California Governor Gavin Newsom has vetoed what would have become one of the most comprehensive policies governing the safety of artificial intelligence in the U.S. The bill would've been among the first to hold AI developers accountable for any severe harm caused by their technologies. It drew fierce criticism from some prominent Democrats and major tech firms, including ChatGPT creator OpenAI and venture capital firm Andreessen Horowitz, who warned it could stall innovation in the state. Newsom described the legislation as "well-intentioned" but said in a statement that it would've applied "stringent standards to even the most basic functions." Regulation should be based on "empirical evidence and science," he said, pointing to his own executive order on AI and other bills he's signed that regulate the technology around known risks such as deepfakes. The debate around California's SB 1047 bill highlights the challenge that lawmakers around the world are facing in controlling the risks of AI while also supporting the emerging technology.

Over the past decade and a half, I have attended many meetings and conferences, and met many people in Western governments, think tanks and academia who have been concerned about the rise of autocracies across the world. Many of them believe that authoritarian tendencies are the biggest threat to the liberal world order and rules-based system. But I beg to differ. I believe the biggest threat to the liberal world order comes from liberal democracies and not their autocratic nemeses. That is because there is a widening chasm between the values Western governments proclaim to uphold and their actual conduct.

Recent years have seen a growing interest in Scene Graph Generation (SGG), a comprehensive visual scene understanding task that aims to predict entity relationships using a relation encoder-decoder pipeline stacked on top of an object encoder-decoder backbone. Unfortunately, current SGG methods suffer from an information loss regarding the entities local-level cues during the relation encoding process. To mitigate this, we introduce the Vision rElation TransfOrmer (VETO), consisting of a novel local-level entity relation encoder. We further observe that many existing SGG methods claim to be unbiased, but are still biased towards either head or tail classes. To overcome this bias, we introduce a Mutually Exclusive ExperT (MEET) learning strategy that captures important relation features without bias towards head or tail classes. Experimental results on the VG and GQA datasets demonstrate that VETO + MEET boosts the predictive performance by up to 47 percentage over the state of the art while being 10 times smaller.

[HHM20] discovered, for 7 pairs (C,D) of seemingly distinct standard electoral control types, that C and D are identical: For each input I and each election system, I is a Yes instance of both C and D, or of neither. Surprisingly this had gone undetected, even as the field was score-carding how many std. control types election systems were resistant to; various "different" cells on such score cards were, unknowingly, duplicate effort on the same issue. This naturally raises the worry that other pairs of control types are also identical, and so work still is being needlessly duplicated. We determine, for all std. control types, which pairs are, for elections whose votes are linear orderings of the candidates, always identical. We show that no identical control pairs exist beyond the known 7. We for 3 central election systems determine which control pairs are identical ("collapse") with respect to those systems, and we explore containment/incomparability relationships between control pairs. For approval voting, which has a different "type" for its votes, [HHM20]'s 7 collapses still hold. But we find 14 additional collapses that hold for approval voting but not for some election systems whose votes are linear orderings. We find 1 additional collapse for veto and none for plurality. We prove that each of the 3 election systems mentioned have no collapses other than those inherited from [HHM20] or added here. But we show many new containment relationships that hold between some separating control pairs, and for each separating pair of std. control types classify its separation in terms of containment (always, and strict on some inputs) or incomparability. Our work, for the general case and these 3 important election systems, clarifies the landscape of the 44 std. control types, for each pair collapsing or separating them, and also providing finer-grained information on the separations.

Over a tumultuous four years, the Trump administration has steadily emphasized the importance of artificial intelligence to American competitiveness. Now President Trump must decide whether to veto what would be the government's biggest-ever funding and strategy boost to AI. The National Defense Authorization Act would provide $6.4 billion in federal money over five years for research on AI and its applications, and it would push Washington toward developing a national strategy on the technology. The bill, approved by both houses of Congress, would increase federal AI spending with $4.8 billion for the National Science Foundation, $1.2 billion via the Energy Department, and $400 million for the National Institute of Standards and Technology. Martijn Rasser, a senior fellow of the Technology and National Security Program at the Center for New American Security, a strategy think tank in Washington, DC, says the funding is significant.

Much of the tech industry's focus on the National Defense Authorization Act has revolved around President Trump's threat to veto the must-pass defense spending bill because it does not repeal Section 230. But the final version of the NDAA, passed by a vast majority of both chambers this week, contains a little-noticed provision that promises to reverberate across the industry: a pledge to increase government spending on artificial intelligence, quantum computing and 5G technology by $10 billion annually over the next five years. It's unclear exactly how much the government spends to bolster those technologies today, but it's likely closer to $1.5 billion. The Industries of the Future Act of 2020, which was supported by IBM and software industry trade group BSA, was introduced earlier this year amid a broader push from the White House -- and Ivanka Trump -- to invest more government resources in "industries of the future," meaning emerging technologies like quantum computing and artificial intelligence. It's part of a broader effort to funnel more resources toward ensuring the U.S. has a leg up on China in the so-called "race" to technological dominance.

Understanding when and how computational complexity can be used to protect elections against different manipulative actions has been a highly active research area over the past two decades. A recent body of work, however, has shown that many of the NP-hardness shields, previously obtained, vanish when the electorate has single-peaked or nearly single-peaked preferences. In light of these results, we investigate whether it is possible to reimpose NP-hardness shields for such electorates by allowing the voters to specify partial preferences instead of insisting they cast complete ballots. In particular, we show that in single-peaked and nearly single-peaked electorates, if voters are allowed to submit top-truncated ballots, then the complexity of manipulation and bribery for many voting rules increases from being in P to being NP-complete.

It is well known that strategic behavior in elections is essentially unavoidable; we therefore ask: how bad can the rational outcome be? We answer this question via the notion of the price of anarchy, using the scores of alternatives as a proxy for their quality and bounding the ratio between the score of the optimal alternative and the score of the winning alternative in Nash equilibrium. Specifically, we are interested in Nash equilibria that are obtained via sequences of rational strategic moves. Focusing on three common voting rules — plurality, veto, and Borda — we provide very positive results for plurality and very negative results for Borda, and place veto in the middle of this spectrum.

Successive elimination of candidates is often a route to making manipulation intractable to compute. We prove that eliminating candidates does not necessarily increase the computational complexity of manipulation. However, for many voting rules used in practice, the computational complexity increases. For example, it is already known that it is NP-hard to compute how a single voter can manipulate the result of single transferable voting (the elimination version of plurality voting). We show here that it is NP-hard to compute how a single voter can manipulate the result of the elimination version of veto voting, of the closely related Coombs’ rule, and of the elimination versions of a general class of scoring rules.

Successive elimination of candidates is often a route to making manipulation intractable to compute. We prove that eliminating candidates does not necessarily increase the computational complexity of manipulation. However, for many voting rules used in practice, the computational complexity increases. For example, it is already known that it is NP-hard to compute how a single voter can manipulate the result of single transferable voting (the elimination version of plurality voting). We show here that it is NP-hard to compute how a single voter can manipulate the result of the elimination version of veto voting, of the closely related Coombs' rule, and of the elimination versions of a general class of scoring rules.