What’s up everyone, today we have the pleasure of sitting down with the acclaimed Britney Muller, Founder and Consultant at Data Sci 101 and former Senior SEO Scientist at Moz.
Summary: Britney takes us on a wild ride through the intersection of marketing and AI, emphasizing the importance of adaptability, continuous learning, and ethical considerations. Britney's journey from SEO to AI illustrates the need for data literacy and strategic decision-making in marketing. She delves into the ethical nuances of AI, discussing the limitations of LLMs and the importance of transparency and responsible development. Highlighting the human element in AI, Britney advocates for balancing technological advancements with human creativity and intuition, and underscores the transformative potential of AI across various sectors. This episode is a compelling call to action for professionals to harmoniously blend technical expertise with ethical mindfulness in the rapidly evolving martech landscape.
About Britney
Embracing Machine Learning: A Journey from SEO to AI
Britney's journey from SEO expertise to machine learning is a testament to the power of curiosity and continuous learning. Nearly a decade ago, while most in the martech field were focused solely on traditional methods, Britney's unique passion for learning and experimentation led her to explore machine learning. This shift was fueled by her desire for a new challenge, as she felt she had reached the zenith of her SEO experiments.
The pivotal moment came when she took the Harvard CS 109 course on machine learning. This experience opened her eyes to the transformative potential of feeding data to models and letting them learn patterns independently. The tangible results and potential applications she witnessed were not just intellectually stimulating but also professionally inspiring. As machine learning evolved, so did Britney's skills. She recalls the early days of TensorFlow, where complex lines of code were required for basic functions, which have now been simplified drastically.
Britney's approach to machine learning is unique. She enjoys taking existing models and reengineering them for different applications, a process she describes as akin to being a 'Frankenstein developer.' This creative tinkering led to practical applications and fun experiments, like her first MNIST model, which could recognize handwritten numbers with high accuracy. Her pride in this achievement underscores her deep connection to her work and the joy it brings her.
Key takeaway: Britney's transition from SEO to machine learning highlights the importance of pursuing passions and continuous learning in professional development. Her success stems from her willingness to embrace new challenges and innovate by reapplying existing technologies in novel ways. This story is a reminder that staying curious and adaptable is crucial in the ever-progressing field of martech.
Data Literacy: Bridging the Gap in Marketing
Britney's endeavor with Data Sci 101 aligns perfectly with her goals of educating the martech community and fostering a well-informed approach to AI and ML. She emphasizes the importance of statistical knowledge in marketing, a skill often overlooked in traditional marketing education. Britney's passion for sharing knowledge is driven by her discovery of the significant gap in data literacy within the marketing industry. This gap, she believes, hinders marketers from making more strategic decisions and finding better insights.
Her approach to education in this field is both innovative and practical. Britney focuses on creating content that is engaging and accessible, breaking down complex topics into understandable segments. She draws inspiration from her friend Daisy Quaker's approach, emphasizing the need to repurpose extensive resources into more digestible formats - akin to turning a large turkey into multiple turkey sandwiches. This analogy perfectly encapsulates her method of making complex data science concepts more palatable for the average marketer.
Britney's journey in educating others began with her own realization of the lack of statistical training in her marketing career. This led her to delve deeper into data science, allowing her to identify and address the gaps in knowledge within the marketing community. Her efforts are not just about imparting knowledge but also about empowering marketers to leverage data more effectively in their strategies.
Key takeaway: Britney's initiative with Data Sci 101 highlights the critical need for data literacy in the marketing world. Her commitment to educating her peers about the importance of statistical knowledge and her innovative approach to content creation serve as a model for making complex subjects accessible and engaging. This endeavor not only enhances the skill set of marketers but also paves the way for more data-informed and strategic decision-making in the industry.
Deciphering the Alien Nature of Large Language Models
Britney's analogy of large language models (LLMs) as aliens provides a unique perspective on the intricacies of AI in the martech world. She recalls one of the more technical textbooks she read on LLMs and how the author compares LLMs to beings in a black cave, fed with the world's texts but lacking a true understanding of human experiences and languages' nuances. This vivid imagery conveys the idea that, while LLMs are proficient in processing and mimicking language patterns, they fall short in grasping the depth and context of real-world experiences and specialized knowledge.
Britney's approach to explaining complex concepts through relatable analogies reflects her commitment to making the abstract more accessible. Her use of post-it notes to jot down everyday analogies like baseball references showcases her inventive method of communication. This approach is crucial in a field where the technology is often abstract and difficult for the average person to grasp.
“LLMs are essentially aliens from a different universe: while they have access to all our world’s text, they lack genuine comprehension of languages, nuances of our reality, and the intricacies of human experience and knowledge.” - Britney Muller,
Introduction to LLMs, part 1.
This alien analogy underlines a significant limitation of LLMs: their inability to truly understand or experience the world as humans do. They can replicate language patterns and provide information based on vast datasets, but they lack the depth of understanding that comes from real-world experiences. For instance, while an LLM could technically participate in a podcast, it would miss the subtleties and unspoken understandings that humans naturally bring to such interactions.
Britney's insights into LLMs highlight the gap between artificial intelligence and human cognition. While LLMs can mimic language to a remarkable degree, their understanding remains surface-level. This distinction is crucial for anyone in martech to understand as it underscores the limitations and capabilities of AI in marketing applications.
Key takeaway: Britney’s alien analogy vividly illustrates the capabilities and limitations of large language models in understanding human language and experience. Her creative communication style effectively bridges the gap between complex AI concepts and the general public. Understanding this 'alien' aspect of LLMs is essential for those in martech, emphasizing the need to recognize the boundaries of AI's comprehension and application.
The Complex Debate on LLMs as Reasoning Engines
Britney delves into the ongoing debate about whether large language models (LLMs) like GPT are reasoning engines or mere probabilistic tools. This topic, often clouded by marketing jargon and hype, challenges the fundamental understanding of what reasoning truly is. Britney highlights the importance of clear definitions in this context, emphasizing the industry's tendency to overstate the capabilities of these technologies.
The CEO and founder of Every: Dan Shipper wrote this
article in which he claims attending a Sequoia talk where Sam Altman said that GPT models are actually reasoning engines not knowledge databases. Interestingly when asked, ChatGPT sides with Britney, that it can simulate reasoning-like responses but is not a reasoning engine.
In Britney's opinion LLMs, at their core, are sophisticated word-predicting machines without reasoning capabilities. However, the notion of reasoning involves more than just generating confident responses to various domain questions. Britney points out the danger in carelessly using terms without concrete definitions, leading to misconceptions about the perfection and human-like abilities of these models. This misalignment in understanding is a critical issue in the field of AI and martech.
Britney’s experience at
NeurIPS, a prominent event in the machine learning field, illustrates the significant interest and varied opinions surrounding LLM reasoning. The packed room at a session dedicated to this topic shows the industry's keen interest, yet it also reveals the lack of consensus. While some experts assert that LLMs are not reasoning engines, others explore their potential in this area.
This dichotomy is further complicated by the inadequacy of current benchmarks used to evaluate LLMs. Human exams and arbitrary benchmarks fall short in accurately assessing these models' capabilities. Britney references Emily Bender's work, which critiques the current methods of evaluating LLMs, emphasizing the challenge in assessing a system designed to be a generalist.
Brit’s suggested resources:
Key takeaway: The debate over whether LLMs are reasoning engines highlights a crucial need for clear definitions and realistic understanding of AI capabilities in martech. Britney’s insights reveal the complexities and varied perspectives in this field, urging caution against overstating these technologies' abilities. Understanding the true nature and limitations of LLMs is essential for anyone working with AI in marketing, emphasizing the importance of informed discussions and realistic expectations.
Navigating the Ambiguity of AI at NeurIPS
Britney's experience at the NeurIPS conference underscores a fundamental truth about the current state of AI and machine learning: the field is rife with uncertainties and contradictions. Her expectation of gaining concrete insights was met with the reality that even experts in the field grapple with conflicting information and research. This revelation is a sobering reminder of the complexities and evolving nature of AI, particularly in the context of martech.
The biggest takeaway for Britney was the realization that definitive answers in AI are elusive. The diverse range of opinions and findings she encountered at NeurIPS paints a picture of a field still in its formative stages, where concrete conclusions are hard to come by. This fuzziness is not just a challenge but also an opportunity for continuous learning and adaptation in the ever-changing landscape of AI and machine learning.
Another significant aspect of her experience was the humanization of the experts behind the technology. Often, discussions about AI and its impact are abstract and depersonalized. Seeing these experts in person, Britney was reminded that they, too, are navigating this complex field, striving to balance technological advancement with ethical considerations and public interest.
Britney’s observations about the financial interests of big tech companies versus public interest add another layer to the conversation. It highlights the need for critical thinking and vigilance in how AI technologies are developed and deployed, especially in a commercial context.
Key takeaway: Britney’s insights from NeurIPS reveal the inherent complexities and ambiguities in the field of AI and machine learning. This understanding is crucial for professionals in martech, emphasizing the need for continuous learning, adaptability, and a balanced approach to technological advancements. Recognizing the human element behind these technologies and being mindful of ethical considerations are key in navigating this evolving landscape.
Unpacking the Ethical Complexities of AI Development
Britney's hot dog analogy for large language models serves as a stark reminder of the complexities and ethical considerations in AI development. Her call for transparency, embodied in her idea for T-shirts stating 'What's in the data?', emphasizes the need for a deeper understanding of the components that constitute these AI models. This question is crucial because, as Britney points out, the datasets used to train models like ChatGPT and MidJourney often contain disturbing elements, including extreme and unethical content.
The ethical dilemma in AI development stems from the pursuit of more human-like, confident sounding models. This pursuit led researchers to feed vast amounts of data into these models, often overlooking the quality and ethical implications of the data used. As a result, these models can inadvertently magnify the darker aspects of society. Britney's revelation about the uncurated and unconsented nature of these datasets adds a layer of urgency to this issue, highlighting the potential harms and unintended consequences of AI technology.
Britney's insights raise critical questions about the responsibility of big tech companies in AI development. The outsourcing of data collection and the lack of transparency in these processes contribute to the ethical complexities of AI. Her call for public awareness is not just about exposing these issues but also about fostering a collective understanding of AI's implications.
The challenge, as Britney notes, is engaging more people in these conversations. It's about making the public aware not just of AI's capabilities but also of its composition and the ethical considerations that come with it. As AI becomes more integrated into work and daily life, this understanding becomes imperative for informed decision-making and responsible use.
Key takeaway: Britney's analogy and insights highlight the critical need for transparency and ethical considerations in AI development. Understanding the composition of the data that feeds these models is essential for both developers and users. The future of AI technology hinges on this awareness and the public's ability to engage in informed discussions about the ethical and technical aspects of AI. This understanding is vital for responsible development and application of AI in various fields, including martech.
Addressing Bias in AI: The Road to Ethical Marketing
Britney's discussion in
Part Two of her LLM guide illuminates the crucial issue of bias in AI and its implications for martech. The C4 dataset, including contributions from Wikipedia, reveals a skewed representation due to its predominantly male, young, educated, and single demographic. This lack of diversity in contributors leads to a narrow viewpoint, influencing AI models like GPT-3 and T5 and resulting in biases related to race, geography, and gender.
The key to creating a more balanced and unbiased dataset lies in thoughtful curation and documentation. While the industry is still far from achieving this ideal, acknowledging the issue and working towards a more representative corpus is a step in the right direction. Britney points out the surprising lack of documentation even at major tech companies regarding the data used to train their models. This lack of transparency exacerbates the issue, as users and developers are often unaware of the biases ingrained in these models.
One striking example Britney discusses is the
PGA's use of AI to complete player headshots, which inadvertently showcased racial biases. This instance highlights the need for marketers to be vigilant and critically assess how AI tools might perpetuate stereotypes or biases, especially in culturally sensitive markets. Marketers must be equipped to recognize these pitfalls and prevent them from shaping public perception and influencing young minds.
To address these challenges, marketers must prioritize awareness and understanding of the inherent biases in AI tools. By being informed about the composition of datasets and the potential biases they contain, marketers can more ethically use AI in their campaigns. This awareness also enables them to prime AI tools for diversity and avoid perpetuating stereotypes.
Key takeaway: The journey towards unbiased AI in martech requires a deep understanding of data sources and their inherent biases. Britney's insights emphasize the need for thoughtfully curated and well-documented datasets. Marketers must remain vigilant, constantly assessing AI tools for biases, especially in culturally sensitive contexts. Ethical use of AI in marketing is not just about leveraging technology but also about ensuring fair and balanced representation in all campaigns.
The Dark Side of LLMs and AI Content Moderation
In several of her talks, Britney mentions the documentary "
Cleaners" and brings to light a seldom-discussed yet critical aspect of AI development: the ethical management and support of content moderators. These individuals, often working in less than ideal conditions for minimal compensation, are tasked with sifting through and labeling the most gruesome and awful content to prevent its appearance on social platforms. It's a disturbing reality that, despite its importance, remains largely hidden from public discourse, especially by big tech companies.
This conversation uncovers the stark realities faced by content moderators, such as those in Kenya employed by OpenAI for ChatGPT,
who earn less than $2 an hour to filter out toxic content. The emotional and psychological toll on these workers is immense, raising serious ethical questions about their treatment and the responsibilities of tech companies. This issue goes beyond just the biases in AI models; it touches on the human element behind AI's operational processes, demanding a more humane approach to technological advancement.
The discussion raises awareness about the often unseen human cost behind AI technologies. The stark contrast between the end product and the process of content moderation is a reminder of the ethical responsibilities that come with the development and use of AI. Britney hopes for a future where AI can assist in this process, reducing the burden on human moderators and mitigating their exposure to traumatic content.
However, until such technological advancements are made, there is a pressing need for ethical standards and support systems for content moderators. This includes adequate mental health services, fair compensation, and a more responsible distribution of tasks by tech companies. Britney's call to action is not just for greater awareness among users but also for industry-wide pressure on tech companies to adopt more ethical practices.
This conversation extends beyond just understanding the biases in AI models. It delves into the human element of AI's backend processes, emphasizing the need for a holistic approach to ethical AI development. The industry must acknowledge and address the challenges faced by content moderators, ensuring their well-being is prioritized.
Key takeaway: The ethical treatment of content moderators in AI development is a crucial issue that needs more attention and action. As users and developers of AI, there is a responsibility to understand and address the human cost behind these technologies. The industry must work towards establishing ethical standards and support systems for content moderators, ensuring their mental health and well-being are safeguarded. This approach is essential for responsible and humane AI development.
Balancing AI Advancements with Human Creativity
One of the things most loved about Marketing Operations is all the puzzle solving that’s required. Britney wrote a
compelling tweet that beautifully captures the human essence of problem-solving “The idea in the shower, a conclusion in your dream, the solution at dinner” and the fear that AI's dehumanization might rob future generations of this.
Britney's reflection on the interplay between AI and human creativity in problem-solving provides a compelling insight into the future of martech. Drawing from her personal experience in solving a complex technical problem for a client, she highlights the beauty and significance of the human problem-solving process. Her journey, filled with moments of trial and error and eureka instances, underlines the importance of human intuition and creativity in the face of advancing AI technologies.
The conversation brings to the forefront the concern that AI developments could potentially rob future generations of these enriching human experiences. Erik Larson's book "
The Myth of Artificial Intelligence" highlights the distinct nature of human intelligence and the overlooked role of abductive reasoning in AI research today. That being said, it’s probably just a matter of time until research evolves from deductive and inductive inference to embracing abductive inference and enabling this form of reasoning – the kind of intuitive problem-solving that could be positioned to replace the uniquely human experience of the 'eureka' moment.
The advancement of AI research towards incorporating aspects of human cognition, as discussed at the neurips conference, is a fascinating development. This approach aims to mimic the 'thinking' process of humans in AI models, leading to more nuanced and effective outcomes. Andrew Ng's suggestion to test AI prompts on humans first is a testament to the need for clarity and explicitness in AI instructions, mirroring the challenges marketers face in articulating their offerings.
Britney's observation about the clarity of communication in AI implementation is a critical point for marketers to consider. The success of AI in marketing not only hinges on the sophistication of the technology but also on how well stakeholders can articulate their needs and goals. This is a common challenge in martech, where there’s often a gap between the potential of technology and the ability to clearly define its application. Our episode with
Sara McNamara highlights this issue, underscoring the importance of precise communication in leveraging AI effectively.
Integrating AI with other systems and models that excel in deterministic tasks can lead to groundbreaking solutions in martech. This synergy between AI's probabilistic nature and other deterministic systems opens up a realm of possibilities for more nuanced and efficient problem-solving. However, Britney expresses a valid concern about the accessibility of such advanced capabilities, fearing that they might remain exclusive to tech giants due to high costs.
Key takeaway: The integration of AI in problem-solving should not overshadow the intrinsic value of human creativity and intuition. As AI continues to evolve, striking a balance between technological efficiency and preserving the human essence of problem-solving is crucial. This balance is not just about technological advancement but also about ensuring that the unique and enriching experience of human discovery is not lost in the pursuit of AI-driven solutions.
The Transformative Potential of Customized AI in Diverse Fields
Britney's excitement about the possibilities of customized AI, particularly with tools like GPT-4, highlights a significant shift in the field of martech and beyond. Her approach, focusing on enhancing AI capabilities through thoughtful integration with specific tasks and datasets, is a testament to the versatility of AI. Despite not having created her own GPT models, Britney's engagement with AI applications reflects a keen interest in exploring its practical uses.
Britney is particularly enthused about the applications of AI in areas like customer support, where AI agents can be optimized for efficient and effective responses. This approach isn't about creating complex, groundbreaking innovations but rather about thoughtfully applying AI to save time and improve experiences for both marketers and end-users. Her observations from NeurIPS further underline the importance of thoughtful application over flashy, complex solutions in AI.
Britney is drawn to AI's applications in healthcare, wildlife conservation, and particularly in empowering communities in the global South. The use of AI in educational contexts, especially where resources like Wi-Fi are limited, points to the inclusive potential of this technology. However, she also raises a crucial point about the disparity in resource allocation and the inverse effect of AI on populations with fewer resources. The ethical dimension of AI development and deployment, particularly in terms of its environmental and societal impacts, remains a significant concern.
Britney's perspective is rooted in a desire to see AI used for genuinely impactful purposes, beyond just commercial gains. The challenge lies in securing funding and support for AI applications that may not offer immediate financial returns but have the potential to make meaningful differences in critical areas of society.
Key takeaway: The future of AI, as seen through Britney's lens, is not just in its technological sophistication but in its thoughtful application across various fields. There's immense potential in customizing AI for specific tasks, improving efficiency, and enhancing user experiences. However, it's crucial to address the ethical and resource-related challenges in AI development, focusing on inclusive and impactful applications that benefit all sections of society. This approach will ensure that the advancements in AI are not just confined to tech giants but also contribute positively to broader societal needs.
Embracing AI in Marketing: Balancing Technical Skills with Human Interaction
On top of the need for basic data science knowledge, Britney’s
predicting that the AI surge may lead to an increased demand for soft skills. She’s really excited about these two things converging.
Britney foresees a future where AI tools like GPT-4 are not just technical assistants but catalysts for enhancing human interaction and creativity. The integration of AI in workshops and meetings, for example, enables professionals to be more present and engaged, fostering better communication and accountability within teams. This approach illustrates how AI can enhance soft skills like communication and empathy, crucial in any industry.
The use of AI in recording and analyzing meetings, identifying who contributes most or asks insightful questions, offers unique insights into team dynamics, making meetings more productive and inclusive. Britney emphasizes that AI technology, particularly for marketers, is now an indispensable assistant. It democratizes technical knowledge, allowing individuals to easily seek help on tasks like Google Sheets without overburdening their colleagues. This accessibility empowers more people to engage in technical problem-solving and innovation.
However, Britney doesn’t overlook the importance of soft skills, especially in the AI industry. She observes a disparity between high IQ (technical skills) and low EQ (emotional intelligence) among many professionals in the field. The advancement of AI technology will increasingly depend on individuals who can blend technical expertise with empathy, creativity, and interpersonal skills.
Moreover, Britney highlights the crucial role of domain experts in the AI landscape. The bottleneck in AI development has shifted from data and AI researchers to those with specialized knowledge in various fields. It's these experts who can identify practical applications of AI, driving innovations that genuinely benefit society.
Key takeaway: The future of AI in marketing and beyond is not just about enhancing technical capabilities but also about bolstering human skills and interactions. AI tools can serve as powerful assistants in problem-solving and creativity, but the real value lies in their ability to enhance human qualities like empathy and communication. As AI continues to evolve, the focus should be on empowering domain experts and encouraging a blend of technical and soft skills, ensuring the development of AI solutions that are both innovative and human-centric.
Finding Meaning Over Happiness in a Multifaceted Career
Britney's approach to balancing a diverse and demanding career is a unique blend of passion, purpose, and prioritization. Her perspective shifts the common narrative from seeking happiness to pursuing meaning in work. This philosophy, centered on impact and fulfillment, resonates deeply in a world often focused on immediate gratification and superficial achievements.
For Britney, the key to balancing her roles as an outdoor adventurer, snowboarder, international keynote speaker, writer, and SEO consultant lies in choosing quality over quantity. She emphasizes the importance of being passionately invested in her work, ensuring that it positively impacts others. This approach underscores a deeper sense of satisfaction that goes beyond fleeting moments of joy, driving her to create work that is both significant and rewarding.
Her journey hasn't been without challenges. There was a time when she felt a lack of meaningful engagement in her work, a realization that prompted a shift in her approach. Now, she uses this sense of purpose as her guiding principle, finding joy in achievements that have a substantial impact on others. This mindset not only fuels her professional pursuits but also brings a more profound sense of fulfillment than mere happiness.
Britney’s story serves as a reminder that success in a multifaceted career is not just about juggling different roles but about finding meaning and purpose in each of them. It’s about creating work that resonates with one's values and has a lasting impact on others.
Key takeaway: The essence of balancing a multifaceted career lies in prioritizing meaningful work over fleeting happiness. Britney’s approach highlights the importance of passion, purpose, and impact in professional endeavors. By focusing on creating value and positive change, one can achieve a deeper sense of fulfillment and success in their career. This philosophy can serve as a guiding principle for professionals navigating the complexities of their diverse roles and responsibilities.
Episode Recap
Britney offers a comprehensive view of the intersection of marketing and LLMs, blending technical know-how with ethical mindfulness and human-centric approaches. Her perspectives encourage professionals in the field to not only embrace AI for its efficiencies but to also understand and address its complexities, ensuring its development and application are both responsible and inclusive.
Britney takes us through a thought-provoking journey that intersects the evolving world of marketing with the intricacies of AI and ethical considerations. Her insights are not just informative; they're a call to action for every professional navigating the complex landscape of LLMs.
Britney's transition from SEO to machine learning underscores the significance of adaptability and continuous learning. Her story is a powerful reminder that success in martech is rooted in the willingness to embrace new challenges and apply existing technologies creatively. This ethos is mirrored in her work with Data Sci 101, where she champions the importance of data literacy. Britney's efforts in making complex subjects accessible and engaging are paving the way for more strategic and data-informed decision-making in marketing.
However, Britney's insights extend beyond technical expertise. She delves into the ethical complexities of AI development, emphasizing the need for transparency and responsible application. Her discussions around the limitations of LLMs and the debate over their reasoning capabilities are particularly enlightening. These conversations reveal the need for clear definitions and realistic expectations about AI capabilities in martech, highlighting the importance of understanding both the potential and the boundaries of these technologies.
Britney also tackles the often-overlooked human element behind AI technologies. Her observations at NeurIPS, coupled with her thoughts on the ethical treatment of content moderators, reveal a multifaceted AI world that's both ambiguous and ethically challenging. It's a reminder that as we navigate this terrain, recognizing the human impact and ethical considerations is as important as understanding the technology itself.
Britney's belief in the transformative potential of customized AI across various fields, including marketing, healthcare, and wildlife conservation, is inspiring. Yet, she remains grounded in the reality that the future of AI should not just be about technological advancement but about fostering a world where technology enhances human capabilities and addresses broader societal needs.