Hi everyone, In order to start data manipulation or to train my AI by using a set of database. What is the minimal requirements that my devices must have to deal with such data and what is the most important component that deal with such data (RAM,CPU,etc..)? Also can I use the cloud as an alternative solution to my slow device?

Hello everyone. Just wanted to share something small. I read this blog which talked about how Large Language Models (LLMs) have evolved over the past months. Firstly, we have pre-trained LLMs which give the user output based on the data they've been trained with. A good example is OpenAI's Chat GPT trained on GPT 3.5 and GPT 4.0 (premium). Now, you can also have access to these LLMs and train them with specific data to perform a specific task. This is how many AI websites are springing up - the take the base LLMs, repeatedly train it with data until it is good enough to produce reasonable output. The following trend is going to be a break in the AI evolution. Recently, developers are trying to build AI interfaces in a way such that they produce a key based on user input, index all relevant data, use that data and info from your input to give you an output. It's similar to how CPUs and RAMs work together. When processing data, CPUs retrieve data necessary from the RAM which accesses this data from the HDD/SSD. It then uses this processes user input with any relevant data from the RAM into output. After this the data is discarded from the RAM This means that LLMs won't have to be trained over and over again for better accuracy and wouldn't consume large amounts of data. This will also greatly boost generative AI capabilities. Hope what I shared was good enough. Let me know if you need any clarifications.

The self-assessment on ML knowledge and experience is based on the common 7 steps of machine learning (ML): 1. Data Collection: Gather and prepare a dataset. 2. Data Preprocessing: Clean and format the data. 3. Split Data: Divide it into training and testing sets. 4. Model Selection: Choose the appropriate algorithm. 5. Model Training: Teach the model on the training data. 6. Model Evaluation: Assess performance on the testing data. 7. Model Deployment: Implement the model in real-world applications and maintain it as needed. Self-assessment: evaluating our knowledge and experience in each step of the machine learning process. Data Collection: 1: No experience, unfamiliar with data collection. 2: Limited experience, but can collect simple datasets. 3: Moderate experience, can collect and preprocess data effectively. 4: Proficient, experienced in collecting diverse datasets. 5: Expert, can handle complex data collection scenarios. Data Preprocessing: 1: Minimal knowledge, need help with data cleaning. 2: Some familiarity with data cleaning techniques. 3: Can handle basic data cleaning tasks. 4: Proficient in data preprocessing, can deal with complex datasets. 5: Expert in data cleaning and transformation. Split Data: 1: No understanding of data splitting. 2: Familiar with the concept but need guidance. 3: Can split data into training and testing sets. 4: Experienced in data splitting strategies (e.g., cross-validation). 5: Expert in data partitioning and cross-validation techniques. Model Selection: 1: Little knowledge, unsure how to choose models. 2: Basic understanding, but difficulty selecting models. 3: Can choose models for simple problems. 4: Proficient in selecting models for various tasks. 5: Expert at model selection and tuning. Model Training: 1: Limited experience, not familiar with model training. 2: Some understanding but require assistance. 3: Can train models on straightforward datasets. 4: Proficient in training a variety of models. 5: Expert in training complex models and deep learning.

Hello, everyone ~ I’m a student in graduate school, studying for my master degree. I would like to ask you guys, which parts in your work that have already been using Ai, as a tool to help your work more efficiently. Or you and your team are preparing to add some new methods with Ai, to achieve your goals. Because I have an oral presentation tomorrow night, I wish some of you could share with me~thanks😄

Potential!