What the data says about Americans’ views of artificial intelligence - Pew Research Center
<a href="https://news.google.com/rss/articles/CBMiswFBVV95cUxNbHdveVdhU05ad0psbzA1THNxbzFGYThRcXFqRnBmQUpCVERtd2pfRnV1cjIwUkpNV1Y2WmhIaXZLZVVsQ3BNVGdIWFNTeFloTWpCM1QxcTNvVHBYR1paV2JTTWFmN2ZlYmJfRGgyb3lwTTFIOVR2WkRjcmhJbkJZaHJHUDhJako5YTVpYjVDTndjcmo4bV9VcjhQZFZRM0hKdVZyZjFjQmk5eGxsYm9tRk9abw?oc=5" target="_blank">What the data says about Americans’ views of artificial intelligence</a> <font color="#6f6f6f">Pew Research Center</font>
Could not retrieve the full article text.
Read on GNews AI USA →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
research
“Alignment” and “Safety”, part one: What is “AI Safety”?
If you’re already familiar with the history of the field, you might wanna skip this one… I like to imagine future historians trying to follow the discourse around AI during the time I’ve been in the field… “Wait, so the AI ethics people think that the AI safety people are the same as the accelerationists and hate them? And the accelerationists think the safety people are the same as the ethicists and hate them ? And the AI safety people want to be friends with both of them!?” In a recent conversation with a researcher, they told me: “Yeah, I work on that, but I just do alignment, not that crazy safety stuff”. Five years ago, they might’ve said the opposite! When I wrote my PhD thesis in 2021, I said: > Until recently, “AI safety” was the most commonly used term for technical work on reduci
Direct Access for Answers to Conjunctive Queries with Aggregation
arXiv:2303.05327v3 Announce Type: replace Abstract: We study the fine-grained complexity of conjunctive queries with grouping and aggregation. For common aggregate functions (e.g., min, max, count, sum), such a query can be phrased as an ordinary conjunctive query over a database annotated with a suitable commutative semiring. We investigate the ability to evaluate such queries by constructing in loglinear time a data structure that provides logarithmic-time direct access to the answers ordered by a given lexicographic order. This task is nontrivial since the number of answers might be larger than loglinear in the size of the input, so the data structure needs to provide a compact representation of the space of answers. In the absence of aggregation and annotation, past research establishe
Human-Robot Copilot for Data-Efficient Imitation Learning
arXiv:2604.03613v1 Announce Type: new Abstract: Collecting human demonstrations via teleoperation is a common approach for teaching robots task-specific skills. However, when only a limited number of demonstrations are available, policies are prone to entering out-of-distribution (OOD) states due to compounding errors or environmental stochasticity. Existing interactive imitation learning or human-in-the-loop methods try to address this issue by following the Human-Gated DAgger (HG-DAgger) paradigm, an approach that augments demonstrations through selective human intervention during policy execution. Nevertheless, these approaches struggle to balance dexterity and generality: they either provide fine-grained corrections but are limited to specific kinematic structures, or achieve generalit
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!