Inflection AI’s New 175B Parameter Model Challenges GPT-4 🔥👊🔥
Plus: ElevenLabs’s latest Speech to Speech update. Google Bard answering your YT video questions.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 154th edition of The AI Edge newsletter. This edition brings you “Inflection AI's new model: ‘Inflection-2’, Which is hot on heels now + It surpasses Google and Meta models.
And a huge shoutout to our incredible readers. We appreciate you😊
In today’s edition:
👊 Inflection AI’s massive 175B parameter model challenges GPT-4
.🗣️ElevenLabs’s latest Speech to Speech transformation
▶️ Google Bard answering your questions about YouTube videos
📚 Knowledge Nugget: Tutorial: How to fine-tune ChatGPT by
Let’s go!
Inflection AI’s massive 175B parameter model challenges GPT-4
Inflection AI has released the Massive 175B Parameter Model- Inflection-2. It is the latest language model developed by Inflection, aiming to create a personal AI for everyone. It has been trained on 5K NVIDIA H100 GPUs and demonstrates improved factual knowledge, stylistic control, and reasoning abilities compared to its predecessor, Inflection-1.
Despite being larger, Inflection-2 is more cost-effective and faster in serving. The model outperforms Google's PaLM 2 Large model on various AI benchmarks. Inflection takes safety, security, and trustworthiness seriously and supports global alignment and governance mechanisms for AI technology. Inflection-2 will undergo alignment steps before being released on Pi, and it performs well compared to other powerful external models.
Why does this matter?
Despite its larger size, it's cost-effective and quicker in serving, outperforming the largest, 70 billion parameter version of LLaMA 2, Elon Musk’s xAI startup’s Grok-1, Google’s PaLM 2 Large and startup Anthropic’s Claude 2, as per the information.
ElevenLabs’s latest Speech to Speech transformation
The company has added Speech-to-speech (STS) to Speech Synthesis, allowing users to convert one voice to sound like another and control emotions, tone, and pronunciation. This tool can extract more emotions from a voice or be used as a reference for speech delivery.
Changes are also being made to premade voices, with new ones added and information on voice availability provided. Other updates include the addition of normalization, a pronunciation dictionary, and more customization options to Projects. The Turbo model and uLaw 8khz format have been introduced, and ACX submission guidelines and metadata can now be applied to Projects.
Watch this video created by one of their community members:
You can try it here.
Why does this matter?
STS technology gives power to users to transform voices, control emotions, and refine pronunciation. This means more expressive and tailored speech synthesis, enhancing the quality and customization of voice output for various applications. This can be used in various industries like Entertainment, Media, education, Customer service, and more.
Google Bard answering your questions about YouTube videos
Google's Bard AI chatbot can now answer specific questions about YouTube videos, expanding its capabilities beyond just finding videos. Users can now ask Bard questions about the content of a video, such as the number of eggs in a recipe or the location of a place shown in a travel video.
This update comes after YouTube recently introduced new generative AI features, including an AI conversational tool that answers questions about video content and a comments summarizer tool that organizes discussion topics in comment sections.
Why does this matter?
These advancements aim to provide users with a richer and more engaging experience with YouTube videos. Users can now find information within videos more efficiently, aiding in learning, recipe following, travel planning, and other practical applications, streamlining information retrieval directly from video content.
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Knowledge Nugget: Tutorial: How to fine-tune ChatGPT
This tutorial explains how to fine-tune OpenAI's ChatGPT model, specifically GPT-3.5 Turbo, to customize its responses. Fine-tuning involves training the model on new examples to make it more suitable for specific use cases. The tutorial provides a simplified approach to fine-tuning using OpenAI's APIs without needing a GPU cluster.
It covers the steps involved in fine-tuning, including creating training data and running the fine-tuning job. The tutorial also discusses when fine-tuning is necessary and when prompt engineering can be a more suitable approach.
Why does this matter?
Businesses can now fine-tune AI models for industry-specific needs with the help of this guide, such as crafting personalized responses in customer service or generating content tailored to specific audiences, enhancing user experiences in various domains.
What Else Is Happening❗
🚀 Adobe acquired Bengaluru-based AI-video creation platform Rephrase.ai
The transaction will help Adobe accelerate its ability to provide AI video content tools to its customers. Rephrase.ai uses generative AI to convert text to video and helps influencers and video creators build digital avatars. (Link)
🚀 AI tool screenshot-to-code will help you build the entire code
Upload any screenshot of a website and watch AI build the entire code, It will improve the generated code by comparing it against the screenshot repeatedly. Try it out. (Link)
🚀 iPhone’s Siri is now replaceable with ChatGPT’s voice assistant
OpenAI's ChatGPT Voice feature is now available to all free users, allowing iPhone users to replace Siri with ChatGPT as their voice assistant. The new Action Button on the iPhone 15 Pro and Pro Max can be configured to launch ChatGPT's Voice access feature. To set it up, users must go to the Action Button menu in the iOS Settings, choose the Shortcut option, and select ChatGPT. (Link)
🚀 New update in Cloudflare's Workers AI
Workers AI now includes Stable Diffusion and Code Llama in over 100 cities worldwide. The platform aims to make it easy to generate both images and code. (Link)
🚀 After the OpenAI drama, major AI players investing in different AI startups
Companies like Salesforce, Qualcomm, Nvidia, and Eric Schmidt are investing in open-source AI startups such as Hugging Face and Mistral AI. The OpenAI saga has been resolved, with Sam Altman reinstated as CEO and a new board, but it has caused a reassessment of relying on a single, proprietary service for generative AI. (Link)
That's all for now!
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