OpenAI Future Plans and Mathematics, DIDACT, & Japan's AI Copyrights
06/02/23 • 17 min
In today's episode of AI Daily, we bring you three exciting news stories that are shaping the world of AI. First, we delve into Japan's groundbreaking stance on copyrights, allowing the use of all data for training AI models. This move showcases Japan's commitment to advancing its AI ecosystem and embracing the potential of AI to transform society. In our second story, we discuss DIDACT, the first code model that mirrors the thinking process of real software developers. By understanding the entire coding process, DIDACT brings a new level of accuracy and efficiency to code generation and debugging. Lastly, we explore OpenAI's innovative approach to mathematical reasoning through process supervision. By rewarding each step in finding a mathematical answer, OpenAI is revolutionizing how AI models learn and improving their performance. Join us as we uncover the latest developments in AI and its wide-ranging implications.
Key Take-Aways:
Japan & AI Copyrights:
Japan reaffirms its stance on copyrights, allowing the use of all data, regardless of commercial use or copyright, for training AI models and applications.
Japan sees AI as a way to save its declining society and drive future progress, taking a progressive and serious approach to its development.
Other countries are likely to follow Japan's lead in adopting similar copyright policies for AI, considering the advantages it offers in terms of workforce and economic growth.
The copyright law in Japan applies only to content produced within the country, exempting foreign-owned content from its regulations.
DIDACT:
DIDACT is the first code language model trained to mimic the step-by-step reasoning and process of a software developer, going beyond just providing the final output of code.
Google's Monorepo, with data from years of developer activity, enabled the training of DIDACT to understand the full software development stack, including error fixing, code editing, and unit testing.
Understanding the history and context of a developer's actions is crucial for DIDACT's ability to predict and suggest the next steps in the coding process.
The development of models like DIDACT reflects a parallel to human cognition, where language and reasoning abilities have evolved over time, leading to the emergence of metacognitive processes. This advancement in AI cognition has potential applications in fields like medicine and law, enabling a step-by-step understanding of complex processes rather than just the final output.
Open AI Mathematics & Future Plans:
OpenAI has implemented process supervision to improve mathematical reasoning in their models, enabling a deeper understanding of the step-by-step process of solving math problems, rather than focusing solely on the final output.
Process supervision aligns with the way humans learn, as it provides feedback at each step of the problem-solving process, reinforcing learning and understanding.
This approach signifies a shift towards considering the entire process and not just the end result, mirroring the way education is conducted in the real world.
OpenAI's focus on improving GPUs to enhance the performance and affordability of GPT-4 demonstrates their commitment to addressing limitations and advancing AI capabilities. Additionally, they discussed the challenges with plug-ins and the need for seamless integration into existing platforms to provide a more efficient user experience.
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In today's episode of AI Daily, we bring you three exciting news stories that are shaping the world of AI. First, we delve into Japan's groundbreaking stance on copyrights, allowing the use of all data for training AI models. This move showcases Japan's commitment to advancing its AI ecosystem and embracing the potential of AI to transform society. In our second story, we discuss DIDACT, the first code model that mirrors the thinking process of real software developers. By understanding the entire coding process, DIDACT brings a new level of accuracy and efficiency to code generation and debugging. Lastly, we explore OpenAI's innovative approach to mathematical reasoning through process supervision. By rewarding each step in finding a mathematical answer, OpenAI is revolutionizing how AI models learn and improving their performance. Join us as we uncover the latest developments in AI and its wide-ranging implications.
Key Take-Aways:
Japan & AI Copyrights:
Japan reaffirms its stance on copyrights, allowing the use of all data, regardless of commercial use or copyright, for training AI models and applications.
Japan sees AI as a way to save its declining society and drive future progress, taking a progressive and serious approach to its development.
Other countries are likely to follow Japan's lead in adopting similar copyright policies for AI, considering the advantages it offers in terms of workforce and economic growth.
The copyright law in Japan applies only to content produced within the country, exempting foreign-owned content from its regulations.
DIDACT:
DIDACT is the first code language model trained to mimic the step-by-step reasoning and process of a software developer, going beyond just providing the final output of code.
Google's Monorepo, with data from years of developer activity, enabled the training of DIDACT to understand the full software development stack, including error fixing, code editing, and unit testing.
Understanding the history and context of a developer's actions is crucial for DIDACT's ability to predict and suggest the next steps in the coding process.
The development of models like DIDACT reflects a parallel to human cognition, where language and reasoning abilities have evolved over time, leading to the emergence of metacognitive processes. This advancement in AI cognition has potential applications in fields like medicine and law, enabling a step-by-step understanding of complex processes rather than just the final output.
Open AI Mathematics & Future Plans:
OpenAI has implemented process supervision to improve mathematical reasoning in their models, enabling a deeper understanding of the step-by-step process of solving math problems, rather than focusing solely on the final output.
Process supervision aligns with the way humans learn, as it provides feedback at each step of the problem-solving process, reinforcing learning and understanding.
This approach signifies a shift towards considering the entire process and not just the end result, mirroring the way education is conducted in the real world.
OpenAI's focus on improving GPUs to enhance the performance and affordability of GPT-4 demonstrates their commitment to addressing limitations and advancing AI capabilities. Additionally, they discussed the challenges with plug-ins and the need for seamless integration into existing platforms to provide a more efficient user experience.
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Previous Episode
StyleAvatar3D, Gorilla, & GILL
In today's episode, we have three exciting stories to share with you. First up is GILL, a groundbreaking method that infuses image recognition capabilities into language models. With GILL, you can now send images to chatbots and receive responses in the form of edited images or detailed explanations. It offers a unique approach to understand and respond to images without the need for extensive multimodal training. Next, we have StyleAvatar3D, a remarkable advancement in 3D avatar generation. This technology allows for high-fidelity and consistent 3D avatars with various poses and styles. Unlike previous methods, StyleAvatar3D maps out the three-dimensional space to create a more realistic and immersive experience. This development opens up new possibilities in gaming and social applications. Lastly, we explore Gorilla, the API app store for language models. Gorilla connects LLMs with thousands of APIs, offering users a vast selection of tools to complete tasks. What sets Gorilla apart is its ability to eliminate hallucinations and provide accurate and reliable API suggestions. With 1,640 APIs available, this model proves to be a powerful and valuable resource. The AI revolution continues, and these stories demonstrate the incredible progress being made in the field.
Key Take-Aways:
GILL:
Gil is a method that infuses image encoder and decoder into Ella lambs, enabling them to recognize, understand, and respond to images.
Gil offers a unique approach by injecting image embeddings into LLMs, allowing for various use cases such as image editing, image explanations, and image injection into conversations.
The integration of an encoder in Gil enables both image generation and image retrieval, expanding its capabilities beyond traditional multimodal models.
Gil's open-source code sets it apart from Meta's multimodal work, offering accessibility and potential real-world applications in image-based communication.
StyleAvatar3D:
StyleAvatar3D introduces image text diffusion for high-fidelity 3D avatar generation, allowing for a wide range of avatars with different poses and styles in a complete 3D space.
The significance of the 3D aspect lies in the visual accuracy and consistency that is challenging to achieve with traditional stable diffusion methods. StyleAvatar3D offers both the generation of 3D images and the ability to maintain consistency in attributes and appearance.
Unlike previous avatar generators that relied on stitching together 2D images, StyleAvatar3D maps out the three-dimensional space, providing a more consistent and immersive experience for games and social platforms.
The introduction of true 3D assets has marked a significant leap forward, enabling the creation of realistic and dynamic visuals in game development and other applications.
Gorilla:
Gorilla is an API app store for LLMs that connects the LLM world with the vast world of APIs, offering thousands of APIs for completing user tasks.
One of Gorilla's key achievements is addressing hallucinations that exist in models like GPT-4, providing accurate API recommendations instead of generating random information.
The Gorilla model is entirely open source, with the training still in progress. However, the inferencing, dataset, and evaluations are openly available. It boasts a wide range of 1,640 APIs that can be called, demonstrating its capabilities against built-in spotlights like Apple's and showcasing superior performance.
Fine-tuning the model on APIs proves to be more effective than prompting, reducing hallucinations and improving accuracy. The architecture's ability to quickly update APIs within the model allows for faster contributions and continuous improvement without the need for complete retraining.
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Next Episode
Nueralangelo by NVIDIA, StyleDrop by Google, & OpenAI's Cyber Security Grant Program
In this episode of AI Daily, we cover three exciting news stories. First, NVIDIA introduces Neuralangelo, an impressive evolution of nerfs that allows you to accurately 3D scan any scene or object using your phone or drone camera. This breakthrough technology opens up a world of possibilities in various industries, from media to drones. Next, we discuss StyleDrop by Google, a remarkable advancement in image styling. With just one reference image, StyleDrop can generate a wide range of styles, including 3D and 2D characters, producing pixel-perfect outputs that surpass previous models like Dream Booth. Finally, we delve into OpenAI's Cybersecurity Grant Program, a $1 million fund aimed at advancing the future of cybersecurity using AI. OpenAI is determined to defend against AI aggressors and improve cybersecurity through innovative projects like developing honey pots and leveraging cutting-edge technology. Tune in to this episode for all the details and insights on these groundbreaking developments!
Key Points:
Nueralangelo by NVIDIA:
NVIDIA introduces Neuralangelo, an evolution of Nerfs that enables accurate 3D scanning of objects using a phone or drone camera.
The improved Neuralangelo pushes the boundaries of what Nerfs can achieve, thanks to better graphics cards and algorithms, offering more use cases in various industries, including media and drones.
The technology starts from a 2D representation and uses multiple angles to create a detailed 3D model, refining it until the desired level of accuracy is achieved.
Neuralangelo is one of the 30 projects presented by NVIDIA at a computer vision conference, showcasing their rapid development and impressive capabilities, such as Michelangelo.
StyleDrop by Google:
Google introduces Style Drop, an evolution of Dream Booth and textual inversion that can generate a wide range of styles based on a single reference image.
Style Drop surpasses its competitors in terms of accuracy and similarity to the desired style, making it an impressive tool for generating content in specific styles, such as company branding.
The model achieves pixel-perfect outputs and requires fine-tuning on less than 1% of its parameters, making it easy to use and customize with just one image.
Style Drop's ability to produce high-quality results with a single image sets it apart from previous models that required multiple images for comparable outcomes.
OpenAI’s Cyber Security Grant Program:
Open AI announces a $1 million cybersecurity grant program aimed at advancing AI-based cybersecurity and defending against AI aggressors.
The program offers $10,000 grants to support the development of innovative cybersecurity solutions and technologies.
Open AI emphasizes the importance of fostering a high-level AI and cybersecurity discourse and encourages applications that leverage state-of-the-art AI technology for cybersecurity purposes.
The grant program aims to address various cybersecurity challenges, including the development of private GPU compute and the creation of deceptive honey pots to deceive hackers.
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