PublicSphereTech

Reinforcement Fine-Tuning refers to the process of refining or adapting a pre-trained reinforcement learning (RL) model to perform better in a specific task or environment. Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. Fine-tuning in this context involves further training an already trained RL model using additional data, expert guidance, or specific constraints to improve its performance on a particular problem or set of tasks. The goal is to make the model more effective, efficient, or specialized in solving the desired task by adjusting its parameters, often using techniques like supervised learning, imitation learning, or other domain-specific modifications. This approach is particularly useful when a general RL model has already been trained on broad data, but more precise, task-specific behavior is required for optimal performance. #12_Days_Of_OpenAi #12DaysOfOpenAi

Day2: preview of reinforcement Fine tuning gpt-o1 The Open Ai's team introduced a preview of an automation system in reinforcement fine-tuning in which you can fine-tune one of Open Ai's Models with your dataset in your own domain of knowledge just by uploading a json file and a grader. They did offer this automation with supervised learning before but now they added the reinforcement fine-tuning that you can choose while configuring your automation The difference between them is that while supervised fine-tuning focuses on learning from labeled data, reinforcement learning (RL) involves learning from feedback based on the actions taken by the model. In supervised fine-tuning, the model is trained to minimize the error between its predictions and the actual outputs, whereas in RL, the model learns to maximize a reward signal based on its actions. This distinction is crucial for understanding the different methodologies in model training. #12_Days_Of_OpenAi #12DaysOfOpenAi

Artificial Neural Network https://lnkd.in/ex9PA6XR #Artificial_Neural_Network #ArtificialNeuralNetwork #ANN

Day 1: OpenAI o1 and o1 pro mode in ChatGPT Open AI's started their "12 days of Open AI" journey by introducing the gpt o1 and gpt o1 pro! Let's start checking out the full o1 model which Sam Alton claims to be the smartest model in the world right after their own o1-pro model by going through whats new! 1.supporting multi-model inputs meaning it can reason through text and images jointly for example if you have a question that features a graph as the entry data you can now attach the photo along with your question and o1 will process the image and the question's text (using different models) and then finds the relations between them and solves the question at last. you can see the demo provided by Open AI's team in [figure 5] that responded in only 10 seconds which is quite impressive when you think about the whole process. 2.it is much faster than the o1-preview model since it can now detect the difficulty of the task and spends the right amount of computing time needed and you no longer need to wait for it to finish "thinking" for simple tasks therefore making it more suitable for everyday use cases. The Open Ai's Team compared the gpt o1 and o1-preview's speed, live, by asking them the same question which o1 answered within 14 seconds while o1-preview took 33. [figure 4] 3.powerful logical reasoning which makes it enable to solve complex problems. In the demo provided by The Open Ai's team, they asked gpt o1-pro a very complex chemistry problem that took the model 53 seconds to respond.[figure 7] 4.Increased accuracy compared to the gpt-4o model [figure 1] and the gpt o1-preview model [figure 2] in solving Competition Math (AIME 2024) and PhD-Level Science questions according the charts provided by the Open AI's team. 5.Improved reliability especially in o1-pro due to its stricter protocol that attempts solving each question four times that considers the question solved if all four attempts lead to the same answer [figure 3]. According to a detailed suite of human evaluations they ran not only it's 50% faster at thinking it also makes major mistakes 34% less often than o1 preview . The Open AI also hinted at future tools and features they are going to add such as web browsing, file uploads and bringing gpt o1 to the API for developers to implement. Open AI introduced a new pricing plan called pro which includes unlimited access to gpt o1, gpt o1-pro, GPT-4o, and Advanced Voice mode for the price of 200$ per month. The company will continue to offer the Plus tier for $20 a month that includes early access to new features, access to all the company’s models except the more powerful o1 version.

Preview of "12 Days of Open AI" Open AI recently announced "12 Days of Open Ai" event in which they will introduce a new product, Ai model or feature every weekday on livestream One thing for sure is that it's gonna be full of surprises but here are some predictions of what we might see in the coming 12 days! --Sora, Open Ai's text to video model, is their most anticipated product to get a stable public release since they previewed multiple clips created by it on February 15, 2024. Event though it's previous release date was rumored to be on August 24, 2024, it is still not available for public use. --Full release of the reasoning o1 model, which will likely allow it to get access to ChatGPT memory GPTs, and live search data --More stable GPT-4o image generation, since not all users can access it or get desirable result or even being locked down by Open Ai  ---Advanced Voice improvements, this could include new features like live search access or even the ability of providing real-time analysis by looking through your phone camera or webcam! ---Canvas, might get new features like stacking different canvas elements within a chat. ---SearchGPT, a new AI search features that give you fast and timely answers with clear and relevant sources. preview of o2 or GPT-5o, Sora-Turbo with a "Sora-full", Voice Engine, OpenAI's text-to-speech tool and 'operator' are also among the prediction experts and people have made about this event We will cover each and everyone of them immediately after they get announced so stay tuned the event starts today  

🤖 **How AI Assists in Discovering New Medicines**   AI is revolutionizing the pharmaceutical industry by speeding up drug discovery, reducing costs, and improving success rates. Here's how AI is playing a pivotal role in the process: 1. **Drug Discovery & Design**:     AI can predict which compounds may work as effective drugs, by analyzing vast amounts of chemical data. Platforms like **Atomwise** use AI to identify promising drug candidates. 2. **Predicting Drug Interactions**:     AI algorithms can predict how different drugs will interact with each other and with the body, reducing trial and error in drug development. 3. **Personalized Medicine**:     AI helps tailor treatments to individual patients based on their genetic data, improving effectiveness and minimizing side effects. 4. **Clinical Trials Optimization**:     AI streamlines patient selection and recruitment for clinical trials, making the process faster and more accurate. AI is making the future of medicine brighter. The possibilities are endless! 💡💊 #AI #DrugDiscovery #PharmaceuticalInnovation

**Cooking Robots: Will Our Future Meals Be Made by Machines?** The intersection of robotics and **artificial intelligence (AI)** is transforming industries, and the food sector is no exception. Robotic kitchens, AI-driven food preparation, and automated cooking systems are redefining what’s possible in food production. But as AI and robotics continue to evolve, the question remains: Can machines replicate the art of cooking, or will humans always have a role in the kitchen? ### **Key Developments in Cooking Robots**: 1. **AI-Powered Automation in Cooking**:     **Moley Robotics**, a UK-based company, has created the world’s first fully functional robotic kitchen capable of preparing gourmet meals. Their system uses **AI algorithms** and **machine learning (ML)** to analyze the cooking techniques of professional chefs, which it then applies through robotic arms. The system can replicate intricate cooking techniques like **sautéing**, **baking**, and **grilling**, and is capable of preparing complex dishes such as **beef Wellington** and **lobster bisque** with precision and consistency. This technology is opening up the possibility of automating high-end cooking in commercial kitchens. 2. **Efficiency and Consistency in High-Volume Food Production**:     In commercial kitchens, particularly in fast food and chain restaurants, consistency and speed are key. **Spyce**, a Boston-based restaurant, uses a robotic kitchen system that integrates **machine learning** and **robotics** to prepare meals with speed and precision. The system uses sensors and cameras to ensure that each meal is cooked to perfection every time. By automating the cooking process, **Spyce** eliminates human error and ensures a consistent product, even during peak hours. The robot chef can prepare dishes in minutes, offering not only speed but also high quality and consistency across servings. 3. **The Human Touch in Culinary Arts**:     While robotic kitchens like those from **Moley Robotics** and **Spyce** offer impressive efficiency and consistency, one crucial element is missing: the **creativity and artistry** of human chefs. AI can follow a recipe, and robotics can replicate cooking steps, but they lack the **creativity** and **innovation** that human chefs bring to the table. The art of creating new dishes, experimenting with flavors, and pairing ingredients is something that AI systems cannot yet replicate. For example, AI may be able to prepare a meal like **beef Wellington**, but it cannot invent a new dish that blends flavors in a completely novel way. **AI lacks intuition** — the sensory feedback that chefs rely on for tasting, adjusting, and innovating.  #CookingRobots #AI #FoodTech

📊🚀 **How to Transform Your Business with Data Science?**   Data science is not just a trend; it’s a game-changer for businesses. By harnessing the power of **data analysis**, **predictive modeling**, and **machine learning**, you can unlock new growth opportunities, optimize operations, and better serve your customers. Here’s how: 1. **Make Data-Driven Decisions**:     Use data to guide business decisions, from marketing strategies to product development. Tools like **Google Analytics** and **CRM software** can provide insights into customer behavior and market trends. 2. **Optimize Operations**:     Data science can streamline processes and improve efficiency. Predictive maintenance models help prevent machine failures, and **supply chain optimization** ensures better resource management. 3. **Personalize Customer Experience**:     By leveraging customer data, you can create personalized experiences and improve customer satisfaction. For example, **Amazon** uses recommendation systems to personalize product suggestions based on browsing history. 4. **Predict Future Trends**:     Data science allows you to anticipate market changes and customer needs. **Predictive analytics** can forecast demand, helping you stay ahead of the competition. Start using data science today and transform your business! 🚀📊 #DataScience #BusinessGrowth #PredictiveAnalytics