Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.

1. THE CURRENT STATE OF
GENERATIVE AI: A
COMPREHENSIVE OVERVIEW
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We are entering an exciting new era in arti몭cial 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
 

2. leave us in awe. This remarkable technological advancement is not just
science 몭ction; 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 pro몭ciency 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 intensi몭es 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 몭elds such as
animation, gaming, art, cinema, and architecture are experiencing profound
changes, spurred on by powerful text-to-image programs like DALL-E, Stable
Di몭usion, 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 signi몭cant turning point was reached,
marking a pivotal moment in the history of arti몭cial 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

3. 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 Adversarial Networks (GANs)
Transformer-based models
The evolution of generative AI and its current state
Historical context of generative AI development
Major achievements and milestones of generative AI
Where do we currently stand in generative AI research and development?
The state of Large Language Models (LLMs)
OpenAI models
Google’s GenAI foundation models
DeepMind’s Chinchilla model
Meta’s LlaMa models
The Megatron Turing model by Microsoft & Nvidia
GPT-Neo models by Eleuther
Hardware and cloud platforms transformation
How is generative AI explored in other modalities?
How is generative AI driving value across major industries?
Customer operations
Marketing and Sales
Software engineering
Research and development
Retail and CPG
Banking
Pharmaceutical and medical
The ethical and social considerations and challenges of generative AI
Current trends of generative AI

4. Understanding generative AI
Generative AI refers to a branch of arti몭cial 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)
Generator
Random input Real
examples
Real
examples
Real
examples
At the core of generative AI, we 몭nd 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

5. “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
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

6. 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 di몭erent data elements
within a sequence interact with and in몭uence each other. Additionally, the
ability of transformers to process multiple sequences in parallel signi몭cantly
accelerates the training phase, further enhancing their e몭ectiveness.
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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 몭rst-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.

7. The foundation for contemporary generative AI lies in deep learning, a
concept dating back to the 1950s. Despite its early inception, the 몭eld of
deep learning experienced a slowdown until the 80s and 90s, when it
underwent a resurgence powered by the introduction of Arti몭cial Neural
Networks (ANNs) and backpropagation algorithms. The advent of the new
millennium brought a signi몭cant leap in data availability and computational
prowess, turning deep learning from theory to practice.
The real turning point arrived in 2012 when Geo몭rey 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 classi몭cation 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 re몭nements 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, di몭usion models for
image generation, and various 몭ow model architectures for audio, image,
and video. Parallel advancements in the 몭eld 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

8. 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 di몭usion-based image services like MidJourney,
Dall-E 2, Stable Di몭usion, 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 signi몭cantly 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 signi몭cant
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 signi몭cant 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

9. 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 o몭ered 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 signi몭cant and relevant dataset was arduous. The introduction of BERT
and the 몭rst 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
“몭ne-tuning,” which involved adapting large models to speci몭c tasks or
smaller datasets, thus improving performance in a speci몭c 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 signi몭cant 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 bene몭cial aspects of Instruction Tuning was
aligning these models with human values, thereby preventing the
generation of undesired or potentially dangerous content. OpenAI
implemented a speci몭c technique for instruction tuning known as
Reinforcement Learning with Human Feedback (RLHF), wherein human
responses trained the model. Further leveraging Instruction Tuning,

10. 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 di몭erent actors, such as providers of
LLM APIs like OpenAI, Cohere, and Anthropic. On the consumer end,
products like ChatGPT and Bing o몭er access to LLMs, simplifying interaction
with these advanced models.
The speed of innovation in this 몭eld 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 di몭erent 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
bene몭ts of democratized access to this technology, what is the future of
open-source LLMs?

11. Here is a table showing the leading LLM models:
Company Model Release Date
Meta LLaMA February 2023
EleutherAI NeoX February 2022
Meta Galactica November 2022
Cohere Cohere XLarge February 2022
Anthropic Anthropic­LM v4­s3 April 2022
Google Google LaMDA May 2021
Google GLaM (Mixture
of Experts)
December 2021
Google Deepmind DeepMind Gopher December 2021
Meta OPT May 2022
Open AI GPT­3 June 2020
A121 A121 Jurassic­1 August 2021
BigScience Bloom August 2022
Baidu Ernie 3.0 Titan December 2021
Meta LLaMA February 2023
Google PaLM April 2022
Open AI GPT­4 March 2023
Google Deepmind DeepMind Chinchilla March 2022
Mosaic MosaicML GPT September 2022
Nvidia & Microsoft MT­NLG October 2021
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Our deep domain knowledge and technical expertise
allow us to develop e몭cient and e몭ective generative

12. AI solutions tailored to your unique needs.
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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
classi몭cation
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 nonpro몭t entity in 2015
in San Francisco, OpenAI aimed to create Arti몭cial 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

13. 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-pro몭t company.
The decision stipulated that investors’ earnings would be limited to a 몭xed
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 signi몭cant portion of this amount. Since then,
OpenAI has raised approximately $13 billion, a fundraising e몭ort led by
Microsoft. This partnership with Microsoft facilitated the development of an
enhanced version of Bing and a more interactive suite of Microsoft O몭ce
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
pro몭ciency 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 signi몭cant 82% reduction in the generation of
inappropriate content.
Google’s GenAI foundation models

14. Google AI, the scienti몭c research division under Google, has been at the
forefront of promising advancements in machine learning. Its most
signi몭cant 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 몭rst
“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,
signi몭cantly 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

15. each training cycle. This con몭guration 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,
e몭ectively 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 classi몭cation 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 speci몭cally 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 몭ne-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

16. 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 arti몭cial intelligence research lab
established in 2010, came under the ownership of Alphabet Inc. in 2015,
following its acquisition by Google in 2014. A signi몭cant 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 pro몭cient software in Go
and Shogi games using reinforcement learning techniques. In 2020,
DeepMind’s AlphaFold took signi몭cant 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 uni몭ed 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 signi몭cant revelation in
the Chinchilla paper was that prior LLMs had been trained on insu몭cient
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 e몭cient models with a smaller parameter
size, o몭ering huge bene몭ts for inference costs. These costs, associated with
operating and using the 몭nished model, scale with parameter size.

17. 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 Arti몭cial Intelligence Research
(FAIR), is an arti몭cial 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 signi몭cant milestone in 2018 was the release of PyText, an
open-source modeling framework designed speci몭cally 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-a몭liated 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.

18. 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 di몭erent 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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Our deep domain knowledge and technical expertise
allow us to develop e몭cient and e몭ective generative
AI solutions tailored to your unique needs.
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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

19. demonstrates the company’s relentless pursuit of innovation in AI. To
promote accessibility and practical use, Nvidia o몭ers 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, o몭ering 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-pro몭t research laboratory specializing in arti몭cial
intelligence. The organization has gained recognition in the 몭eld 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-e몭ective
computational resources. February 2022 saw the release of the GPT-NeoX-

20. 20b model, becoming the largest open-source language model at the time. In
January 2023, EleutherAI formalized its status as a non-pro몭t research
institute.
GPT-NeoX-20B, EleutherAI’s 몭agship 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 몭ve 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,
o몭ering a deviation from learned positional embeddings commonly found in
GPT models.
Hardware and cloud platforms transformation
The advent of generative AI has considerably in몭uenced 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 speci몭cally 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-speci몭c integrated circuits (ASICs) – has
played a critical role in this transformation. These TPUs, developed
speci몭cally for e몭cient machine learning tasks, are closely integrated with
TensorFlow, Google’s machine learning framework. Google Cloud Platform

21. 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) o몭er potent GPU-equipped
instances like the Amazon Elastic Compute Cloud (EC2) P3 instances. They
are armed with Nvidia V100 GPUs, o몭ering 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, exempli몭es this. DALL-E can
generate unique, high-resolution images and manipulate existing ones by
utilizing a modi몭ed 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

22. independent research lab, allows users to generate images through
Discord bot commands, enhancing user interactivity. The Stable Di몭usion
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 o몭ers a range
of user-friendly features.
Audio generation: OpenAI’s Whisper, Google’s AudioLM, and Meta’s
AudioGen are signi몭cant 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 몭les.
Search engines: Neeva and You.com 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. You.com 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 o몭ers context-sensitive solutions,
catering to the speci몭c needs of the coding environment. Impressively,
GitHub Copilot can generate functional code across a variety of
programming languages, e몭ectively 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

23. transformative potential across industries. As these technologies continue
to evolve, they will play an increasingly crucial role in shaping our digital
future.
How is generative AI driving value across
major industries?
Total, %
of
Industry
Revenue
Administrative &
Professional Services
0.9­1.4 150­250
Total,
$ Billion
760­
1,200
340­
470
230­
420
580­
1,200
280­
530
180­
260
120­
260
40­
50
60­
90
Advance Electronics
& Semiconductors
100­170
1.3­2.3
Advanced Manufacturing 170­290
1.4­2.4
Agriculture 40­70
0.6­1.0
Banking 200­340
2.8­4.7
Basic Materials 120­200
0.7­1.2
Chemical 80­140
0.8­1.3
Construction 90­150
0.7­1.2
Consumer Packaged Goods 160­270
1.4­2.3
Education 120­230
2.2­4.0
Energy 150­240
1.0­1.6
Healthcare 150­250
1.8­3.2
Sign Tech 240­460
4.8­9.3
Insurance 50­70
1.8­2.8
Media and Entertainment 60­110
1.5­2.6
Pharmaceuticals &
Medical Products
60­110
2.6­4.5
Public and Social Sector 70­110
0.5­0.9
Real Estate 110­180
1.0­1.7
Retail 240­390
1.2­1.9
Marketing
&
Sales
Customer
Operations
Product
&
R&D
Software
Engineering
Supply
Chain
&
Operations
Risk
&
Legal
Strategy
&
Finance
Corporate
IT
2
Talent
&
Organization
Low Impact High Impact

24. 2,600­4,400
Telecommunications 60­100
2.3­3.7
Travel, Transport, &
Logistics
180­300
1.2­2.0
LeewayHertz
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
pro몭ciency through digital self-service and skill augmentation. The
technology has already found a 몭rm 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 speci몭c 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 e몭ciency 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 몭rst contact: Generative AI can promptly access data
speci몭c to a customer, enabling a human customer service representative

25. to address queries and resolve issues more e몭ectively during the 몭rst
interaction.
Reduced response time: Generative AI can decrease the time a human
sales representative takes to respond to a customer by o몭ering 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 o몭ers 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 signi몭cant 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 e몭ects 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.
Partner with LeewayHertz for robust generative AI
solutions
Our deep domain knowledge and technical expertise
allow us to develop e몭cient and e몭ective generative
AI solutions tailored to your unique needs.
Learn More
Marketing and sales

26. 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 speci몭c 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 su몭cient 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:
E몭cient and e몭ective content creation: Generative AI can signi몭cantly
expedite the ideation and content drafting process, saving time and e몭ort.
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 uni몭ed 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

27. 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 pro몭les.
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 signi몭cantly 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 pro몭les from structured and
unstructured data, suggesting actions to sta몭 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 pro몭les. 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.

28. 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 signi몭cantly 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 signi몭cant 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

29. 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, work몭ow, 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 bene몭ts 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 몭ne-
tuning, have wider application scopes compared to models optimized for a
single task. Consequently, they can hasten time-to-market and expand the

30. 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 e몭ciently. 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 pro몭le testing candidates.
It also identi몭ed a new R&D use case for non-generative AI: deep learning
surrogates, which can be combined with generative AI to produce even
greater bene몭ts. Integration of these technologies will necessitate the
development of speci몭c 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.

31. 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 o몭erings 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 signi몭cant
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
tra몭c, 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

32. 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.
Banking
Generative AI is poised to create signi몭cant 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 bene몭ts 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 bene몭ts. This potential is further ampli몭ed by
certain characteristics of the industry, such as sustained digitization e몭orts,
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

33. managers swiftly access and synthesize answers from a massive internal
knowledge base.
Generative AI can also signi몭cantly reduce back-o몭ce costs. Customer-facing
chatbots could assess user requests and select the best service expert based
on topic, level of di몭culty, 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 bene몭cial 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
ine몭ciencies. This results in more robust, e몭ective code.
Furthermore, generative AI can signi몭cantly streamline content generation by
drawing on existing documents and data sets. It can create personalized
marketing and sales content tailored to speci몭c client pro몭les 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 signi몭cantly in몭uence the
pharmaceutical and medical-product industries, with an anticipated impact
between $60 billion to $110 billion annually. This signi몭cant 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 identi몭cation 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

34. 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 몭nding.
During the lead identi몭cation stage, scientists can employ foundation models
to automate the preliminary screening of chemicals. They seek chemicals
that will have speci몭c e몭ects 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 speci몭c medication or
treatment is critical in the indication-몭nding 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 몭ve indications recommended by a
foundation model for a tested drug. Consequently, these drugs progress
smoothly into Phase 3 trials, signi몭cantly 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.

35. Intellectual Property (IP): Training data and model outputs can pose
signi몭cant 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 identi몭able 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 signi몭cantly a몭ect workforces,
potentially causing a disproportionately negative impact on speci몭c 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 su몭cient information to provide meaningful responses, leading to the
creation of plausible yet 몭ctitious sources.
Bias: Generative AI might exhibit cultural, con몭rmation, 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.

36. Generative AI’s ethical, democratic, environmental, and social risks should be
thoroughly considered. Ethically, it can generate a large volume of
unveri몭able 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.
Partner with LeewayHertz for robust generative AI
solutions
Our deep domain knowledge and technical expertise
allow us to develop e몭cient and e몭ective generative
AI solutions tailored to your unique needs.
Learn More
Current trends of generative AI
Coordination with Multiple Agents
Estimates Post­Recent
Median Top Quartile Line Represents Range
Of Export Estimates
Top Quartile
Median
Estimates Pre­Generative AI (2017)1
Estimates AI Developments (2023)1
2010 2020 2030 2040 2050 2060 2070 2080
Creativity
Logical Reasoning & Problem
Solving
Natural­Language Generation
Natural­Language Understanding
Output Articulation &
Presentation
Generating Novel Patterns
& Categories
Sensory Perception

37. Sensory Perception
Social & Emotional Output
Social & Emotional Reasoning
Social & Emotional Sensing
LeewayHertz
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 e몭cient data access, improve
personalized medicine by organizing various medical and genetic
information, and o몭er intelligent transcription to save time and simplify

38. complex data. It can also boost patient engagement by o몭ering
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 e몭ects on organizations and
the environment, better simulations of rare events could help mitigate
their adverse impacts cost-e몭ectively.
Reliability through hybrid models: The future of generative AI might lie in
combining di몭erent models to counter the inaccuracies in large language
models. Hybrid models fusing LLMs’ bene몭ts with accurate narratives from
symbolic AI can drive innovation, productivity, and e몭ciency, 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-speci몭c applications: Generative AI can provide tailored solutions
for speci몭c domains, like healthcare or customer service. Industry-speci몭c
insights and automation can signi몭cantly improve work몭ows. 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,

39. reducing errors and boosting e몭ciency.
Endnote
Generative AI stands at the forefront of technology, potentially rede몭ning
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
signi몭cant, posing a barrier to widespread accessibility.
Despite these challenges, the progress made in the 몭eld 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-speci몭c tasks. This approach could streamline the
resource-intensive process associated with these models, making them more
cost-e몭ective 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 몭eld 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 bene몭ts 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 몭lled with exciting
possibilities, albeit accompanied by challenges. The industry’s concerted
e몭orts 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

40. 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 speci몭c needs!
Author’s Bio
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.
Write to Akash
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