Falcon 180B: Largest and Most Powerful Open LLM
Plus: Apple's million dollars/day spend on AI, Microsoft and Paige's largest AI model for cancer.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 101st edition of The AI Edge newsletter. This edition brings you the most powerful and largest open LLM, Falcon 180B.
And a huge shoutout to our amazing readers. We appreciate you😊
In today’s edition:
🤖 Introducing Falcon 180B, largest and most powerful open LLM
🔥 Apple is spending millions of dollars a day to train AI
🚀 Microsoft and Paige to build the largest image-based AI model to fight cancer
🧠 Knowledge Nugget: Vector Database: The Secret Behind Large Language Models Capabilities by
Let’s go!
Introducing Falcon 180B, largest and most powerful open LLM
UAE’s Technology Innovation Institute (TII) has released Falcon 180B, a new state-of-the-art for open models. It is the largest openly available language model, with 180 billion parameters, trained on a massive 3.5 trillion tokens using TII's RefinedWeb dataset. It's currently at the top of the Hugging Face Leaderboard for pre-trained Open LLMs and is available for both research and commercial use.
The model performs exceptionally well in various tasks like reasoning, coding, proficiency, and knowledge tests, even beating competitors like Meta's LLaMA 2. Among closed-source models, it ranks just behind OpenAI's GPT 4 and performs on par with Google's PaLM 2 Large, which powers Bard, despite being half the model's size.
Why does it matter?
It is a great contribution to open source. But there’s a catch: You’ll need 400GB of memory for inference, which can cost too much to host this for inference. Moreover, code is only 5% in the training mix, which is by far the most useful data to boost reasoning, master tool use, and power AI agents.
However, this indicates the continuous pushing of the boundaries of generative AI, and we may be only a few months away from GPT-4-level open-source models.
(Source)
Apple is spending millions of dollars a day to train AI
Reportedly, Apple has been expanding its budget for building AI to millions of dollars a day. It has a unit of around 16 members, including several former Google engineers, working on conversational AI. It is working on multiple AI models to serve a variety of purposes.
Apple wants to enhance Siri to be your ultimate digital assistant, doing multi-step tasks without you lifting a finger and using voice commands.
It is developing an image generation model and is researching multimodal AI, which can recognize and produce images or video as well as text.
A chatbot is in the works that would interact with customers who use AppleCare.
Why does it matter?
OpenAI, too, splashed out a whopping $100 million for GPT-4 alone. Perhaps this shouldn’t be surprising, given that Apple has been a visionary in the past, consistently pushing the boundaries of what's possible in technology. It is also reported that Apple created a team four years ago, indicating it may not be as much of a laggard in the AI race as we thought.
(Source)
Microsoft and Paige to build the largest image-based AI model to fight cancer
Paige, a technology disruptor in healthcare, has joined forces with Microsoft to build the world’s largest image-based AI models for digital pathology and oncology.
Paige developed the first Large Foundation Model using over one billion images from half a million pathology slides across multiple cancer types. Now, it is developing a new AI model with Microsoft that is orders-of-magnitude larger than any other image-based AI model existing today, configured with billions of parameters.
Paige will utilize Microsoft’s advanced supercomputing infrastructure to train the technology at scale and ultimately deploy it to hospitals and laboratories across the globe using Azure.
Why does this matter?
This will help realize the potential of generative AI at an unprecedented scale, introduce completely novel capabilities of AI, and serve as the cornerstone for the next generation of clinical/healthcare applications built with AI.
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Knowledge Nugget: Vector Database: The Secret Behind Large Language Models Capabilities
Have you ever wondered how language models like GPT-3, BERT, and others seem to understand and generate text with astonishing accuracy? The answer lies in vector embeddings.
In this article,
takes you on an exciting journey through the world of Vector Databases, shedding light on their significance in modern language processing and machine learning. It also discusses how a vector database works, vector index creation algorithms, and similarity measurement methods.Whether you are a seasoned data scientist or simply curious about the inner workings of powerful AI models, this article is for you.
Why does this matter?
The article demystifies the crucial role and inner workings of vector embeddings and databases in the functioning of LLMs. This can lead to more effective development of AI applications that are efficient and accurate.
What Else Is Happening❗
🚀OpenAI to host its first developer conference on November 6 in San Fransico (Link)
🆕IBM rolls out new generative AI models and capabilities across Watsonx (Link)
🔓Anthropic launches its first consumer-facing premium subscription plan for Claude 2 (Link)
🔄Slack launches new Workflow Builder to help better automate your tasks (Link)
💼Hubspot unveils HubSpot AI, platform-wide AI capabilities for marketing, sales, and service teams (Link)
🤝SAP acquires LeanIX to focus on AI-assisted IT modernization (Link)
🌟📝Friday Featured Prompt
This Week's Prompt: Act as a Fullstack Software Developer
I want you to act as a software developer. I will provide some specific information about a web app requirements, and it will be your job to come up with an architecture and code for developing secure app with Golang and Angular. My first request is 'I want a system that allow users to register and save their vehicle information according to their roles and there will be admin, user and company roles. I want the system to use JWT for security'
👨💻✨ Easy-peasy development with ChatGPT? Probably yes!
This ChatGPT prompt helps you architect and code apps, simplifying and streamlining your development tasks. It can also help you improve your development skills, productivity, and creativity.
So, more of crafting robust apps and less of stressing!
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That's all for now!
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