ChatGPT's Newest Upgrade: Real-time Web Browsing

In a world that values current and reliable information, OpenAI has stepped up its game with the latest enhancement to ChatGPT. The beloved AI, which previously had a knowledge cutoff in September 2021, is no longer restricted to that timeline. Excitingly, ChatGPT can now actively browse the internet, ensuring users receive up-to-the-minute and authoritative insights.

What does this mean for users? It means that every query can now be supplemented with direct links to sources from the web. So, not only will you get the vast knowledge already embedded in ChatGPT, but you'll also have the added benefit of real-time, sourced data from the expansive digital universe.

The future of AI-assisted searches and data extraction looks brighter with this evolution. Dive in and explore the limitless bounds of information with the newly empowered ChatGPT!


Machine Learning for Everybody – Full Course


"Machine Learning for Everybody – Full Course" is a comprehensive guide designed to introduce beginners to the fascinating world of machine learning (ML). This course takes you step-by-step from the foundational concepts to advanced techniques, ensuring that you gain a deep understanding of how ML algorithms work and how they can be applied to real-world scenarios. With illustrative examples, hands-on projects, and clear explanations, this course is perfect for anyone looking to dive into ML, whether you're a student, professional, or just a curious learner.


Bridging The AI Gap: Microsoft and Meta Join Forces

The rapid development of AI technology is transforming the way we engage with the digital world, and it’s clear that the power of collaboration can amplify this transformation.

Last week, we provided a glimpse into our vision of creating a seamless AI copilot experience aimed at helping individuals effortlessly tackle any task. At the heart of this vision lies Bing. It's not just a search engine; it’s the underpinning of our AI experiences, ensuring they are deeply rooted in the freshest web data and information available.

While Microsoft’s AI innovations power a range of products within our ecosystem, our mission doesn’t stop there. We pride ourselves on being more than just a product company; we’re a platform that empowers others to realize their AI aspirations. This mindset has opened the doors to some exhilarating partnerships, and today, I’m overjoyed to share one such development.

We’re embarking on a new journey with Meta. Our collaboration will see the integration of Bing into Meta AI’s chat experiences. This means that users will receive answers that are not only accurate but also in tune with real-time search data. From engaging with Meta AI to chatting on platforms like WhatsApp, Messenger, and Instagram, users will witness an enriched AI interaction.

Our commitment to Meta is a testament to our shared ambition: harnessing AI to foster innovation and enhance user experiences. As we further this partnership, our primary goal remains - to infuse the magic of powerful and relevant AI into the tools and platforms that are indispensable to people’s daily lives.

Here's to shaping a future where technology intuitively complements every facet of our lives.

OpenAI and Jony Ive's Potential Collaboration on an AI Device

In the ever-evolving world of technology, leaders from top-notch firms often discuss possible innovations, and it seems like Jony Ive, the design genius behind many of Apple's iconic products, and Sam Altman, the head of OpenAI, are no exception.

Recent reports suggest that the duo is brainstorming a novel device with AI capabilities. Interestingly, this isn’t just a discussion between Altman and Ive. Masayoshi Son, the visionary behind SoftBank, also weighed in on the concept. However, whether Son will continue to be involved in this potential project remains uncertain.

Details about the device remain shrouded in mystery. The design, functionalities, and even the decision to take the idea from concept to reality are yet to be ascertained. However, the fact that both Ive and Altman have discussed potential designs does pique interest.

If realized, such a device could bolster OpenAI’s standing in the tech industry, providing them with a significant edge. The question of who would take the responsibility to release this device is also in the air. While it remains speculative, Sam Altman's conversation with the manufacturer, Humane, indicates potential collaboration routes.

As always, such discussions amongst tech titans often lead to innovative products that can redefine user experiences. It remains to be seen what this collaboration might bring to the world of AI and technology.

At AILab, we have been diligently developing our AI device, Pocket AI, for over six months.

We are on track to release it in the coming year.

Stay tuned for more updates on this intriguing development!


Microsoft's Leap into Advanced AI: Bing’s Upgrades and Beyond

In a recent event in New York, Microsoft unveiled a series of AI-driven enhancements to Bing and various Windows features, signaling the tech giant's commitment to staying at the forefront of artificial intelligence innovation.

Bing Welcomes DALL-E 3

One of the headline announcements was the integration of OpenAI's DALL-E 3 model into Bing. This advancement follows Microsoft’s previous step, where it enabled consumers to generate images using DALL-E in Bing Chat earlier this year. At that time, Microsoft remained mum about the specific DALL-E version but has now confirmed the transition to DALL-E 3. This means users can expect more intricate image renderings, with a particular focus on the nuances of features like fingers, eyes, and shadows.

Promoting Responsible AI Use

Microsoft is not just aiming for better AI capabilities; it is equally focused on responsible AI usage. The latest iteration will see the addition of invisible digital watermarks on all AI-generated images, aptly termed as Content Credentials. Backed by cryptographic measures and abiding by the standards of the "Coalition for Content Provenance and Authenticity (C2PA)", this watermarking ensures greater transparency in the realm of AI images. It’s worth noting that other tech giants like Adobe, Intel, and Sony are also backing the C2PA initiative.

A More Personal Bing Experience

Bing is also evolving to offer a more tailored search experience. Drawing upon your prior interactions with Bing Chat, the search engine will now provide answers that align more with your personal interests. Microsoft illustrates this with a simple example: If you've previously searched for your favorite sports team, Bing might notify you if that team has a match in a city you plan to visit.

Although this personalization might raise eyebrows, Microsoft assures users that they have the option to opt-out. This means that if someone isn’t keen on having their chat history influence their search results, they can easily turn this feature off.

Making Searches More Efficient

Microsoft's research suggests that a significant chunk of users - more than 60%, to be exact - end up modifying their initial search query multiple times. This often arises due to the lack of personalized context. By tapping into a user's previous searches or current research trends, Microsoft believes the search process can be made more seamless and efficient.

Expansion to Microsoft 365

Lastly, the tech behemoth announced that Bing Chat Enterprise will now support multimodal Visual Search and Image Creator. This is great news for the 160 million-plus Microsoft 365 users who will soon benefit from enhanced AI chatbot capabilities in their workplace.

In Conclusion

Microsoft's recent announcements underscore their commitment to not only advancing AI capabilities but also ensuring its responsible use. As the line between technology and daily life continues to blur, it’s reassuring to see tech leaders like Microsoft prioritize both innovation and ethics. As users, all we can do is eagerly await these features and perhaps, keep tweaking those search queries a little less.


ChatGPT: The Next Evolution with Voice and Image Capabilities

OpenAI is thrilled to announce the rollout of new voice and image features in ChatGPT! This evolution offers a more intuitive interaction, allowing users to voice chat with ChatGPT and visually show its context by sharing images.

Broadening the Horizons: The What and Why

Using these new features, users can:

  • Snap and Share: Whether it's a fascinating landmark while traveling or a snapshot of the fridge's contents, ChatGPT can provide insights, recipes, and more.
  • Math Homework Assistance: Parents can help their children by snapping a photo of a math problem and receiving hints.
  • Availability: Over the next two weeks, Plus and Enterprise users can look forward to accessing these voice and image features. Voice capability will be available on both iOS and Android, while the image feature will be available across all platforms.

Diving Deeper into the Features

1. Engage in Voice Conversations with ChatGPT

Users can now verbally converse with ChatGPT, opening a plethora of opportunities such as bedtime stories or settling debates.

Getting Started with Voice:

  • Navigate to Settings → New Features on the mobile app.
  • Opt into voice conversations.
  • Tap the headphone button on the home screen and choose a voice from five options.

This innovation is backed by a new text-to-speech model and leverages Whisper, OpenAI's open-source speech recognition system.

2. Chat About Images

By tapping the photo button, users can now provide ChatGPT with visual context.

Getting Started with Images:

  • For iOS or Android users, tap the plus button first.
  • Share the desired image or use the drawing tool for more specificity.

The image understanding hinges on the prowess of multimodal GPT-3.5 and GPT-4 models.

Safety and Gradual Deployment

At OpenAI, our mission is to foster AGI that's both safe and beneficial. Here's our approach:


While voice technology heralds immense potential, it can also be misused. We are committed to limiting its scope to specific use cases, such as voice chat. Notable collaborations, such as with Spotify, are leveraging this technology responsibly.

Image Input:

Challenges with vision-based models include hallucinations and high-stakes interpretations. To ensure responsible deployment, we've taken significant measures:

  • User Experience: Collaborating with "Be My Eyes," an app for the visually impaired, has enriched our understanding of the feature's practical applications and limitations.
  • Technical Safeguards: We've curtailed ChatGPT's capability to analyze or comment on individuals to uphold privacy.

Feedback from real-world usage will be paramount in refining these safeguards.

Model Limitations:

ChatGPT excels in specific domains but has limitations, particularly with non-English, non-roman scripts. Users are urged to use ChatGPT responsibly, especially for specialized topics.

Expanding Access

The excitement doesn't end here! Following the initial rollout to Plus and Enterprise users, we're eager to introduce these capabilities to a broader user base, including developers.

Stay tuned, and dive into the next-gen ChatGPT experience!


Diving into Deep Learning with PyTorch: A Beginner’s Guide

In this course, you learn all the fundamentals to get started with PyTorch and Deep Learning.

Deep Learning, with its potential to transform industries and the way we approach data, has taken the tech world by storm. If you've been curious about this revolutionary field and have been seeking a comprehensive introduction, then you're in the right place.

Why PyTorch?

PyTorch, developed by Facebook's AI Research lab, has rapidly gained popularity among researchers and developers alike. It is recognized for its dynamic computation graph, which means the graph builds on-the-fly as operations are created, making it highly flexible and intuitive. This is particularly useful for those just beginning their deep learning journey, as it allows for easy debugging and a more natural understanding of the flow of operations.

What Will You Learn?

In this course, you'll be taken on a deep dive into the fascinating world of deep learning. Some highlights include:

Understanding the Basics: Grasp the fundamental concepts of neural networks, how they're structured, and how they function.
PyTorch Essentials: Get hands-on experience with PyTorch's tensors, autograd, and other essential components.
Building Neural Networks: By the end of this course, you'll be constructing your very own neural networks, and training them to recognize patterns, images, and more.
Practical Applications: Witness the real-world utility of deep learning as you work on exciting projects and real-life datasets.
Beginner-Friendly Approach

This course is crafted keeping beginners in mind. Whether you're entirely new to programming, or an experienced developer wanting to switch to deep learning, you'll find the content accessible and engaging. The blend of theory and hands-on exercises ensures that you not only learn but also apply your newfound knowledge practically.


With the increasing demand for professionals skilled in deep learning and AI, there's no better time than now to dive in. By familiarizing yourself with PyTorch and deep learning fundamentals through this course, you're equipping yourself with the tools and knowledge necessary to be at the forefront of technological innovation.

Get started today, and embark on a journey of endless learning and opportunities!


Unlocking Creative Horizons: DALL-E 3's Integration with ChatGPT and Enhanced Safety Measures

OpenAI’s DALL-E 3: The Next Evolution in Generative AI Visual Art

OpenAI has once again made a groundbreaking move in the realm of AI-driven art with the announcement of DALL-E 3, the third iteration of its generative AI visual art platform. With DALL-E’s proven capability to convert text prompts into artful images, this new version promises enhanced contextual understanding and user-friendly features.

What’s New with DALL-E 3?

One of the most exciting updates is the seamless integration of DALL-E 3 with ChatGPT. This feature allows users to leverage ChatGPT for generating detailed prompts, a task that could previously be a hurdle for those not adept at crafting specific prompts. By initiating a dialogue with ChatGPT, users can have the chatbot craft a descriptive paragraph which DALL-E 3 then interprets into creative visuals.

A striking demo was showcased to The Verge where Aditya Ramesh, the spearhead of the DALL-E team, used ChatGPT to brainstorm a logo for a hypothetical ramen restaurant situated in the mountains. The result? An imaginative art piece featuring a mountain adorned with ramen-inspired snowcaps, a broth-resembling waterfall, and pickled eggs artistically presented as garden stones. While the output was more artistic merch than a traditional logo, it exemplifies the innovative potential of DALL-E 3.

DALL-E’s Evolution: A Brief Look Back

The inception of DALL-E dates back to January 2021, pioneering the field before its counterparts like Stability AI and Midjourney. As DALL-E 2 emerged in 2022, OpenAI addressed certain concerns by introducing a waitlist system to regulate its access, primarily due to potential content biases and explicit image generations. The platform later became publicly accessible in September of the same year.

Now, with DALL-E 3, OpenAI is planning a phased release, initially rolling it out to ChatGPT Plus and ChatGPT Enterprise users, with research labs and API service access to follow in the fall. As of now, a timeline for a free public version remains under wraps.

Safety Enhancements in DALL-E 3

Amid the advancements, safety remains paramount. OpenAI has fortified DALL-E 3 with robust safety measures, rigorously tested by external red teamers. One notable advancement is the implementation of input classifiers designed to screen out explicit or potentially harmful prompts. Another significant upgrade ensures the inability to reproduce images of public figures when their names are explicitly mentioned in the prompt.

Sandhini Agarwal, OpenAI's policy researcher, expressed strong belief in these safety measures but also reminded users that continuous improvement is underway and perfection is still a work in progress.

Additionally, in response to concerns from the artist community, DALL-E 3 comes with an in-built ethical code: it won't attempt to recreate art in the style of living artists. OpenAI is also offering artists the option to prevent their art from being used in future AI iterations by allowing them to request removal of specific copyrighted images.

This move comes in light of legal challenges faced by DALL-E's competitors, Stability AI and Midjourney, and art platform DeviantArt, which were sued by artists alleging copyright infringements.

In Conclusion

DALL-E 3 stands as a testament to OpenAI's commitment to innovation, accessibility, and ethics in the ever-evolving domain of AI-generated art. As we await its broader release, the art and tech community watches with anticipation, eager to explore the limitless horizons that DALL-E 3 promises.


China's Rising LLM Wave: The Opportunities, Challenges, and Future Predictions in the AI Arena

  • China accounts for 40% of the global volume of LLM.
  • However, it slightly lags behind the US, whose share is already 50%.
  •  China's enthusiasm for generative AI, sparked by OpenAI's ChatGPT, has led to a surge in product announcements from various tech firms.
  • The country now hosts around 130 large language models (LLMs), making up 40% of the global market, second only to the US.
  • Despite the growth, investors are concerned about the similarities between offerings, increasing costs, and the lack of sustainable business models.
  • US-China tensions have impacted the AI sector in China, with fewer US funds investing and AI chip shortages becoming problematic.
  • Esme Pau from Macquarie Group predicts a shakeup in the sector with price wars and the phasing out of less capable LLMs in the coming year.
  • Opinions vary on which firms will dominate the market, but there's a belief that major tech giants like Alibaba, Tencent, and Baidu have the edge due to their established ecosystems.
  • Tony Tung of Gobi Partners GBA highlights that many startups in the space are struggling, with investors now showing more caution than earlier in the year.


How LLMs Help Improve Your Business

In the dynamic business world, it's crucial to remain updated with the latest advancements in technology and methodologies to ensure consistent growth and relevance. Among these advancements is the rise of Logical Learning Models (LLMs). But what are LLMs, and how can they be pivotal in improving your business? Let's dive in.

Understanding LLMs

Logical Learning Models or LLMs are a type of artificial intelligence (AI) model that focuses on reasoning and problem-solving using a combination of logic and machine learning. Unlike traditional machine learning, which relies solely on data to make predictions, LLMs integrate structured knowledge and logic to make informed decisions. This fusion of logic and learning offers a robust solution for complex problems faced by businesses today.

Benefits of Integrating LLMs into Business Operations

  1. Enhanced Decision Making: With the power of reasoning, LLMs can make decisions by understanding the why and how behind a situation, offering insights that purely data-driven approaches might miss.
  2. Reduced Operational Costs: LLMs can optimize processes, reduce redundancies, and predict potential pitfalls, thereby leading to cost-saving in the long run.
  3. Improved Customer Experience: By analyzing patterns and logically reasoning out preferences, LLMs can offer personalized experiences to customers, enhancing their satisfaction and loyalty.
  4. Mitigating Risks: By understanding potential outcomes logically, LLMs can predict risks and offer solutions to mitigate them.
  5. Versatility Across Industries: From healthcare to finance, from retail to manufacturing, the application of LLMs is vast, making it a valuable asset for various sectors.
  6. Scalability: As your business grows, LLMs can adapt and scale to ensure that the operations run smoothly, without needing constant manual intervention.

AILAB: Your Partner in Integrating LLMs

Incorporating LLMs into your business might seem daunting, but with the right partner, the transition can be smooth and rewarding. That's where AILAB comes in. As industry leaders in the world of AI and logical learning, AILAB has a track record of helping businesses seamlessly integrate LLMs into their operations.

AILAB not only offers tailored solutions to fit your unique business needs but also ensures post-integration support, ensuring that your business reaps the maximum benefits from LLMs. Their expertise and dedication to pushing the boundaries of what AI can achieve make them the ideal partner for businesses looking to stay ahead of the curve.


In the modern era of business, staying competitive means being open to the innovations that promise growth and efficiency. LLMs, with their power to reason and learn, offer businesses an opportunity to drive success in unprecedented ways. And with partners like AILAB, integrating these advanced models into your business operations becomes not just feasible but also a game-changing move. Embrace the future of business with LLMs and watch your enterprise soar to new heights.


LLM compression

LLM pruning

Large language models (LLMs) consist of many components, but not all are essential for output. Such non-critical components can be pruned to maintain performance while reducing model size.

Unstructured Pruning:
  • Involves removing parameters without considering the model's structure.
  • Sets insignificant parameters to zero, creating a sparse model.
  • It is easy to implement but hard to optimize due to its random weight distribution.
  • Requires additional processing to compress and might need retraining.
  • Notable advancements include SparseGPT (eliminates retraining) and LoRAPrune (combines low-rank adaptation with pruning).

Structured Pruning:
  • Removes whole sections, like neurons or layers.
  • Simplifies model compression and boosts hardware efficiency.
  • Requires a deep understanding of the model and might significantly impact accuracy.
  • LLM-Pruner is a promising technique that uses gradient information to prune without relying heavily on original training data.
  • Both methods aim to optimize the balance between model size and performance.

LLM Knowledge Distillation

Knowledge distillation involves training a smaller "student" model to emulate a more complex "teacher" model, effectively creating a compact yet proficient model. In the context of LLMs, this technique has two primary categories:

Standard Knowledge Distillation:

  • Transfers the broad knowledge of the teacher to the student.
  • Can use prompts and responses from models like ChatGPT to train smaller LLMs, though there are constraints related to data from commercial models.
  • MiniLLM, developed by Tsinghua University and Microsoft Research, improves the process by using specialized objective and optimization functions, addressing the challenge of accurately capturing data distributions.

Emergent Ability Distillation:

  • Targets the transfer of a specific capability from the teacher model.
  • Examples include extracting math or reasoning skills from GPT-4 to a smaller model, such as Vicuna.
  • Focusing on a narrower task set makes measuring EA distillation easier, but it's essential to recognize the limitations in transferring emergent behaviors to smaller LLMs

LLM Quantization

Large Language Models (LLMs) like GPT-3 store parameters as floating-point values, with models like GPT-3 using hundreds of gigabytes of memory. To reduce this size, a technique called quantization is used, converting parameters to smaller integers.

Benefits of Quantization:

  • Allows LLMs to run on everyday devices.
  • Examples of quantized LLMs include GPT4All and Llama.cpp.

Quantization Approaches:

  • Quantization-Aware Training (QAT): Integrates quantization during training, allowing models to learn low-precision representations. The downside is it requires training from the beginning.
  • Quantization-Aware Fine-Tuning (QAFT): Adapts a pre-trained model for lower-precision weights. Techniques like QLoRA and PEQA are used in this approach.
  • Post-Training Quantization (PTQ): Reduces precision after training without changing the architecture. It's simple and efficient but might affect accuracy.

For an in-depth exploration of LLM compression, the paper "A Survey on Model Compression for Large Language Models" is recommended.


Revolutionizing Audio Generation: An Introduction to Stable Audio's Latent Diffusion Models

Stable Audio introduces a new approach to audio generation using latent diffusion models. Traditional audio diffusion models have been limited to generating fixed-size outputs, creating challenges when generating variable-length audios, such as full songs. Stable Audio is designed to overcome this limitation by conditioning on text metadata, audio file duration, and start time, allowing for controlled content and length. This architecture can render 95 seconds of stereo audio in less than one second using an NVIDIA A100 GPU. It combines a variational autoencoder (VAE), a text encoder, and a U-Net-based conditioned diffusion model to achieve this. The model is trained using a vast dataset from AudioSparx, totaling over 19,500 hours of audio. Stable Audio represents the advanced work of Stability AI's research lab, Harmonai, with promising future developments including open-source models.


Code Llama: Revolutionizing Coding with AI-driven Language Models

Unlocking The Potential of AI in Code Generation

In a landscape dominated by innovation, it's not uncommon to stumble upon tools that redefine the paradigms of technology. Enter Code Llama: A new entrant that promises to redefine how we perceive coding, offering unprecedented assistance in the coding domain.

What is Code Llama?

Code Llama is not just another AI tool; it's a revolution. Built atop the robust Llama 2, this large language model (LLM) harnesses the power of AI to generate and discuss code. What sets Code Llama apart is its state-of-the-art performance among publicly available LLMs, aiming to augment developers' productivity and diminish the entry barrier for coding novices.

This breakthrough LLM can be a game-changer for both productivity and education. It holds the promise to aid programmers in crafting more efficient, thoroughly documented software, ensuring robustness.

Openness in AI: The Way Forward

Our vision at OpenAI is clear: to foster innovation, safety, and responsibility. In line with our commitment to open-source AI development, we're proud to announce the release of Code Llama, not just for research but also for commercial usage. This mirrors the community license adopted by its predecessor, Llama 2.

Code Llama isn't just a repackaged Llama 2. It is Llama 2, reimagined. This LLM, after being extensively trained on code-specific datasets, has evolved to possess enhanced coding prowess. Whether it's generating code, interpreting natural language about code, or even assisting in code completion and debugging across a multitude of popular programming languages, Code Llama has got it covered.

Different Sizes for Different Needs

Understanding that one size doesn't fit all, we're launching Code Llama in three distinct sizes, with parameter capacities of 7B, 13B, and 34B. Each of these models has been rigorously trained with a staggering 500B tokens of code and related data. The adaptability of these models ensures that they cater to varying requirements, from serving capacities to latency needs.

Specialized Versions for Focused Utility

In addition to the core models, we've introduced two specialized variants:

  • Code Llama – Python: Given the prominence of Python in the AI and coding community, this variant is further fine-tuned on a colossal 100B tokens of Python code.
  • Code Llama – Instruct: This iteration is meticulously fine-tuned to comprehend natural language instructions, making it proficient at discerning user expectations from their prompts.

A Vision for the Future

It's exhilarating to see programmers employ LLMs to enhance a myriad of tasks. Our aspiration is to alleviate the mundane, repetitive aspects of coding, allowing developers to concentrate on the quintessentially human facets of their roles. By ushering in AI models like Code Llama, we're not just propelling innovation but also fortifying safety. It's an invitation to the community to appraise, refine, and innovate.

We envisage Code Llama as the ally for software engineers across diverse sectors. However, the horizon is vast, and there are innumerable use cases to explore. Our hope? That Code Llama sparks inspiration, encouraging the tech community to leverage the prowess of Llama 2, spawning pioneering tools for both research and commercial avenues.

With Code Llama, the future of coding looks not just promising, but revolutionary. We're just getting started!


Understanding Large Language Models with Sergey: A Deep Dive

Language models, especially the large ones, have been making waves in the world of artificial intelligence. Their ability to generate human-like text, answer questions, and even code has left many amazed. But how do these behemoths work? Sergey's latest video is your ticket to understanding the intricate world of large language models (LLMs).

What's Inside the Video?

Sergey's comprehensive guide will unpack several layers of LLMs:

Core ML Principles: Before diving deep into LLMs, it's crucial to understand the basic machinery of Machine Learning. Sergey begins by breaking down these foundational concepts in a digestible manner.

The Transformer Architecture: At the heart of many LLMs lies the Transformer architecture. Sergey delves into how this ingenious design works and why it's pivotal to the success of models like GPT and BERT.

Notable LLMs: From the early models to the latest ones, Sergey walks viewers through the hall of fame of LLMs, discussing their unique features and impact.

Pretraining Dataset Composition: An LLM is only as good as the data it's trained on. Sergey discusses the importance of dataset composition in pretraining, revealing insights into how these models get their vast knowledge.

Why Watch?

This video isn't just a lecture; it's a journey. Sergey's expertise, combined with illustrative examples and visuals, ensures a learning experience that's both informative and engaging. Whether you're an AI enthusiast, a student, or someone curious about the ongoing AI revolution, this guide offers a window into one of the most talked-about innovations in recent years.


Introducing Falcon 180B: The Next-Gen Open-Source Language Model Surpassing Previous Benchmarks


The Hugging Face AI community announced the release of Falcon 180B, an open-source large language model (LLM) with 180 billion parameters trained on 3.5 trillion tokens. This latest LLM surpasses prior models, including the previously top-ranked LLaMA 2, in scale and performance. Falcon 180B, trained using Amazon SageMaker on 4,096 GPUs, competes closely with commercial models like Google's PaLM-2. The release signifies rapid advancement in LLMs, with Falcon 180B benefiting from techniques such as LoRAs and Nvidia’s Perfusion. It is expected to see further improvement as the community fine-tunes it.

Hardware requirements

Falcon 180B Training Full fine-tuning 5120GB 8x 8x A100 80GB

Falcon 180B Training LoRA with ZeRO-3 1280GB 2x 8x A100 80GB

Falcon 180B Training QLoRA 160GB 2x A100 80GB

Falcon 180B Inference BF16/FP16 640GB 8x A100 80GB

Falcon 180B Inference GPTQ/int4 320GB 8x A100 40GB


SQLCoder: a state-of-the-art LLM for SQL generation

  •   SQLCoder, an open-source product by Defog, converts natural language questions into SQL queries.
  •   It surpasses the performance of many open-source models, even edging out models like gpt-3.5-turbo and text-davinci-003 which are 10 times its size.
  •   You can test SQLCoder using the provided interactive demo.

Technical Details

  •   SQLCoder is a 15B parameter Language Learning Model (LLM) that's a refined version of StarCoder.
  •   It's optimized for hand-crafted SQL queries of varying complexity.
  •   On certain individual database schemas, SQLCoder rivals or even surpasses GPT-4 in performance.


  •   Over the past three months, enterprises in healthcare, finance, and government have used SQLCoder.
  •   The primary advantage: it can be self-hosted, ensuring sensitive data stays on the server.
  •   The release is Defog's way of contributing back to the community, given they built upon existing models like StarCoder.


  •   Defog crafted a unique dataset centered on text-to-SQL tasks derived from 10 varied schemas. An additional evaluation dataset was produced from 7 new schemas.
  •   The dataset's complexity was ensured by selecting intricate schemas comprising 4-20 tables.
  •   Each question was categorized based on difficulty, using a method inspired by the Spider dataset.
  •   The model fine-tuning process was split into two stages, beginning with simpler questions, leading up to the more complex ones.


  •   Assessing the accuracy of SQL queries is inherently tricky due to multiple valid solutions for a single query.
  •   Therefore, Defog had to create a unique framework to gauge the correctness of SQL queries. They've open-sourced this framework and the accompanying dataset.


  •   SQLCoder excels against all notable models, save for GPT-4, based on Defog's evaluation mechanism.
  •   Especially, it bests some models that are much larger in size.
  •   For specific database schemas, its performance and responsiveness match or surpass OpenAI's GPT-4.

Future Prospects

  • Defog plans to enhance SQLCoder by:
  • Incorporating more curated data and broader questions.
  • Utilizing advanced training techniques like Reward Modeling and RLHF.
  • Introducing a specialized model for data analysis combining SQL and Python.


  The model can be explored and tested via Defog's interactive demo.

This summary encapsulates the primary features, approach, and future plans for SQLCoder by Defog.


SQL Coder Model


Create a Large Language Model from Scratch with Python – Tutorial


🚀 Dive Deep into the World of Language Models! 🚀

🔍 Discover: "Create a Large Language Model from Scratch with Python – Tutorial" 🔍

📌 Highlights:

Step-by-Step Guide: From setting up your environment to coding the intricate algorithms.

Hands-on Python Coding: Break down the complexities of language models with clear, concise Python code.

Deep Learning Explained: Understand the underlying mechanics behind large language models like GPT and BERT.

🖥 Why Watch?

Practical Knowledge: Go beyond theory and dive into hands-on implementation.

Expand Your Skill Set: Be a pioneer in one of the most sought-after domains in AI.

For Everyone: Whether you're a student, a software engineer, or an AI enthusiast, this tutorial will enhance your knowledge and skills.

📢 Don't Miss Out! Join us on this fascinating journey and start building your very own language model. Tap into the future of AI with Python! 🐍


PyTorch for Deep Learning & Machine Learning – Full Course

 🚀 Dive into the world of AI with our comprehensive course on 'PyTorch for Deep Learning & Machine Learning'. Whether you're a beginner eager to dive into neural networks or an expert aiming to sharpen your skills, this course is tailored for you. Unravel the power of PyTorch and transform your ideas into powerful ML models. Enroll now! 🔥


A step-by-step roadmap for getting started with machine learning

A step-by-step roadmap for getting started with machine learning:

Foundational Knowledge

  • Linear Algebra (vectors, matrices, eigenvalues, etc.)
  • Calculus (derivatives and integrals, partial derivatives for multivariate functions)
  • Probability and Statistics (Bayes' theorem, mean, median, variance, standard deviation, etc.)


  • Python (most popular for ML): Get comfortable with libraries like NumPy and pandas.

  • Understand relational databases (SQL) and NoSQL databases.

Machine Learning Basics

Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (kNN)

Unsupervised Learning
  • Clustering (K-means, hierarchical clustering)
  • Dimensionality Reduction (PCA, t-SNE)

- Regularization: L1 and L2 regularization
- Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC, AUC, etc.
- Tools and Libraries: Scikit-learn

Intermediate Machine Learning

- **Ensemble Methods:** Bagging, Boosting (e.g., AdaBoost, Gradient Boosting, XGBoost)

- **Neural Networks:** Basics of feedforward neural networks

- **Deep Learning:**

- Convolutional Neural Networks (CNNs)

- Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), GRU

- **Tools and Libraries:** TensorFlow, Keras, PyTorch

- **Validation:** Understand overfitting, underfitting, and how to split data (train/test/validation splits, k-fold cross-validation)

Advanced Machine Learning & Specializations

- **Natural Language Processing (NLP):** Tokenization, embeddings, transformers, BERT, etc.

- **Computer Vision:** Advanced CNN architectures, object detection, segmentation.

- **Reinforcement Learning:** Q-learning, deep Q networks, policy gradient methods.

- **Transfer Learning:** Utilizing pre-trained models.

- **Generative Adversarial Networks (GANs):** Basics and applications.

- **Explainable AI:** Techniques to understand and interpret machine learning models.

Real-world Application & Production

- **Model Deployment:** Tools like TensorFlow Serving, Flask, FastAPI.

- **Cloud Platforms:** AWS (SageMaker), Google Cloud ML, Azure ML.

- **Model Monitoring & Maintenance:** Ensure your model stays accurate over time.

- **Optimization:** Real-time processing, reducing latency, serving models efficiently.

- **MLOps:** Continuous integration and deployment (CI/CD) for ML tools like MLflow.

Stay Updated and Continuous Learning

- **Research & Papers:** Websites like arXiv, conferences like NeurIPS, ICML, etc.

- **Online Courses & Certifications:** Coursera, edX, Udacity, and others offer advanced courses.

- **Work on Projects:** Build your own projects and participate in hackathons and Kaggle competitions.

Soft Skills & Miscellaneous

Ethics in AI: Understand the ethical implications of ML models and their decisions.
Communication: Being able to explain complex ML concepts to non-experts is crucial.
Networking: Engage with the community and attend workshops, webinars, and meetups.

Remember that the field of machine learning is vast, and it's okay not to know everything. Instead, focus on building a strong foundational understanding and then dive deeper into areas that interest you the most. The best way to learn is by doing, so working on projects and hands-on experimentation is crucial to understanding the nuances and intricacies of ML algorithms and tools.