Apple Silently Making AI Moves 🍎🤫💭
Plus: Salesforce’s Einstein can customize AI. NExT-GPT's human-like AI research.
Hello, Engineering Leaders and AI Enthusiasts!
Welcome to the 104th edition of The AI Edge newsletter. This edition brings you Apple’s silent moves toward AI. 🤐
And a huge shoutout to our incredible readers. You all rock! 😊
In today’s edition:
🍎 Apple silently making AI moves
and 👏💯Salesforce’s Einstein can customize AI for you
🔍 NExT-GPT advances human-like AI research
📚 Knowledge Nugget: Large language models, explained with a minimum of math and jargon by
Let’s go!
Apple silently making AI moves
Apple is quietly incorporating artificial intelligence into its new iPhones and watches to improve basic functions. The company showcased new gadgets with improved semiconductor designs that power AI features, such as better call quality and image capture.
Apple's AI efforts have been reshaping its core software products behind the scenes without explicitly mentioning AI at its developer conference. Apple's new watch chip includes a four-core "Neural Engine" that enhances Siri's accuracy by 25% and enables new ways to interact with the device. The iPhone also automatically recognizes people in the frame for improved image capture.
Why does this matter?
Apple's subtle integration of AI enhances the user experience and sets it apart from competitors like Microsoft and Google, who prioritize more ambitious AI transformations. These new AI additions can contribute to more efficient, intuitive, and enjoyable experiences for end users.
Salesforce’s Einstein can customize AI for you
Salesforce introduced Einstein Copilot Studio, which allows customers to customize their AI offerings. The tool consists of three elements: prompt builder, skills builder, and model builder.
With the prompt builder, customers can add their own custom prompts for their products or brands.
The skills builder enables companies to add actions to prompts, such as competitor analysis or objection handling.
The model builder allows customers to bring their own models or use supported third-party offerings.
Salesforce is also working on a system called "the Einstein Trust Layer" to address issues like bias and inappropriate responses.
Why does this matter?
Customizing AI offerings will allow more personalized and tailored experiences to end users to align with their specific products or brands. This offering by Salesforce gives a competitive edge to its competitors by offering a comprehensive toolset for AI customization.
NExT-GPT advances human-like AI research
The NExT-GPT system is a multimodal language model that can understand and generate content in various modalities, such as text, images, videos, and audio. It fills the gap in existing models by allowing for any multimodal understanding and generation.
NExT-GPT leverages pre-trained encoders and decoders, requiring only a small amount of parameter tuning. It also introduces a modality-switching instruction tuning (MosIT) and a curated dataset for complex cross-modal understanding.
Why does this matter?
This research demonstrates the potential for building AI agents to model universal modalities, advancing human-like AI research. It addresses the limitations in current models by enabling comprehensive multimodal understanding and generation, supporting seamless interactions and processing across various input formats.
Knowledge Nugget: Large language models, explained with a minimum of math and jargon
Large language models like ChatGPT have become popular, but not many people understand how they work. In this enlightening article, the authors TIMOTHY B LEE and SEAN TROTT explains how LLMs are trained to predict the next word using huge amounts of text.
Unlike conventional software, LLMs are built on neural networks trained on billions of words, making their inner workings a mystery. However, experts have some understanding of these models. This article aims to explain LLMs without technical jargon or advanced math. It covers word vectors, the transformer (the basic building block of LLMs), and the training process, which requires large amounts of data for good performance.
The article will start by explaining word vectors; the surprising way language models represent and reason about language. Then the transformer, the basic building block for systems like ChatGPT. And in last it will explain how these models are trained and explore why good performance requires such phenomenally large quantities of data.
Why does this matter?
The article empowers users to grasp the underlying mechanisms of these models, sharing better understanding and informed interactions with AI systems without jargons. Also understanding LLMs is crucial for advancing in AI. By shedding light on word vectors, the transformer, and the training process, this article contributes to the collective knowledge and promotes further research and development in language modeling.
What Else Is Happening❗
✅ Roblox, a popular online gaming platform, is set to introduce an AI chatbot called Roblox Assistant. (Link)
✅ Alibaba made its AI model ‘Tongyi Qianwen’, available to the public. (Link)
✅ Coca-Cola has used AI to create a new flavor called Y3000 Zero Sugar. (Link)
✅ 15 US companies have taken an oath to develop AI responsibly. (Link)
✅ Character.ai outperforms ChatGPT in mobile app usage within the US. (Link)
✅ Pika Lab have Introduced Camera Movement Parameter. (Link)
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🤩 Wednesday WOW!
A mesmerizing world where imagination meets technology!
A visionary artist
created this animation using the power of Midjourney, Pika Labs, Runway, CapCut, and Good Vibes.We applaud the artist's expertise, which shines through in the flawless editing and seamless transitions made possible by Generative AI capabilities.
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