How AI Will Change Science Forever - Ep. 43 with Alice Albrecht
Dec 18, 2024
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Alice Albrecht, a machine learning researcher and head of AI product at SmartNews, dives into the transformative impact of AI on scientific research. She discusses the shift from traditional research papers to open data initiatives, emphasizing the importance of N-of-1 studies. Albrecht argues for data unbundling in science and media, and the need for big tech to open-source data for collaborative research. Her insights highlight how AI can improve accessibility and reduce workloads for researchers, setting the stage for a new era of scientific discovery.
AI is transforming scientific research by shifting focus from traditional papers to open data gathering and contextual predictions.
The emergence of 'N of 1' studies illustrates the importance of individual experiments in an AI-driven research landscape.
AI tools are designed to enhance human intelligence, fostering a collaborative environment that enriches creativity and productivity in research.
Deep dives
The Need for Data Transparency in Scientific Publishing
Publishing scientific papers traditionally has focused on successful experiments and findings, often neglecting failures and attempts that could provide valuable insights. This narrow focus creates a skewed understanding of scientific progress, particularly in fields like machine learning, where data and its failures are just as crucial as successful outcomes. Open data initiatives are emerging as solutions to encourage researchers to share everything they have, not just successful findings, leading to a richer scientific discourse. By releasing comprehensive datasets, researchers can foster a collaborative atmosphere that allows various scientists to build upon each other's work and generate new hypotheses.
Recollect.ai and its Evolution
Recollect.ai was developed to assist knowledge workers by connecting and synthesizing information in a personalized manner, catering to their specific needs rather than merely feeding them data. Initially focused on retrieving information, the platform evolved to prioritize user objectives, effectively curating and synthesizing resources tailored to individual goals as interest in AI surged. The evolution from basic functionality to a more nuanced approach highlights the growing understanding that AI should augment human intelligence rather than strive toward complete automation. Despite challenges in building a product amidst rapidly changing AI technologies, the experience revealed the importance of adaptability and responsiveness in a fast-paced environment.
The Cyborg Hypothesis: Humans and AI Collaboration
The notion that technology should be designed to enhance human capabilities rather than replace them is the foundation of the cyborg hypothesis. This approach posits that AI should work alongside individuals to aid in complex cognitive tasks, like understanding vast amounts of information or enhancing creative thinking, rather than function independently. As AI tools become more common, the challenge is identifying how best they can integrate into existing workflows so that humans can continue to engage creatively and analytically. By focusing on how AI can fill gaps in human capabilities, a path to a symbiotic relationship emerges, ultimately fostering enhanced productivity and creativity.
The Future of Media and Storytelling
Recent advancements in AI are set to redefine media and storytelling, enabling the production of content that was previously too expensive or impractical to create. With AI-generated podcasts and other media formats, companies can share internal updates and create stories around lesser-known narratives, democratizing the production of information. Lowering the production barriers offers the potential for underserved stories, particularly in local journalism, to be told and accessed, thus enriching public discourse. This shift indicates a future where stories can be disseminated more widely, allowing for diverse voices and experiences to be represented in media.
Reconceptualizing Science and Prediction
The relationship between data and hypotheses in scientific research is slowly shifting towards a focus on prediction rather than purely causal explanations. Data asymmetry, where valuable datasets are locked within proprietary domains, complicates the scientific process and creates obstacles to impactful research. By embracing an 'N of one' approach, scientists can run individual experiments based on self-reported data and personal insights, supporting real-time insights and predictive analytics. This perspective opens pathways for scientists to generate predictions and gather actionable insights, moving towards a more individualized understanding of complex issues.
AI is going to change science forever.
Small scale studies will give way to large scale open data gathering efforts. We’ll shift from seeking broad general theories to making contextual predictions in individual cases. The traditional research paper will change fundamentally.
That’s why I had Alice Albrecht on the show. Few people straddle the worlds of science and AI like she does: She holds a Ph.D. in cognitive neuroscience from Yale and is a machine learning researcher with almost a decade of experience. Her startup re:collect built an app to augment human intelligence with AI and was acqui-hired by SmartNews earlier this year. She now heads up AI product there.
We get into the contours of this new paradigm in science:
- Whether research papers are still the best format to “release” science in
- The increasing importance of data in scientific discovery
- Why AI is making N-of-1 studies imperative—when they’re normally seen as unscientific
- The case for big tech to open-source their data for scientific research
- The power of unbundling data and interpretations, in science and media
This is a must-watch for anyone interested in how AI is changing the future of scientific research.
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Timestamps:
Introduction: (00:00:59)
Everything Alice learned about growing an AI startup: (00:04:50)
Alice’s thesis about how AI can augment human intelligence: (00:09:08)
Whether chat is the best way for humans to interface with AI: (00:12:47)
Ideas to build an AI model that predicts OCD symptoms: (00:23:55)
Why Alice thinks LLMs aren’t the right models to do predictive work: (00:37:12)
How AI is broadening the horizons of science: (00:38:39)
The new format in which science will be released: (00:40:14)
Why AI makes N-of-1 studies more relevant: (00:45:39)
The power of separating data from interpretations: (00:50:42)
Links to resources mentioned in the episode:
Alice Albrecht: @AliceAlbrecht
The company that recently acquired Alice’s startup: SmartNews
The piece Alice wrote for Every about how AI can augment human intelligence: The Case for Cyborgs Every’s product incubations that we discuss in the context of how AI is changing media: Extendable Articles, TLDR
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