machine_learning.md

Here are the Reddit posts categorized, sorted by score, and summarized:

Hardware:

1. [D] confused between M1 Max 16 inch 64GB vs m3 max 14 inch (32GB) (Score: 0) - Choosing between a 16" M1 Max with 64GB RAM and a 14" M3 Max with 32GB RAM for software development, machine learning, and gaming. Considering performance and longevity. Link
2. [D] AI workstation purchase advice (Score: 0) - Seeking advice on purchasing a workstation with an A100 or H100 GPU for training generative AI models like diffusion models for 256x256 image generation. Budget around $35k. Link
3. Suggestions for Hardware for AI subreddits or discussion forums. [Discussion] (Score: 1) - Looking for subreddits or forums to discuss AI/ML hardware research and collaborate on projects related to hardware accelerator systems design for AI/DL workloads. Link

Language Models:

1. [D] What is the current best in tiny (say, <10,000 parameters) language models? (Score: 46) - Inquiring about state-of-the-art in small language models with <10,000 parameters that can be built from scratch in C/C++ without dependencies. Wondering if anything beats HMMs and n-gram models at this size. Link
2. [D] Best language model to work locally (Score: 1) - Seeking opinions on the best language model for local use, particularly for code writing, without requiring a supercomputer. Link
3. [D] Real good use cases for LLMs and GenAI (Score: 0) - Looking for impactful and innovative use cases, demos, or projects leveraging LLMs or GenAI beyond customer service and coding copilots. Bonus for identifying overhyped "wrapper" products. Link

Conferences and Advice:

1. [D] Advice for attending ICLR 2024 as a first conference (Score: 5) - A junior college student seeking advice on making the most of their first conference (ICLR) and whether talking to professors could help with PhD prospects despite a rough start in college. Link

Data and Evaluation:

1. [D] What databases would you use for large amounts of csv data? (Score: 7) - Seeking recommendations for databases to store and quickly query large datasets (20 million rows) for machine learning projects, as an alternative to slow pandas processing. Link
2. [P] Choosing the right F1 score for sentiment classification by an Automatic Speech Recognition LLM (Score: 2) - A PM working on a conversational intelligence platform is seeking guidance on choosing the right F1 score range for sentiment classification (happy, unhappy, neutral) to balance accuracy and avoid overfitting. Current F1 scores are below 60%. Link

Theory:

1. [D] Intension vs Extension point of view? (Score: 0) - Inquiring about approaches to interpret neural networks using the philosophical framework of intension (e.g., temperature, speed) vs. extension (e.g., geometry). Link

Summary: The posts cover a range of topics relevant to AI engineers, including hardware considerations, language model architectures, conference advice, data storage and evaluation metrics, and theoretical frameworks.

In the hardware category, users are seeking advice on choosing between M1 Max and M3 Max MacBook configurations for AI development and purchasing workstations with high-end GPUs like the A100 or H100 for training generative models. There is also interest in finding forums to discuss AI hardware research and collaborate on projects.

Language models are another focus, with discussions on the state-of-the-art in tiny models (<10,000 parameters) that can be built from scratch without dependencies, as well as the best models for local use, particularly for code writing tasks. Users are also curious about innovative and impactful applications of LLMs and GenAI beyond customer service and coding assistance.

For those new to the field, there is a request for advice on making the most of attending ICLR as a first conference and leveraging the opportunity to connect with professors for potential PhD pursuits.

Data-related topics include recommendations for databases to efficiently store and query large datasets (e.g., 20 million rows) for machine learning projects, as an alternative to slower pandas processing. Additionally, a PM working on a conversational intelligence platform is seeking guidance on choosing the right F1 score range for sentiment classification to balance accuracy and avoid overfitting.

Finally, there is a theoretical question about interpreting neural networks using the philosophical framework of intension vs. extension, which could lead to new insights and approaches in understanding these models.

Overall, these posts highlight the diverse range of challenges and opportunities in the AI field, from hardware and architecture optimization to data management, evaluation metrics, and theoretical frameworks. Collaboration, innovation, and continuous learning are key themes throughout the discussions.

** OLD VERSION BELOW **

Hardware for AI/ML

• Suggestions for Hardware for AI subreddits or discussion forums: Looking for subreddits or forums to discuss research and collaborate on projects related to hardware accelerator systems and their complexities for AI/DL workloads. (1 upvote)
• Confused between M1 Max 16 inch 64GB vs M3 Max 14 inch (32GB): Seeking advice on choosing between a 16" M1 Max (64GB RAM, 1TB SSD) for INR 219,900 and a 14" M3 Max (36GB RAM, 1TB SSD) for INR 282,000 for software development, machine learning, and occasional gaming. (0 upvotes)
• AI workstation purchase advice: Looking to buy a workstation with an NVIDIA A100 or H100 GPU for training generative AI models like diffusion models for 256x256 image generation tasks, with a budget of around $35k. (0 upvotes)

Language Models & Architectures

• What is the current best in tiny (say, <10,000 parameters) language models?: Inquiring about the state-of-the-art in small language models with fewer than 10,000 parameters, and whether anything beats HMMs, n-gram models, etc. when restricted to this size. (46 upvotes)
• Best language model to work locally: Seeking opinions and experiences on choosing a language model that can work locally without requiring a supercomputer, primarily for code writing tasks. (1 upvote)

Applications & Use Cases

• Real good use cases for LLMs and GenAI: Looking for impactful and out-of-the-box use cases, demos, or projects showcasing the potential of LLMs and GenAI beyond customer service and coding copilots. (0 upvotes)
• Choosing the right F1 score for sentiment classification by an Automatic Speech Recognition LLM: Seeking guidance on determining the appropriate range of F1 scores for happy and unhappy sentiment classification in a Conversational Intelligence platform, considering the risk of overfitting. (2 upvotes)

Data & Databases

• What databases would you use for large amounts of csv data?: Inquiring about database options for quick query and storage of large datasets (20 million rows) to scale model development beyond pandas and CSV files. (7 upvotes)

Conferences & Networking

• Advice for attending ICLR 2024 as a first conference: A junior college student seeking advice on making the most out of attending ICLR 2024 as a first-time conference goer and presenter, and whether talking to professors from other schools could help with PhD prospects despite a rough start in college. (5 upvotes)

Theoretical Concepts

• Intension vs Extension point of view?: Inquiring about approaches to interpret neural networks in terms of the intension vs extension framework, a philosophical divide where intension refers to concepts like temperature and speed, while extension refers to geometry. (0 upvotes)

The discussions on the Machine Learning subreddit cover a wide range of topics, from hardware considerations for AI/ML workloads to the applications and use cases of large language models (LLMs) and generative AI (GenAI). There is a strong interest in finding the best language models that can work locally without requiring extensive computational resources, as well as determining appropriate evaluation metrics for specific tasks like sentiment classification.

Hardware-related questions revolve around choosing the right workstation configurations for training generative AI models, with users seeking advice on specific setups like M1 Max vs M3 Max MacBook Pros and workstations with NVIDIA A100 or H100 GPUs. There is also a desire to find communities and forums dedicated to discussing the complexities of hardware accelerator systems for AI/DL workloads.

In terms of language models, there is curiosity about the current state-of-the-art in tiny language models with fewer than 10,000 parameters, and whether traditional architectures like HMMs and n-gram models can be outperformed in this size range. Users are also looking for language models that can work locally without requiring supercomputers, particularly for code writing tasks.

The potential applications and use cases of LLMs and GenAI are a topic of interest, with users seeking impactful and out-of-the-box examples beyond customer service and coding copilots. One specific application discussed is sentiment classification using an Automatic Speech Recognition LLM, where guidance is sought on determining the appropriate range of F1 scores to avoid overfitting.

Data management and storage are also concerns, with users inquiring about database options for handling large CSV datasets to scale model development beyond pandas and CSV files. Additionally, there is interest in exploring theoretical concepts like the intension vs extension framework in the context of interpreting neural networks.

Lastly, the importance of networking and knowledge sharing is highlighted through a post seeking advice on attending ICLR 2024 as a first-time conference goer, with the added goal of potentially improving PhD prospects through interactions with professors from other schools.