The question of whether AI-generated outputs can be patented is impacting how technology companies can protect their intellectual property. Some of the most hyped up AI technologies are systems that can produce surprisingly creative outputs. Uncanny poems, short stories, and striking digital art have all been generated by machines. The human effort required to initiate these processes are often trivial: a few clicks or typing a text description can guide the machine towards producing something useful. Similar generative AI models are also being applied in scientific and technological applications.

Meta's new chatbot has made the day for many on social media after giving honest opinions about its own boss, Mark Zuckerberg. The company recently released a new chatbot called BlenderBot 3, and the internet took it on a test run. A lot of users, including the media, also asked Meta AI about its opinion on the company's founder, to which it gave some interesting answers. The system can chat with a human on almost every topic by searching the internet. Meta previously warned that the chatbot could be "rude" or "offensive" to some as it "learned" the human language from widely available public data.

Government agencies collect a lot of data, and have access to even more of it in their archives. The trick has always been trying to tap into that store of information to improve decision-making, which is a major focus in government these days. The President's Management Agenda, for example, emphasizes the importance of data-driven decision-making to improve federal services. The volume of data that most agencies are working with is such that humans can't easily tap into it for help with that decision-making. And even if they can perform searches into that data, the process is slow.

Role requiring'No experience data provided' months of experience in Chicago A Series B SaaS company in the cyber security realm is looking for a Senior Java Developer to join a fast paced agile team in their Mclean office, although this role would offer partial remote. The ideal candidate will be brought in to expand a highly scalable fault tolerant cloud service, working with micro-service architecture, auto scaling, and application development. Qualified candidates will have experience with J2ee technologies, developed web services from scratch, interfaced external API's with Rest, understanding of spring frameworks, experience with AWS and exposure to CI/CD with Jenkins. If you are interested in join an industry favorite start-up please apply. Applicants must be currently authorized to work in the United States on a full-time basis now and in the future.

The transferability of adversarial examples (AEs) across diverse models is of critical importance for black-box adversarial attacks, where attackers cannot access the information about black-box models. However, crafted AEs always present poor transferability. In this paper, by regarding the transferability of AEs as generalization ability of the model, we reveal that vanilla black-box attacks craft AEs via solving a maximum likelihood estimation (MLE) problem. For MLE, the results probably are model-specific local optimum when available data is small, i.e., limiting the transferability of AEs. By contrast, we re-formulate crafting transferable AEs as the maximizing a posteriori probability estimation problem, which is an effective approach to boost the generalization of results with limited available data. Because Bayes posterior inference is commonly intractable, a simple yet effective method called MaskBlock is developed to approximately estimate. Moreover, we show that the formulated framework is a generalization version for various attack methods. Extensive experiments illustrate MaskBlock can significantly improve the transferability of crafted adversarial examples by up to about 20%.

Future wireless networks (5G and beyond) are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have been dramatically growth with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated in applications throughout all layers of the network. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB's 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks, while making models more robust against any attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack against the channel estimation model. The results indicated that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.

3D posture estimation is important in analyzing and improving ergonomics in physical human-robot interaction and reducing the risk of musculoskeletal disorders. Vision-based posture estimation approaches are prone to sensor and model errors, as well as occlusion, while posture estimation solely from the interacting robot's trajectory suffers from ambiguous solutions. To benefit from the advantages of both approaches and improve upon their drawbacks, we introduce a low-cost, non-intrusive, and occlusion-robust multi-sensory 3D postural estimation algorithm in physical human-robot interaction. We use 2D postures from OpenPose over a single camera, and the trajectory of the interacting robot while the human performs a task. We model the problem as a partially-observable dynamical system and we infer the 3D posture via a particle filter. We present our work in teleoperation, but it can be generalized to other applications of physical human-robot interaction. We show that our multi-sensory system resolves human kinematic redundancy better than posture estimation solely using OpenPose or posture estimation solely using the robot's trajectory. This will increase the accuracy of estimated postures compared to the gold-standard motion capture postures. Moreover, our approach also performs better than other single sensory methods when postural assessment using RULA assessment tool.

Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters. We argue that storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of knowledge and resource requirements. We posit that a more efficient alternative is to provide explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. We present LM-CORE -- a general framework to achieve this -- that allows \textit{decoupling} of the language model training from the external knowledge source and allows the latter to be updated without affecting the already trained model. Experimental results show that LM-CORE, having access to external knowledge, achieves significant and robust outperformance over state-of-the-art knowledge-enhanced language models on knowledge probing tasks; can effectively handle knowledge updates; and performs well on two downstream tasks. We also present a thorough error analysis highlighting the successes and failures of LM-CORE.

Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making. This work examines obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform's legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset.

The US Court of Appeals for the Federal Circuit found that an artificial intelligence (AI) software system cannot be listed as an inventor on a patent application because the Patent Act requires an "inventor" to be a natural person. Stephen Thaler develops and runs AI systems that generate patentable inventions, including a system that he calls his "Device for the Autonomous Bootstrapping of Unified Science" (DABUS). In 2019, Thaler sought patent protection for two of DABUS's putative inventions by filing patent applications with the US Patent & Trademark Office (PTO). Thaler listed DABUS as the sole inventor on both applications. The PTO found that the patent applications lacked valid inventorship and sent a Notice of Missing Parts requesting that Thaler identify a valid inventor.