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The current state of Generative AI: A comprehensive overview

Current state of generative AI

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We are entering an exciting new era in artificial intelligence, where generative AI takes center stage, seamlessly blending human imagination with machine intelligence. It propels machine learning models to a new level of cognition, where they can create art, compose music, design, and generate ideas that leave us in awe. This remarkable technological advancement is not just science fiction; it’s the reality we are experiencing today.

Over the past year, generative AI has evolved from an intriguing concept to a mainstream technology, commanding attention and attracting investments on a scale unprecedented in its brief history. Generative AI showcases remarkable proficiency in producing coherent text, images, code, and various other impressive outputs based on simple textual prompts. This capability has captivated the world, fueling a growing curiosity that intensifies with each iteration of a generative AI model released. It’s worth noting that the true potential of generative AI is far more profound than performing traditional Natural Language Processing tasks.

This technology has found a home in a multitude of industries, paving the way for sophisticated algorithms to be distilled into clear, concise explanations. It’s helping us build bots, develop apps, and convey complex academic concepts with unprecedented ease. Creative fields such as animation, gaming, art, cinema, and architecture are experiencing profound changes, spurred on by powerful text-to-image programs like DALL-E, Stable Diffusion, and Midjourney.

We have been laying the groundwork for over a decade for today’s AI. However, it was in the year 2022 that a significant turning point was reached, marking a pivotal moment in the history of artificial intelligence. It was the year when ChatGPT was launched, ushering in a promising era of human-machine cooperation. As we bask in the radiance of this newfound enlightenment, we are prompted to delve deeper into the reasons behind this sudden acceleration and, more importantly, the path that lies ahead.

In this article, we will embark on an expedition to understand the origins, trajectory, and champions of the present-day generative AI landscape. We’ll explore the array of tools that are placing the creative, ideation, development, and production powers of this transformative technology into the hands of users. With industry analysts forecasting a whopping $110 billion valuation by 2030, there’s no denying that the future of AI is not just generative; it’s transformative. So, join us as we traverse this uncharted territory, tracing the story of the greatest technological evolution of our time.

Understanding generative AI

Generative AI refers to a branch of artificial intelligence focused on creating models and systems that have the ability to generate new and original content. These AI models are trained on large datasets and can produce outputs such as text, images, music, and even videos. This transformative technology, underpinned by unsupervised and semi-supervised machine learning algorithms, empowers computers to create original content nearly indistinguishable from the human-created output. To fully appreciate the magic of this innovative technology, it is vital to understand the models that drive it. Here are some important generative AI models:

Generative Adversarial Networks (GANs)

Generative Adversarial Networks

At the core of generative AI, we find two main types of models, each with its unique characteristics and applications. First, Generative Adversarial Networks (GANs) excel at generating visual and multimedia content from both text and image data. Invented by Ian Goodfellow and his team in 2014, GANs pit two neural networks, the generator and the discriminator, against each other in a zero-sum game. The generator’s task is to create convincing “fake” content from a random input vector, while the discriminator’s role is to distinguish between real samples from the domain and fake ones produced by the generator. The generator and discriminator, typically implemented as Convolutional Neural Networks (CNNs), continuously challenge and learn from each other. When the generator creates a sample so convincing that it fools not only the discriminator but also human perception, the discriminator evolves to get better, ensuring continuous improvement in the quality of generated content.

Transformer-based models

Transformer-based models

These deep learning networks are predominantly used in natural language processing tasks. Pioneered by Google in 2017, these networks excel in understanding the context within sequential data. One of the best-known examples is GPT-3, built by the OpenAI team, which produces human-like text, crafting anything from poetry to emails, with uncanny authenticity. A transformer model operates in two stages: encoding and decoding. The encoder extracts features from the input sequence, transforming them into vectors representing the input’s semantic and positional aspects. These vectors are then passed to the decoder, which derives context from them to generate the output sequence. By adopting a sequence-to-sequence learning approach, transformers can predict the next item in the sequence, adding context that brings meaning to each item. Key to the success of transformer models is the use of attention or self-attention mechanisms. These techniques add context by acknowledging how different data elements within a sequence interact with and influence each other. Additionally, the ability of transformers to process multiple sequences in parallel significantly accelerates the training phase, further enhancing their effectiveness.

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The evolution of generative AI and its current state

Historical context of generative AI development

The fascinating journey of generative AI commenced in the 1960s with the pioneering work of Joseph Weizenbaum, who developed ELIZA, the first-ever chatbot. This early attempt at Natural Language Processing (NLP) sought to simulate human conversation by generating responses based on the text it received. Even though ELIZA was merely a rules-based system, it began a technological evolution in NLP that would unfold over the coming decades.

The foundation for contemporary generative AI lies in deep learning, a concept dating back to the 1950s. Despite its early inception, the field of deep learning experienced a slowdown until the 80s and 90s, when it underwent a resurgence powered by the introduction of Artificial Neural Networks (ANNs) and backpropagation algorithms. The advent of the new millennium brought a significant leap in data availability and computational prowess, turning deep learning from theory to practice.

The real turning point arrived in 2012 when Geoffrey Hinton and his team demonstrated a breakthrough in speech recognition by deploying Convolutional Neural Networks (CNNs). This success was replicated in the realm of image classification in 2014, propelling substantial advancements in AI research.

That same year, Ian Goodfellow unveiled his ground-breaking paper on Generative Adversarial Networks (GANs). His innovative approach involved pitting two networks against each other in a zero-sum game, generating new images that mimicked the training images yet were distinct. This milestone led to further refinements in GAN architecture, yielding progressively better image synthesis results. Eventually, these methods started being used in various applications, including music composition.

The years that followed saw the emergence of new model architectures like Recurrent Neural Networks (RNNs) for text and video generation, Long Short-term Memory (LSTM) for text generation, transformers for text generation, Variational Autoencoders (VAEs) for image generation, diffusion models for image generation, and various flow model architectures for audio, image, and video. Parallel advancements in the field gave rise to Neural Radiance Fields (NeRF) capable of building 3D scenes from 2D images and reinforcement learning that trains agents through reward-based trial and error.

More recent achievements in generative AI have been astonishing, from creating photorealistic images and convincing deep fake videos to believable audio synthesis and human-like text produced by sophisticated language models like OpenAI’s GPT-1. However, it was only in the latter half of 2022, with the launch of various diffusion-based image services like MidJourney, Dall-E 2, Stable Diffusion, and the deployment of OpenAI’s ChatGPT, that generative AI truly caught the attention of the media and mainstream. New services that convert text into video (Make-a-Video, Imagen Video) and 3D representations (DreamFusion, Magic3D & Get3D) also significantly highlight the power and potential of generative AI to the wider world.

Major achievements and milestones

Generative AI has witnessed remarkable advancements in recent times, owing to the emergence of powerful and versatile AI models. These advancements are not standalone instances; they are a culmination of scaling models, growing datasets, and enhanced computing power, all interacting to propel the current AI progress.

  • The dawn of the modern AI era dates back to 2012, with significant progress in deep learning and Convolutional Neural Networks (CNNs). CNNs, although conceptualized in the 90s, became practical only when paired with increased computational capabilities. The breakthrough arrived when Stanford AI researchers presented ImageNet in 2009, an annotated image dataset for training computer vision algorithms. When AlexNet combined CNNs with ImageNet data in 2012, it outperformed its closest competitor by nearly 11%, marking a significant step forward in computer vision.
  • In 2017, Google’s “Transformer” model bridged a critical gap in Natural Language Processing (NLP), a sector where deep learning had previously struggled. This model introduced a mechanism called “attention,” enabling it to assess the entire input sequence and determine relevance to each output component. This breakthrough transformed how AI approached translation problems and opened up new possibilities for many other NLP tasks. Recently, this transformative approach has also been extended to computer vision.
  • The advancements of Transformers led to the introduction of models like BERT and GPT-2 in 2018, which offered novel training capabilities on unstructured data using next-word prediction. These models demonstrated surprising “zero-shot” performance on new tasks, even without prior training. OpenAI continued to push the boundaries by probing the model’s potential to scale and handle increased training data. The major challenge faced by researchers was sourcing the appropriate training data. Although vast amounts of text were available online, creating a significant and relevant dataset was arduous. The introduction of BERT and the first iteration of GPT began to leverage this unstructured data, further boosted by the computational power of GPUs. OpenAI took this forward with their GPT-2 and GPT-3 models. These “generative pre-trained transformers” were designed to generate new words in response to input and were pre-trained on extensive text data.
  • Another milestone in these transformer models was the introduction of “fine-tuning,” which involved adapting large models to specific tasks or smaller datasets, thus improving performance in a specific domain at a fraction of the computational cost. A prime example would be adapting the GPT-3 model to medical documents, resulting in a superior understanding and extraction of relevant information from medical texts.
  • In 2022, Instruction Tuning emerged as a significant advancement in the generative AI space. Instruction Tuning involves teaching a model, initially trained for next-word prediction, to follow human instructions and preferences, enabling easier interaction with these Language Learning Models (LLMs). One of the beneficial aspects of Instruction Tuning was aligning these models with human values, thereby preventing the generation of undesired or potentially dangerous content. OpenAI implemented a specific technique for instruction tuning known as Reinforcement Learning with Human Feedback (RLHF), wherein human responses trained the model. Further leveraging Instruction Tuning, OpenAI introduced ChatGPT, which restructured instruction tuning into a dialogue format, providing an accessible interface for interaction. This paved the way for widespread awareness and adoption of generative AI products, shaping the landscape as we know it today.

Where do we currently stand in generative AI research and development?

The state of Large Language Models (LLMs)

The present state of Large Language Model (LLM) research and development can be characterized as a lively and evolving stage, continuously advancing and adapting. The landscape includes different actors, such as providers of LLM APIs like OpenAI, Cohere, and Anthropic. On the consumer end, products like ChatGPT and Bing offer access to LLMs, simplifying interaction with these advanced models.

The speed of innovation in this field is astonishing, with improvements and novel concepts being introduced regularly. This includes, for instance, the advent of multimodal models that can process and understand both text and images and the ongoing development of Agent models capable of interacting with each other and different tools.

The rapid pace of these developments raises several important questions. For instance:

  • What will be the most common ways for people to interact with LLMs in the future?
  • Which organizations will emerge as the key players in the advancement of LLMs?
  • How fast will the capabilities of LLMs continue to grow?
  • Given the balance between the risk of uncontrolled outputs and the benefits of democratized access to this technology, what is the future of open-source LLMs?

Here is a table showing the leading LLM models:

LLM models

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OpenAI’s models

Model Function
GPT4 Most capable GPT model, able to do complex tasks and optimized for chat
GPT 3.5 Turbo Optimized for dialogue and chat, most capable GPT 3.5 model
Ada Capable of simple tasks like classification
Davinci Most capable GPT3 model
Babbage Fast, lower cost and capable of straightforward tasks
Curie Faster, lower cost than Davinci
DALL-E Image model
Whisper Audio model

OpenAI, the entity behind the transformative Generative Pre-trained Transformer (GPT) models, is an organization dedicated to developing and deploying advanced AI technologies. Established as a nonprofit entity in 2015 in San Francisco, OpenAI aimed to create Artificial General Intelligence (AGI), which implies the development of AI systems as intellectually competent as human beings. The organization conducts state-of-the-art research across a variety of AI domains, including deep learning, natural language processing, computer vision, and robotics, aiming to address real-world issues through its technologies.

In 2019, OpenAI made a strategic shift, becoming a capped-profit company. The decision stipulated that investors’ earnings would be limited to a fixed multiple of their original investment, as outlined by Sam Altman, the organization’s CEO. According to the Wall Street Journal, the initial funding for OpenAI consisted of $130 million in charitable donations, with Tesla CEO Elon Musk contributing a significant portion of this amount. Since then, OpenAI has raised approximately $13 billion, a fundraising effort led by Microsoft. This partnership with Microsoft facilitated the development of an enhanced version of Bing and a more interactive suite of Microsoft Office apps, thanks to the integration of OpenAI’s ChatGPT.

In 2019, OpenAI unveiled GPT-2, a language model capable of generating remarkably realistic and coherent text passages. This breakthrough was superseded by the introduction of GPT-3 in 2020, a model trained on 175 billion parameters. This versatile language processing tool enables users to interact with the technology without the need for programming language proficiency or familiarity with complex software tools.

Continuing this trajectory of innovation, OpenAI launched ChatGPT in November 2022. An upgrade from earlier versions, this model exhibited an improved capacity for generating text that closely mirrors human conversation. In March 2023, OpenAI introduced GPT-4, a model incorporating multimodal capabilities that could process both image and text inputs for text generation. GPT-4 boasts a maximum token count of 32,768 compared to its predecessor, enabling it to generate around 25,000 words. According to OpenAI, GPT-4 demonstrates a 40% improvement in factual response generation and a significant 82% reduction in the generation of inappropriate content.

Google’s GenAI foundation models

Google AI, the scientific research division under Google, has been at the forefront of promising advancements in machine learning. Its most significant contribution in recent years is the introduction of the Pathways Language Model (PaLM), which is Google’s largest publicly disclosed model to date. This model is a major component of Google’s recently launched chatbot, Bard.

PaLM has formed the foundation of numerous Google initiatives, including the instruction-tuned model known as PaLM-Flan and the innovative multimodal model PaLM-E. This latter model is recognized as Google’s first “embodied” multimodal language model, incorporating both text and visual cues.

The training process for PaLM used a broad text corpus in a self-supervised learning approach. This included a mixture of multilingual web pages (27%), English literature (13%), open-source code from GitHub repositories (5%), multilingual Wikipedia articles (4%), English news articles (1%), and various social media conversations (50%). This expansive data set facilitated the exceptional performance of PaLM, enabling it to surpass previous models like GPT-3 and Chinchilla in 28 out of 29 NLP tasks in the few-shot performance.

PaLM variants can scale up to an impressive 540 billion parameters, significantly more than GPT-3’s 175 billion. The model was trained on 780 billion tokens, again outstripping GPT-3’s 300 billion. The training process consumed approximately 8x more computational power than GPT-3. However, it’s noteworthy that this is likely considerably less than what’s required for training GPT-4. PaLM’s training was conducted across multiple TPU v4 pods, harnessing the power of Google’s dense decoder-only Transformer model.

Google researchers optimized the use of their Tensor Processing Unit (TPU) chips by using 3072 TPU v4 chips linked to 768 hosts across two pods for each training cycle. This configuration facilitated large-scale training without the necessity for pipeline parallelism. Google’s proprietary Pathways system allowed the seamless scaling of the model across its numerous TPUs, demonstrating the capacity for training ultra-large models like PaLM.

Central to this technological breakthrough is Google’s latest addition, PaLM 2, which was grandly introduced at the I/O 2023 developer conference. Touted by Google as a pioneering language model, PaLM 2 is equipped with enhanced features and forms the backbone of more than 25 new products, effectively demonstrating the power of multifaceted AI models.

Broadly speaking, Google’s GenAI suite comprises four foundational models, each specializing in a unique aspect of generative AI:

  1. PaLM 2: Serving as a comprehensive language model, PaLM 2 is trained across more than 100 languages. Its capabilities extend to text processing, sentiment analysis, and classification tasks, among others. Google’s design enables it to comprehend, create, and translate complex text across multiple languages, tackling everything from idioms and poetry to riddles. The model’s advanced capabilities even stretch to logical reasoning and solving intricate mathematical equations.
  2. Codey: Codey is a foundational model specifically crafted to boost developer productivity. It can be incorporated into a standard development kit (SDK) or an application to streamline code generation and auto-completion tasks. To enhance its performance, Codey has been meticulously optimized and fine-tuned using high-quality, openly licensed code from a variety of external sources.
  3. Imagen: Imagen is a text-to-image foundation model enabling organizations to generate and tailor studio-quality images. This innovative model can be leveraged by developers to create or modify images, opening up a plethora of creative possibilities.
  4. Chirp: Chirp is a specialized foundation model trained to convert speech to text. Compatible with various languages, it can be used to generate accurate captions or to develop voice assistance capabilities, thus enhancing accessibility and user interaction.

Each of these models forms a pillar of Google’s GenAI stack, demonstrating the breadth and depth of Google’s AI capabilities.

DeepMind’s Chinchilla model

DeepMind Technologies, a UK-based artificial intelligence research lab established in 2010, came under the ownership of Alphabet Inc. in 2015, following its acquisition by Google in 2014. A significant achievement of DeepMind is the development of a neural network, or a Neural Turing machine, that aims to emulate the human brain’s short-term memory.

DeepMind has an impressive track record of accomplishments. Its AlphaGo program made history in 2016 by defeating a professional human Go player, while the AlphaZero program overcame the most proficient software in Go and Shogi games using reinforcement learning techniques. In 2020, DeepMind’s AlphaFold took significant strides in solving the protein folding problem and by July 2022, it had made predictions for over 200 million protein structures. The company continued its streak of innovation with the launch of Flamingo, a unified visual language model capable of describing any image, in April 2022. Subsequently, in July 2022, DeepMind announced DeepNash, a model-free multi-agent reinforcement learning system.

Among DeepMind’s impressive roster of AI innovations is the Chinchilla AI language model, which was introduced in March 2022. The claim to fame of this model is its superior performance over GPT-3. A significant revelation in the Chinchilla paper was that prior LLMs had been trained on insufficient data. An ideal model of a given parameter size should utilize far more training data than GPT-3. Although gathering more training data increases time and costs, it leads to more efficient models with a smaller parameter size, offering huge benefits for inference costs. These costs, associated with operating and using the finished model, scale with parameter size.

With 70 billion parameters, which is 60% smaller than GPT-3, Chinchilla was trained on 1,400 tokens, 4.7 times more than GPT-3. Chinchilla AI demonstrated an average accuracy rate of 67.5% on Measuring Massive Multitask Language Understanding (MMLU) and outperformed other major LLM platforms like Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (300 parameters and 530B parameters) across a wide array of downstream evaluation tasks.

Meta’s LlaMa models

Meta AI, previously recognized as Facebook Artificial Intelligence Research (FAIR), is an artificial intelligence lab renowned for its contributions to the open-source community, including frameworks, tools, libraries, and models to foster research exploration and facilitate large-scale production deployment. A significant milestone in 2018 was the release of PyText, an open-source modeling framework designed specifically for Natural Language Processing (NLP) systems. Meta further pushed boundaries with the introduction of BlenderBot 3 in August 2022, a chatbot designed to improve conversational abilities and safety measures. Moreover, the development of Galactica, a large language model launched in November 2022, has aided scientists in summarizing academic papers and annotating molecules and proteins.

Emerging in February 2023, LLaMA (Large Language Model Meta AI) represents Meta’s entry into the sphere of transformer-based large language models. This model has been developed with the aim of supporting the work of researchers, scientists, and engineers in exploring various AI applications. To mitigate potential misuse, LLaMA will be distributed under a non-commercial license, with access granted selectively on a case-by-case basis to academic researchers, government-affiliated individuals and organizations, civil society, academia, and industry research facilities. By sharing codes and weights, Meta allows other researchers to explore and test new approaches in the realm of LLMs.

The LLaMA models boast a range of 7 billion to 65 billion parameters, positioning LLaMA-65B in the same league as DeepMind’s Chinchilla and Google’s PaLM. The training of these models involved the use of publicly available unlabeled data, which necessitates fewer computing resources and power for smaller foundational models. The larger variants, LLaMA-65B and 33B, were trained on 1.4 trillion tokens across 20 different languages. According to the FAIR team, the model’s performance varies across languages. Training data sources encompassed a diverse range, including CCNet (67%), GitHub, Wikipedia, ArXiv, Stack Exchange, and books. However, like other large-scale language models, LLaMA is not without issues, including biased and toxic generation and hallucination.

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The Megatron Turing model by Microsoft & Nvidia

Nvidia, a pioneer in the AI industry, is renowned for its expertise in developing Graphics Processing Units (GPUs) and Application Programming Interfaces (APIs) for a broad range of applications, including data science, high-performance computing, mobile computing, and automotive systems. With its forefront presence in AI hardware and software production, Nvidia plays an integral role in shaping the AI landscape.

In 2021, Nvidia’s Applied Deep Learning Research team introduced the groundbreaking Megatron-Turing model. Encompassing a staggering 530 billion parameters and trained on 270 billion tokens, this model demonstrates the company’s relentless pursuit of innovation in AI. To promote accessibility and practical use, Nvidia offers an Early Access program for its MT-NLG model through its managed API service, enabling researchers and developers to tap into the power of this model.

Further cementing its commitment to advancing AI, Nvidia launched the DGX Cloud platform. This platform opens doors to a myriad of Nvidia’s Large Language Models (LLMs) and generative AI models, offering users seamless access to these state-of-the-art resources.

GPT-Neo models by Eleuther

EleutherAI, established in July 2020 by innovators Connor Leahy, Sid Black, and Leo Gao, is a non-profit research laboratory specializing in artificial intelligence. The organization has gained recognition in the field of large-scale Natural Language Processing (NLP) research, with particular emphasis on understanding and aligning massive models. EleutherAI strives to democratize the study of foundational models, fostering an open science culture within NLP and raising awareness about these models’ capabilities, limitations, and potential hazards.

The organization has undertaken several remarkable projects. In December 2020, they created ‘the Pile,’ an 800GiB dataset, to train Large Language Models (LLMs). Following this, they unveiled GPT-Neo models in March 2021, and in June of the same year, they introduced GPT-J-6B, a 6 billion parameter language model, which was the most extensive open-source model of its kind at that time. Moreover, EleutherAI has also combined CLIP and VQGAN to build a freely accessible image generation model, thus founding Stability AI. Collaborating with the Korean NLP company TUNiB, EleutherAI has also trained language models in various languages, including Polyglot-Ko.

The organization initially relied on Google’s TPU Research Cloud Program for its computing needs. However, by 2021, they transitioned to CoreWeave for funding. They also utilize TensorFlow Research Cloud for more cost-effective computational resources. February 2022 saw the release of the GPT-NeoX-20b model, becoming the largest open-source language model at the time. In January 2023, EleutherAI formalized its status as a non-profit research institute.

GPT-NeoX-20B, EleutherAI’s flagship NLP model, trained on 20 billion parameters, was developed using the company’s GPT-NeoX framework and CoreWeave’s GPUs. It demonstrated a 72% accuracy on the LAMBADA sentence completion task and an average 28.98% zero-shot accuracy on the Hendrycks Test Evaluation for Stem. The Pile dataset for the model’s training comprises data from 22 distinct sources spanning five categories: academic writing, web resources, prose, dialogue, and miscellaneous sources.

EleutherAI’s GPT-NeoX-20B, a publicly accessible and pre-trained autoregressive transformer decoder language model, stands out as a potent few-shot reasoner. It comprises 44 layers, a hidden dimension size of 6144, and 64 heads. It also incorporates 1.1. Rotary Positional Embeddings, offering a deviation from learned positional embeddings commonly found in GPT models.

Hardware and cloud platforms transformation

The advent of generative AI has considerably influenced the evolution of hardware and the cloud landscape. Recognizing the processing power needed to train and run these complex AI models, companies like Nvidia have developed powerful GPUs like the ninth-generation H100 Tensor Core. Boasting 80 billion transistors, this GPU is specifically designed to optimize large-scale AI and High-performance Computing (HPC) models, following the success of its predecessor, the A100, in the realm of deep learning.

Meanwhile, Google, with its Tensor Processing Units (TPUs) – custom-designed accelerator application-specific integrated circuits (ASICs) – has played a critical role in this transformation. These TPUs, developed specifically for efficient machine learning tasks, are closely integrated with TensorFlow, Google’s machine learning framework. Google Cloud Platform has further embraced generative AI by launching its TPU v4 on Cloud, purpose-built for accelerating NLP workloads and developing TPU v5 for its internal applications.

Microsoft Azure has responded to the call for generative AI by providing GPU instances powered by Nvidia GPUs, such as the A100 and P40, tailored for various machine learning and deep learning workloads. Their partnership with OpenAI has enabled the training of advanced generative models like GPT-3 and GPT-4 and made them accessible to developers through Azure’s cloud infrastructure.
On the other hand, Amazon Web Services (AWS) offer potent GPU-equipped instances like the Amazon Elastic Compute Cloud (EC2) P3 instances. They are armed with Nvidia V100 GPUs, offering over 5,000 CUDA cores and an impressive 300 GB of GPU memory. AWS has also designed its own chips, Inferentia for inference tasks and Trainium for training tasks, thus catering to the computational demands of generative AI.

This transformation in hardware and cloud landscapes has also facilitated the creation of advanced models like BERT, RoBERTa, Bloom, Megatron, and the GPT series. Among them, BERT and RoBERTa, both trained using transformer architecture, have delivered impressive results across numerous NLP tasks, while Bloom, an openly accessible multilingual language model, was trained on an impressive 384 A100–80GB GPUs.

How is generative AI explored in other modalities?

  • Image generation: State-of-the-art tools for image manipulation have emerged due to the amalgamation of powerful models, vast datasets, and robust computing capabilities. OpenAI’s DALL-E, an AI system that generates images from textual descriptions, exemplifies this. DALL-E can generate unique, high-resolution images and manipulate existing ones by utilizing a modified version of the GPT-3 model. Despite certain challenges, such as algorithmic biases stemming from its training on public datasets, it’s a notable player in the space. Midjourney, an AI program by an independent research lab, allows users to generate images through Discord bot commands, enhancing user interactivity. The Stable Diffusion model by Stability AI is another key player, with its capabilities for image manipulation and translation from the text. This model has been made accessible through an online interface, DreamStudio, which offers a range of user-friendly features.
  • Audio generation: OpenAI’s Whisper, Google’s AudioLM, and Meta’s AudioGen are significant contributors to the domain of audio generation. Whisper is an automatic speech recognition system that supports a multitude of languages and tasks. Google’s AudioLM and Meta’s AudioGen, on the other hand, utilize language modeling to generate high-quality audio, with the latter being able to convert text prompts into sound files.
  • Search engines: Neeva and are AI-powered search engines prioritize user privacy while delivering curated, synthesized search results. Neeva leverages AI to provide concise answers and enables users to search across their personal email accounts, calendars, and cloud storage platforms. categorizes search results based on user preferences and allows users to create content directly from the search results.
  • Code generation: GitHub Copilot is transforming the world of software development by integrating AI capabilities into coding. Powered by a massive repository of source code and natural language data, GitHub Copilot provides personalized coding suggestions, tailored to the developer’s unique style. Furthermore, it offers context-sensitive solutions, catering to the specific needs of the coding environment. Impressively, GitHub Copilot can generate functional code across a variety of programming languages, effectively becoming an invaluable asset to any developer’s toolkit.
  • Text generation: Jasper.AI is a subscription-based text generation model that requires minimal user input. It can generate various text types, from product descriptions to email subject lines. However, it does have limitations, such as a lack of fact-checking and citation of sources.
    The rapid rise of consumer-facing generative AI is a testament to its transformative potential across industries. As these technologies continue to evolve, they will play an increasingly crucial role in shaping our digital future.

LLM fine-tuning: Tailoring AI to meet domain-specific needs

LLMs are highly capable, but their true potential is unlocked through fine-tuning, tailoring their abilities to specific tasks and domains. Pre-trained language models, though immensely powerful, are like broad encyclopedic resources. They possess extensive knowledge but may lack the depth required for specific tasks or domains. Enter fine-tuning, the process used to customize and refine these models to perform effectively in specific contexts, such as handling tasks such as sentiment analysis in customer reviews or answering domain-specific queries.

Fine-tuning LLMs has the following merits:

  • Efficiency: Instead of reinventing the wheel, fine-tuning uses the foundational knowledge of pre-trained models, conserving time and computational energy.
  • Precision: Once fine-tuned, a model can navigate the intricacies of a specific domain, offering results with greater accuracy and relevancy.

Why should businesses invest in fine-tuned LLMs? Given the generic nature of base LLMs, businesses often turn to their fine-tuned versions. Here’s why:

  • Customization: Each business is unique, with its own set of challenges and objectives. A fine-tuned model can cater to these individual needs, whether it’s generating personalized content or parsing user interactions on a platform.
  • Regulatory adherence & data sensitivity: Businesses can’t afford to take risks with mounting concerns around data privacy and industry-specific regulations. A fine-tuned model can be designed to respect these boundaries, ensuring responses that align with compliance standards.
  • Domain mastery: Every industry has its lexicon. Finance isn’t tech, and healthcare isn’t entertainment. Fine-tuning equips LLMs with the vocabulary and context to understand and interact effectively within a particular industry.
  • Boost in performance: When tailored for specific tasks, LLMs exhibit enhanced performance. This specialization, be it in sentiment analysis, document categorization, or data extraction, can elevate business operations, ensuring data-driven, efficient, and accurate decisions.
  • Elevated user experience: At the end of the day, it’s all about the end-user. A fine-tuned model can significantly enhance user interaction through chatbots, virtual aides, or support systems. This leads not just to satisfied customers but often to loyal ones.

As businesses become more aware of the power of AI and its subsets like NLP, the demand for custom NLP-based solutions grows. Today, LLM fine-tuning is not a luxury, it’s a necessity that ensures that AI understands and resonates with a business’s specific needs and nuances. Fine-tuning an LLM is the key to distinguishing between a standard, one-size-fits-all response and one that genuinely understands and resonates with the user’s specific needs and context.

Challenges associated with LLM fine-tuning approaches

While fine-tuned LLMs have become indispensable in various fields, the conventional fine-tuning approaches present several challenges. Let’s delve into the intricacies and hurdles involved.

Data constraints and discrepancies

  • Issue: An ideal scenario for fine-tuning requires ample, task-specific data. However, obtaining such data can often be difficult. What complicates things further is the disparity between pre-training data and domain-specific data.
  • Implication: Insufficient data can cause the model to miss the intricacies of the task, rendering it less effective.

The paradox of catastrophic forgetting

  • Issue: Fine-tuning may hone LLMs for new tasks but can inadvertently make them forget previously learned tasks.
  • Implication: The trade-off between adapting to new tasks and retaining previous knowledge can be delicate, affecting the model’s versatility.

The problem of overfitting

  • Issue: It’s a fine balance to strike – ensuring LLMs learn from the training data without them becoming overly tailored to it.
  • Implication: Overfit models might fail to generalize for real-world scenarios, making them narrowly effective.

Bias amplification pitfalls

  • Issue: Existing biases in pre-trained models can get accentuated during fine-tuning.
  • Implication: Outputs from biased LLMs can inadvertently perpetuate and exacerbate societal stereotypes and prejudices.

Navigating the maze of hyperparameter tuning

  • Issue: Fine-tuning involves multiple hyperparameters that can influence the model’s adaptation process. Determining the ideal set can be taxing.
  • Implication: Incorrect hyperparameters can hinder model efficiency, requiring repeated efforts and resource consumption.

Evaluating the metrics

  • Issue: Traditional metrics may not encapsulate the nuanced capabilities of fine-tuned LLMs, leading to potential performance mismatches.
  • Implication: Inaccurate evaluation can mislead developers about the model’s real-world applicability.

While fine-tuning LLMs promises specificity and increased efficiency, the path is challenging. Addressing these hurdles requires a combination of technological innovation, domain expertise, and iterative learning. Only then can LLMs truly be harnessed for their potential in varied applications.

Addressing challenges associated with fine-tuning: 8 prompt engineering approaches

Using vector embeddings to ensure prompts are translated into a model-friendly format

Vector embeddings serve as a cornerstone for fine-tuning LLMs, offering a nuanced approach to data representation. Unlike traditional databases that manage discrete information, vector databases are adept at capturing the subtle nuances in data, especially concerning complex areas like language. These databases are repositories of high-dimensional vectors representing linguistic data in a mathematical space, facilitating similarity searches.

The idea at the core of this approach is simple: words, phrases, or even entire sentences can be mapped into vectors in a multi-dimensional space. Within this space, linguistic similarities translate to spatial proximities, making retrieving related content a matter of calculating the distance between vectors.

Here’s where the synergy with LLMs becomes evident. LLMs are exceptional at understanding and generating language but often benefit from a structured data retrieval method. By integrating vector databases into the fine-tuning process, LLMs can gain a deeper grasp of the information, ensuring pinpoint response accuracy.

The utilization of vector embeddings allows for targeted information extraction. Instead of sifting through heaps of textual data, LLMs can instantly pull up the most contextually relevant data when paired with vector databases. This streamlined process not only boosts the accuracy of the LLM but also enhances its efficiency, conserving computational resources.

However, as with any sophisticated technology, there are hurdles. The effectiveness of this approach hinges on the quality and diversity of the vector embeddings. An incomplete or biased database could distort the model’s understanding. Moreover, the integration process demands a keen understanding of language models and vector databases—a combination requiring expertise.

In essence, marrying vector embeddings with LLMs is a promising avenue for bolstering the capabilities of these models. While the approach is laden with potential, it also demands meticulous implementation and a deep understanding of both domains.

Generative models, such as Large Language Models (LLMs), are created to produce outputs that make sense and align with the provided information. The way data is given to the model, mainly through prompts or input instructions, significantly impacts what the model generates. This approach of crafting effective prompts to control the model’s output is referred to as ‘Prompt Engineering.’ Essentially, it’s about using carefully constructed prompts to guide the model’s responses in a desired direction. At its core, prompt engineering appears straightforward and user-friendly. However, as LLMs evolve, this technique is also evolving into a more intricate and sophisticated practice. Here are 8 modern approaches:

Approach 1: Static prompts

Static prompts are pivotal in fine-tuning LLMs to optimize their generative capabilities. These prompts, distinct in their unwavering and fixed nature, serve as foundational cues, directing the model’s response behavior.

Prompts can be categorized based on the amount of guiding information they carry.

  • Zero-shot learning: The model is provided with no prior examples in the prompt and is expected to comprehend and execute the task.
  • One-shot learning: The model is given a single example within the prompt to illustrate the desired output or format.
  • Few-shot learning: Multiple examples are embedded within the prompt, offering the model various perspectives of the desired task.

Among these, one-shot and few-shot learning approaches have been observed to enhance the generative abilities of LLMs, as they provide clear context and expectations to the model.

By incorporating examples into prompts, users can effectively communicate the format or structure of the desired output. These examples act as guiding posts, illustrating how the LLM should process and present its response.

Static prompts stand out in their simplicity. As the name suggests, these are unchanging and straightforward textual cues devoid of any dynamic templating or external information injections. The model is presented with clear, consistent instruction, leaving minimal room for ambiguity. In a world where prompt engineering is evolving with more complex structures, static prompts’ simplicity and clarity often prove advantageous, especially during the fine-tuning process.

Fine-tuning LLMs is a nuanced process that requires a deep understanding of how models perceive and respond to inputs. Static prompts, with their straightforward and unaltered nature, serve as a reliable tool. By leveraging static prompts and understanding their placement within the broader spectrum of prompt engineering, developers and researchers can harness the true potential of LLMs, ensuring precise, consistent, and contextually apt outputs.

Approach 2: Prompt templates

While static prompts are valuable for providing LLMs with a clear and unchanging directive, fine-tuning often demands a more dynamic and adaptable approach. Enter prompt templating, an evolution from the static prompt methodology, offering flexibility, adaptability, and precision.

At its core, prompt templating is the art of transforming a static prompt into a dynamic template. Instead of fixed text, these templates contain placeholders that can be filled with varying data or application values at runtime. Essentially, these templates act as molds, ready to be filled with context-specific details to guide the LLM’s generative process.

Benefits of templating:

  • Flexibility: One template can adapt to many scenarios, as different data can be fed into its placeholders.
  • Consistency: Even with dynamic data, the structure of the response remains consistent due to the fixed template.
  • Efficiency: Templates can be stored, reused, and shared, eliminating the need to craft a new prompt for every slight variation in a task.
  • Programmability: With placeholders in the template, developers can seamlessly integrate LLMs into applications, ensuring real-time and context-aware responses.

Entity injection and prompt injection are often used interchangeably with templating, hinting at the process’s essence. ‘Entity injection’ refers to introducing specific entities or data into the prompt, while ‘prompt injection’ alludes to the insertion of dynamic data into a preset prompt structure.

Prompt templates, with their adaptability, can be easily incorporated into software programs. As a result, developers can programmatically control the input (via placeholders) and optimize the output generated by the LLM. This ensures that the generated content is relevant and aligns with the real-time data or context the program deals with.

With templating, the know-how of crafting an effective prompt doesn’t need to be reinvented each time. Once a template is designed, it can be stored for future use, shared with other teams or projects, and even integrated into various applications. This saves time and ensures the consistency and reliability of the responses generated by the LLM.

Prompt templating is a testament to the adaptability and evolution in the world of LLM fine-tuning. Moving beyond the confines of static prompts, templating allows for dynamic interactions, tailored responses, and a seamless marriage of LLMs with modern-day applications. As we continue to push the boundaries of what LLMs can achieve, the role of prompt templates in fine-tuning and application integration remains paramount.

Approach 3: Prompt composition

The ever-evolving landscape of LLM fine-tuning serves as a testament to the intricate synergy between human ingenuity and the capabilities of artificial intelligence. Within this realm, one particularly advanced technique is the craft of “prompt composition.” Here’s a deep dive into how prompt composition aids in fine-tuning LLMs and ensures they remain at the forefront of versatility and precision.

While prompt templates are molds waiting to be filled with specific data, prompt composition takes it further. Here, rather than relying on one mold, multiple prompt templates from a library are dynamically combined to craft a more advanced and contextual prompt. It’s akin to piecing together a puzzle, where each template uniquely creates the bigger picture.

Prompt composition’s core strength lies in its unparalleled flexibility. Depending on the task or context, different templates can be chosen and pieced together, leading to a highly adaptive LLM response mechanism. This modularity not only aids in generating precise outputs but also allows for seamless programmatic control.

While the flexibility of prompt composition is a boon, it comes with its own set of challenges. Selecting the right templates, ensuring they align seamlessly, and managing the hierarchy or sequence can be complex. But when done right, this complexity translates into nuanced and contextually rich responses from the LLM, surpassing what individual static prompts or singular templates could achieve.

One of the primary advantages of prompt composition is the reusability of individual templates. Since a library of templates is maintained, any part of a frequently used prompt can be stored as a template and seamlessly incorporated into various prompt compositions. This ensures consistency and significantly reduces the time and effort required to craft new prompts.

A rich and contextual prompt is birthed by injecting specific variables into these placeholders and subsequently combining the templates. This exemplifies the dynamic nature of prompt composition, where templates can be interchanged, variables can be injected, and prompts can be tailored to the exact requirement.

Prompt composition, while intricate, heralds a new era in LLM fine-tuning. By leveraging a library of templates and dynamically composing prompts, we usher in a level of granularity and adaptability previously unattained.

Approach 4: Contextual prompts

In the intricate world of LLM fine-tuning, ensuring the accuracy and relevance of model responses is paramount. Enter “contextual prompting,” a game-changing approach that tackles these challenges head-on. Let’s delve into how contextual prompting enhances LLM capabilities and solidifies their place in the rapidly evolving tech landscape.

Beyond the basic prompts, contextual prompting provides LLMs with background or situational information to guide their responses. It’s like setting the stage for a performance, ensuring the LLM understands the task and the broader environment in which the task is set.

As indicated, even a small addition of context can vastly elevate the quality and accuracy of the LLM’s responses. Just as humans rely on context to decipher ambiguous situations or statements, LLMs can generate outputs that align more closely with user intentions and real-world scenarios when given a frame of reference.

One of the challenges in LLM outputs is “hallucination,” — where the model generates information that, while plausible-sounding, is inaccurate or not based on the data it was trained on. Contextual prompting acts as a shield against such instances. Grounding the LLM’s responses in the provided context minimizes the chances of the model going off tangents or producing misleading outputs.

Contextual prompting complements other advanced techniques, like prompt engineering, by enhancing the prompts’ precision. An engineered prompt might dictate the format or structure of the LLM’s response, but adding context ensures that the response’s content is accurate and relevant.

Consider tasks like sentiment analysis on product reviews. While a basic prompt might ask the LLM to determine the sentiment of a review, a contextual prompt would provide information on the type of product, its features, and other relevant data. This context helps the LLM better understand nuances in the review and produce a more accurate sentiment analysis.

Another advantage of contextual prompting is its ability to balance the LLM’s vast knowledge with the specific context of the query. This balance ensures the model doesn’t provide overly generic answers and instead tailors its responses based on the context provided.

Contextual prompting is a cornerstone for more robust and accurate LLM responses. By anchoring the LLM’s vast capabilities within a defined frame of reference, we not only improve the accuracy of its outputs but also unlock its potential to handle complex, real-world scenarios with finesse. As LLM fine-tuning continues to advance, the role of contextual prompting as a vital tool in the toolkit will only become more pronounced.

Approach 5: Prompt chaining

In advanced language model applications, prompt chaining is a sophisticated technique that optimizes the model’s efficiency and capability. Here’s a detailed explanation of how prompt chaining functions and its role in refining LLM outputs.

At its core, prompt chaining is the strategic process of feeding the output of one LLM prompt as the input to the next. Think of it like a relay race, where each runner (or model call) passes the baton (the output) to the next runner.

Each “node” or step in the chain is designed to accomplish a specific, narrowly defined sub-task. LLMs can address each component with greater precision by breaking down complex questions or problems into smaller, more manageable pieces. This process is reminiscent of the divide-and-conquer strategy in problem-solving.

Chain of Thought Prompting, central to prompt chaining, is rooted in sequential reasoning. Just as humans often break down complicated problems step by step, the chain of thought prompting guides LLMs to construct layered, comprehensive answers by tackling each layer individually.

While prompt chaining is a technique in its own right, its principles, especially the chain of thought prompting, are echoed in other areas like agent-based systems and advanced prompt engineering. This universality underscores the technique’s importance and applicability.

Prompt chaining by dividing a query into distinct segments ensures each segment receives focused attention from the LLM. This improves the accuracy of the final combined output and allows for a more in-depth exploration of the subject matter.

One of the challenges with LLMs is their potential to provide overly broad or general responses to complex queries. With its segmented approach, prompt chaining acts as a countermeasure, directing the model to provide specific, concise answers for each segment of the larger task.

Imagine a scenario where an LLM is tasked with drafting a detailed business plan. Using prompt chaining, the process could be broken down into generating an executive summary, market analysis, financial projections, and marketing strategy. Each segment would be tackled individually, ensuring depth and precision before amalgamating into the comprehensive business plan.

Prompt chaining represents a significant leap in harnessing the full potential of LLMs. Embracing the chain of thought prompting principle empowers LLMs to dissect, analyze, and reconstruct complex tasks with enhanced precision and depth. As the field of LLM fine-tuning continues to evolve, techniques like prompt chaining will be instrumental in driving innovation and refining model outputs.

Approach 6: Prompt pipelines

In the expanding universe of LLMs, fine-tuning techniques and strategies are evolving to get the most out of these sophisticated tools. “Prompt Pipelines” is a potent method to refine and enhance the model’s output. Let’s delve into its intricate workings.

A prompt pipeline can be visualized as a series of connected stages, much like an assembly line in a factory. Every stage has a specific function, from interpreting a user’s request to crafting the perfect query for the LLM to process.

In essence, a prompt pipeline provides a structured pathway for user requests. The pipeline navigates through various stages, beginning with an initiating trigger, typically a user query. These can include interpreting the user’s intent, selecting an appropriate prompt template, and determining the contextual parameters required for the final prompt.

While prompt templates offer a predefined structure, pipelines bring fluidity to this framework. Depending on the specifics of a user request, pipelines can autonomously choose, modify, and even combine multiple templates. This allows for a higher degree of personalization in responses.

A major strength of prompt pipelines lies in their ability to weave in contextual information. By doing so, they provide the LLM with a more grounded frame of reference, ensuring that the response is accurate and contextually relevant. This is especially valuable for few-shot training scenarios, where the model is provided with limited examples.

Prompt pipelines minimize manual interventions. Once set up, they can dynamically craft prompts based on the user’s request and the available knowledge. This ensures that the LLM receives the most optimal input tailored to the specifics of the query.

For end-users, prompt pipelines streamline the process of interacting with LLMs. By automating the crafting of prompts and ensuring they are contextually robust, users receive more accurate and relevant responses, enhancing their overall experience.

With prompt pipelines, organizations can scale their interactions with LLMs. As new scenarios, intents, or knowledge bases emerge, pipelines can be adapted or extended without overhauling the entire system.

Prompt pipelines represent the next step in the evolution of LLM interactions. By bridging the gap between static templates and dynamic user needs, they provide a structured yet flexible framework for crafting optimal prompts. As fine-tuning LLMs becomes an increasingly nuanced art, the role of prompt pipelines in ensuring high-quality, contextually relevant outputs will only grow in importance.

Approach 7: Autonomous agents

As the LLMs expand, the methods to streamline and refine their operations continually evolve. One such innovative method is the integration of autonomous agents. Here’s a closer look at how these agents reshape the landscape of LLM fine-tuning.

Given the complexities and vast potentialities of LLMs, automation becomes indispensable. Manual operations can be error-prone and inefficient. This is where autonomous agents come into play, streamlining processes and introducing a degree of intelligent self-direction.

While prompt chaining follows a fixed, predefined sequence of actions, autonomous agents operate differently. They do not adhere to a strict sequence, offering more flexibility. Instead of executing a linear series of tasks, they dynamically choose the most appropriate path based on the context.

What sets agents apart is their ability to act with a high degree of autonomy. Instead of rigidly following a set protocol, they assess the situation, leverage their tools, and decide on the best course of action. This adaptability allows them to respond to diverse requests more effectively.

Autonomous agents come equipped with a variety of tools and capabilities. Whenever a request is presented, they scan through their toolkit to find the most suitable tools for addressing it. The execution pipeline, a dynamic path to reaching a solution, grants the agents the autonomy they are known for. They might cycle through various tools and even revert or change strategies mid-way, all in pursuit of the most accurate response.

One of the standout features of autonomous agents is their iterative nature. Rather than settling for the first solution that comes their way, they may undergo multiple iterations. They evaluate, refine, and re-evaluate until they converge on the optimal answer.

Autonomous agents bring a fresh perspective to the fine-tuning of LLMs. By introducing dynamic, context-aware decision-making, they ensure that the LLM’s operations are accurate and efficient. Their iterative approach can be particularly beneficial in training phases, where models are refined based on feedback loops.

Autonomous agents are changing the way we interact with and fine-tune LLMs. Their adaptability, dynamic nature, and intelligent decision-making capabilities make them invaluable assets in the evolving world of language models. As the field progresses, the synergy between LLMs and autonomous agents is set to become even more pronounced, offering unprecedented levels of efficiency and precision.

Approach 8: Prompt tuning/Soft prompts

As the world of LLMs evolves, researchers and developers continually seek ways to refine their performance and make them more effective. One such method is through the use of soft prompts or prompt tuning. Here is a more detailed exploration of how soft prompts play a role in enhancing the capabilities of LLMs:

Soft prompts differ from traditional or “hard” prompts. While hard prompts are textual cues provided to a model, soft prompts consist of embeddings—numerical representations that can steer an LLM’s behavior. These embeddings aren’t directly interpretable as text, making them more abstract than their hard counterparts.

Soft prompts emerge during the prompt tuning phase. This phase involves adjusting the embeddings to guide the model towards generating more accurate and relevant responses for specific tasks.

One of the most significant benefits of soft prompts is their ability to act as surrogates for additional training data. Soft prompts can fill the gap when acquiring more labeled data is challenging or expensive. As highlighted in recent studies, an effective soft prompt can be as valuable as hundreds or even thousands of additional data points, offering a cost-effective way to improve model performance.

However, it’s essential to understand the limitations and challenges associated with soft prompts. The primary issue lies in their lack of interpretability. While a hard prompt can be read, understood, and edited by humans, soft prompts remain enigmatic. Their embedded nature means that while they can significantly influence a model’s behavior, discerning the exact reasoning behind their effect is challenging. This opacity mirrors the broader concerns about interpretability in deep learning, where models, despite their prowess, often operate as “black boxes.”

While soft prompts come with the challenge of interpretability, their potential benefits—especially as substitutes for extensive training data—cannot be understated. In applications where gathering more data is infeasible or in rapid development scenarios, soft prompts offer a compelling alternative for refining LLMs.

Soft prompts or prompt tuning is a fascinating frontier in the world of LLMs. Researchers can coax models into generating more precise outputs by leveraging these numerical embeddings, even when traditional training data is scarce. As with many cutting-edge technologies, there are trade-offs to consider, and in the case of soft prompts, the balance lies between their utility and the challenges of opacity. As the field advances, strategies to harness the power of soft prompts while mitigating their limitations will become increasingly essential.

How is generative AI driving value across major industries?

Gen AI potential in industries

Image reference – McKinsey

Let us explore the potential operational advantages of generative AI by functioning as a virtual specialist across various applications.

Customer operations

Generative AI holds the potential to transform customer operations substantially, enhancing customer experience and augmenting agent proficiency through digital self-service and skill augmentation. The technology has already found a firm footing in customer service because it can automate customer interactions via natural language processing.

Here are a few examples showcasing the operational enhancements that generative AI can bring to specific use cases:

  • Customer self-service: Generative AI-driven chatbots can deliver immediate and personalized responses to complex customer queries, independent of the customer’s language or location. Generative AI could allow customer service teams to handle queries that necessitate human intervention by elevating the quality and efficiency of interactions through automated channels. Our research revealed that approximately half of the customer contacts in sectors like banking, telecommunications, and utilities in North America are already managed by machines, including AI. We project that generative AI could further reduce the quantity of human-handled contacts by up to 50 percent, contingent upon a company’s current automation level.
  • Resolution during the first contact: Generative AI can promptly access data specific to a customer, enabling a human customer service representative to address queries and resolve issues more effectively during the first interaction.
  • Reduced response time: Generative AI can decrease the time a human sales representative takes to respond to a customer by offering real-time assistance and suggesting subsequent actions.
  • Increased sales: Leveraging its capability to analyze customer data and browsing history swiftly, the technology can identify product suggestions and offers tailored to customer preferences. Moreover, generative AI can enhance quality assurance and coaching by drawing insights from customer interactions, identifying areas of improvement, and providing guidance to agents.

As per an estimation report by McKinsey, applying generative AI to customer care functions could cause significant productivity improvements, translating into cost savings that could range from 30 to 45 percent of current function costs. However, their analysis only considers the direct impact of generative AI on the productivity of customer operations. It does not factor in the potential secondary effects on customer satisfaction and retention that could arise from an enhanced experience, including a deeper understanding of the customer’s context that could aid human agents in providing more personalized assistance and recommendations.

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Marketing and sales

Generative AI has swiftly permeated marketing and sales operations, where text-based communications and large-scale personalization are primary drivers. This technology can generate personalized messages tailored to each customer’s specific interests, preferences, and behaviors. It can even create preliminary drafts of brand advertising, headlines, slogans, social media posts, and product descriptions.

However, the introduction of generative AI into marketing operations demands careful planning. For instance, there are potential risks of infringing intellectual property rights when AI models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition are utilized. Moreover, a virtual try-on application might produce biased representations of certain demographics due to limited or skewed training data. Therefore, substantial human supervision is required for unique conceptual and strategic thinking pertinent to each company’s needs.

Potential operational advantages that generative AI can provide for marketing include the following:

  • Efficient and effective content creation: Generative AI can significantly expedite the ideation and content drafting process, saving time and effort. It can also ensure a consistent brand voice, writing style, and format across various content pieces. The technology can integrate ideas from team members into a unified piece, enhancing the personalization of marketing messages targeted at diverse customer segments, geographies, and demographics. Mass email campaigns can be translated into multiple languages with varying imagery and messaging tailored to the audience. This ability of generative AI could enhance customer value, attraction, conversion, and retention at a scale beyond what traditional techniques allow.
  • Enhanced data utilization: Generative AI can help marketing functions overcome unstructured, inconsistent, and disconnected data challenges. It can interpret abstract data sources such as text, images, and varying structures, helping marketers make better use of data like territory performance, synthesized customer feedback, and customer behavior to formulate data-informed marketing strategies.
  • SEO optimization: Generative AI can assist marketers in achieving higher conversion and lower costs via Search Engine Optimization (SEO) for various technical components such as page titles, image tags, and URLs. It can synthesize key SEO elements, aid in creating SEO-optimized digital content, and distribute targeted content to customers.
  • Product discovery and search personalization: Generative AI can personalize product discovery and searches based on multimodal inputs from text, images, speech, and a deep understanding of customer profiles. Technology can utilize individual user preferences, behavior, and purchase history to facilitate the discovery of the most relevant products and generate personalized product descriptions.

McKinsey’s estimations indicate that generative AI could boost the productivity of the marketing function, creating a value between 5 and 15 percent of total marketing expenditure.

Additionally, generative AI could significantly change the sales approach of both B2B and B2C companies. Here are two potential use cases for sales:

  • Increase sales probability: Generative AI could identify and prioritize sales leads by forming comprehensive consumer profiles from structured and unstructured data, suggesting actions to staff to enhance client engagement at every point of contact.
  • Improve lead development: Generative AI could assist sales representatives in nurturing leads by synthesizing relevant product sales information and customer profiles. It could create discussion scripts to facilitate customer conversation, automate sales follow-ups, and passively nurture leads until clients are ready for direct interaction with a human sales agent.

McKinsey’s analysis proposes that the implementation of generative AI could boost sales productivity by approximately 3 to 5 percent of current global sales expenditures. This technology could also drive value by partnering with workers, enhancing their work, and accelerating productivity. By rapidly processing large amounts of data and drawing conclusions, generative AI can provide insights and options that can significantly enhance knowledge work, speed up product development processes, and allow employees to devote more time to tasks with a higher impact.

Software engineering

Viewing computer languages as another form of language opens up novel opportunities in software engineering. Software engineers can employ generative AI for pair programming and augmented coding and can train large language models to create applications that generate code in response to a natural-language prompt describing the desired functionality of the code.

Software engineering plays a crucial role in most companies, a trend that continues to expand as all large enterprises, not just technology giants, incorporate software into a broad range of products and services. For instance, a significant portion of the value of new vehicles derives from digital features such as adaptive cruise control, parking assistance, and Internet of Things (IoT) connectivity.

The direct impact of AI on software engineering productivity could be anywhere from 20 to 45 percent of the current annual expenditure on this function. This value would primarily be derived from reducing the time spent on certain activities, like generating initial code drafts, code correction and refactoring, root-cause analysis, and creating new system designs. By accelerating the coding process, generative AI could shift the skill sets and capabilities needed in software engineering toward code and architecture design. One study discovered that software developers who used Microsoft’s GitHub Copilot completed tasks 56 percent faster than those who did not use the tool. Moreover, an empirical study conducted internally by McKinsey on software engineering teams found that those trained to use generative AI tools rapidly decreased the time required to generate and refactor code. Engineers also reported a better work experience, citing improvements in happiness, workflow, and job satisfaction.

Large technology companies are already marketing generative AI for software engineering, including GitHub Copilot, now integrated with OpenAI’s GPT-4, and Replit, used by over 20 million coders.

Research and development

The potential of generative AI in Research and Development (R&D) may not be as readily acknowledged as in other business functions, yet studies suggest that this technology could yield productivity benefits equivalent to 10 to 15 percent of total R&D expenses.

For instance, industries such as life sciences and chemicals have started leveraging generative AI foundation models in their R&D processes for generative design. These foundation models can generate candidate molecules, thereby accelerating the development of new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. However, the same principles can be employed in the design of many other products, including large-scale physical items and electrical circuits, among others.

While other generative design techniques have already unlocked some potential to implement AI in R&D, their costs and data requirements, such as using “traditional” machine learning, can restrict their usage. Pretrained foundation models that support generative AI, or models enhanced via fine-tuning, have wider application scopes compared to models optimized for a single task. Consequently, they can hasten time-to-market and expand the types of products to which generative design can be applied. However, foundation models lack the capabilities to assist with product design across all industries.

Besides the productivity gains from quickly generating candidate designs, generative design can also enhance the designs themselves. Here are some examples of the operational improvements generative AI could bring:

  • Enhanced design: Generative AI can assist product designers in reducing costs by selecting and using materials more efficiently. It can also optimize manufacturing designs, leading to cost reductions in logistics and production.
  • Improved product testing and quality: Using generative AI in generative design can result in a higher-quality product, increasing attractiveness and market appeal. Generative AI can help to decrease the testing time for complex systems and expedite trial phases involving customer testing through its ability to draft scenarios and profile testing candidates.

It also identified a new R&D use case for non-generative AI: deep learning surrogates, which can be combined with generative AI to produce even greater benefits. Integration of these technologies will necessitate the development of specific solutions, but the value could be considerable because deep learning surrogates have the potential to accelerate the testing of designs proposed by generative AI.

Retail and CPG

Generative AI holds immense potential for driving value in the retail and Consumer Packaged Goods (CPG) sector. It is estimated that the technology could enhance productivity by 1.2 to 2.0 percent of annual revenues, translating to an additional value of $400 billion to $660 billion. This enhancement could come from automating key functions such as customer service, marketing and sales, and inventory and supply chain management.

The retail and CPG industries have relied on technology for several decades. Traditional AI and advanced analytics have helped companies manage vast amounts of data across numerous SKUs, complex supply chains, warehousing networks, and multifaceted product categories. With highly customer-facing industries, generative AI can supplement existing AI capabilities. For example, generative AI can personalize offerings to optimize marketing and sales activities already managed by existing AI solutions. It also excels in data management, potentially supporting existing AI-driven pricing tools.

Some retail and CPG companies have already begun leveraging generative AI. For instance, technology can improve customer interaction by personalizing experiences based on individual preferences. Companies like Stitch Fix are experimenting with AI tools like DALL·E to suggest style choices based on customers’ color, fabric, and style preferences. Retailers can use generative AI to provide next-generation shopping experiences, gaining a significant competitive edge in an era where customers expect natural-language interfaces to select products.

In customer care, generative AI can be combined with existing AI tools to improve chatbot capabilities, enabling them to mimic human agents better. Automating repetitive tasks will allow human agents to focus on complex customer problems, resulting in improved customer satisfaction, increased traffic, and brand loyalty.

Generative AI also brings innovative capabilities to the creative process. It can help with copywriting for marketing and sales, brainstorming creative marketing ideas, speeding up consumer research, and accelerating content analysis and creation.

However, integrating generative AI in retail and CPG operations has certain considerations. The emergence of generative AI has increased the need to understand whether the generated content is fact-based or inferred, demanding a new level of quality control. Also, foundation models are a prime target for adversarial attacks, increasing potential security vulnerabilities and privacy risks.

To address these concerns, companies will need to strategically keep humans in the loop and prioritize security and privacy during any implementation. They will need to institute new quality checks for processes previously managed by humans, such as emails written by customer reps, and conduct more detailed quality checks on AI-assisted processes, such as product design. Thus, as the economic potential of generative AI unfolds, retail and CPG companies need to harness its capabilities strategically while managing the inherent risks.


Generative AI is poised to create significant value in the banking industry, potentially boosting productivity by 2.8 to 4.7 percent of the industry’s annual revenues, an additional $200 billion to $340 billion. Alongside this, it could enhance customer satisfaction, improve decision-making processes, uplift the employee experience, and mitigate risks by enhancing fraud and risk monitoring.

Banking has already experienced substantial benefits from existing AI applications in marketing and customer operations. Given the text-heavy nature of regulations and programming languages in the sector, generative AI can deliver additional benefits. This potential is further amplified by certain characteristics of the industry, such as sustained digitization efforts, large customer-facing workforces, stringent regulatory requirements, and the nature of being a white-collar industry.

Banks have already begun harnessing generative AI in their front lines and software activities. For instance, generative AI bots trained on proprietary knowledge can provide constant, in-depth technical support, helping frontline workers access data to improve customer interactions. Morgan Stanley is building an AI assistant with the same technology to help wealth managers swiftly access and synthesize answers from a massive internal knowledge base.

Generative AI can also significantly reduce back-office costs. Customer-facing chatbots could assess user requests and select the best service expert based on topic, level of difficulty, and customer type. Service professionals could use generative AI assistants to access all relevant information to address customer requests rapidly and instantly.

Generative AI tools are also beneficial for software development. They can draft code based on context, accelerate testing, optimize the integration and migration of legacy frameworks, and review code for defects and inefficiencies. This results in more robust, effective code.

Furthermore, generative AI can significantly streamline content generation by drawing on existing documents and data sets. It can create personalized marketing and sales content tailored to specific client profiles and histories. Also, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates, creating alerts for relevant shifts.

Pharmaceutical and medical

Generative AI holds the potential to significantly influence the pharmaceutical and medical-product industries, with an anticipated impact between $60 billion to $110 billion annually. This significant potential stems from the laborious and resource-intensive process of new drug discovery, where pharmaceutical companies spend approximately 20 percent of revenues on R&D, and new drug development takes around ten to 15 years on average. Therefore, enhancing the speed and quality of R&D can yield substantial value.

For instance, the lead identification stage in drug discovery involves identifying a molecule best suited to address the target for a potential new drug, which can take several months with traditional deep learning techniques. Generative AI and foundation models can expedite this process, completing it in just a few weeks.

Two key use cases for generative AI in the industry include improving the automation of preliminary screening and enhancing indication finding.

During the lead identification stage, scientists can employ foundation models to automate the preliminary screening of chemicals. They seek chemicals that will have specific effects on drug targets. The foundation models allow researchers to cluster similar experimental images with higher precision than traditional models, facilitating the selection of the most promising chemicals for further analysis.

Identifying and prioritizing new indications for a specific medication or treatment is critical in the indication-finding phase of drug discovery. Foundation models allow researchers to map and quantify clinical events and medical histories, establish relationships, and measure the similarity between patient cohorts and evidence-backed indications. This results in a prioritized list of indications with a higher probability of success in clinical trials due to their accurate matching with suitable patient groups.

Pharmaceutical companies that have used this approach report high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. Consequently, these drugs progress smoothly into Phase 3 trials, significantly accelerating drug development.

The ethical and social considerations and challenges of Generative AI

Generative AI brings along several ethical and social considerations and challenges, including:

  • Fairness: Generative AI models might unintentionally produce biased results because of imperfect training data or decisions made during their development.
  • Intellectual Property (IP): Training data and model outputs can pose significant IP challenges, possibly infringing on copyrighted, trademarked, or patented materials. Users of generative AI tools must understand the data used in training and how it’s utilized in the outputs.
  • Privacy: Privacy risks may occur if user-fed information is identifiable in model outputs. Generative AI might be exploited to create and spread malicious content, including disinformation, deepfakes, and hate speech.
  • Security: Cyber attackers could harness generative AI to increase the speed and sophistication of their attacks. Generative AI is also susceptible to manipulation, resulting in harmful outputs.
  • Explainability: Generative AI uses neural networks with billions of parameters, which poses challenges in explaining how a particular output is produced.
  • Reliability: Generative AI models can generate varying answers to the same prompts, which could hinder users from assessing the accuracy and reliability of the outputs.
  • Organizational impact: Generative AI may significantly affect workforces, potentially causing a disproportionately negative impact on specific groups and local communities.
  • Social and environmental impact: Developing and training generative AI models could lead to adverse social and environmental outcomes, including increased carbon emissions.
  • Hallucination: Generative AI models, like ChatGPT, can struggle when they lack sufficient information to provide meaningful responses, leading to the creation of plausible yet fictitious sources.
  • Bias: Generative AI might exhibit cultural, confirmation, and authority biases, which users need to be aware of when considering the reliability of the AI’s output.
  • Incomplete data: Even the latest models, like GPT-4, lack recent content in their training data, limiting their ability to generate content based on recent events.

Generative AI’s ethical, democratic, environmental, and social risks should be thoroughly considered. Ethically, it can generate a large volume of unverifiable information. Democratically, it can be exploited for mass disinformation or cyberattacks. Environmentally, it can contribute to increased carbon emissions due to high computational demands. Socially, it might render many professional roles obsolete. These multifaceted challenges underscore the importance of managing generative AI responsibly.

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Current trends of generative AI

Image reference – McKinsey

  • Prompts-based creation: Generative AI’s impressive applications in art, music, and natural language processing are causing a growing demand for skills in prompt engineering. Companies can transform content production by enhancing user experience via prompt-based creation tools. However, IT decision-makers must ensure data and information security while utilizing these tools.
  • API integration to enterprise applications: While the spotlight is currently on chat functionalities, APIs will increasingly simplify the integration of generative AI capabilities into enterprise applications. These APIs will empower all kinds of applications, ranging from mobile apps to enterprise software, to leverage generative AI for value addition. Tech giants such as Microsoft and Salesforce are already exploring innovative ways to integrate AI into their productivity and CRM apps.
  • Business process transformation: The continuous advancement of generative AI will likely lead to the automation or augmentation of daily tasks, enabling businesses to rethink their processes and extend the capabilities of their workforce. This evolution can give rise to novel business models and experiences that allow small businesses to appear bigger and large corporations to operate more nimbly.
  • Advancement in healthcare: Generative AI can potentially enhance patient outcomes and streamline tasks for healthcare professionals. It can digitalize medical documents for efficient data access, improve personalized medicine by organizing various medical and genetic information, and offer intelligent transcription to save time and simplify complex data. It can also boost patient engagement by offering personalized recommendations, medication reminders, and better symptom tracking.
  • Evolution of synthetic data: Improvements in generative AI technology can help businesses harness imperfect data, addressing privacy issues and regulations. Using generative AI in creating synthetic data can accelerate the development of new AI models, boost decision-making capabilities, and enhance organizational agility.
  • Optimized scenario planning: Generative AI can potentially improve large-scale macroeconomic or geopolitical events simulations. With ongoing supply chain disruptions causing long-lasting effects on organizations and the environment, better simulations of rare events could help mitigate their adverse impacts cost-effectively.
  • Reliability through hybrid models: The future of generative AI might lie in combining different models to counter the inaccuracies in large language models. Hybrid models fusing LLMs’ benefits with accurate narratives from symbolic AI can drive innovation, productivity, and efficiency, particularly in regulated industries.
  • Tailored generative applications: We can expect a surge in personalized generative applications that adapt to individual users’ preferences and behaviors. For instance, personalized learning or music applications can optimize content delivery based on a user’s history, mood, or learning style.
  • Domain-specific applications: Generative AI can provide tailored solutions for specific domains, like healthcare or customer service. Industry-specific insights and automation can significantly improve workflows. For IT decision-makers, the focus will shift towards identifying high-quality data for training purposes and enhancing operational and reputational safety.
  • Intuitive natural language interfaces: Generative AI is poised to foster the development of Natural Language Interfaces (NLIs), making system interactions more user-friendly. For instance, workers can interact with NLIs in a warehouse setting through headsets connected to an ERP system, reducing errors and boosting efficiency.


Generative AI stands at the forefront of technology, potentially redefining numerous facets of our existence. However, as with any growing technology, the path to its maturity comes with certain hurdles.

A key challenge lies in the vast datasets required for developing these models, alongside the substantial computational power necessary for processing such information. Additionally, the costs associated with training generative models, particularly large language models (LLMs), can be significant, posing a barrier to widespread accessibility.

Despite these challenges, the progress made in the field is undeniable. Studies indicate that while large language models have shown impressive results, smaller, targeted datasets still play a pivotal role in boosting LLM performance for domain-specific tasks. This approach could streamline the resource-intensive process associated with these models, making them more cost-effective and manageable.
As we progress further, it’s imperative to remain mindful of the security and safety implications of generative AI. Leading entities in the field are adopting human feedback mechanisms early in the model development process to ensure safer outcomes. Moreover, the emergence of open-source alternatives paves the way for increased access to next-generation LLM models. This democratization benefits practitioners and empowers independent scientists to push the boundaries of what’s possible with generative AI.

In conclusion, the current state of generative AI is filled with exciting possibilities, albeit accompanied by challenges. The industry’s concerted efforts in overcoming these hurdles promise a future where generative AI technology becomes an integral part of our everyday lives.

Ready to transform your business with generative AI? Contact LeewayHertz today and unlock the full potential of robust generative AI solutions tailored to meet your specific needs!

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Author’s Bio


Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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