For starters, we can easily create value at the intersection of

• our skills,
• the overall market need, and
• our passions.

Finding the intersection seems hard but is often trivial from the outside.

A serious cognitive bias prevents us from seeing the obvious.

But the good news is – AI can help us discover it easily!

~~~

To create a (digital) product that sells do two things:

(1) Pose and answer simple questions such as:

• What were the last 10 YT videos you watched?
• Which products do you spend money on?
• What do people need help with?
• … (the more the better)

(2) Copy your answers & paste into Gemini, Claude, ChatGPT, Grok (ALL OF THEM!) asking for ‘3 perfect niche ideas’ or sth.

This ~10-min exercise will help you get clarity WHERE to focus your building energy.

Then move on to figuring out WHAT to build…

Blueprint: Find a more detailed guide at FirstSale.ai

The post What Your Last 10 YouTube Videos Say About Your Future Business appeared first on Be on the Right Side of Change.

Here’s the thing most people miss: AI isn’t about keeping up with the latest models.

It’s about finding small, practical ways to improve your workflow every single day.

Think about it. If you could improve your productivity by just 1% each day, what would that look like over a year? Thanks to the magic of compounding, that’s not a 365% improvement—it’s a mind-blowing 3,778% increase! That’s right, small daily improvements don’t add up—they multiply.

This is why I’ve become obsessed with finding AI newsletters that don’t just regurgitate tech news but actually deliver actionable tips you can implement immediately. The kind of tips that might save you 10 minutes today, 30 minutes tomorrow, and eventually hours every week.

I’ve subscribed to dozens of AI newsletters (so you don’t have to), and I’m cutting through the noise to bring you the 10 that actually deliver practical value. These aren’t just random picks—they’re the newsletters that have genuinely helped me and thousands of others transform our productivity through consistent, applicable AI advice.

At the top of my list? Finxter’s Daily AI Tips Newsletter—a game-changer that’s helped me implement those crucial 1% improvements day after day. But I’ll also cover nine other stellar options that might better fit your specific needs.

Let’s dive in and find the perfect AI productivity companion for your inbox.

1. Finxter Daily AI Tips Newsletter: Your Daily 1% Productivity Boost

Subscribers: 160,000+
Frequency: Daily
Cost: Free

Subscribe here (100% free)

Look, I’m not going to sugarcoat this—Finxter’s newsletter changed my life. And I don’t say that lightly.

While everyone else is busy debating whether AGI will take over the world by 2030, Finxter is sending me practical tips I can use TODAY to get more done in less time. It’s like having a productivity coach who happens to be an AI expert in your inbox every morning.

What makes Finxter stand out is their laser focus on small, implementable improvements. They understand that productivity isn’t about massive overhauls—it’s about tiny tweaks that compound over time. Each daily tip might only save you 5-10 minutes, but implement these consistently and suddenly you’re saving hours every week.

Their tips range from clever prompt engineering techniques to automate repetitive tasks, to Python scripts that can eliminate hours of manual work, to AI tool combinations I would never have thought of myself.

For example, one recent tip showed how to use ChatGPT to analyze your writing patterns and create a custom style guide—then use that guide with another AI tool to ensure all your content maintains a consistent voice. Brilliant stuff that took me maybe 15 minutes to implement but saves me hours of editing time every month.

The newsletter also includes PDF cheat sheets that serve as quick reference guides when you’re in the middle of work. I’ve printed several and keep them on my desk for those “how did I do that again?” moments.

What I love most is that Finxter doesn’t assume you’re a tech genius. Their explanations are clear, their examples are practical, and they never make you feel stupid for not knowing something. It’s like learning from a smart friend rather than a condescending tech bro.

If you only subscribe to one newsletter from this list, make it Finxter. Those daily 1% improvements will compound faster than you can imagine.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

2. The Rundown: AI News That Actually Matters

Subscribers: 600,000+
Frequency: Daily
Cost: Free

The Rundown has exploded in popularity, and for good reason. Founded by Rowan Cheung, this newsletter cuts through the noise to deliver AI updates that actually impact how you work.

What I appreciate about The Rundown is their ruthless filtering. They don’t waste your time with every minor update to every AI tool—they focus on developments that could genuinely change your workflow. Their “3 Things You Can Use Today” section at the end of each newsletter is pure gold for productivity hunters.

Related: GitHub Curated List of AI Newsletters

While not as laser-focused on daily productivity improvements as Finxter, The Rundown excels at keeping you informed about new AI tools that could revolutionize specific aspects of your work. They were how I discovered several text-to-video tools that cut my content creation time in half.

The writing style is refreshingly conversational—like getting updates from a tech-savvy friend who respects your time too much to bore you with unnecessary details.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

3. Superhuman AI: Become the Productivity Hero in Your Office

Subscribers: 650,000+
Frequency: Daily
Cost: Free

With over 650,000 subscribers, Superhuman AI has clearly struck a chord. Created by Zain Kahn (aka “The AI Guy”), this newsletter focuses on practical ways to leverage AI in your career.

What sets Superhuman apart is their step-by-step guides. Rather than just telling you about a cool AI tool, they walk you through exactly how to use it to solve specific problems. Their recent guide on using Claude to replicate your writing style was a game-changer for my content creation process.

The newsletter strikes a nice balance between news, tools, and tutorials. Each edition takes about 3 minutes to read but often delivers insights that can save you hours of work.

I’ve found their AI automation tips particularly valuable—they’ve helped me automate several tedious aspects of my work that I didn’t even realize could be automated. That’s the kind of 1% improvement that adds up fast.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

4. Ben’s Bites: Practical AI Without the Fluff

Subscribers: 100,000+
Frequency: Daily (Quick Bites) and Weekly (Deep Dives)
Cost: Free, with paid option

Ben Tossell’s newsletter stands out for its no-nonsense approach to AI. Ben started this newsletter to learn more about AI for his own projects, and that practical focus shines through.

What I love about Ben’s Bites is the dual format: daily “Quick Bites” for essential updates and weekly deep dives for more comprehensive analysis. The daily emails are perfect for busy professionals who need to stay informed without getting overwhelmed.

Ben excels at finding practical use cases for new AI tools. Rather than just announcing a new tool’s existence, he shows you exactly how it might fit into your workflow. His breakdown of AI tools for data analysis saved me countless hours on a recent project.

The writing style is refreshingly straightforward—Ben doesn’t waste time with hype or jargon. He gets straight to the point, respecting your time and intelligence.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

5. Mindstream: 5-Minute AI Updates for Busy Professionals

Subscribers: 130,000+
Frequency: Daily
Cost: Free

If you’re perpetually short on time but still want to stay informed about AI, Mindstream might be your perfect match. Co-founded by Adam Biddlecombe, this newsletter promises AI updates in just 5 minutes a day.

What makes Mindstream special is their focus on productivity tricks. Each newsletter includes a walkthrough of how Adam is using a new AI tool in his own workflow. These aren’t theoretical use cases—they’re real examples from someone who’s actually implementing these tools daily.

I’ve found their AI tool recommendations particularly valuable. They seem to have a knack for finding lesser-known tools that solve specific problems better than the big-name alternatives. Their recommendation of a specialized AI writing assistant for technical documentation saved me hours of frustration.

The writing is concise and practical—perfect for scanning during your morning coffee before diving into work.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

6. TLDR AI: Complex Concepts Made Simple

Subscribers: 500,000+
Frequency: Daily
Cost: Free

TLDR AI does exactly what the name suggests—it distills complex AI developments into short, easy-to-understand summaries. This is particularly valuable in the AI space, where academic papers and technical announcements can be impenetrable to non-specialists.

What I appreciate about TLDR AI is how they translate technical breakthroughs into practical implications. They don’t just tell you that a new language model has improved performance on certain benchmarks—they explain how this might change the tools you use daily.

Their summaries of academic papers have been especially valuable for me. They extract the key insights from dense research without dumbing it down too much. This has helped me stay ahead of AI trends without spending hours wading through technical jargon.

The writing style is clear and accessible, making complex topics digestible even for busy professionals reading on their commute.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

7. The Neuron: Signal Without Noise

Subscribers: 425,000+
Frequency: Daily
Cost: Free

Created by Northwestern students Pete Huang and Noah Edelman, The Neuron has quickly gained a reputation for high-quality AI content without the fluff. Their tagline—”all signal, no noise”—perfectly captures their approach.

What sets The Neuron apart is their balanced coverage. They don’t just focus on the latest ChatGPT update or the newest image generator—they provide a comprehensive view of the AI landscape, including developments that might fly under the radar of other newsletters.

I’ve found their detailed breakdowns of industry news particularly helpful for understanding the broader implications of AI developments. Their analysis of recent AI regulation proposals helped me understand how these changes might affect my work in ways I hadn’t considered.

The writing includes a touch of humor that makes technical topics more engaging without sacrificing substance. It’s like learning from the smartest, funniest person in your CS class.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

8. AI Breakfast: In-Depth Analysis to Start Your Day

Subscribers: 54,000+
Frequency: 3x per week
Cost: Free

AI Breakfast offers a slightly different approach from the daily quick-hit newsletters. With three issues per week, they go deeper into each topic they cover.

What I appreciate about AI Breakfast is their focus on the global impact of AI. They don’t just cover new tools and features—they explore how AI is transforming industries and societies worldwide. This broader perspective helps me think more strategically about how AI might affect my field in the long term.

Their coverage includes relevant news stories and research papers to back up their analysis, which I find refreshingly thorough in an era of hot takes and superficial coverage.

The writing style is thoughtful and nuanced, perfect for readers who want to develop a deeper understanding of AI trends rather than just skimming the surface.

9. Prompt Engineering Daily: Master the Art of AI Communication

Subscribers: 100,000+
Frequency: Daily
Cost: Free

If you’ve ever spent 20 minutes trying to get an AI tool to understand exactly what you want, you know that prompt engineering is a crucial skill. Prompt Engineering Daily, founded by Aadit Sheth, focuses specifically on this vital aspect of AI productivity.

What makes this newsletter valuable is its practical approach to prompting. Each issue includes prompt tutorials you can immediately apply to your work. I’ve saved countless hours by implementing their prompting techniques with tools like ChatGPT and Claude.

The newsletter also covers machine learning news and tool recommendations, but always with a focus on how you can apply these developments to your daily work. Their recent breakdown of function calling in the OpenAI API helped me automate a tedious data processing task that used to eat up hours of my week.

The writing is clear and instructional, with step-by-step guides that make complex techniques accessible to everyone.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

10. Future Tools: Tomorrow’s Productivity Tools, Today

Subscribers: 50,000+
Frequency: Weekly
Cost: Free

Rounding out our list is Future Tools, a newsletter focused on emerging AI tools that can transform your workflow. Founded by Matt Wolfe, this newsletter has a knack for spotting useful tools before they hit the mainstream.

What I love about Future Tools is their categorization. They organize AI tools by use case—productivity, generative art, copywriting, code generation, and more—making it easy to find solutions for your specific needs.

The weekly frequency is perfect for this type of content. Rather than overwhelming you with daily tool recommendations, they curate a manageable selection each week, giving you time to actually try them out.

I’ve discovered several game-changing tools through this newsletter, including an AI detection tool that has become essential for my content creation process and a generative code tool that has dramatically accelerated my development workflow.

The writing is straightforward and practical, focusing on what each tool does and how it might fit into your work rather than getting lost in technical specifications.

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

The Compound Effect of Daily AI Tips

After reading through these newsletters, you might be tempted to subscribe to all of them. I get it—FOMO is real, especially when it comes to staying ahead with AI. But remember what we talked about at the beginning: the power of small, consistent improvements.

You don’t need to read every AI newsletter out there. You need one (or maybe two) that consistently deliver actionable tips you’ll actually implement. Because a tip you don’t use is just digital clutter.

This is why I put Finxter at the top of the list. Their daily format and focus on small, implementable improvements embodies that 1% better philosophy. Each tip builds on the last, creating a compound effect that can transform your productivity over time.

Think about it this way: If you implement just one 1% improvement each workday, by the end of the year, you’ll be 1.01^250 = 12x more productive. That’s not just an incremental gain—it’s a life-changing transformation in how much you can accomplish.

So pick the newsletter that resonates with you, commit to implementing one tip each day, and watch as those small improvements compound into massive productivity gains. Your future self will thank you.

Which newsletter will you choose to fuel your daily 1% improvements?

Subscribe – Free AI Nuggets and Tools to Supercharge Your Productivity

The post The 10 Best AI Newsletters That Will Skyrocket Your Productivity in 2025 appeared first on Be on the Right Side of Change.

In 2023, AI’s rapid progress saw the release of numerous new large language models, with two-thirds being open-source. Notably, Gemini Ultra reached human-level performance on the MMLU benchmark, and GPT-4 achieved a 0.96 mean win rate on the HELM benchmark.

Despite a decline in global private AI investment, funding for generative AI surged. AI’s integration into daily life is growing, with increased mentions in Fortune 500 earnings calls and evidence of AI boosting worker productivity.

What are the skills most in demand among the AI job postings in 2023? See here:

What do computer scientists earn? See here:

With these interesting nuggets out of the way, let’s dive into the top 10 takeaways of the new Stanford report on Artificial Intelligence.

Takeaway 1: AI Outperforms Humans in Specific Areas

AI excels in tasks like image classification, visual reasoning, and English comprehension, but struggles with more complex tasks such as advanced mathematics, visual common-sense reasoning, and planning.

GPT-4 still outperforms most alternative models in many benchmarks:

The Massive Multitask Language Understanding (MMLU) test is one of the best tests that asseses model performance in 57 subject areas including humanities, STEM, social science. We have reached human baseline performance!

For the average person, this means that while AI can handle certain routine and straightforward tasks, it still requires human intelligence for more intricate problem-solving.

Takeaway 2: Industry Leads AI Research

In 2023, the industry developed 51 notable machine learning models, compared to academia’s 15. Additionally, 21 notable models resulted from industry-academia collaborations, marking a new high.

This dominance means that businesses are at the forefront of driving AI innovation, potentially leading to faster commercialization of AI technologies that can improve everyday life.

Takeaway 3: Rising Costs for Frontier AI Models

The training costs for state-of-the-art AI models are skyrocketing. For instance, OpenAI’s GPT-4 training costs were estimated at $78 million, while Google’s Gemini Ultra cost $191 million.

These escalating costs could lead to higher prices for AI-driven products and services, impacting consumers’ access to advanced technologies.

Takeaway 4: U.S. Dominates AI Model Development

In 2023, U.S.-based institutions created 61 notable AI models, significantly surpassing the European Union’s 21 and China’s 15.

However, China has long granted most AI patents:

For the common person, this indicates that the U.S. remains a global leader in AI innovation, potentially providing Americans with earlier access to cutting-edge AI applications and services.

Takeaway 5: Lack of Standardized Evaluations for LLM Responsibility

There is a significant absence of standardized benchmarks for responsible AI. Leading developers like OpenAI, Google, and Anthropic use different benchmarks, complicating systematic risk and limitation assessments.

This inconsistency means that consumers might face varying levels of safety and ethical standards in AI products. Here’s an initial evaluation of the “trustworthiness” of different models:

However, there are multiple standardized tests for LLM performance (e.g., MMMU) which might be more important for now — given the difficulty of “testing” the ethical standards of an AI model anyways.

Takeaway 6: Surge in Generative AI Investment

Despite a general decline in AI private investment, funding for generative AI surged to $25.2 billion in 2023, nearly eight times the amount in 2022. Companies like OpenAI, Anthropic, Hugging Face, and Inflection reported significant fundraising.

This boom means more innovative AI tools and applications are likely to enter the market, enhancing creativity and productivity in everyday life.

However, it is very interesting to see that we’re not at peak investment. The year 2021 has seen far higher investments in AI:

This might indicate that the amazing AI skills we see in 2024 are just the beginning. We’re not at the peak of a hype phase yet.

Takeaway 7: AI Enhances Worker Productivity

Studies from 2023 show that AI improves worker productivity and output quality. AI also helps bridge the skill gap between low- and high-skilled workers. However, improper use without oversight can decrease performance.

For workers, this means that integrating AI into their workflows can make their jobs easier and more efficient, but it’s essential to use these tools correctly.

Takeaway 8: Accelerated Scientific Progress with AI

AI significantly advanced scientific discovery in 2023 with applications like AlphaDev for efficient algorithmic sorting and GNoME for facilitating materials discovery.

For instance, AlphaDev has already made it in the C++ sorting library — proof that it’s a superior AI-generated sorting algorithm. The impact not only on science but on all computing applications is significant!

Here’s the groundbreaking work on materials research:

Check out my article on the topic:

Google Deep Learning – 800 Years of Human Experimentation in One Discovery

This progress means that breakthroughs in science and technology could occur more frequently, leading to faster advancements in health, energy, and other crucial areas that affect everyday life.

Takeaway 9: Increase in U.S. AI Regulations

AI-related regulations in the U.S. have grown substantially, from one in 2016 to 25 in 2023, with a 56.3% increase last year alone.

For the public, this indicates that the government is taking steps to ensure that AI technologies are developed and deployed responsibly, potentially reducing risks associated with these technologies.

Takeaway 10: Growing Public Awareness and Anxiety About AI

A survey by Ipsos revealed that the percentage of people who believe AI will dramatically impact their lives in the next 3-5 years rose from 60% to 66%. Additionally, 52% express nervousness about AI products and services, up from 37% in 2022. In the U.S., Pew data shows 52% of Americans feel more concerned than excited about AI, up from 37% in 2022.

This growing awareness and concern reflect a need for better public education on AI and more transparency from AI developers to build trust with the public.

The post Top 10 Takeaways from Stanford’s AI Index Report 2024 appeared first on Be on the Right Side of Change.

Massive Performance Boost

Trillium TPUs deliver a whopping 4.7x increase in peak compute performance per chip compared to the previous TPU v5e. This leap is achieved through expanded matrix multiply units and increased clock speeds. What this means is faster training and serving of AI models, making AI more efficient and accessible.

This is impressive given that the previous TPU v5p was already 2.8x times faster than TPU v4. The improvements in the space are mind-boggling!

By the way, here’s an interesting breakdown on when to use TPUs, according to Google:

Doubling Down on Memory and Bandwidth

Trillium doubles the capacity and bandwidth of High Bandwidth Memory (HBM) and the Interchip Interconnect (ICI). This allows the TPU to handle larger models and more data at faster speeds. Essentially, it can process twice the amount of model weights and key-value caches, improving the efficiency and speed of AI workloads.

Specialized for Advanced AI

Equipped with third-generation SparseCore, Trillium is tailored for processing ultra-large embeddings, crucial for advanced ranking and recommendation systems. This specialization helps in accelerating workloads and reducing latency, making AI applications smoother and faster.

Scalability and Efficiency

Trillium can scale up to 256 TPUs in a single high-bandwidth, low-latency pod. With multislice technology and Titanium Intelligence Processing Units (IPUs), it can connect thousands of chips in a supercomputer setup. Plus, it’s over 67% more energy-efficient than its predecessor, making it Google’s most sustainable TPU yet.

Real-World Applications

From autonomous vehicles to drug discovery, Trillium TPUs are set to power the next wave of AI models. Companies like Essential AI, Nuro, and Deep Genomics are already gearing up to leverage Trillium for groundbreaking advancements in their fields.

AI Hypercomputer Integration

Trillium is a key component of Google Cloud’s AI Hypercomputer, a supercomputing architecture designed for cutting-edge AI tasks. This setup integrates Trillium TPUs with open-source software frameworks and flexible consumption models, empowering developers to push the boundaries of AI.

Partnerships and Support

Google has teamed up with Hugging Face, SADA, and Lightricks to enhance AI model training and serving. These collaborations ensure that Trillium’s performance gains are easily accessible to AI developers and businesses.

Google’s Trillium TPUs are set to be available later this year.

Valuing $NVIDIA as a Real Estate Company That Sells Housing to AI Agents ($100k/Share in 2034)

The post Google Big AI Chip Announcement – Trillium: 6-th Gen TPU (4.7x) appeared first on Be on the Right Side of Change.

The Backbone of Next-Gen AI

These new clusters represent a significant stride forward in hardware capability, providing the computational power necessary for developing and deploying advanced AI models, including the much-discussed Llama 3, the successor to Meta’s Llama 2 language model.

With plans to expand to 350,000 NVIDIA H100 GPUs by the end of 2024, Meta is gearing up for a future where it can harness compute power equivalent to nearly 600,000 H100 GPUs. This infrastructure will form the backbone of Meta’s AI ambitions, from research and development to real-world applications across its suite of products and services.

Valuing $NVIDIA as a Real Estate Company That Sells Housing to AI Agents ($100k/Share in 2034)

State-of-the-Art Network and Storage Solutions

Meta’s new AI clusters are not just about raw GPU power; they also feature cutting-edge network and storage innovations. One cluster utilizes RDMA over Converged Ethernet (RoCE), while the other employs NVIDIA Quantum2 InfiniBand technology, ensuring robust, high-speed interconnects capable of handling vast amounts of data with minimal latency. This is critical for tasks that require extensive data communication between nodes, such as training large-scale AI models.

On the storage front, Meta has implemented a Linux Filesystem in Userspace (FUSE) API backed by their proprietary ‘Tectonic’ distributed storage solution, optimized for flash media. This setup is designed to meet the intensive data and checkpointing demands of large AI workloads, enabling high throughput and exabyte-scale storage. Additionally, a collaboration with Hammerspace has introduced a parallel network file system (NFS) that enhances the development experience by allowing interactive debugging and immediate code changes across thousands of GPUs.

Commitment to Open Compute and AI Innovation

Meta continues to champion open compute and open-source solutions, leveraging platforms like Grand Teton and OpenRack, which are part of the Open Compute Project (OCP).

These efforts demonstrate Meta’s ongoing commitment to fostering innovation and collaboration in the AI field, making these advanced technologies accessible to the broader community and setting new standards for responsible AI development.

Looking Forward

The implications of Meta’s latest infrastructure expansion are vast, promising not only to accelerate the company’s own AI research but also to influence the broader landscape of AI technology. By continuing to push the boundaries of what’s possible in AI hardware and network capabilities, Meta is paving the way for future advancements that could redefine how AI is integrated into our digital lives.

For those of us following these developments, Meta’s infrastructure roadmap is a clear indication that the AI revolution is not just continuing—it’s accelerating. Stay tuned as we keep an eye on how these advancements unfold and what they mean for the future of technology:

Join 160k tech enthusiasts and AI developers

The post $2 Billion: Llama 3 Is Trained on Two 24k NVIDIA H100 GPU Clusters appeared first on Be on the Right Side of Change.

From a technical standpoint, a large language model (LLM) can be seen as a massive file on a computer, containing billions or even trillions of numerical values, known as parameters. These parameters are fine-tuned through extensive training on diverse datasets, capturing the statistical properties of human language.

However, such a dry description hardly does justice to the magic of LLMs. From another perspective, they function almost like an oracle. You call upon them with a query, such as llm("What is the purpose of life"), and they may respond with something witty, insightful, or enigmatic, like "42" (a humorous nod to Douglas Adams’ The Hitchhiker’s Guide to the Galaxy).

By the way, you can check out my article on using LLMs like this in the command line here:

Recommended: How to Run Large Language Models (LLMs) in Your Command Line?

Isn’t it wild to think about how Large Language Models (LLMs) can turn math into something almost magical? It’s like they’re blending computer smarts with human creativity, and the possibilities are just getting started.

Now, here’s where it gets really cool.

These LLMs take all kinds of complex patterns and knowledge and pack them into binary files full of numbers. We don’t really understand what these numbers represent but together they encode a deep understanding of the world. LLMs are densely compressed human wisdom, knowledge, and intelligence. Now imagine having these files and being able to copy them millions of times, running them all at once.

It’s like having a huge team of super-smart people, but they’re all in your computer.

So picture this: Millions of brainy helpers in your pocket, working day and night on anything you want.

You know how doctors are always trying to figure out the best way to treat illnesses? Imagine having millions of super-smart helpers to quickly find the answers.

Or think about your savings and investments; what if you had a team of top financial experts guiding you 24/7 to make the smartest choices with your money?

And for kids in school, picture having a personal tutor for every subject, making sure they understand everything perfectly. LLMs is like having an army of geniuses at your service for anything you need.

LLMs, what Willison calls alien technology, have brought us closer to solving the riddle of intelligence itself, turning what was once the exclusive domain of human cognition into something that can be copied, transferred, and harnessed like never before.

I’d go as far as to say that the age-old process of reproducing human intelligence has been transcended. Intelligence is solved. LLMs will only become smarter from now on. Like the Internet, LLMs will stay and proliferate and penetrate every single sector of our economy.

How Do LLMs Work?

The underlying mechanism of Large Language Models (LLMs) might seem almost counterintuitive when you delve into how they operate. At their core, LLMs are essentially word-prediction machines, fine-tuned to anticipate the most likely next word (more precisely: token) in a sequence.

For example consider ChatGPT’s LLM chat interface that has reached product market fit and is used by hundreds of millions of users. The ingenious “hack” that allows LLMs to participate in a chat interface is all about how the input is framed. In essence, the model isn’t inherently conversing with a user; it’s continuing a text, based on a conversational pattern it has learned from vast amounts of data.

Consider this simplified example:

You are a helpful assistant

User: What is the purpose of life?
Assistant: 42
User: Can you elaborate?
Assistant:

Here’s what’s happening under the hood:

1. Setting the Scene: The introductory line, "You are a helpful assistant" sets a context for the LLM. It provides an instruction to guide its responses, influencing its persona.
2. User Input: The following lines are framed as a dialogue, but to the LLM, it’s all part of a text it’s trying to continue. When the user asks, "What is the purpose of life?" the LLM looks at this as the next part of a story, or a scene in a play, and attempts to predict the next word or phrase that makes the most sense.
3. Assistant Response: The assistant’s response, "42" is the model’s guess for the next word, given the text it has seen so far. It’s a clever completion, reflecting the model’s training on diverse and creative texts. In the second run, however, the whole conversation is used as input and the LLM just completes the conversation.
4. Continuing the Conversation: When the user follows up with "Can you elaborate?" the LLM is once again seeking to continue the text. It’s not consciously leading a conversation but following the statistical patterns it has learned, which, in this context, would typically lead to an elaboration.

The magic is in how all these elements come together to create an illusion of a conversation. In reality, the LLM doesn’t understand the conversation or its participants. It’s merely predicting the next word, based on an intricately crafted pattern.

This “dirty little hack” transforms a word-prediction engine into something that feels interactive and engaging, demonstrating the creative application of technology and the power of large-scale pattern recognition. It’s a testament to human ingenuity in leveraging statistical learning to craft experiences that resonate on a very human level.

Prompt Engineering is a clever technique used to guide the behavior of Large Language Models (LLMs) by crafting specific inputs, or prompts, that steer the model’s responses. It’s akin to creatively “hacking” the model to generate desired outputs.

For example, if you want the LLM to act like a Shakespearean character, you might begin with a prompt like "Thou art a poet from the Elizabethan era". The model, recognizing the pattern and language style, will respond in kind, embracing a Shakespearean tone.

This trickery through carefully designed prompts transforms a word-prediction machine into a versatile and interactive tool that can mimic various styles and tones, all based on how you engineer the initial prompt.

Prompt Engineering with Python and OpenAI

You can check out the whole course on OpenAI Prompt Engineering using Python on the Finxter academy. We cover topics such as:

• Embeddings
• Semantic search
• Web scraping
• Query embeddings
• Movie recommendation
• Sentiment analysis

Academy: Prompt Engineering with Python and OpenAI

What’s the Secret of LLMs?

The secret to the magical capabilities of Large Language Models (LLMs) seems to lie in a simple and perhaps surprising element: scale.

The colossal nature of these models is both their defining characteristic and the key to their unprecedented performance.

Tech giants like Meta, Google, and Microsoft have dedicated immense resources to developing LLMs. How immense? We’re talking about millions of dollars spent on cutting-edge computing power and terabytes of textual data to train these models. It’s a gargantuan effort that converges in a matrix of numbers — the model’s parameters — that represent the learned patterns of human language.

The scale here isn’t just large; it’s virtually unprecedented in computational history. These models consist of billions or even trillions of parameters, fine-tuned across diverse and extensive textual datasets. By throwing such vast computational resources at the problem, these corporations have been able to capture intricate nuances and create models that understand and generate human-like text.

However, this scale comes with challenges, including the enormous energy consumption of training such models, the potential biases embedded in large-scale data, and the barrier to entry for smaller players who can’t match the mega corporations’ resources.

The story of LLMs is a testament to the “bigger is better” philosophy in the world of artificial intelligence. It’s a strategy that seems almost brute-force in nature but has led to a qualitative leap in machine understanding of human language. It illustrates the power of scale, paired with ingenuity and extensive resources, to transform a concept into a reality that pushes the boundaries of what machines can achieve.

Attention Is All You Need

The 2017 paper by Google “Attention is All You Need” marked a significant turning point in the world of artificial intelligence. It introduced the concept of transformers, a novel architecture that is uniquely scalable, allowing training to be run across many computers in parallel both efficiently and easily.

This was not just a theoretical breakthrough but a practical realization that the model could continually improve with more and more compute and data.

Key Insight: By using unprecedented amount of compute on unprecedented amount of data on a simple neural network architecture (transformers), intelligence seems to emerge as a natural phenomenon.

Unlike other algorithms that may plateau in performance, transformers seemed to exhibit emerging properties that nobody fully understood at the time. They could understand intricate language patterns, even developing coding-like abilities. The more data and computational power thrown at them, the better they seemed to perform. They didn’t converge or flatten out in effectiveness with increased scale, a behavior that was both fascinating and mysterious.

OpenAI, under the guidance of Sam Altman, recognized the immense potential in this architecture and decided to push it farther than anyone else. The result was a series of models, culminating in state-of-the-art transformers, trained on an unprecedented scale. By investing in massive computational resources and extensive data training, OpenAI helped usher in a new era where large language models could perform tasks once thought to be exclusively human domains.

This story highlights the surprising and yet profound nature of innovation in AI.

A simple concept, scaled to extraordinary levels, led to unexpected and groundbreaking capabilities. It’s a reminder that sometimes, the path to technological advancement isn’t about complexity but about embracing a fundamental idea and scaling it beyond conventional boundaries. In the case of transformers, scale was not just a means to an end but a continually unfolding frontier, opening doors to capabilities that continue to astonish and inspire.

Ten Tips to Use LLMs Effectively

As powerful and versatile as Large Language Models (LLMs) are, harnessing their full potential can be a complex endeavor.

Here’s a series of tricks and insights to help tech enthusiasts like you use them effectively:

1. Accept that No Manual Exists: There’s no step-by-step guide to mastering LLMs. The field is still relatively new, and best practices are continually evolving. Flexibility and a willingness to experiment are essential.
2. Iterate and Refine: Don’t reject the model’s output too early. Your first output might not be perfect, but keep iterating. Anyone can get an answer from an LLM, but extracting good answers requires persistence and refinement. You can join our prompt engineering beginner and expert courses to push your own understanding to the next level.
3. Leverage Your Domain Knowledge: If you know coding, use LLMs to assist with coding tasks. If you’re a marketer, apply them for content generation. Your expertise in a particular area will allow you to maximize the model’s capabilities.
4. Understand How the Model Works: A rough understanding of the underlying mechanics can be immensely beneficial. Following tech news, like our daily Finxter emails, can keep you informed and enhance your ability to work with LLMs.
5. Gain Intuition by Experimenting: Play around with different prompts and settings. Daily hands-on practice can lead to an intuitive feel for what works and what doesn’t.
6. Know the Training Cut-off Date: Different models have different cut-off dates. For example, OpenAI’s GPT-3.5 models were trained until September 2021, while Claude 2 Anthropic and Google PaLM 2 are more recent. This can affect the accuracy and relevance of the information they provide.
7. Understand Context Length: Models have limitations on the number of tokens (words, characters, spaces) they can handle. It’s 4000 tokens for GPT-3, 8000 for GPT-4, and 100k for Claude 2. Tailoring your input to these constraints will yield better results.
8. Develop a “Sixth Sense” for Hallucinations: Sometimes, LLMs may generate information that seems plausible but is incorrect or hallucinated. Developing an intuition for recognizing and avoiding these instances is key to reliable usage.
9. Stay Engaged with the Community: Collaborate with others, join forums, and stay abreast of the latest developments. The collective wisdom of the community is a powerful asset in mastering these technologies.
10. Be Creative: Prompt the model for creative ideas (e.g., "Give me 20 ideas on X"). The first answers might be obvious, but further down the list, you might find a spark of brilliance.

Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) represents an intriguing intersection between the vast capabilities of Large Language Models (LLMs) and the power of information retrieval. It’s a technique that marries the best of both worlds, offering a compelling approach to generating information and insights.

Here’s how it works and why it’s making waves in the tech community:

What is Retrieval Augmented Generation?

RAG is a method that, instead of directly training a model on specific data or documents, leverages the vast information already available on the internet. By searching for relevant content, it pulls this information together and uses it as a foundation for asking an LLM to generate an answer.

How Does RAG Work?

1. Search for Information: First, a search is conducted for content relevant to the query or task at hand. This could involve scouring databases, the web, or specialized repositories.
2. Prepend the Retrieved Data: The content found is then prepended to the original query or prompt. Essentially, it’s added to the beginning of the question or task you’re posing to the LLM.
3. Ask the Model to Answer: With this combined prompt, the LLM is then asked to generate an answer or complete the task. The prepended information guides the model’s response, grounding it in the specific content retrieved.

Why is RAG Valuable?

• Customization: It allows for tailored responses based on real-world data, not just the general patterns an LLM has learned from its training corpus.
• Efficiency: Rather than training a specialized model, which can be costly and time-consuming, RAG leverages existing models and augments them with relevant information.
• Flexibility: It can be applied to various domains, from coding to medical inquiries, by merely adapting the retrieval component to the area of interest.
• Quality: By guiding the model with actual content related to the query, it often results in more precise and contextually accurate responses.

Retrieval Augmented Generation represents an elegant solution to some of the challenges in working with LLMs. It acknowledges that no model, no matter how large, can encapsulate the entirety of human knowledge. By dynamically integrating real-time information retrieval, RAG opens new horizons for LLMs, making them even more versatile and responsive to specific and nuanced inquiries.

In a world awash with information, the fusion of search and generation through RAG offers a sophisticated tool for navigating and extracting value. Here’s my simple formula for RAG:

USEFULNESS ~ LLM_CAPABILITY * CONTEXT_DATA

or more simply: 
USEFULNESS ~ Intelligence * Information

Let’s examine an advanced and extremely powerful technique to provide helpful context to LLMs and, thereby, get the most out of it:

Embeddings and Vector Search: A Special Case of Retrieval Augmented Generation (RAG)

In the broader context of RAG, a specialized technique called “Embeddings and Vector Search” takes text-based exploration to a new level, allowing for the construction of semantic search engines that leverage the capabilities of LLMs.

Here’s how it works:

Transforming Text into Embeddings

1. Text to Vector Conversion: Any string of text, be it a sentence, paragraph, or document, can be transformed into an array of floating-point numbers, or an “embedding”. This embedding encapsulates the semantic meaning of the text based on the LLM’s mathematical model of human language.
2. Dimensionality: These embeddings are positioned in a high-dimensional space, e.g., 1,536 dimensions. Each dimension represents a specific aspect of the text’s semantic content, allowing for a nuanced representation.

Example: Building a Semantic Search Engine

1. Cosine Similarity Distance: To find the closest matches to a given query, the cosine similarity distance between vectors is calculated. This metric measures how closely the semantic meanings align between the query and the existing embeddings.
2. Combining the Brain (LLM) with Application Data (Embedding): By pairing the vast understanding of language embedded in LLMs with specific application data through embeddings, you create a bridge between generalized knowledge and specific contexts.
3. Retrieval and Augmentation: The closest matching embeddings are retrieved, and the corresponding text data is prepended to the original query. This process guides the LLM’s response, just as in standard RAG.

Why is this Technique Important?

You can use embeddings as input to LLM prompts to provide context in a highly condensed and efficient form. This solves one half of the problem of using LLMs effectively!

• Precision: It offers a finely-tuned mechanism for retrieving content that semantically resonates with a given query.
• Scalability: The method can be applied to vast collections of text, enabling large-scale semantic search engines.
• Customization: By building embeddings from specific data sources, the search process can be tailored to the unique needs and contexts of different applications.

Embeddings are a powerful extension of the RAG paradigm, enabling a deep, semantic understanding of text. By translating text into numerical vectors and leveraging cosine similarity, this technique builds bridges between the abstract mathematical understanding of language within LLMs and the real-world applications that demand precise, context-aware responses.

Using embeddings in OpenAI is as simple as running the following code:

response = openai.Embedding.create(
    input="Your text string goes here",
    model="text-embedding-ada-002"
)
embeddings = response['data'][0]['embedding']

Possible output:

{
  "data": [
    {
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        ...
        -4.547132266452536e-05,
        -0.024047505110502243
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-ada-002",
  "object": "list",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

If you want to dive deeper into embeddings, I recommend checking out our blog post and the detailed OpenAI guide!

Recommended: What Are Embeddings in OpenAI?

ChatGPT Plugins

OpenAI has recently announced the initial support for plugins in ChatGPT. As part of the gradual rollout of these tools, the intention is to augment language models with capabilities that extend far beyond their existing functionalities.

ChatGPT plugins are tools specifically designed for language models to access up-to-date information, run computations, or use third-party services such as Expedia, Instacart, Shopify, Slack, Wolfram, and more.

The implementation of plugins opens up a vast range of possible use cases. From giving parents superpowers with Milo Family AI to enabling restaurant bookings through OpenTable, the potential applications are expansive. Examples like searching for flights with KAYAK or ordering groceries from local stores via Instacart highlight the practical and innovative utilization of these plugins.

OpenAI is also hosting two plugins, a web browser and a code interpreter (see below) to broaden the model’s reach and increase its functionality. An experimental browsing model will allow ChatGPT to access recent information from the internet, further expanding the content it can discuss with users.

Recommended: Top 5 LLM Python Libraries Like OpenAI, LangChain, Pinecone

ChatGPT Code Interpreter: What Is It and How Does It Work?

The ChatGPT Code Interpreter is a revolutionary feature added to OpenAI’s GPT-4 model, enabling users to execute Python code within the ChatGPT environment.

It functions as a sandboxed Python environment where tasks ranging from PDF conversion using OCR to video trimming and mathematical problem-solving can be carried out.

Users can upload local files in various formats, including TXT, PDF, JPEG, and more, as the Code Interpreter offers temporary disk space and supports over 300 preinstalled Python packages.

Whether it’s data analysis, visualization, or simple file manipulations, the Code Interpreter facilitates these actions within a secure, firewalled environment, transforming the chatbot into a versatile computing interface.

Accessible to ChatGPT Plus subscribers, this feature amplifies the range of possibilities for both coders and general users, blending natural language interaction with direct code execution.

Here’s a list of tasks that can be solved by Code Interpreter that were previously solved by specialized data scientists:

1. Explore Your Data: You can upload various data files and look into them. It’s a handy way to see what’s going on with your numbers.
2. Clean Up Your Data: If your data’s a little messy, you can tidy it up by removing duplicates or filling in missing parts.
3. Create Charts and Graphs: Visualize your data by making different types of charts or graphs. It’s a straightforward way to make sense of complex information.
4. Try Out Machine Learning: Build your own machine learning models to predict outcomes or categorize information. It’s a step into the more advanced side of data handling.
5. Work with Text: Analyze texts to find out what’s being said or how it’s being expressed. It’s an interesting dive into natural language processing.
6. Convert and Edit Files: Whether it’s PDFs, images, or videos, you can convert or modify them as needed. It’s quite a practical feature.
7. Gather Data from Websites: You can pull data directly from web pages, saving time on collecting information manually.
8. Solve Mathematical Problems: If you have mathematical equations or problems, you can solve them here. It’s like having a calculator that can handle more complex tasks.
9. Experiment with Algorithms: Write and test your algorithms for various purposes. It’s a useful way to develop custom solutions.
10. Automate Tasks: If you have repetitive or routine tasks, you can write scripts to handle them automatically.
11. Edit Images and Videos: Basic editing of images and videos is possible, allowing for some creative applications.
12. Analyze IoT Device Data: If you’re working with Internet of Things (IoT) devices, you can analyze their data in this environment.

Here’s an example run in my ChatGPT environment:

Yay you can now run Python code and plot scripts in your ChatGPT environment!

If you click on the “Show work” button above, it toggles the code that was executed:

A simple feature but powerful — using ChatGPT has now become even more convincing for coders like you and me.

To keep learning about OpenAI and Python, you can download our cheat sheet here:

Recommended: Python OpenAI API Cheat Sheet (Free)

Resources:

• https://openai.com/blog/chatgpt-plugins
• https://simonwillison.net/2023/Aug/3/weird-world-of-llms/
• https://beebom.com/chatgpt-code-interpreter/
• https://chat.openai.com

The post Alien Technology: Catching Up on LLMs, Prompting, ChatGPT Plugins & Embeddings appeared first on Be on the Right Side of Change.

Image credits

This discovery represents an advancement equivalent to nearly 800 years’ worth of knowledge in material science. With such a massive scientific impact, Google Brain undoubtedly deserved to get their paper accepted for the world’s most credible research publication, Nature.

The traditional approach to discovering new materials has been a slow, expensive, and often inefficient process, typically involving making incremental changes to known crystals or experimenting with new element combinations.

GNoME revolutionizes this process by employing two distinct deep-learning models.

• The first model generates structures by tweaking elements in existing materials, while
• The second model makes predictions based solely on chemical formulas, thereby broadening the scope of potential discoveries.

GNoME’s efficiency is notable: initially, it predicted materials’ stability with about 5% accuracy, but this figure increased significantly through iterative learning, reaching over 80% accuracy for one model. The use of AI in this domain isn’t new; projects like the Materials Project have previously used similar techniques to discover and improve the stability of materials.

However, GNoME’s scale and precision are unparalleled, making it a standout in the field.

One of the most exciting aspects of this development is the potential applications of these new materials.

For instance, GNoME identified 52,000 new layered compounds similar to graphene, which could revolutionize electronics with the development of superconductors. It also discovered 528 potential lithium-ion battery conductors, which could lead to more efficient batteries, a crucial component in energy storage and electric vehicles.

A superionic conductor is a type of material that conducts electric current primarily through the movement of ions, rather than electrons. These conductors exhibit high ionic conductivity, which means ions can move through them easily, especially at elevated temperatures. This unique property makes superionic conductors particularly useful in applications like solid-state batteries and fuel cells, where efficient ion transport is crucial for their performance.

In parallel, Berkeley Lab’s new autonomous laboratory, the A-Lab, has been instrumental in synthesizing these materials.

The A-Lab integrates robotics with machine learning to optimize the development of new materials, managing to synthesize 41 out of 58 proposed compounds over 17 days. This level of efficiency far surpasses that of traditional, human-led labs.

This breakthrough in materials science is not just a testament to the power of AI in accelerating discovery but also holds promise for significant advancements in various sectors, including energy, computing, and electronics.

The synthesis of these materials could herald a new era of technological innovation, particularly in areas critical to addressing global challenges like the climate crisis.

Let’s end this article with an insightful conclusion provided by the DeepLearning researchers:

“The application of machine-learning methods for scientific discovery has traditionally suffered from the fundamental challenge that learning algorithms work under the assumption of identically distributed data at train and test times, but discovery is inherently an out-of-distribution effort. Our results on large-scale learning provide a potential step to move past this dilemma, by demonstrating that GNoME models exhibit emergent out-of-distribution capabilities at scale.” — Nature 2023

Also, they showed that these improvements seem to be consistent with the AI Scaling Laws:

“The test loss performance of GNoME models exhibit improvement as a power law with further data. These results are in line with neural scaling laws in deep learning and suggest that further discovery efforts could continue to improve generalization.” — Nature 2023

So, this is only the beginning of a series of expected scientific breakthroughs that will lift humanity to new heights.

Recommended: AI Scaling Laws – A Short Primer

The post Google Deep Learning – 800 Years of Human Experimentation in One Discovery appeared first on Be on the Right Side of Change.

But in a fresh exclusive with The Verge, Sam Altman dropped the following bombshell:

• Interviewer: The reports about the Q* model breakthrough that you all recently made, what’s going on there?
• Sam Altman: No particular comment on that unfortunate leak. But what we have been saying — two weeks ago, what we are saying today, what we’ve been saying a year ago, what we were saying earlier on — is that we expect progress in this technology to continue to be rapid and also that we expect to continue to work very hard to figure out how to make it safe and beneficial.

In my logic: If Sam calls it a leak, it must be real. (?)

Diving deeper into the topic, I found the following interesting nugget:

“The […] breakthrough allowed OpenAI to overcome limitations on obtaining enough high-quality data to train new models […] The research involved using computer-generated [data], rather than real-world data like text or images pulled from the internet, to train new models. That appears to be a reference to the idea of training algorithms with so-called synthetic training data, which has emerged as a way to train more powerful AI models.” — Wired

Like Tesla’s new end-to-end full-self-driving (FSD) system, synthetic data is now being used by OpenAI to train neural nets!

I share this with you because I believe the following take is not well-understood by the general public:

• The AI Scaling Laws suggest we have not reached saturation in performance – all we need to do is throw more compute and more data on the problem.
• Many argue that data is the limiting factor – e.g., in humanoid robotics, we don’t have a lot of real-world data.
• However, if we can effectively train models on synthetic data, we’ve mitigated this bottleneck!

We’re already seeing a double-digit percentage productivity improvement for our economy due to LLMs. This trend is sustainable for at least a couple of years. Maybe decades!

Have a look at this research result in a recent first-class paper “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”:

“Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. […]”

Another big takeaway is this: when synthetic data is an effective option, the bottleneck for AI will be compute. Singularity will take our hunger for compute to the moon.

10x, 100x, 1000x of demand for AI compute in a few years wouldn’t surprise me in the least.

Bullish for AI compute providers like NVDA, AMZN, and TSLA!

Be on the right side of change!
Chris

Original Publication in my newsletter. Join us free!

The post OpenAI Q*: Data May Not Be the Bottleneck Anymore. AI Compute Is! (NVDA, TSLA, AMZN) appeared first on Be on the Right Side of Change.

This is what GPTs look like:

And here’s how you can use the GPT builder at chat.openai.com/gpts/editor:

Here are a couple of examples extracted from the recent talk by Sam Altman:

• Globe Guru – Discover cultures with AI travel insights.
• Star Tracker – Night sky exploration with AI assistance.
• Golf Tracker – AI to improve your golf game.
• Meditation Guide – Personalized AI meditation and mindfulness coach.
• Study Buddy – AI-driven personalized study and learning assistant.
• Hiking Helper – Trail recommendations and safety tips AI.
• In Style – Fashion trends and advice with AI.
• Pun Generator – Humorous pun creation using AI.
• Code Checker – AI for efficient code error detection.
• Pocket Sommelier – AI wine recommendations for any meal.
• Racing League – AI to strategize car racing tactics.
• Repair It – AI guidance for home repairs.
• Origami Expert – Step-by-step AI origami instructions.
• Garden Guide – AI advice for gardening and planting.
• Gift Finder – AI to find perfect gifts.
• Fantasy Football – AI-powered fantasy football strategy guide.
• Poker Mentor – AI coaching for poker strategy.
• Study Buddy – Another AI academic assistance tool.
• Back Roads – Discover scenic routes with AI.
• Algebra 101 – AI algebra tutoring and solutions.
• Cafe Finder – Find cafes nearby with AI.
• The Professor – AI for academic research and writing.
• Build a Robot – DIY robot-building with AI guidance.
• Tree Houses – AI designs for treehouse projects.
• Text Extractor – AI to extract text from images.
• Hiking Helper – Safety and route AI for hikers.
• Pixel Artist – AI-driven pixel art creation tool.
• Pun Generator – Creative pun crafting with AI.
• Pocket Sommelier – Your AI wine pairing advisor.
• Buddy Buddy – AI for social interaction suggestions.
• Code Checker – AI for debugging and code review.
• Back Roads – AI to explore less-traveled paths.
• all the jokes – Endless AI-generated humor.

Opening up user-generated bots is an insane new feature that’ll change the world, much like the app store (GPT store) did for iPhones.

Recommended: GPT-4 Turbo: 12 New Incredible Features at a 3x Reduced Price vs GPT-4

If you want to stay tuned, check out my free email academy:

The post OpenAI Wants Us to Create GPTs, i.e., Miniature AI Agent Helpers appeared first on Be on the Right Side of Change.

First things first: why Grok?

Definition Grok:

Grok means “to understand”, of course, but Dr. Mahmoud, who might be termed the leading Terran expert on Martians, explains that it also means, “to drink” and “a hundred other English words, words which we think of as antithetical concepts. ‘Grok’ means all of these. It means ‘fear’, it means ‘love’, it means ‘hate’ – proper hate, for by the Martian ‘map’ you cannot hate anything unless you grok it, understand it so thoroughly that you merge with it and it merges with you – then you can hate it. By hating yourself. But this implies that you love it, too, and cherish it and would not have it otherwise. Then you can hate – and (I think) Martian hate is an emotion so black that the nearest human equivalent could only be called mild distaste.

Grok means “identically equal”. The human cliché “This hurts me worse than it does you” has a distinctly Martian flavor. The Martian seems to know instinctively what we learned painfully from modern physics, that observer acts with observed through the process of observation. Grok means to understand so thoroughly that the observer becomes a part of the observed – to merge, blend, intermarry, lose identity in group experience. It means almost everything that we mean by religion, philosophy, and science and it means as little to us as color does to a blind man.

The Martian Race had encountered the people of the fifth planet, grokked them completely, and had taken action; asteroid ruins were all that remained, save that the Martians continued to praise and cherish the people they had destroyed.

All that groks is God.

— Robert Heinlein in Stranger in a Strange Land

xAI’s Grok model does more than just answer questions—it nudges users to ponder what questions to ask, offering a blend of wit along with its responses. Grok is not just a monotonous query resolver; it’s designed with a rebellious streak, welcoming questions other AI systems might shy away from.

The uniqueness of Grok lies in its real-time knowledge of the world, thanks to the 𝕏 platform it operates on. While it’s still in its early beta phase, the journey to its creation is a testament to xAI’s ambition of crafting AI tools that bridge the gaps in human understanding and knowledge.

The vision is grand; the team at xAI envisions Grok as a digital companion in the relentless human quest for knowledge, helping to quickly access relevant information, process data, and foster new ideas.

Behind Grok is an engine known as Grok-1, a frontier Large Language Model (LLM) that underwent meticulous development over the last four months. Initially, a prototype LLM named Grok-0 was trained with 33 billion parameters, achieving promising results but still lagging behind in terms of resource efficiency.

However, the evolution didn’t stop there. Continuous improvements over the next two months propelled Grok-1 to achieve remarkable scores on machine learning benchmarks like HumanEval, MMLU, and others, showcasing substantial enhancements in reasoning and coding capabilities.

The technical prowess of Grok-1 is not to be underestimated. When pitted against other models in its compute class on benchmark tests, it outshone models like ChatGPT-3.5 and Inflection-1. Although models with more training data and computing resources like GPT-4 surpassed Grok-1, the results underscore the rapid strides xAI is making in training LLMs efficiently.

These advancements are not solely attributed to algorithmic enhancements but also to a robust infrastructure built on a foundation of Kubernetes, Rust, and JAX. The challenges of training such models are myriad, from hardware failures to configuration missteps. Yet, xAI’s custom distributed systems and a focus on maximizing useful compute per watt have helped to navigate these hurdles, minimizing downtime, and maintaining a high Model Flop Utilization (MFU) even amidst unreliable hardware scenarios.

As xAI is gearing up for the next leap in model capabilities, the journey of Grok embodies the essence of innovation and the unyielding pursuit of understanding that xAI stands for. The future of Grok, although in its infancy, resonates with the boundless possibilities that AI holds, not just as tools of utility, but as companions in the human endeavor of unraveling the mysteries of the universe.

Recommended: GPT-4’s Hindsight Portfolio for Painfully Obvious Investments Looking Back from 2030

The post Elon Musk’s xAI Releases a New Humorous LLM Called Grok Based on Hitchhiker’s Guide to the Galaxy appeared first on Be on the Right Side of Change.