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Bryan McCann
Cofounder | CTO at you.com
Professional Background
Bryan McCann has carved out a significant niche in the field of artificial intelligence and natural language processing (NLP), currently serving as the Co-founder and Chief Technology Officer (CTO) at You.com, a pioneering search engine that aims to personalize user experience through its advanced AI capabilities. With a wealth of experience that spans various roles in research and technology development, Bryan has demonstrated a commitment to pushing the frontiers of what AI and NLP can achieve.
Prior to co-founding You.com, Bryan accumulated invaluable experience as a Lead Research Scientist at Salesforce Research, where he focused on advancing deep learning technologies specifically tailored for natural language processing. His ambitious projects have contributed to making challenging new concepts in NLP accessible and feasible for broader implementation. Leading his own research team, Bryan specialized in essential aspects of conversational AI, including intent recognition, named entity recognition, and voice assistance technologies. His work often merged the theoretical and practical aspects of AI application, allowing for innovative and effective solutions to complex AI challenges.
Bryan's journey into deep learning began with an exploration of transfer learning techniques that ultimately resulted in the development of contextualized word embeddings, paving the way for transformative models like ELMo and BERT. Following this success, he shifted focus to unified text-to-text models for multitask learning, significantly influencing the emerging landscape of models like T5 and the groundbreaking GPT-2/3. His research interests are diverse, extending to explainable AI methods, controllable text generation, and even protein generation through advanced language modeling techniques, demonstrating his versatile skill set and innovative approach to AI research.
Education and Achievements
Bryan's academic background is rooted in prestigious institutions; he completed both his Master of Science (M.S.) and Bachelor of Science (B.S.) degrees in Computer Science, with an emphasis on Artificial Intelligence, at Stanford University, alongside his Bachelor of Arts (B.A.) in Philosophy. This unique combination of disciplines has enriched his perspective on AI and its intersections with human cognition and language. His philosophical research touched upon profound questions of meaning, existence, and aesthetics, culminating in a notable defense of conceptual analysis that portrays it as a tool for learning rather than merely a means of discovering absolute truths.
Bryan's contributions to the field of deep learning and NLP have led him to explore various philosophical inquiries, particularly within the philosophy of science, engaging with the works of esteemed thinkers such as Karl Popper and Thomas Kuhn. His academic pursuits and research passion are mirrored in his literary pursuits as well, with Bryan being an avid reader who finds inspiration in the writings of notable authors like Italo Calvino and Marcel Proust.
Bryan's interests extend beyond the conventional boundaries of computer science; he has an enthusiastic appreciation for mathematics, particularly Set Theory and Topological Analysis, which he explores for enjoyment and intellectual engagement. His creative endeavors include dabbling in poetry and essay writing, primarily reflecting on the intersections of philosophy, literature, and his research experiences, often shared through live readings rather than traditional publications.
Contributions to Organizations
Throughout his career, Bryan McCann has been associated with numerous organizations that reflect his leadership and expertise in technology and research. Before stepping into his role as the CTO and Co-founder at You.com, Bryan served as an Advisor at a variety of organizations, including HUMAN CAPITAL, Builders Fund, and Moonhub. His contributions have successfully guided these organizations in leveraging AI technologies and strategies.
In his tenure at Salesforce, Bryan progressed through various roles, starting as a Research Scientist and advancing to Senior Research Scientist and eventually Lead Research Scientist. His trajectory through Salesforce exemplifies a continuous commitment to innovation in the fields of deep learning and NLP. Additionally, his earlier positions at Stanford University as a Course Assistant and System Architect for etcML illustrate his foundational experiences in research and technology development.
Overall Impact and Future Directions
Bryan McCann's diverse background and wealth of experience in artificial intelligence, natural language processing, and philosophical inquiry position him as a thought leader in his field. His innovative research and application of deep learning technologies have not only contributed to significant advancements in NLP but have also inspired others in the industry to explore new directions
Highlights
The future of engineering in the age of agents and autopilots is a leading indicator for all knowledge work, and begs the question...
Who are we really building AI agents for?
Earlier this week, I discussed what might happen to entry-level engineers and suggested engineering education might evolve like a medical residency. But the implications go well beyond engineers.
Will tech giants dominate further, or will AI agents enable more disruptive startups?
What role can open source play in determining the answers to these questions?
If you'd like to explore these questions together at #SXSW, vote for our session on exactly these topics!
You can vote here:
The pendulum swings back and forth between large and small AI models.
One of the most fascinating parts of watching AI go mainstream has been watching an ever-increasing number of people see the pendulum swing for the first time.
We should all be rightly excited as small models improve, but it is just a matter of time before an even bigger, more unified model sets a new record for blowing our minds.
Then we'll spend some time as a community distilling (broadly speaking, not only in the precise technical way that word is used) all the learnings that went into reaching that new high point into smaller models again.
Bigger, larger models are better for searching for new, mind-blowing capabilities.
All those parameters help us search for new solutions.
But once we've found those new solutions, we can package them up into smaller models that only need to execute on that solution or serve as a base for a comparatively small optimization for a particular application.
In reality, the pendulum swinging is an illusion -- it reflects the community's attention on either small or large models as the immediate next step.
But AI advances in bipedal strides -- left foot small models, right foot big models.
For those always searching for the new solutions there is a constant stream of effort being poured into the main line of research, driving that right foot forward while the left is planted.
For those always searching to apply the latest and greatest to their applications and products, there is an equally constant stream of effort, driving the left forward once the right is planted yet again.
And so we advance step by step...
#AI
