filtering is usually limited to larger categories (e.g country). I haven't examples of filters of the type : `{user_id: {not_in: ['123', '456' , ... ]}}`. A user might have already seen 1000s of recommendations. What is the usual approach for this type of history-aware ANN? submitted by /u/overflozz [link] [comments]

Hi everyone, I want to know if there are developments and research topics that are outside/completely orthogonal to LLMs and generative AI. To be honest, I am bored of LLMs. I don't care about the performance of Model X vs Models A,B,C,D etc. Moreover, at least 8 out of 10 projects in my organization are focused on generative AI and RAG. While I understand the usefulness of these ideas, I think there's an overload of information that is not particularly helpful for my brain. Personally, I am interested in scientific machine learning- drug discovery, climate change, physics simulations. If there are other research areas that you are aware of, please feel free to share. From a "long term career perspective", I want to transition to companies that work on problems in imaging and communications (I have a background in signal/image processing and computer vision). I am very much interested in novel imaging techniques that use some kind of computational imaging and ML algorithms. Qualcomm and Samsung come to my mind- but I could be wrong. submitted by /u/madhav1113 [link] [comments]

So, nowadays, everyone is distilling rationales gathered from a large language model to another relatively smaller model. However, I remember from the old days that we did we train the small network to match the logits of the large network when doing distillation. Is this forgotten /tried and did not work today? submitted by /u/miladink [link] [comments]

What's the process/approach:architecture of combining multiple models of different modality to generate sane output? Just curious what your experiences have been. Any pointers or links to research would be handy. submitted by /u/hophophop1233 [link] [comments]

I've been examining the real-world context limits of large language models (LLMs), and I wanted to share some enlightening findings from a recent benchmark (RULER) that cuts through the noise. What’s the RULER Benchmark? Developed by NVIDIA, RULER is a benchmark designed to test LLMs' ability to handle long-context information. It's more intricate than the common retrieval-focused NIAH benchmark. RULER evaluates models based on their performance in understanding and using longer pieces of text. Table highlighting RULER benchmark results and effective context lengths of leading LLMs Performance Highlights from the Study: Llama2-7B (chat): Shows decent initial performance but doesn't sustain at higher context lengths. GPT-4: Outperforms others significantly, especially at greater lengths of context, maintaining above 80% accuracy. Command-R (35B): Performs comparably well, slightly behind GPT-4. Yi (34B): Shows strong performance, particularly up to 32K context length. Mixtral (8x7B): Similar to Yi, holds up well until 32K context. Mistral (7B): Drops off in performance as context increases, more so after 32K. ChatGLM (6B): Struggles with longer contexts, showing a steep decline. LWM (7B): Comparable to ChatGLM, with a noticeable decrease in longer contexts. Together (7B): Faces difficulties maintaining accuracy as context length grows. LongChat (13B): Fares reasonably up to 4K but drops off afterwards. LongAlpaca (13B): Shows the most significant drop in performance as context lengthens. Key Takeaways: All models experience a performance drop as the context length increases, without exception. The claimed context length by LLMs often doesn't translate into effective processing ability at those lengths. GPT-4 emerges as a strong leader but isn't immune to decreased accuracy at extended lengths. Why Does This Matter? As AI developers, it’s critical to look beyond the advertised capabilities of LLMs. Understanding the effective context length can help us make informed decisions when integrating these models into applications. What's Missing in the Evaluation? Notably, Google’s Gemini and Claude 3 were not part of the evaluated models. RULER is now open-sourced, paving the way for further evaluations and transparency in the field. Sources I recycled a lot of this (and tried to make it more digestible and easy to read) from the following post, further sources available here: Harmonious.ai Weekly paper roundup: RULER: real context size of LLMs (4/8/2024) submitted by /u/ParsaKhaz [link] [comments]

Hello Reddit! We're a group of students from Carnegie Mellon passionate about data. We've been working on a project that tackles one of the biggest challenges in AI today: data bias. Our tool is designed to audit your datasets and generate synthetic, privacy-safe data that helps promote fairness in AI applications. Here’s why we think it could be a game-changer: Detect and Correct Data Biases: Ensures your AI models are built on fair and balanced data. Enhance Data Quality: Improves the reliability and performance of your AI systems. Generate Synthetic Data: Expands your dataset without compromising privacy. We believe in a future where data-driven technologies are equitable and just, and we're eager to contribute to making that a reality. We’d love to get your insights and feedback to further refine our tool. Join Us in Shaping the Future of Fair AI Are you interested in AI fairness? Do you have experiences with biased data or ideas on how to address these issues? Let’s start a conversation! Comment here or send us a DM. We’re here to discuss and collaborate on getting you the data you deserve! submitted by /u/ComplexAnalysis42 [link] [comments]

I've a CS degree and I'm currently working as a new grad Data Analyst. Aside from building dashboards and reports, I also do a lot of data engineering and some ML modelling. I'm planning to get a CS Masters after 1 - 2 years of work, then switch to DS. But looking at this sub, it seems like a lot of people recommend switching to SWE instead? I do enjoy software engineering work so I wouldn't mind the transition, but I'm interested in ML and thought DS would be a good career path for that. Is Data Science really a dying field and pay less than SWE? I even heard that switching from DS to SWE is more difficult than the other way around, because any SWE learning ML can do DS work. And I've seen people saying that SWE is more stable and easier to get a job than DS. Is any of this true? submitted by /u/---Imperator--- [link] [comments]

Was super excited to read the news from Meta this morning, particularly around the HumanEval scores the 70B model got. Thought it'd be useful to make the new Llama 3 70B available to anyone that wants to try it, so I added it to my VS Code coding copilot extension double.bot. Also making it free for the first 50 messages so everyone gets a chance to try it while we wait for the quantized versions to run locally submitted by /u/geepytee [link] [comments]

Hi everyone, I’m a marketing major about to graduate in May. A year and a half ago I took basic hypothesis testing/linear modeling class to try out an analytics certificate at my school, and I fell in love with statistics for the first time. When I began looking for job/internship opportunities around that time I was worried because while I didn’t mind marketing, I also didn’t love it. That’s when I made the decision to continue my degree in business, and work every week towards becoming a data analyst (and eventually, a data scientist! But I’m patient, and I wanted to wait until I’m ready. There’s a lot to statistics/programming, as I now know… lol). It’s been a long and very hard road. But I now have a job as a data analyst and I’m working on a large machine learning personal project now. I see a lot of negative discussion in this sub (which is entirely fair); however, I just wanted you all to know that as someone who is not taking the traditional route into data science, I think your job is awesome. I think what you do is fascinating and what statistical modeling might accomplish in the future is inspiring. Have a great day, and I hope I have the pleasure of meeting some of you in the field one day. submitted by /u/Wqrped [link] [comments]

Llama-3 is making waves in the AI community. I was curious how it will perform in the medical domain, Here are the evaluation results for Llama-3 (7B and 70B) on a medical domain benchmark consisting of 9 diverse datasets https://preview.redd.it/sdwx5tglxbvc1.png?width=1464&format=png&auto=webp&s=d32585a69244d44c83e2b1e8a85301a7a8676ea2 I'll be fine-tuning, evaluating & releasing Llama-3 & different LLMs over the next few days on different Medical and Legal benchmarks. Follow the updates here: https://twitter.com/aadityaura https://preview.redd.it/9egbcayv9avc1.png?width=1344&format=png&auto=webp&s=436a972421d5568e1a544962b8cfd1c7b14efe04 submitted by /u/aadityaura [link] [comments]

I have been hunting jobs for almost 4 months now. It was after 2 years, that I opened my eyes to the outside world and in the beginning, the world fell apart because I wasn't aware of how much the industry has changed and genAI and LLMs were now mandatory things. Before, I was just limited to using chatGPT as UI. So, after preparing for so many months it felt as if I was walking in circles and running across here and there without an in-depth understanding of things. I went through around 40+ job posts and studied their requirements, (for a medium seniority DS position). So, I created a plan and then worked on each task one by one. Here, if anyone is interested, you can take a look at the important tools and libraries, that are relevant for the job hunt. Github, Notion I am open to your suggestions and edits, Happy preparation! submitted by /u/xandie985 [link] [comments]

I have been hunting jobs for almost 4 months now. It was after 2 years, that I opened my eyes to the outside world and in the beginning, the world fell apart because I wasn't aware of how much the industry has changed and genAI and LLMs were now mandatory things. Before, I was just limited to using chatGPT as UI. So, after preparing for so many months it felt as if I was walking in circles and running across here and there without an in-depth understanding of things. I went through around 40+ job posts and studied their requirements, (for a medium seniority DS position). So, I created a plan and then worked on each task one by one. Here, if anyone is interested, you can take a look at the important tools and libraries, that are relevant for the job hunt. Github, Notion I am open to your suggestions and edits, Happy preparation! submitted by /u/xandie985 [link] [comments]