Why AI is harder than we think

By Melanie Mitchell. 2021

134 citations recorded by the source corpus.

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI Spring") and periods of disappointment, loss of confidence, and reduced funding ("AI Winter"). Even with today's seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this talk I will discuss some fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I will also speculate on what is needed for the grand challenge of making AI systems more robust, general, and adaptable --- in short, more intelligent.

Subfields: Artificial Intelligence, Philosophy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Philosophy

Citations in the corpus

AI and the Everything in the Whole Wide World Benchmark (2021, background): “…issues have long been acknowledged broadly by the machine learning community as a particularly difﬁcult task in benchmark design and development [Mitchell, 2021, Jacobs and Wallach, 2021, Malik, 2020] — however as Bowman and Dahl note, it remains a generally neglected issue as “[t]his…”
Can language models learn from explanations in context? (2022, background): “LMs could beneﬁt from explanations without necessarily “understanding” explanations in a human-like way (cf. Mitchell, 2021).”
Multimodal datasets: misogyny, pornography, and malignant stereotypes (2021, background): “The emergence of deep learning aided computer vision as a notable ﬁeld of Artiﬁcial Intelligence (AI) ushered the so-termed AI spring [1] and has been characterized by its voracious need for vast volumes of data.”
Human-Centered AI (2021, background): “[2] As a result, aligning system values and actions with the objective of the human continues to be harder than we think [3], [4] .”
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “Of course, we are not referring to the human understanding of “awareness” [73], but rather a new kind of specialized intelligence that is able to understand signiﬁcant amounts of numerical data and draw domain-speciﬁc conclusions from it faster than a human expert could.”
Optimal Policies Tend To Seek Power (2019, background): “However, another possibility is that such concerns simply arise from the anthropomorphization of AI systems [LeCun and Zador, 2019, Various, 2019, Pinker and Russell, 2020, Mitchell, 2021].”