neuroAI E-Book

A. K. Pradeep

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 610

Veröffentlichungsjahr: 2024

Beschreibung

It is the most powerful revolution of this century.

Neuroscience-powered GenAi enables massive impact on everything from medicine to marketing, entertainment to education, flavors to fragrances, and much more. Simply by blending cutting edge neuroscience with bleeding edge GenAi. Put humanity back at the center of GenAi.

neuroAi: Winning the Minds of Consumers with Neuroscience Powered GenAi is the master guide for everyone seeking to understand this breakthrough technology; what it is, how it works, and most especially how to put it to work for competitive advantage in the marketplace.

neuroAi combines learnings from advanced neuroscience with deep GenAi expertise and practical how-to's. This is a 'force multiplier,' enabling readers to gain the fullest understanding of how to apply neuroscience-powered GenAi to appeal most effectively to the hidden driver of 95% of consumer behavior: the non-conscious mind. Innovators, creatives, and corporate executives now have a blueprint of how to unleash neuroAi at scale in the enterprise. The focus is on “Top Line Growth”—build and grow revenues while exciting and winning consumers.

Written by Dr. A. K. Pradeep and his team of experts at Sensori.ai, the world's only firm combining advanced neuroscience learnings with GenAi, neuroAi features a primer on neuroscience, GenAi, and the core memory structures and functions of the human brain. Dr. Pradeep's original book, The Buying Brain, broke new ground by bringing neuroscience into marketing. neuroAi takes continues this innovative journey even farther now by combining advanced neuroscience with GenAi.

The book explores key topics including:

• How the non-conscious mind interacts with GenAi to trigger the most relevant and impactful consumer responses
• What are key learnings from teen brains, boomer brains, mommy brains, middle age brains that GenAi must be aware of
• How activating desireGPT, the brain's desire framework, strongly drives purchase intent and brand loyalty
• How TV shows, movies, and music can achieve higher Ratings by applying neuroscience powered GenAi to writing Scripts and Dialogs
• How to create Fragrance, and Flavors using neuroAi
• How a wide range of consumer product categories worldwide are applying neuroscience powered GenAi to foster innovation, spur sales, and build brands
• How to build scalable capability in neuroAi within the enterprise

For business leaders and all who seek expert insight and practical guidance on how to harness this astounding technology with maximum effect for business and personal success, neuroAi serves as an inspiring and accessible resource for successful marketing in the Age of the Machine.

Leseprobe

DR. A.K. PRADEEP · DR. ANIRUDH ACHARYA

DR. RAJAT CHAKRAVARTY · RATNAKAR DEV

neuroAI

Winning the Minds of Consumers with Neuroscience‐Powered GenAI

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Library of Congress Cataloging‐in‐Publication Data:

Names: Pradeep, A. K., 1963‐ author. | Chakravarty, Rajat, author. | Acharya, Anirudh, author. | Dev, Ratnakar, author.Title: neuroAi : winning the minds of consumers with neuroscience powered GenAi / Dr. A. K. Pradeep, Dr. Rajat Chakravarty, Dr. Anirudh Acharya, Ratnakar Dev.Description: Hoboken, New Jersey : Wiley, [2024] | Includes bibliographical references and index.Identifiers: LCCN 2024020698 (print) | LCCN 2024020699 (ebook) | ISBN 9781394261963 (hardback) | ISBN 9781394261987 (adobe pdf) | ISBN 9781394261970 (epub)Subjects: LCSH: Artificial intelligence—Marketing applications. | Consumers—Psychology.Classification: LCC HF5415.125 .P73 2024 (print) | LCC HF5415.125 (ebook) | DDC 658.8/342028563—dc23/eng/20240601LC record available at https://lccn.loc.gov/2024020698LC ebook record available at https://lccn.loc.gov/2024020699

Cover Design: WileyCover Image: © Africa Studio/Adobe Stock

Dr. A. K. Pradeep dedicates this book to his three adult kids – Alexis Pracar, Shane Pracar, and Devin Pracar – for keeping him curious, humble, and honest, to his Dad – Professor Anantha Krishnan – for his unwavering support, and to his Mom – Saraswathi as she sleeps on the waves on Stinson Beach, California. He thanks his sister, Dr. A. K. Bhargavi, and his brother, A. K. Prasad, for their unwavering support.

Dr. Anirudh Acharya dedicates this book to the wonderfully clever and dedicated colleagues he has had the pleasure of working with. Thank you for making every project an experience. This book would not have been possible without the love and patience of Reha.

Dr. Rajat Chakravarty dedicates this book to his wife, Dr. Torsa Ghosal – Here's to all the quirky moments that inspired these pages. Without you, this book would still be well‐written but oh so dull.

Ratnakar Dev dedicates this book to Malhar and Arya, the future, and Sonali for all the support.

All authors together as a group dedicate this book to our dear friend Tom Robbins, for his infectious enthusiasm, joy, hard work, and sheer brilliance.

Introduction

There are moments in human history that are as rare as they are revolutionary. They change the very nature of life, largely for the good of all humankind.

While these historical moments are few, they create an outsized and lasting impact. Witness the printing press … the Industrial Revolution … the discovery of cures for diseases such as cancer, and several more.

We are witnessing just such a singular moment now: the explosive creation of generative Ai (GenAi). Never before has a technology swept across the globe so quickly, transforming virtually every aspect of life, from commerce to medicine to education, entertainment, governance, and even thought itself.

This chapter serves as a doorway through which you can pass, to encounter and understand the full sweep of this technology, and grasp its import and promise.

But this book introduces an even larger and more impactful event: the integration of GenAi with neuroscience. The implications of this unique combination are, in a very real sense, limitless.

Understanding how the human brain works will give you a new and insightful view into life and all its component aspects. Understanding GenAi, and even more specifically the interplay between neuroscience and AI, will take you well into the new world which we are all inhabiting more and more every day.

But this chapter, and this book, are much more than an elite lecture on generative Ai and neuroscience. The parallel purpose of this book, and each of its chapters, is to equip you with the knowledge and the tools that this revolutionary invention offers, that which you can use in your career and your life.

Given the proliferation of GenAi, it is important to ask the question, “if everyone has GenAi, how does it create a differentiator? Does generative Ai rapidly become generic Ai?”

We posit that the intelligent blending of neuroscience and GenAi creates true marketplace differentiators, and this the natural evolution of GenAi to neuroAi.

Welcome to the natural evolution of GenAi – neuroAi. It is natural to love the pretty pictures generated by GenAi, and to fall in love with the prose it generates, and falsely believe that humans are no longer necessary. This is just an arrogant, and erroneous premise, and this book refutes that. We believe that integrating the painstaking work of neuroscientists who have spent years deciphering the human brain is critical to the success of GenAi. We also assert that the painstaking work done by market researchers who have sought to understand the motivations of customers' needs to be humbly integrated into GenAi. The breakthrough work of cognitive scientists and psychologists in creating breakthroughs in understanding unconscious motivations cannot be brushed aside. Last, but not least, the thousands of professors, graduate students, and undergraduates who have submitted themselves to surveys, batteries of psychological assessments, EEG recordings, and fMRI analyses need to be acknowledged and form the guardrails and guiding principles of GenAi.

Humility in honoring and integrating these neuroscientific, psychological, and market insights findings into GenAi, does not water it down, but rather powers it to truly realize its full potential – this is the premise of this book.

Our approach to GenAi, furthermore, does NOT eliminate humans – it effectively carves a role for humans in an enterprise. We offer the following paradigm to those who seek to unleash the power of GenAi in an enterprise. If you walk in with the assumption that the single value of GenAi will be to bring your costs down by eliminating a chunk of your workforce – you will be proven wrong, and more so you will embark on a detrimental path.

This book is about top‐line growth – how to create products that truly mesmerize and delight consumers. How to create services that blow their mind. How to inspire, challenge, and fulfill our deepest sensory cravings while growing revenues. The focus of this book is not on saving your way to prosperity by eliminating people and slimming costs through the use of neuroAi. We want you to be inspired to grow your revenues and win the mindshare of consumers. We therefore urge you to consider the following guidelines:

Verify GenAi's understanding:

Use humans to verify if GenAi has understood what it has been asked to do perfectly. Humans migrate to verify and validate that the algorithm understands what it has been asked to do. In particular, confirm what its parameters, boundaries, and constraints are and how precisely it must deliver what is expected of it.

Challenge and verify GenAi reasoning:

Use humans to verify and validate the reasoning GenAi has used to create its results – look for flaws and proof points that no constraints were violated and confirm that the output conforms to what was requested.

Push GenAi creative domains:

Use humans to challenge and gently push the boundaries of GenAi and selectively explore newer areas that it was not quite programmed to explore. This is a place that humans excel at – extrapolation – and what better way to explore than with a machine that “knows it all.”

Transfer heuristics to GenAi:

Use humans to selectively transfer their “heuristics,” “rules of thumb,” and “life experience” as observations for GenAi. This preserves the expertise in an enterprise in a meaningful way, by embedding it into algorithms so it does not walk out the door when an employee leaves.

Create institutional memory:

Use GenAi to create an enterprise memory of every single creative query, attempt, and conclusion reached by humans in the enterprise as the collective “creative treasure” for the next generation to use – so we never forget the lessons learned.

Our approach in this book is to give you the excitement of humanity embracing GenAi as a tool that honors our humanity, values it, and builds a better future for us with our understanding of ourselves.

Engaging Brains and Brands

Journey into the workings of the human mind and you'll find a labyrinth as complex as any plotline crafted by an avant‐garde filmmaker. Our brains are wired for story, with each twist and turn of a narrative sparking connections across the convoluted neural networks. Now, imagine harnessing that power – not to perplex but to persuade. For marketers, this is not just a feat of creativity; it is a call to delve into the rich realm of neuroscience, because the nonconscious mind drives 95%+ of our decision‐making (Kramer & Block 2011).

Here's where our tale takes a scientific spin: When we listen to stories, our brains light up not just in the language‐processing regions, but across areas tasked with deciphering human emotions, motives, and experiences. This means the stories engulf us, resonating on a deeply personal level. An effective marketer sees not merely an opportunity but a responsibility to tell a tale that matters.

But how to hold that treasured attention? Contemporary evidence suggests that neural engagement peaks when humans can draw parallels between the story and their own lives, triggering a sense of personal relevance. As the science progresses, so does our understanding that the most compelling marketing narratives are those in which consumers can see themselves playing a starring role.

Transitioning these neuroscientific revelations into a practical marketing strategy invokes the delicate art of bridging dimensions: from firing synapses to firing up sales. The story must envelop the product in such a way that potential customers feel it was crafted just for them. Personalized marketing has shifted from being just a trend to becoming a necessity, justified by neuroscience (Montgomery & Smith 2009). It is important to teach GenAi that stories matter, and that is how humans perceive reality. Algorithms must learn how to create narratives that captivate attention, and drive resonance with emotion.

Employ subtle humor and watch as the guardrails of skepticism lower. Laughter, after all, is a universal language that the brain interprets as a signal of trust and camaraderie. When humor intertwines with a product's story, it can form an irresistible combination that charms the amygdala, the brain's bastion of emotion. Can algorithms really create humor? The breakthroughs in large language models (LLM) today make many things possible. Will they create the next Larry David or Dave Chappelle? That remains to be seen. But we urge you, dear reader, not to Curb your Enthusiasm.

Embark further down this neuroAi path, and we encounter the crucial element of sensory marketing. Research has shown the effectiveness of targeting multiple senses, cementing brand memories as undeniably as an unforgettable jingle or a haunting perfume. By engaging more senses, you stake a stronger claim in consumers' “neural real estate.” GenAi algorithms need to understand that neurological resonance through multisensory stimuli are important for the brain. When authors begin a story with the sounds of a thunderstorm and smell of damp earth, they really engage neurological resonance.

In the world of experiences, where services and goods are staged like theatrical productions, we must consider the enveloping plot. Consumers are not mere observers but are part of the act. By curating immersive experiences, marketers are directors, orchestrating a play in which the product is a character, and the consumer the protagonist, deeply involved in the unfolding drama.

Let's not forget the power of visuals, which reign supreme in the kingdom of cognition. The human brain processes images quicker than words – an evolutionary trace to the days when survival hinged on instant recognition. In a market saturated with text, a well‐crafted image resonates in a split second, striking the electric chords of our visual cortex. The brain has a complex set of rules developed over thousands of years on how best to parse visual imagery. The algorithms of GenAi must understand these rules to generate compelling and persuasive imagery, packaging designs, and point of sale materials.

Consider a product launch as a sort of first date. The initial impression matters greatly, and familiarity breeds affinity. Repetition in branding is not merely about consistency; it's about creating a rhythm that the brain grows fond of, building a comfort zone within which loyalty blossoms.

To recap as we conclude this narrative: Keep the tales woven, the humor clever, the senses captivated, and the visuals arresting. Make the mundane into the magical, for in the realm of neuroAi, the line between science and story is effectively blurred.

Driving Innovation by Mimicking Human Ingenuity

Human ingenuity is the masterful art of problem‐solving, often breaking through established norms and practices. In neuroscience, our understanding of human behavior and the brain's mechanisms plays a pivotal role in advancing this art.

Neural mechanisms function as engines of cognition – intricate and profound, much like the awe‐inspiring mechanics inside a high‐performance automobile. And just as a sophisticated car demands a skilled driver, the complexities of the human brain require adept navigation. Current research into the frontal lobes, the "drivers" of executive functions, unveils the delicate interplay between cognition and control.

As we delve deeper into the neural realm, we recognize patterns and comprehend that the prefrontal cortex, akin to a dashboard laden with control buttons, regulates our thoughts, actions, and emotions with precision. The mastery lies not only in the impeccable design of this neural dashboard but also in the efficiency with which we use it to conduct our daily lives.

The concept of neuroplasticity presents the brain's remarkable ability to adapt – to learn how to drive itself more effectively over time, akin to refining one's driving skills continuously. This malleability is the cerebral counterpart to upgrades in vehicular technology, offering us the capacity to become better pilots of our mental faculties, constantly adjusting to the new routes of experiences and knowledge.

What really is innovation? Distilling and extracting lessons, ideas and breakthroughs from one field and creating their rich analogs in another field is indeed innovation. Scientific breakthroughs, product and service breakthroughs follow this paradigm. GenAi looks for breakthroughs in one area and thoughtfully asks what the analogs in another area are – this creates foundational breakthroughs in product innovation. It extracts breakthroughs and trends in one area and asks two fundamental questions:

Can these breakthroughs and trends be directly transferred into another area?

What are the analogs of this area that can be brought to the forefront into the other area?

These truly become the foundational paradigms of inspiration and innovation. The power of GenAi is the ability to perform this creative transfer across categories in an unceasing and unrelenting manner. Algorithms that create product innovation must take these evolutionary paradigms into creating product innovations across categories.

Protect the enterprise continuously by filing for patents and protections using the ability of GenAi to take a generated idea, and rapidly create a provisional patent. Imagine every brainstorming session coming up, not only with ideas for products, but a provisional patent accompanying the idea as well. The ability to create innovations, and protect them in real time is a powerful new use of GenAi.

Brains, Brands, and Bots

Most chatbots are boring, burdensome, and frustrating.

Most learning apps lose customers after two attempts or about two days of app usage.

Most healthcare and patient condition management applications are abandoned after just a few tries.

Billions of dollars are wasted in creating cold, unimaginative, unhelpful human machine interactions that rarely deliver on the promised benefits.

The burgeoning field of neuroscience offers an exquisite maze of knowledge, the understanding of which becomes a linchpin to building bridges between human cognition and artificial intelligence.

We begin with a dive into the unseen; the nonconscious processes that sculpt our interactions with the world. It's within this labyrinthine nonconscious that much of our decision‐making takes center stage. Our neural processors handle a myriad of information, silently steering our likes, dislikes, and ultimately the choices we make, without the presence of conscious thought.

How do we get GenAi to understand how to seduce and persuade the nonconscious of the human mind? What data might it rely on to understand the nonconscious? These vexing puzzles have been solved well by generations of neuroscientists and marketers. We now know that music persuades the nonconscious mind, and so does reinforced entertainment – explaining reach, reinforcement, and effectiveness measures of content.

These nonconscious processes are pivotal when it comes to persuasion. Subtle cues, processed below the level of awareness, can have profound impacts on our choices. Understanding these subtle triggers, and the neural algorithms they set off, is key to creating messages that motivate and delight.

When it comes to coupling this intricate knowledge with the power of generative Ai, we open a portal of possibilities. AI, in its vast processing capabilities, can dissect, learn, and mimic the neural patterns that lead to these positive states of human experience. With AI as a partner, creative minds gain a powerful ally in their quest for innovation, engagement, and memorability.

The practical applications of this knowledge are manifold. Consumer experiences can be designed to be habit forming, to be delightful and truly exciting. Gone are the days of learning apps that fail and cold chatbots that bore. GenAi coupled with learning applications, and habit forming applications create newer possibilities for an informed healthier world. Passing the Turing test should not turn into passing the Boring test. Chatbots that engage, inspire, and motivate can create newer commercial opportunities.

Entertainment and sensory delights become tailored experiences that can adapt in real time to the neural feedback of the audience. Here, AI becomes an interactive companion, responding to the nonconscious cues of the participants, creating a symbiotic relationship between human and machine that elevates each performance to a new pinnacle.

The fusion of neuroscience and GenAi holds the keys to personalizing our world in ways we've only begun to understand. As this book unfolds, we merge deep scientific understanding with the spark of creativity and embark on a journey that reinvents how we think about the world of neuro‐commerce.

The Artistry of AI‐Augmented Creativity

One cannot help but marvel at the intricate interplay of neurons and synapses within the human brain, a duet that orchestrates our every thought, feeling, and creative burst. Our cerebral cortex permits us the luxury of abstract thought and ingenuity. With roughly 86 billion neurons, each functioning as a nexus of possibility, the potential for creative and intellectual achievements is boundless.

Indeed, the creative process is deeply rooted in the unpredictable yet harmonious interplay between various neural networks. The brain's default mode network (DMN), often active when we daydream or engage in introspection, plays a crucial role in generating novel ideas. It's within these moments of apparent rest that the seeds of creativity find fertile ground.

Transitioning into the world of augmentation, we find that generative Ai amplifies these capabilities. It adds a layer of complexity and depth. Machines learn and suggest, challenge, and inspire, functioning much like a muse to an artist, spurring human ingenuity to new heights.

As we harmonize our neural compositions with the algorithms of AI, the combination becomes a catalyst for creativity. Take the world of design. GenAi can sift through terabytes of visual art at superhuman speeds to inspire designers with themes, layouts, imagery and color palettes that might not surface in the isolation of the human mind. Algorithms can now create art “in the style of” that becomes a spectacular minimalistic extraction of what constitutes style. The ability to win with packaging in the aisle can change brand perception for both consumers and retailers.

Beyond suggestion, AI is also a co‐creator. In music, algorithms analyze patterns in rhythm and melody, generating compositions that can evoke the deepest of human emotions – a blend of electronic and organic that enchants both creators and connoisseurs.

The beauty of AI collaboration lies in personalization. Marketing campaigns, once a one‐size‐fits‐all suit, can now be tailored with precision only achievable by understanding and adapting to individual neural preferences. The result? A message that feels like it was crafted just for you, because, in a way, it was.

AI augments the creative process by introducing diversity and reducing the echo chamber effect. Content generation becomes less about rehashed ideas and more about unique narratives that resonate and connect. AI doesn't stifle creativity; it propels it, making laughter more heartfelt and surprises more delightful.

So, as we embark on this AI‐assisted odyssey, let us remember that our neural narrators are not being silenced; they are being amplified. The generative prowess of AI serves as a powerful tool to refine and extend the narratives we wish to tell, painting our stories with a richer palette. AI is not the author of our tale; it is our sophisticated pen.

In essence, AI is a celebration, not a suppression, of our creative spirit. It stands as a testament to our ingenuity, a mirror that reflects our own cerebral prowess, stunningly amplified.

Building neuroAi Capability in the Enterprise Fast – Note for Leaders

The alliance between humans and machines is grounded in this principle. As we frame the context for our AI counterparts, the human brain finely hones the generative processes through strategic, creative prompts, guiding the technology akin to shaping clay on a pottery wheel, where each touch can alter the output. Thus, human involvement remains essential, ensuring the AI's extraordinary potential is channeled into products or solutions with meaningful applications.

When human touch converges with AI's capabilities, the outcome can often surpass what each could achieve in isolation. Consider the human‐Doppler effect where, just as a siren's pitch varies as it passes by, the human contribution dynamically adjusts the direction and intensity of the AI's work based on real‐time feedback and intuition.

Let's not forget that human creativity is not a static construct but a dynamic flow that often involves flashes of serendipity and leaps of imagination. It is these very facets that we entrust to our silicon partners.

Translating these neural principles into business strategizing, we find that human involvement with AI mirrors the core skills of any great CEO: anticipation, adaptation, and improvisation. CEOs often have to steer the company ship through turbulent waters – a knack equally required when calibrating AI to hit the sweet spot between ingenuity and practicality.

Imagine AI as a high‐octane vehicle, the likes of which would leave any car lover filled with admiration. Now consider this: Just as you wouldn't hand your nephew the keys to a brand‐new Ferrari on his sixteenth birthday, the reins to AI require a seasoned hand, someone who knows just when to throttle and when to brake.

AI without human input can generate content at breakneck speeds, but it's the human touch that adds depth, evoking the "oohs" and "aahs" from the audience.

The common approaches to building and enhancing GenAi capability in the enterprise are as follows:

Designate an executive as the GenAi leader within the enterprise – exec assembles multifunctional teams across disciplines.

Create a thousand points of light – by performing a variety of pilots across disciplines to figure where value is maximized within the enterprise.

Work with vendors of many sizes and capabilities to embed GenAi within the enterprise.

Hire some of the large, well‐known consulting companies to lay out a GenAi strategy and implementation plan.

Reluctantly, we posit that most of these approaches will yield very small and mostly meaningless results with little or no ability to scale them. Sadly, millions of dollars will be spent with not much to show for it. The focus will be on unimaginative, brutal, and unnecessary layoffs and cost reductions that will not grow the top line, nor will it build for success.

However, there are two approaches that will work in superior and cost‐effective ways, and will grow the top line, and we urge our reader, and business leader to consider them carefully.

Acquire and build:

Rapidly acquire small GenAi or neuroAi companies that have domain knowledge, deep expertise, and proprietary data that range in size from 10 to 50 people. Integrate them into the enterprise and utilize their “entrepreneurial energy” and domain knowledge to transform the enterprise.

Build‐operate‐transfer:

Use experienced vendors with deep knowledge of both the technology and the category to build neuroAi labs or Centers of Excellence, or neuroAi studios within an enterprise. Have them operate it with staff in the enterprise, and eventually transfer it to inheritors within the enterprise. Rotate employees through these in‐house labs or studios to get the entire organization trained in the language and techniques of neuroAi. This will upscale the skill sets of the entire organization.

These two approaches will yield remarkable results, accelerating GenAi capability and knowledge within the enterprise, and will be most cost effective while delivering true business outcomes and value.

The Importance of the Nonconscious in GenAi

In the field of human cognition, the nonconscious mind plays the elusive but dominant partner – guiding, influencing, but seldom seen. Recent neuroscience posits that our nonconscious processes direct much of our cognitive output, from snap decisions to complex problem‐solving tasks. Indeed, 95% of daily human decision‐making is in the nonconscious.

The anatomical seat of the nonconscious lies within the vast networks of the brain, such as the basal ganglia, orchestrating movements and learning patterns without a whisper to our conscious awareness. The brain's nonconscious systems operate silently, efficiently processing vast amounts of sensory data, often solving puzzles we didn't consciously know were being attempted.

Think of the nonconscious as the über‐efficient personal assistant who's always got your back, fielding calls you didn't know were coming. It's the nonconscious that ties your shoelaces while your conscious mind is still wrestling with which sock to put on first.

This nonconscious machinery works relentlessly and gracefully. Heuristics, the brain's innate shortcuts, allow it to function efficiently, guiding our daily navigation with minimal cognitive effort. Neuroscience indicates that these rapid‐fire mechanisms are governed by neural circuits within subcortical structures, such as the amygdala and hippocampus, which process emotional and environmental stimuli long before our conscious mind has had a chance to catch up.

GenAi engines, thus enlightened with nonconscious data are equipped to deliver outputs that resonate with our own cognitive harmonies.

How to Use This Book

This book is for the practitioner – so do not read it from cover to cover. Read a chapter or two – think about it, reflect on it. See how the neuroscience principles make sense to you, in your own life. Look at things around you with this light of understanding.

Get on any one of the popular LLMs – large language model engines. Write a prompt or two asking it to produce some output without injecting neuroscience into it. Now inject the principles of neuroscience into the prompts giving the LLM precise instruction in what to do, and what to avoid. See for yourself how the output changes.

If you are a creative designer, creating products or innovations or packaging for a demography, read the neuroscience that governs the behavior of the demography in the first half of the book. Now go to the second half of the book and pull the relevant chapters or sections for the task at hand. Put it together and work with GenAi – you will see the difference.

We have deliberately kept the neuroscience application focused and accessible to the practitioner.

At the risk of being redundant, we have tried to make each chapter self‐sufficient, so don't be surprised to see a few facts and concepts show up repeatedly. The repetition not only reinforces, but also makes each chapter a bit independent of the others – so you can pick up any chapter and be able to apply without the burden of all the prior knowledge.

In closing, the elegant interplay between the nonconscious mind and AI presents a frontier teeming with potential. By tracing the neural pathways that govern our cognition, we can sculpt AI systems that speak to the nonconscious mind of the consumer.

Why is having this knowledge so valuable now?

Because as AI, particularly GenAi, assumes more and more of a central role in marketing and product innovation, understanding how best to reach and persuade the nonconscious mind becomes vital and critical to winning the consumer.

Move on from here to learn even more. Remember clearly that humanity is at the center. Human understanding of ourselves must drive GenAi. Humans become supervisors, overseers, and drivers of this powerful engine. Humility, not hubris, will allow us to realize the real power of GenAi.

PART 1Neuroscience and Ai

CHAPTER 1neuroAi for Marketers, Product Designers, and Executives

In short order, LLMs will become one of the most current, important, and ongoing topics of conversation worldwide.

Why? And what are LLMs?

This chapter will take you through the inner workings of GenAi, including large language models – that is, LLMs. With this compelling technology rapidly encircling the globe and penetrating into every corner of commerce and personal life, it makes sense to attain a working grasp of the fundamentals. Soon, fluency with this knowledge will be as necessary, and expected, a basic job and life skill as is our daily reliance on digital communications.

So let's learn about transformers, encoders, decoders, tokens, and the ultimate goal post, the Holy Grail of AI: artificial general intelligence.

It used to be the running joke in AI – that a million monkeys pounding out random keystrokes would hardly produce Shakespeare. It appears that the primates in San Francisco have done just that. The question is how? This chapter lays out the core elements of transformer technology. The goal is to give you a rudimentary understanding of the elements that go into it, but also stimulate a desire in you to build your own transformer for your own industry – whether you make fragrance, flavors, music, or floral arrangements. Understanding transformers will facilitate a newer way to think of your enterprise data.

It has been the lament of CTOs and marketers that despite the numerous “data lakes” and “data warehouses” in an enterprise, nothing new has really come about that facilitated daily use and breakthrough discovery. Transformer tech, in conjunction with large language models, can provide that precise enterprise asset. We anticipate the next generation of tech consulting, and strategic consulting to be practices that house and structure data to build enterprise specific and enterprise proprietary transformer models.

Introduction to Large Language Models

Large language models (LLMs) have clearly marked a paradigm shift in the field of natural language processing (NLP) and artificial intelligence over the last couple of years. So much so that we seem to hear about a new acronymized LLM practically every other day, adding to the list of the now well‐known models – GPTs (OpenAI, March 14, 2023; OpenAI, November 5, 2019; OpenAI, November 30, 2022), PaLM (Google AI, n.d.), LLaMA (Meta, Llama, n.d.), Gemini (Pichai S., Hassabis D., December 6, 2023), and the like. Their successful capture of so much of the world's attention has to do with how adept LLMs are at understanding, generating, and interacting with human language in ways that appear startlingly lifelike and human.

NLP models and chatbots are not new. In fact, the first chatbot can be traced all the way back to ELIZA, developed between 1964 and 1967 at MIT (Weizenbaum 1966), and even the GPT line of models can be traced backed to GPT‐1 released back in 2018 (Radford et al. 2018). So, what has changed in the last couple of years? What magical threshold do we seem to have crossed? In this chapter we will introduce LLMs, and explore LLMs and the specialized niche they have carved out in the domain of human language understanding.

At the heart of the new breed of LLMs is a neural network architecture that was first introduced in a landmark paper “Attention is all you need” (Vaswani et al. 2017). This paper described the transformer model, which has been the key to revolutionizing the process of human language understanding. Before the transformer model, most NLP models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), relied heavily on the sequence of data. One word after another, and one sentence after another. This made it difficult for any of these models to efficiently capture long‐distance dependencies between words or build an understanding on the semantic weight of words given their context.

To the novice reader, neural networks are simply yet another way of fitting models to input‐output data. The same way, lines, curves, splines, polynomials, and regressors fit input data to output data, neural networks are just another way to perform the same input‐output data mapping. The “branding” is that the structure of neural networks vaguely mimic and are inspired by how neurons fire in our brain. The notions of neurons, layers, firing potentials and thresholds, and weighted and reinforced connections between them is a mathematical model of our own biological computer – the brain. Simple neural networks have an input layer, an output layer, and a hidden layer in between. Deep learning networks have millions of layers and neurons. Training typically involves presenting matched input output pairs and letting the neural network learn to adjust its neuronal weights to create perfect mappings between inputs and outputs. The adjustment of the weights of neuronal connections is typically accomplished by choosing paths that minimize the predicted output error. So, with an error function, and training data, and a means to adjust the weights of neuronal connections (similar to adjusting the coefficients of a polynomial in curve fitting), the input output mapping is accomplished. A portion of the training dataset is generally reserved for the purpose of testing. The build neural network that has never seen the test data is then tested on that reserved dataset. If it predicts accurately the outputs given the inputs, then the network is declared as fully trained and the job is done. These of course were the early days. Nothing awesome came from these neural networks, until the invention of the transformer model.

The transformer model addressed these limitations by introducing the concept of “attention.” We will go into this in some detail a little further on, but very briefly, the attention mechanism does not process words one by one but considers a weighted sum of all the words in a sentence simultaneously. These weights determine which words get the most of the model's “attention,” and this allows the model to capture the relationships between words in the sentence.

In addition to this novel neural network architecture, the other factor that has fueled the LLM explosion is computational power and the vast quantities of data that these models churn through and digest in their training. The “large” in LLM isn't just for show after all. The size of an LLM model is typically measured in the number of parameters it has. These parameters are the various numerical values that the model can adjust to better improve its performance. In general, the more parameters, the more capable the model.

The evolution of LLMs has been characterized by a continuous expansion of these parameters, which has led to exponential improvements in performance. From models with millions to billions, and even trillions of parameters, the trajectory has been astonishing.

A Brief Chronology of LLM Evolution

2018 – GPT (Generative Pretrained Transformer): Introduced by OpenAI. It has 117 million parameters (

https://openai.com/index/language‐unsupervised/

)

2018 – BERT (Bidirectional Encoder Representations from Transformers): Introduced by Google. It has 340 million parameters (Devlin

et al

., 2019)

2019 – GPT‐2: Introduced by OpenAI, with 1.5 billion parameters (

https://cdn.openai.com/better‐language‐models/language_models_are_unsupervised_multitask_learners.pdf

)

2019 – T5 (Text‐to‐Text Transfer Transformer): Introduced by Google in 2019. The “base” version has 220 million parameters and the “large” version extends up to 11 billion parameters (Roberts A., Raffel C., February 24, 2020)

2020 – GPT‐3: Introduced by OpenAI. It has a staggering 175 billion parameters (Li C., June 3, 2020)

2022 – PaLM: Introduced by Google, with 540 billion parameters (

https://research.google/blog/pathways-language-model-palm-scaling-to-540-billion-parameters-for-breakthrough-performance/

)

2023 – GPT‐4: Introduced by OpenAI. A reported 1.76 Trillion parameters (Schreiner M., July 11, 2023)

FIGURE 1.1 The rapid growth in size of recent LLMs.

Clearly, what is immediately obvious to anyone interacting with one of the newer LLMs is their ability to generate coherent and contextually relevant responses over extended interactions. This has made them particularly fascinating for both AI researchers and the general public, sparking conversations about the nature of intelligence, artificial general intelligence (AGI), and the future of human‐AI interaction.

In the rest of this chapter, we will unpack the significance of LLMs, providing a walkthrough of the technical breakthroughs and what makes them special – especially the transformer models that underpin them and the accompanying operational mechanisms like tokenization and attention that allow for their unparalleled language understanding.

Transformers

As mentioned briefly, before the advent of transformers, the models in use were largely recurrent networks. The problem with RNNs, as we have seen, is that it tends to forget the beginning of the sentence by the time we come to the end of a very long sentence. Since these are sequential in nature, they process one word at a time. The output from the previous step is used as part of the input for the current step. This dependence on previous steps means that in an RNN each step must wait for the last one to be completed. This prevents parallelization during training and makes long sequences difficult to manage.

Transformers, on the other hand, have no recurrence and instead rely on attention, making it much faster to train and allows for parallelization. We will get into what the attention mechanism is shortly, but in general, such a mechanism allows a model to both process the entire input sentence simultaneously and “pay attention” to the important part of the sentence or input sequence.

FIGURE 1.2 A simplified representation of the transformer architecture. Several intermediate layers have been hidden to focus on the key components.

Figure 1.2 depicts a transformer model. While it certainly looks to be complicated and has several components, at the highest level a complete transformer has two main blocks – an encoder block (on the left) and a decoder block (on the right). The encoder's job is to understand the input content, and the decoder's job is to utilize that understanding to produce the desired outcome, whether this be text generation, machine translation, question answering, or what have you.

An encoder can be thought of as the part of the model that changes or transforms the input and encodes it into a higher, more abstract mathematical representation that the model can digest. The purpose of the encoder is to extract and encode important features from the input that will be useful for the task at hand. In the case of text data, the encoder would take a sentence and convert each word into a mathematical entity called a vector that captures its semantic meaning and contextual relationship with other words in the sentence.

The output of the encoder is then passed to the decoder. The decoder takes the encoded input and generates a readable or useful output from the encoded information. For instance, in a translation task, the encoder will take in sentences of the source language and generate an abstract internal representation capturing the meaning and nuances of the input sentence. The decoder will then take this internal representation and generate a sentence in the target language, word by word. It uses the encoded information and also takes into account what has been translated so far, to generate the next part of the output.

A closer look at the transformer architecture reveals several subblocks marked with the words attention and multiheaded attention. We shall now take a closer look at the attention mechanism that forms the heart of the transformer architecture and explain how it knows what the important part of the sentence is and what it means for a model to pay attention to parts of a sentence.

The Self‐Attention Mechanism

The self‐attention mechanism in language models, in its most intuitive sense, allows the model to “focus” on the relevant parts of the input to make accurate predictions. It's akin to how when we read a complex text, we pay more attention to certain keywords to comprehend its meaning.

Let us consider the sentence:

“The quick brown fox jumps over the lazy dog.”

When you parse through this sentence and notice the word “fox,” you are also able to identify the words that would be most related to the words fox. In this case, they might be “quick,” “brown,” and “jumps.” These other words help you understand what this particular fox is about.

So if you were to construct a query, “What is the fox in this sentence about?” the keys that would help you unlock the puzzle would be the words “quick,” “brown,” and “jumps.” And each of these words carry its own values and associations. You have an understanding of what it means to be “quick” or to “jump,” and therefore an understanding of what this fox is about.

Similarly, what the multiheaded attention mechanism does is to subject each word of the input sentence to the following sort of questioning process:

“For the word q in the input sentence, what are the other words k in the sentence that best help me understand it, and what are their associated values v?”

Through extensive training, the attention mechanism learns how to effectively and correctly represent qs, ks, and vs for itself, so that in any given sentence for each of the qs, it pays attention to the correct ks and retrieves the correct vs.

Let us build on this intuition and make it slightly more concrete. The next section is slightly more complex, and there will be some math involved.

The Details: Tokenization and Embeddings

The first thing we need to sort out before we proceed is to understand how a transformer model represents words mathematically. Let us consider the same sentence:

“The quick brown fox jumps over the lazy dog.”

The typical procedure is that this sentence is first “tokenized” into a sequence of tokens or entities. A word‐level tokenization would give us the following sequence:

“The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”

These tokens are all independent entities; across an entire corpus of data there would be a large, but limited, set of tokens that together define a vocabulary. This would be like a special dictionary where each word would be assigned a specific and unique token number (OpenAI “Tokenizer” n.d).

791, 4062, 14198, 39935, 35308, 927, 279, 16053, 5679

Each token number would map to a high‐dimensional mathematical vector called the embedding vector or word vector. The importance of embedding vectors is that via training, words/tokens are converted into vectors in a way that semantic relationships between words relate to the geometric relationships between their corresponding vectors. For instance, words with similar meanings are located near each other in the vector space, and the geometric direction between words can even capture semantic relationships between words. Remember the neuroscience adage: “Neurons that fire together, wire together.” Words that seem connected seem to cluster together in the vector space of words.