Google Challenges GPT-4 with Gemini 🔥⚔️
Plus: Google Research’s new gen-image dynamics. Microsoft Research's Self-Aligning LLMs.
Hello, Engineering Leaders and AI Enthusiasts!
Welcome to the 106th edition of The AI Edge newsletter. This edition brings you Google challenges OpenAI's GPT-4 model with its powerful Gemini AI.
And a huge shoutout to our incredible readers. You all rock! 😊
In today’s edition:
⚔️ Google Challenges GPT-4 with Gemini
👏🖼️Google Research’s new generative image dynamics
💪 Microsoft Research's self-aligning LLMs
📚 Knowledge Nugget: LLM products: measurement and manipulation by
Let’s go!
Google Challenges GPT-4 with Gemini
Google is reportedly nearing the release of its conversational AI software, Gemini. Which is intended to compete with OpenAI's GPT-4 model. Gemini is a collection of large-language models that can power chatbots, summarize text, generate original text, help write code and create images based on user requests.
Google is currently giving developers access to a version of Gemini, but not the largest version it is developing. The company plans to make Gemini available to companies through its Google Cloud Vertex AI service. Google has invested heavily in generative AI to catch up with OpenAI's ChatGPT.
Why does this matter?
Imagine more efficient customer support through smarter chatbots, faster content creation, and enhanced code development. As Google's Gemini promises advanced conversational AI, benefiting users with more powerful chatbots, which will enhance the user experience in various applications.
Google Research’s new generative image dynamics
Google Research’s new paper introduces a method for turning single still images into seamless looping videos or interactive dynamic scenes. The model is trained on real video sequences with natural motion, such as trees swaying or clothes blowing in the wind.
Given a single image, the model can predict long-term motion patterns in the Fourier domain. These predictions can be converted into dense motion trajectories, which can be used for various applications, such as creating dynamic videos from still images or enabling realistic interactions with objects in pictures.
Why does this matter?
This research enhances user experiences by enabling dynamic videos from still images and realistic interactions. It can also can have broader applications in computer vision and AI, including robotics and autonomous systems.
Microsoft Research's self-aligning LLMs
The paper introduces a method called RAIN that allows language models to align themselves with human preferences without the need for finetuning or extra data. By integrating self-evaluation and rewind mechanisms, unaligned models can produce responses consistent with human preferences through self-boosting.
RAIN operates without training or parameter updates and uses a fixed-template prompt to guide the model's alignment with human preferences. Experimental results show that RAIN significantly improves the harmlessness rate of language models while maintaining their helpfulness. It also establishes a new defense baseline against adversarial attacks.
Why does this matter?
RAIN enhances user safety by allowing language models to align with human preferences, reducing harmful outputs and ensuring more helpful responses in various applications, from customer support to content generation.
Knowledge Nugget: LLM products - measurement and manipulation
This article by
discusses the challenges and implications of using large language model-based products, which result from software-based companies and the internet. While LLM tools allow for the creation of complex products, their performance cannot be guaranteed due to the dominance of deep learning.The lack of progress in mechanistic interpretability research adds to the uncertainty and decreased robustness of these applications. The article focuses on two important characteristics of AI products: measurement of performance and manipulation of users. Both of these aspects are challenging due to the unknown workings of machine learning systems and the impact of internet dynamics.
Why does this matter?
This article underscores not only the ethical concerns surrounding LLMs but also the critical impact on user trust, decision-making processes, and responsible AI development and deployment in real-world scenarios.
What Else Is Happening❗
✅ OpenAI is opening its first European Union (EU) office in Dublin. (Link)
✅ AWS partnering with India’s ISRO to boost AI capabilities in the space via cloud computing. (Link)
✅ Microsoft has open-sourced EvoDiff, a protein-generating AI framework. (Link)
✅ Data analytics and AI software maker Databricks has raised over $500 million in a Series I funding round, increasing its valuation to $43 billion. (Link)
✅ Infosys, India's second-largest software services exporter, has signed a $1.5 billion contract to leverage AI solutions. (Link)
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🌟📝Friday Featured Prompt
This Week's Prompt: Act as a Cyber Security Specialist
I want you to act as a cyber security specialist. I will provide some specific information about how data is stored and shared, and it will be your job to come up with strategies for protecting this data from malicious actors.
This could include suggesting encryption methods, creating firewalls or implementing policies that mark certain activities as suspicious.
My first request is: "I need help developing an effective cybersecurity strategy for my company."In today's interconnected world, safeguarding your company's digital assets is paramount. With ever-evolving threats lurking in the digital landscape, it's time to build an unbreakable shield around your data.
Ready to fortify your defenses and protect what matters most?
ChatGPT can help. 🙂
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