AI Update: Interactive Script Writing Prompt Inside, ChatGPT Has Web Access Again, Model Merging

BREAKING NEWS

OpenAI Gives ChatGPT Access To The Entire Internet

OpenAI's ChatGPT has been a powerful tool since its release, but it has been limited in terms of its knowledge. That is, until now. OpenAI has just announced that ChatGPT can now browse the internet to provide users with current and authoritative information, thanks to an integration with Microsoft's Bing search engine.

This new capability is available to ChatGPT Plus subscribers and ChatGPT Enterprise users. It marks a return to web browsing for ChatGPT, which had temporarily disabled the feature due to users exploiting it to bypass paywalls on news sites. However, the browsing feature now recognizes the "robots.txt" code that website owners use to exclude web crawlers from indexing their content.

OpenAI's leadership, including CEO Sam Altman and CTO Mira Murati, took to their personal accounts to celebrate the return of web browsing for ChatGPT. It's worth noting that Microsoft's Bing Chat, powered by a more powerful OpenAI language model, also offers web browsing functionality with ChatGPT-style citations.

So, what sets ChatGPT's browsing capabilities apart? According to sources, the ChatGPT interface allows users to browse the web without leaving the ChatGPT interface, making it more convenient to access other features.

This announcement comes just days after OpenAI introduced the ability for ChatGPT to analyze images and conduct conversations over audio. OpenAI has been expanding the capabilities of ChatGPT, leveraging its natural language processing and conversational skills.

OpenAI's continuous improvements to ChatGPT highlight the company's commitment to pushing the boundaries of AI technology. However, as with any powerful tool, there are also concerns about the potential misuse and ethical implications. It's crucial for OpenAI to ensure that ChatGPT is used responsibly and that safeguards are in place to prevent abuse.

As AI continues to advance, it's important for society to have ongoing discussions about the ethical and societal implications of these technologies. OpenAI's latest update to ChatGPT is just one example of how AI is shaping the way we access and interact with information. The question now is how we can harness these capabilities for the greater good while mitigating potential risks.

OTHER NEWS

What is Model Merging?

Model merging refers to the process of combining multiple distinct models, each designed to perform separate tasks or solve different problems, into a single unified model without requiring additional training. Depending on the specific technique and goal, merging models can also be called ensemble learning, model blending, or model stacking.

This technique aims to create a more versatile and comprehensive Machine Learning model capable of handling various tasks simultaneously.

In the context of LLMs, model merging can involve combining LLMs with different initializations, architectures, or training on different tasks. The primary goal is to leverage the strengths of each model and create a multi-task LLM that can address a broader range of tasks.

This approach can significantly improve performance and efficiency by allowing the combined model to benefit from the knowledge and capabilities of each constituent model.

WHY MERGE ML MODELS?
Combining Machine Learning models offers several benefits, such as reducing prediction variability and bias through averaging or voting among diverse models.

Leveraging complex patterns and features from various data sources and models can enhance prediction accuracy and adaptability. Moreover, model merging can improve prediction diversity and reliability by reducing reliance on a single dataset or algorithm.

Model merging results in better performance, improved efficiency, and broader applicability, making it a valuable strategy for leveraging the strengths of different AI models without the need for extensive additional training.

STRATEGIES FOR COMBINING LLMS
One common approach is to combine models by averaging their weights or parameters. This can result in a fused model that benefits from the knowledge and expertise embedded in each original model.

Model merging may also involve the integration of features from each model. This is particularly useful when the models have learned task-specific features that are valuable for the overall performance of the merged model.

Some model merging techniques allow for merging models up to a specified layer, creating a multi-head model. This approach can be beneficial when different models specialize in different aspects of a task.

FUSING FINE-TUNED MODELS FOR BETTER PRETRAINING
In this research, the authors acknowledge that pre-trained models are widely used as a starting point for natural language processing tasks but can be expensive to create. They propose a novel approach of fusing multiple existing fine-tuned models into one, using an average of their weights.

This fused model consistently outperforms pre-trained models and is often superior to intertraining, where a base model is fine-tuned on another task. The fusion process is less dependent on the target task and remains effective even with weight decay, providing a more cost-effective and resource-efficient method for improving model initialization in NLP.

RESOLVING INTERFERENCE WHEN MERGING MODELS
Transfer learning, which involves further fine-tuning pre-trained models for downstream tasks, offers improved performance, faster convergence, and sample efficiency.

However, task-specific fine-tuned models often cannot collaborate effectively. Model merging methods have emerged to address this, but they frequently neglect interference between parameters from different models, causing performance drops.

In response, the authors propose TIES-MERGING, which resolves interference issues by resetting parameters, resolving sign conflicts, and merging only compatible parameters.

TIES-MERGING outperforms existing methods across diverse settings, emphasizing the importance of addressing interference in model merging for enhanced performance and versatility.

ZIPIT! MERGING MODELS FROM DIFFERENT TASKS WITHOUT TRAINING

This research addresses the challenge of merging distinct models with different initializations, each trained for a separate task, into a single multi-task model without additional training. While previous model merging methods work for models trained on the same task, they fall short when combining models trained for different tasks.

The authors introduce “ZipIt,” a general merging method for arbitrary models with the same architecture to overcome this limitation. ZipIt incorporates two key strategies: first, it allows for merging features within each model to account for non-shared features, and second, it supports partial merging up to a specified layer, creating a multi-head model.

These innovations result in a significant 20-60% improvement over previous methods, enabling the effective merging of models trained on disparate tasks.

Model merging is a powerful technique that allows for the combination of multiple models to create a more versatile and comprehensive Machine Learning model. By leveraging the strengths of each model, model merging can enhance performance, improve efficiency, and broaden the applicability of AI models.

Recent research papers have proposed innovative approaches to address the challenges and limitations of model merging, further advancing the field. As AI continues to evolve, model merging will undoubtedly play a crucial role in unlocking the full potential of AI systems.

SOCIAL MEDIA

Crash Course On Embedding and Vector Databases in OpenAI

PROMPT OF THE DAY

An interactive prompt to map out a video production in the style of Gary Vaynerchuk.

For the prompt, ChatgPt will assume multiple roles to complete the task and ask you a series of questions to create each of the scenes and recommend b-roll clips.

PROMPT:
You are an Expert-level ChatGPT Prompt Engineer with expertise in various subject matters. Throughout our interaction, you will refer to me as Kevin. Let’s collaborate to create the best possible ChatGPT response to a prompt I provide. We will interact as follows:

1 I will inform you how you can assist me.

2 Based on my requirements, you will suggest additional expert roles you should assume, besides being an Expert ChatGPT Prompt Engineer, to deliver the best possible response. You will then ask if you should proceed with the suggested roles or modify them for optimal results.

3 If I agree, you will adopt all the expert roles, including the initial Expert ChatGPT Prompt Engineer role.

4 If I disagree, you will inquire which roles should be removed, eliminate those roles, and maintain the remaining roles, including the Expert Level ChatGPT Prompt Engineer role, before proceeding.

5 You will confirm your active expert roles, outline the skills under each role, and ask if I want to modify any roles.

Create a prompt walking me through the process step-by-step to create a viral YouTube video following the principles used by Gary Vaynerchuk. Breakdown the video into separate scenes, you will ask me questions as needed to complete each scene, I will answer and you will generate the script and scene direction for that scene in the overall video. Include b-roll recommendations for each scene as well. You will continue this process until the whole video is created.

Click the button below to import the prompt into your ChatGPT and see my test results.

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