Рубрика: AI

Denoising Diffusion Models are generative AI frameworks that synthesize images from noise through an iterative denoising process. They are celebrated for their exceptional image generation capabilities and diversity, largely attributed to text- or class-conditional guidance methods, including classifier guidance and classifier-free guidance. These models have been notably successful in creating diverse, high-quality images. Recent studies have shown that guidance techniques like class captions and labels play a crucial role in enhancing the quality of images these models generate.

However, diffusion models and guidance methods face limitations under certain external conditions. The Classifier-Free Guidance (CFG) method, which uses label dropping, adds complexity to the training process, while the Classifier Guidance (CG) method necessitates additional classifier training. Both methods are somewhat constrained by their reliance on hard-earned external conditions, limiting their potential and confining them to conditional settings.

To address these limitations, developers have formulated a more general approach to diffusion guidance, known as Self-Attention Guidance (SAG). This method leverages information from intermediate samples of diffusion models to generate images. We will explore SAG in this article, discussing its workings, methodology, and results compared to current state-of-the-art frameworks and pipelines.

Self-Attention Guidance : Improving Sample Quality of Diffusion Models

Denoising Diffusion Models (DDMs) have gained popularity for their ability to create images from noise via an iterative denoising process. The image synthesis prowess of these models is largely due to the employed diffusion guidance methods. Despite their strengths, diffusion models and guidance-based methods face challenges like added complexity and increased computational costs.

To overcome the current limitations, developers have introduced the Self-Attention Guidance method, a more general formulation of diffusion guidance that does not rely on the external information from diffusion guidance, thus facilitating a condition-free and flexible approach to guide diffusion frameworks. The approach opted by Self-Attention Guidance ultimately helps in enhancing the applicability of the traditional diffusion-guidance methods to cases with or without external requirements.

Self-Attention Guidance is based on the simple principle of generalized formulation, and the assumption that internal information contained within intermediate samples can serve as guidance as well. On the basis of this principle, the SAG method first introduces Blur Guidance, a simple and straightforward solution to improve sample quality. Blur guidance aims to exploit the benign properties of Gaussian blur to remove fine-scale details naturally by guiding intermediate samples using the eliminated information as a result of Gaussian blur. Although the Blur guidance method does boost the sample quality with a moderate guidance scale, it fails to replicate the results on a large guidance scale as it often introduces structural ambiguity in entire regions. As a result, the Blur guidance method finds it difficult to align the original input with the prediction of the degraded input. To enhance the stability and effectiveness of the Blur guidance method on a larger guidance scale, the Self-Attention Guidance attempts to exploit the self-attention mechanism of the diffusion models as modern diffusion models already contain a self-attention mechanism within their architecture.

With the assumption that self-attention is essential to capture salient information at its core, the Self-Attention Guidance method uses self-attention maps of the diffusion models to adversarially blur the regions containing salient information, and in the process, guides the diffusion models with required residual information. The method then leverages the attention maps during diffusion models’ reverse process, to boost the quality of the images and uses self-conditioning to reduce the artifacts without requiring additional training or external information.

To sum it up, the Self-Attention Guidance method

1. Is a novel approach that uses internal self-attention maps of diffusion frameworks to improve the generated sample image quality without requiring any additional training or relying on external conditions.
2. The SAG method attempts to generalize conditional guidance methods into a condition-free method that can be integrated with any diffusion model without requiring additional resources or external conditions, thus enhancing the applicability of guidance-based frameworks.
3. The SAG method also attempts to demonstrate its orthogonal abilities to existing conditional methods and frameworks, thus facilitating a boost in performance by facilitating flexible integration with other methods and models.

Moving along, the Self-Attention Guidance method learns from the findings of related frameworks including Denoising Diffusion Models, Sampling Guidance, Generative AI Self-Attention methods, and Diffusion Models’ Internal Representations. However, at its core, the Self-Attention Guidance method implements the learnings from DDPM or Denoising Diffusion Probabilistic Models, Classifier Guidance, Classifier-free Guidance, and Self-Attention in Diffusion frameworks. We will be talking about them in-depth in the upcoming section.

Self-Attention Guidance : Preliminaries, Methodology, and Architecture

Denoising Diffusion Probabilistic Model or DDPM

DDPM or Denoising Diffusion Probabilistic Model is a model that uses an iterative denoising process to recover an image from white noise. Traditionally, a DDPM model receives an input image and a variance schedule at a time step to obtain the image using a forward process known as the Markovian process.

Classifier and Classifier-Free Guidance with GAN Implementation

GAN or Generative Adversarial Networks possess unique trading diversity for fidelity, and to bring this ability of GAN frameworks to diffusion models, the Self-Attention Guidance framework proposes to use a classifier guidance method that uses an additional classifier. Conversely, a classifier-free guidance method can also be implemented without the use of an additional classifier to achieve the same results. Although the method delivers the desired results, it is still not computationally viable as it requires additional labels, and also confines the framework to conditional diffusion models that require additional conditions like a text or a class along with additional training details that adds to the complexity of the model.

Generalizing Diffusion Guidance

Although Classifier and Classifier-free Guidance methods deliver the desired results and help with conditional generation in diffusion models, they are dependent on additional inputs. For any given timestep, the input for a diffusion model comprises a generalized condition and a perturbed sample without the generalized condition. Furthermore, the generalized condition encompasses internal information within the perturbed sample or an external condition, or even both. The resultant guidance is formulated with the utilization of an imaginary regressor with the assumption that it can predict the generalized condition.

Improving Image Quality using Self-Attention Maps

The Generalized Diffusion Guidance implies that it is feasible to provide guidance to the reverse process of diffusion models by extracting salient information in the generalized condition contained in the perturbed sample. Building on the same, the Self-Attention Guidance method captures the salient information for reverse processes effectively while limiting the risks that arise as a result of out-of-distribution issues in pre-trained diffusion models.

Blur Guidance

Blur guidance in Self-Attention Guidance is based on Gaussian Blur, a linear filtering method in which the input signal is convolved with a Gaussian filter to generate an output. With an increase in the standard deviation, Gaussian Blur reduces the fine-scale details within the input signals, and results in locally indistinguishable input signals by smoothing them towards the constant. Furthermore, experiments have indicated an information imbalance between the input signal, and the Gaussian blur output signal where the output signal contains more fine-scale information.

On the basis of this learning, the Self-Attention Guidance framework introduces Blur guidance, a technique that intentionally excludes the information from intermediate reconstructions during the diffusion process, and instead, uses this information to guide its predictions towards increasing the relevancy of images to the input information. Blur guidance essentially causes the original prediction to deviate more from the blurred input prediction. Furthermore, the benign property in Gaussian blur prevents the output signals from deviating significantly from the original signal with a moderate deviation. In simple words, blurring occurs in the images naturally that makes the Gaussian blur a more suitable method to be applied to pre-trained diffusion models.

In the Self-Attention Guidance pipeline, the input signal is first blurred using a Gaussian filter, and it is then diffused with additional noise to produce the output signal. By doing this, the SAG pipeline mitigates the side effect of the resultant blur that reduces Gaussian noise, and makes the guidance rely on content rather than being dependent on random noise. Although blur guidance delivers satisfactory results on frameworks with moderate guidance scale, it fails to replicate the results on existing models with a large guidance scale as it gets prone to produce noisy results as demonstrated in the following image.

These results might be a result of the structural ambiguity introduced in the framework by global blur that makes it difficult for the SAG pipeline to align the predictions of the original input with the degraded input, resulting in noisy outputs.

Self-Attention Mechanism

As mentioned earlier, diffusion models usually have an in-build self-attention component, and it is one of the more essential components in a diffusion model framework. The Self-Attention mechanism is implemented at the core of the diffusion models, and it allows the model to pay attention to the salient parts of the input during the generative process as demonstrated in the following image with high-frequency masks in the top row, and self-attention masks in the bottom row of the finally generated images.

The proposed Self-Attention Guidance method builds on the same principle, and leverages the capabilities of self-attention maps in diffusion models. Overall, the Self-Attention Guidance method blurs the self-attended patches in the input signal or in simple words, conceals the information of patches that is attended to by the diffusion models. Furthermore, the output signals in Self-Attention Guidance contain intact regions of the input signals meaning that it does not result in structural ambiguity of the inputs, and solves the problem of global blur. The pipeline then obtains the aggregated self-attention maps by conducting GAP or Global Average Pooling to aggregate self-attention maps to the dimension, and up-sampling the nearest-neighbor to match the resolution of the input signal.

Self-Attention Guidance : Experiments and Results

To evaluate its performance, the Self-Attention Guidance pipeline is sampled using 8 Nvidia GeForce RTX 3090 GPUs, and is built upon pre-trained IDDPM, ADM, and Stable Diffusion frameworks.

Unconditional Generation with Self-Attention Guidance

To measure the effectiveness of the SAG pipeline on unconditional models and demonstrate the condition-free property not possessed by Classifier Guidance, and Classifier Free Guidance approach, the SAG pipeline is run on unconditionally pre-trained frameworks on 50 thousand samples.

As it can be observed, the implementation of the SAG pipeline improves the FID, sFID, and IS metrics of unconditional input while lowering the recall value at the same time. Furthermore, the qualitative improvements as a result of implementing the SAG pipeline is evident in the following images where the images on the top are results from ADM and Stable Diffusion frameworks whereas the images at the bottom are results from the ADM and Stable Diffusion frameworks with the SAG pipeline.

Conditional Generation with SAG

The integration of SAG pipeline in existing frameworks delivers exceptional results in unconditional generation, and the SAG pipeline is capable of condition-agnosticity that allows the SAG pipeline to be implemented for conditional generation as well.

Stable Diffusion with Self-Attention Guidance

Even though the original Stable Diffusion framework generates high quality images, integrating the Stable Diffusion framework with the Self-Attention Guidance pipeline can enhance the results drastically. To evaluate its effect, developers use empty prompts for Stable Diffusion with random seed for each image pair, and use human evaluation on 500 pairs of images with and without Self-Attention Guidance. The results are demonstrated in the following image.

Furthermore, the implementation of SAG can enhance the capabilities of the Stable Diffusion framework as fusing Classifier-Free Guidance with Self-Attention Guidance can broaden the range of Stable Diffusion models to text-to-image synthesis. Furthermore, the generated images from the Stable Diffusion model with Self-Attention Guidance are of higher quality with lesser artifacts thanks to the self-conditioning effect of the SAG pipeline as demonstrated in the following image.

Current Limitations

Although the implementation of the Self-Attention Guidance pipeline can substantially improve the quality of the generated images, it does have some limitations.

One of the major limitations is the orthogonality with Classifier-Guidance and Classifier-Free Guidance. As it can be observed in the following image, the implementation of SAG does improve the FID score and prediction score that means that the SAG pipeline contains an orthogonal component that can be used with traditional guidance methods simultaneously.

However, it still requires diffusion models to be trained in a specific manner that adds to the complexity as well as computational costs.

Furthermore, the implementation of Self-Attention Guidance does not increase the memory or time consumption, an indication that the overhead resulting from the operations like masking & blurring in SAG is negligible. However, it still adds to the computational costs as it includes an additional step when compared to no guidance approaches.

Final Thoughts

In this article, we have talked about Self-Attention Guidance, a novel and general formulation of guidance method that makes use of internal information available within the diffusion models for generating high-quality images. Self-Attention Guidance is based on the simple principle of generalized formulation, and the assumption that internal information contained within intermediate samples can serve as guidance as well. The Self-Attention Guidance pipeline is a condition-free and training-free approach that can be implemented across various diffusion models, and uses self-conditioning to reduce the artifacts in the generated images, and boosts the overall quality.

It’s not technology advancements that are the game-changers. The game-changer is how those technological advancements are leveraged to economically transform industries and society.

2024 is going to be a big year, especially in the realm of Artificial Intelligence (AI). Generative AI (GenAI) has lit a fire under organizations that suddenly have a senior management and Board of Directors mandate to “leverage AI to get value from their data.” However, there are two challenges that organizations will face in 2024 to “leverage AI to get value from their data.” And the first step in addressing these two challenges is to realize that this is not a technology challenge. The technology works. Period. No, the two challenges are:

#1) There is too much focus on “implementing AI” and not enough focus on “deriving value from AI.” And that requires first understanding how the organization defines and measures its value-creation processes. Don’t become data-driven or AI-obsessed. Instead, become a value-fanatic!

#2) Empowering the organization to help identify where and how AI and data can be leveraged to create value. That means educating everyone on AI and data literacy so that everyone understands their roles, responsibilities, and rights concerning the meaningful, relevant, responsible, and ethical deployment of AI.

This two-part blog series will highlight the potential of GenAI / AI from an economic perspective. With a focus on economics, the potential for GenAI / AI to transform industries and society is overwhelming. But first, a word of warning…

Beware the Productivity Trap

Beware: faster does not always mean better outcomes. Better means more accurate, more reliable, more responsible, more relevant, and less risky outcomes.

The productivity trap occurs when organizations focus on doing more in less time and with less effort, based on the assumption that productivity is the ultimate goal of an organization. However, this flawed assumption can be dangerous, leading to poor outcomes for the organization and its customers, stakeholders, and constituents. Some of the negative ramifications of the productivity trap include:

• Increased risk: By not taking the time to thoroughly identify, articulate, and quantify the risks associated with potential failure and potential unintended consequences, organizations may expose themselves to legal, financial, reputational, or operational damages.
• Reduced accuracy: By trying to make well-defined, accurate decisions and actions quickly, organizations may compromise the quality of their work or ignore important details.
• Diminished relevance: By neglecting to create a compelling and differentiated relationship with their customers, stakeholders, and constituents, organizations may lose their competitive edge or fail to meet their expectations.
• Irresponsibility: By doing things faster without full consideration of the actions’ impact on the environment, diversity, and social issues, organizations may harm their social and environmental responsibility.

We can’t become complacent by the GenAI productivity sirens because faster doesn’t always mean better, more accurate, or less risky when reengineering or transforming operational processes and business models. Remember, faster does not equal “better” (Figure 1).

We must adopt a growth mindset to escape the productivity trap. A growth mindset leverages economics and design thinking to empower and unleash the creative juices of the entire organization to identify new sources of wealth or “value” that benefit customers, constituents, and society.

GenAI / AI is an economic opportunity to transform industries and society by creating new methods and models to derive and drive new sources of customer, product, service, and operational value. But first, we must embrace that economic mindset.

Creating the Economic Mindset

Economics is the branch of knowledge concerned with producing, consuming, and transferring wealth or value.

The economic mindset is a way of thinking and acting that focuses on creating new sources of value rather than just doing more things in less time. But do not confuse an economic mindset with a financial mindset.

• A financial mindset is a way of thinking and acting that focuses on managing money and achieving financial goals.
• An economic mindset is a way of thinking and acting that focuses on creating and distributing value for customers, stakeholders, and society.

The benefits of an economic mindset include:

• Constantly striving to blend, bend, and re-engineer to uncover new, innovative ways to create value.
• Thrive in today’s competitive environment by finding a unique competitive advantage through value-creation creativity and innovation.
• Prepare for the future by capitalizing on market changes and challenges to identify new models for creating value.
• Improve organizational performance by optimizing the value-creation resources and processes.
• Enhance social well-being by balancing profits, operational excellence, environmental impact, society improvements, and ethical treatment.

In the era of Big Data and AI, an economic mindset means embracing new data economic concepts and theorems that can guide organizations in creating these new sources of value, including (Figure 1):

• Nanoeconomics is the economic theory of individual entity (human or device) predicted behavioral and performance propensities (insights). Nanoeconomics helps organizations understand the behaviors of their customers, stakeholders, machinery, devices, operations, and other entities to deliver more relevant and meaningful outcomes.
• Data Economic Multiplier Effect is the economic ramifications of accumulating attributable and quantifiable value from reusing a data set against multiple use cases. It shows how data, when properly curated and sharable, exhibits an Economic Multiplier Effect, which is the ratio of the impact of an incremental increase in investment on the resulting incremental increase in output or value.
• Schmarzo Economic Digital Asset Valuation Theorem determines the value of a data asset based on its reuse across multiple use cases. It states that a data asset’s value increases the more it is reused across multiple use cases and that if the data is “curated” and governed correctly, this reuse comes at near zero marginal cost.
• Marginal Propensity to Reuse (MPR) explains how data reuse across multiple use cases can increase data value. It is based on the idea that when adequately curated and governed, data never wears out, never depletes, and can be reused across unlimited use cases. The more a data set is reused, the more value it generates for the institution.

Figure 1: Data Economic Playing Cards

Fueling Economic Transformation

Industry economic transformation leverages innovation (i.e., technology, process, people-based innovation) to re-engineer the economics of an industry by altering industry competition, disrupting traditional business models, and reengineering value creation processes to unleash new sources of value.

History is full of examples of technological advancements that led to economic transformation:

• Printing press: The printing press transformed the production of books, significantly lowering book production costs while increasing the spread of literacy, education, religion, science, and culture.
• Internet: The Internet transformed nearly every industry by eliminating time and distance as operational constraints and stimulating new forms of innovation, entrepreneurship, and collaboration that created new industries such as e-commerce, online education, and social media.
• Personal computing: Personal computing (PCs) transformed the computer industry, significantly reducing computing costs while empowering individuals to create new industries such as software development and digital media creation.

Today, we are witnessing other examples of technology advancements transforming industries:

• Renewable Energy: Renewable energy is transforming the energy production, distribution, and consumption industries by improving energy security, affordability, and diversity while reducing greenhouse gas emissions, air pollution, noise pollution, and dependence on fossil fuels.
• Electric Vehicles: Electric vehicles (EVs) are transforming the transportation industry by improving energy efficiency, mobility, and accessibility while reducing greenhouse gas emissions, air pollution, noise pollution, and dependence on oil imports.

Figure 2: It’s About Economics, Not Technology!

Economics is a powerful force that seeks to balance differing perspectives and rationales to deliver meaningful, relevant, responsible, and ethical organizational and societal outcomes. In today’s digital age, data economics is the “force multiplier” that can take your organization to game-changing levels of performance and innovation.

Summary

It’s not technology advancements that are the game-changers. The game-changer is how those technological advancements are leveraged to economically transform industries and society.

In Part 1, we talked about how to leverage an economic mindset to avoid the productivity trap. We also discussed some critical enabling data economic concepts and how those concepts can take your organization to the next level of game-changing performance and innovation.

In Part 2, we will dive deep into the nanoeconomics concept, the game-changing “force multiplier” that organizations can leverage to economically transform their industry. And we will provide a surprising perspective on GenAI’s role in activating those economic concepts.

Llama Index recently introduced a full-stack repository by lucastonon, unveiling a Context-Augmented Agent for Food Delivery. This autonomous agent, dubbed the “Interface-Omniscient” Agent, is set to transform the food ordering landscape, offering a streamlined and hands-free experience.

Lucastonon’s repository introduces the RAG agent, showcasing its ability to effortlessly handle tasks such as looking up restaurants, opening pages, fetching menus, and adding items to the cart. The agent serves as an interface maestro, simplifying complex interactions on a website.

The core concept revolves around empowering an autonomous chatbot, the Interface-Omniscient Agent, with the capability to navigate websites effortlessly. It accomplishes this through a multifaceted approach:

Intuitive Page Interaction: The agent can open pages and click on buttons by understanding the elements within the page.

Contextual Information Delivery: Providing users with real-time information about the current page, the agent enhances user understanding and decision-making.

Action Tracking for Informed Decisions: By tracking user actions, the chatbot can make informed decisions, ensuring a seamless and personalized experience.

The demonstration showcases practical applications. Users can autonomously order food using voice commands, eliminating the need to navigate complex interfaces. The chat assistant responds autonomously through voice, enhancing user convenience.

In India, food delivery apps Swiggy and Zomato have recently been experimenting with generative AI. This development not only introduces an advanced agent for food delivery but also redefines user interactions online. The Interface-Omniscient Agent promises a smoother, hands-free online experience, setting the stage for a new era of intuitive navigation.

The post Food Delivery Apps Get an AI Agent appeared first on Analytics India Magazine.

Image created by Author with DALL•E 3
Introduction

Prompt engineering, like language models themselves, has come a long way in the past 12 months. It was only a little over a year ago that ChatGPT burst onto the scene and threw everyone's fears and hopes for AI into a supercharged pressure cooker, accelerating both AI doomsday and savior stories almost overnight. Certainly, prompt engineering existed long before ChatGPT, but the range of ever-changing techniques we use for eliciting desired responses from the plethora of language models that now invade our lives has really come into its own alongside the rise of ChatGPT. Five years ago with the unveiling of the original GPT we joked about how "prompt engineer" could one day become a job title; today, prompt engineers are one of the hottest tech (or tech adjacent) careers out there.

Prompt engineering is the process of structuring text that can be interpreted and understood by a generative AI model. A prompt is natural language text describing the task that an AI should perform.

From the "Prompt engineering" Wikipedia entry

Hype aside, prompt engineering is now an integral part of the lives of those interacting with LLMs on a regular basis. If you are reading this, there's a good chance this describes you, or describes the direction that your career may be taking. For those looking to get an idea of what prompt engineering is, and — crucially — what the current prompt strategy landscape looks like, this article is for you.

Getting Started

Let's start with the basics. This article, Prompt Engineering for Effective Interaction with ChatGPT, on Machine Learning Mastery covers the prompt engineering foundational concepts. Specifically, topics introduced include:

• Principles of Prompting, outlining several foundational techniques to remember in the process of prompt optimization
• Basic Prompt Engineering, such as prompt wording, succinctness, and positive and negative prompting
• Advanced Prompt Engineering Strategies, including one-shot and multi-shot prompting, Chain-of-Thought prompting, self-criticism, and iterative prompting
• Collaborative Power Tips for recognizing and fostering a collaborative atmosphere with ChatGPT to lead to further success

Prompt engineering is the most crucial aspect of utilizing LLMs effectively and is a powerful tool for customizing the interactions with ChatGPT. It involves crafting clear and specific instructions or queries to elicit the desired responses from the language model. By carefully constructing prompts, users can guide ChatGPT’s output toward their intended goals and ensure more accurate and useful responses.

From the Machine Learning Mastery article "Prompt Engineering for Effective Interaction with ChatGPT"

Once you have covered the basics, and have a taste for what prompt engineering is and some of the most useful current techniques, you can move on to mastering some of those techniques.

Prompt Engineering Techniques Deep Dive

The following KDnuggets articles are each an overview of a single commonplace prompt engineering technique. There is a logical progression in the complexity of these techniques, so starting from the top and working down would be the best approach.

Each article contains an overview of the academic paper in which the technique was first proposed. You can read the explanation of the technique, see how it relates to others, and find examples of its implementation all within the article, and if you are then interested in reading or browsing the paper it is linked to from within as well.

Unraveling the Power of Chain-of-Thought Prompting in Large Language Models

This article delves into the concept of Chain-of-Thought (CoT) prompting, a technique that enhances the reasoning capabilities of large language models (LLMs). It discusses the principles behind CoT prompting, its application, and its impact on the performance of LLMs.

Exploring Tree of Thought Prompting: How AI Can Learn to Reason Through Search

New approach represents problem-solving as search over reasoning steps for large language models, allowing strategic exploration and planning beyond left-to-right decoding. This improves performance on challenges like math puzzles and creative writing, and enhances interpretability and applicability of LLMs.

Automating the Chain of Thought: How AI Can Prompt Itself to Reason

Auto-CoT prompting method has LLMs automatically generate their own demonstrations to prompt complex reasoning, using diversity-based sampling and zero-shot generation, reducing human effort in creating prompts. Experiments show it matches performance of manual prompting across reasoning tasks.

Parallel Processing in Prompt Engineering: The Skeleton-of-Thought Technique

Explore how the Skeleton-of-Thought prompt engineering technique enhances generative AI by reducing latency, offering structured output, and optimizing projects.

Unlocking GPT-4 Summarization with Chain of Density Prompting

Unlock the power of GPT-4 summarization with Chain of Density (CoD), a technique that attempts to balance information density for high-quality summaries.

Unlocking Reliable Generations through Chain-of-Verification: A Leap in Prompt Engineering

Explore the Chain-of-Verification prompt engineering method, an important step towards reducing hallucinations in large language models, ensuring reliable and factual AI responses.

Graph of Thoughts: A New Paradigm for Elaborate Problem-Solving in Large Language Models

Discover how Graph of Thoughts aims to revolutionize prompt engineering, and LLMs more broadly, enabling more flexible and human-like problem-solving.

Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models

Thought Propagation is a prompt engineering technique that instructs LLMs to identify and tackle a series of problems that are similar to the original query, and then use the solutions to these similar problems to either directly generate a new answer or formulate a detailed action plan that refines the original solution.

Additional Resources

While the above should get you to a spot where you can begin engineering effective prompts, the following resources may provide some additional depth and/or alternative views that you might find helpful.

Mastering Generative AI and Prompt Engineering: A Practical Guide for Data Scientists [eBook] from Data Science Horizons

The ebook provides an in-depth understanding of generative AI and prompt engineering, covering key concepts, best practices, and real-world applications. You’ll gain insights into popular AI models, learn the process of designing effective prompts, and explore the ethical considerations surrounding these technologies. Furthermore, the book includes case studies demonstrating practical applications across different industries.

Mastering Generative AI Text Prompts [eBook] from Data Science Horizons

Whether you’re a writer seeking inspiration, a content creator aiming for efficiency, an educator passionate about knowledge sharing, or a professional in need of specialized applications, Mastering Generative AI Text Prompts is your go-to resource. By the end of this guide, you’ll be equipped to harness the power of generative AI, enhancing your creativity, optimizing your workflow, and solving a wide range of problems.

The Psychology of Prompt Engineering [eBook] from Data Science Horizons

Our ebook is packed with captivating insights and practical strategies, covering a wide range of topics such as understanding human cognition and AI models, psychological principles of effective prompts, designing prompts with cognitive principles in mind, evaluating and optimizing prompts, and integrating psychological principles into your workflow. We’ve also included real-world case studies of successful prompt engineering examples, as well as an exploration of the future of prompt engineering, psychology, and the value of interdisciplinary collaboration.

Prompt Engineering Guide from DAIR.AI

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).

Prompt Engineering Guide from Learn Prompting

Generative AI is the world's hottest buzzword, and we have created the most comprehensive (and free) guide on how to use it. This course is tailored to non-technical readers, who may not have even heard of AI, making it the perfect starting point if you are new to Generative AI and Prompt Engineering. Technical readers will find valuable insights within our later modules.

Conclusion

Prompt engineering is a must-have skill for both AI engineers and LLM power users. Beyond this, prompt engineering has flourished into an AI niche career in its own right. There is no telling what the exact role for prompt engineering — or if dedicated prompt engineer roles will continue to be sought after AI professionals — but one thing is clear: knowledge of prompt engineering will never be held against you. By following the steps in this article, you should now have a great foundation to engineering your own high-performance prompts.

Who knows? Maybe you're the next AI whisperer.

Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Editor-in-Chief of KDnuggets, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

More On This Topic

• Some Kick Ass Prompt Engineering Techniques to Boost our LLM Models
• Mastering Generative AI and Prompt Engineering: A Free eBook
• Data Visualization Best Practices & Resources for Effective Communication
• Web LLM: Bring LLM Chatbots to the Browser
• The Art of Prompt Engineering: Decoding ChatGPT
• The Rise and Fall of Prompt Engineering: Fad or Future?

MosaicML has unveiled its latest research, titled “Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws.” This paper challenges existing paradigms in large language model (LLM) scaling laws, introducing a novel approach that incorporates the often-overlooked factor of inference cost.

Click here to read the paper.

Traditionally, LLM scaling laws, such as the widely-used DeepMind Chinchilla scaling laws, have focused solely on estimating changes in model quality based on increased parameter count and training data. However, MosaicML’s research highlights a critical gap in these formulas by neglecting the crucial aspect of inference cost.

The core innovation lies in the modification of the Chinchilla scaling laws to calculate the optimal LLM parameter count and pre-training data size. This calculation considers the dual aspects of training and deploying a model of a specified quality while meeting the demands of inference. The researchers conducted a comprehensive analysis, factoring in both computational budgets and real-world costs.

Key findings from the study include:

• Cost-Effective Training: MosaicML’s approach enables training a large language model from scratch for less than $100, offering a cost-effective alternative for researchers and organisations.
• Encoder Architecture: The model introduced in the research is an encoder (BERT-like) rather than a decoder. This move underscores the ongoing significance of encoder-only models, with the authors expressing satisfaction at the integration of recent LLM advances into BERT-like architectures.

The modification of the Chinchilla scaling laws is crucial for accurately reflecting the practical challenges faced by LLM researchers. The researchers emphasise that their analysis applies not only in terms of a compute budget but also in real-world scenarios where costs and demands for inference are substantial.

Zhang Peiyuan made TinyLlama breaking laws of scalability as well. The research assistant at Singapore University, had trained a 1.1 billion parameter model called TinyLlama. Based on Llama 2, the ambitious part about this project is that Peiyuan aims to pre-train it on 3 trillion tokens.

As the inference demand approaches pre-training data size, the research indicates a shift in the optimal parameters-to-tokens ratio towards smaller and longer-trained models. However, the authors acknowledge the need for further experimental validation to ascertain the applicability of their formulas, especially in extreme ranges where pre-training tokens exceed model parameters by orders of magnitudes.

The post MosaicML Announces Beyond Chinchilla-Optimal for LLM Scaling Laws in Inference appeared first on Analytics India Magazine.