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From Protoscience to Epistemic Monoculture: How Benchmarking Transformed Artificial Intelligence Research


Core Concepts
The field of artificial intelligence research (AIR) transitioned from an era of diverse, autonomous exploration to an epistemic monoculture focused on building ever-larger deep learning models, driven by the introduction of formal benchmarking evaluation systems.
Abstract
The content traces the historical development of the field of artificial intelligence research (AIR) from its origins in the 1950s to the rise of deep learning in the 2010s. In the early era of "Symbolic AI" (1950s-1980s), the field was characterized by a diversity of theoretical approaches and a lack of consensus, as well as technological limitations that confined researchers to working on simplified "toy problems." This led to an "AI Winter" in the 1980s as the field failed to deliver on its ambitious promises. The introduction of "benchmarking" by DARPA in the late 1980s marked a radical shift. Benchmarking provided a formal, quantitative evaluation system that prioritized a single metric - predictive accuracy. This allowed statistical machine learning approaches to overtake the rule-based symbolic AI methods, as the former could better leverage increasing compute power and data availability to optimize for the benchmark tasks. Benchmarking also required researchers to cede autonomy to external actors like DARPA, who set the agenda and problems to be solved. This transition away from autonomous, basic research towards task-driven science ultimately led to the dominance of deep learning, a machine learning approach uniquely positioned to benefit from scaling up compute and data. The success of deep learning on benchmarks crowded out nearly all other AI approaches, creating an "epistemic monoculture" in the field.
Stats
"What causes advancements in technology is when the money start pouring in. . . that is one way progress happens- when there is one clear application, a clear market, and the challenge has been formulated in a way that it's clear to the community what needs to be done." Elham Tabassi, NIST director and white house policy advisor "Word translation accuracy was acknowledged to be a crude measure that excluded important considerations like a word's frequency of use, semantics, idioms, or syntactic importance in the sentence. Yet it was able to arbitrate between competing approaches in a way that qualitative debates had failed."
Quotes
"ARPA was focused on excellence. Now, people outside the ARPA community of investigators would say that that's an insider's view, that in fact it was highly political; it was just that there was an 'in' group and an 'out' group. If you were a student of Newell, or Simon, or Minsky, or Fano, you were in." Edward Feigenbaum "We are safe in asserting that speech recognition is attractive to money. The attraction is perhaps similar to the attraction of schemes for turning water into gasoline, extracting gold from the sea, curing cancer, or going to the moon." John Pierce

Key Insights Distilled From

by Bernard J. K... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06647.pdf
From Protoscience to Epistemic Monoculture

Deeper Inquiries

How might the spread of deep learning and benchmarking evaluation impact the autonomy and diversity of other scientific fields as generative AI models become more widely adopted

The spread of deep learning and benchmarking evaluation could potentially impact the autonomy and diversity of other scientific fields as generative AI models become more widely adopted. As other scientific fields start to incorporate deep learning techniques and benchmarking evaluation methods, there may be a shift towards a more standardized approach to research. This could lead to a reduction in the diversity of research methodologies and a homogenization of scientific practices across different disciplines. The emphasis on predictive accuracy as the primary metric for success may overshadow other important aspects of research, such as theoretical innovation, creativity, and interdisciplinary collaboration. Additionally, the adoption of benchmarking evaluation systems may prioritize short-term, task-driven goals over long-term, exploratory research, potentially limiting the scope of scientific inquiry and innovation.

What are the potential downsides or unintended consequences of an epistemic monoculture focused solely on predictive accuracy, and how can these be mitigated

The potential downsides of an epistemic monoculture focused solely on predictive accuracy are significant. One major concern is the neglect of other important epistemic values, such as explainability, interpretability, fairness, and ethical considerations. By prioritizing predictive accuracy above all else, there is a risk of overlooking the broader implications and societal impacts of AI technologies. This narrow focus may also lead to the reinforcement of biases and inequalities present in the data used to train deep learning models, exacerbating existing social issues. To mitigate these consequences, it is essential to incorporate a more holistic evaluation framework that considers a range of epistemic values and ethical principles. This could involve developing multidimensional evaluation metrics, promoting transparency and accountability in AI research, and fostering interdisciplinary collaborations to address complex societal challenges.

Given the historical lessons from AI, how can funding agencies and scientific institutions better balance the need for task-driven, applied research with the value of autonomous, basic research for long-term scientific progress

Drawing from the historical lessons of AI, funding agencies and scientific institutions can better balance the need for task-driven, applied research with the value of autonomous, basic research by adopting a flexible and adaptive approach to research funding. Instead of rigidly categorizing research into basic or applied domains, funders can support projects that integrate both approaches and encourage interdisciplinary collaboration. By promoting a culture of innovation and risk-taking, while also emphasizing the importance of practical applications and societal impact, funding agencies can create a conducive environment for scientific progress. Additionally, investing in long-term, curiosity-driven research alongside targeted, goal-oriented projects can help maintain a balance between exploration and application in scientific endeavors. This approach can foster creativity, diversity, and resilience in the scientific community, leading to sustainable and impactful advancements in knowledge and technology.
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