To tackle this problem, we rigorously analyze a representative distillation-based algorithm in the view of kernel regression.

To tackle this problem, we rigorously analyze a representative distillation-based algorithm in the view of kernel regression.

Over the past decade, there is a growing interest in collaborative learning that can enhance AI models of multiple parties. However, it is still challenging to enhance performance them without sharing private data and models from individual parties. One recent promising approach is to develop distillation-based algorithms that exploit unlabeled public data but the results are still unsatisfactory in both theory and practice. To tackle this problem, we rigorously analyze a representative distillation-based algorithm in the view of kernel regression. This work provides the first theoretical results to prove the (nearly) minimax optimality of the nonparametric collaborative learning algorithm that does not directly share local data or models in massively distributed statistically heterogeneous environments. Inspired by our theoretical results, we also propose a practical distillation-based collaborative learning algorithm based on neural network architecture. Our algorithm successfully bridges the gap between our theoretical assumptions and practical settings with neural networks through feature kernel matching. We simulate various regression tasks to verify our theory and demonstrate the practical feasibility of our proposed algorithm.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Biden administration said on Tuesday it was taking the first step toward writing key standards and guidance for the safe deployment of generative artificial intelligence and how to test and safeguard systems. The Commerce Department's National Institute of Standards and Technology (NIST) said it was seeking public input by Feb. 2 for conducting key testing crucial to ensuring the safety of AI systems. Commerce Secretary Gina Raimondo said the effort was prompted by President Joe Biden's October executive order on AI and aimed at developing "industry standards around AI safety, security, and trust that will enable America to continue leading the world in the responsible development and use of this rapidly evolving technology."

Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models from being plagiarized or misused, therefore, motivates us to propose a provable secure model ownership verification scheme using zero-knowledge proof, named FedZKP. It is shown that the FedZKP scheme without disclosing credentials is guaranteed to defeat a variety of existing and potential attacks. Both theoretical analysis and empirical studies demonstrate the security of FedZKP in the sense that the probability for attackers to breach the proposed FedZKP is negligible. Moreover, extensive experimental results confirm the fidelity and robustness of our scheme.

We're clarifying how ChatGPT's behavior is shaped and our plans for improving that behavior, allowing more user customization, and getting more public input into our decision-making in these areas. OpenAI's mission is to ensure that artificial general intelligence (AGI)[1] benefits all of humanity. We therefore think a lot about the behavior of AI systems we build in the run-up to AGI, and the way in which that behavior is determined. Since our launch of ChatGPT, users have shared outputs that they consider politically biased, offensive, or otherwise objectionable. In many cases, we think that the concerns raised have been valid and have uncovered real limitations of our systems which we want to address.

Cities are looking at a number of solutions to these problems. Some require disclosure when an AI model is used in decisions, while others mandate audits of algorithms, track where AI causes harm or seek public input before putting new AI systems in place. What would you like to see cities do to make their use of AI more transparent and fair? It will take time for cities and local bureaucracies to build expertise in these areas and figure out how to craft the best regulations, says Joanna Bryson, a professor of ethics and technology at the Hertie School in Berlin. But such efforts could provide a model for other cities, and even nations that are trying to craft standards of their own, she says.

The Los Angeles Police Department's slow and careful process for developing a policy on how it will deploy drones is imperfect, but Chief Charlie Beck and his department are approaching the question in the proper spirit, taking public input and considering the many very serious concerns about drones being used for unwarranted police snooping. If only L.A. County Sheriff Jim McDonnell would take heed. Both the LAPD and the Sheriff's Department have already acquired the small, remote-controlled and camera-equipped devices that could prove valuable in providing an aerial view of tense standoffs -- or could just as easily be misused to ramp up intrusive public surveillance, ostensibly in the name of crime prevention. McDonnell unveiled his program in January as a done deal and has deployed one drone despite criticism from members of the Sheriff Civilian Oversight Commission, who want publicly vetted standards for using the equipment. Beck, by contrast, has sworn off drone flights pending the drafting of guidelines and a series of public meetings, and amid demonstrations by activists who oppose any use of the devices in their belief -- not altogether unreasonable, given how some departments have used red-light cameras and license-plate readers -- that once police have them they will be prone to misuse them.

We introduce the problem Max#SAT, an extension of model counting (#SAT). Given a formula over sets of variables X, Y, and Z, the Max#SAT problem is to maximize over the variables X the number of assignments to Y that can be extended to a solution with some assignment to Z. We demonstrate that Max#SAT has applications in many areas, showing how it can be used to solve problems in probabilistic inference (marginal MAP), planning, program synthesis, and quantitative information flow analysis. We also give an algorithm which by making only polynomially many calls to an NP oracle can approximate the maximum count to within any desired multiplicative error. The NP queries needed are relatively simple, arising from recent practical approximate model counting and sampling algorithms, which allows our technique to be effectively implemented with a SAT solver. Through several experiments we show that our approach can be successfully applied to interesting problems.

The government can support the development of artificial intelligence by helping educate the public on the technology's potential applications and establishing an ethics policy on its use, several experts told the White House in response to a recent call for public input on future of AI. The White House Office of Science and Technology Policy submitted a request for information earlier this summer to help develop its stance on artificial intelligence, and several organizations responded to the call by outlining benefits and the challenges they believe researchers and policymakers will encounter as AI develops. The government needs to educate the public on artificial intelligence and dispel theories that it will lead to a robot apocalypse, Joshua New, policy analyst for the Center for Data Innovation, told FedScoop. "Destigmatizing it in the public sphere, saying, 'This is like a great technological benefit, we should be pursuing it aggressively,' I think that's the most important thing that the government can do," New said. The Center for Data Innovation responded to the RFI by noting that government should continue to talk about the benefits of AI and attempt to dispel fears of the technology.