Google has just been hit with a $32.5 million penalty for infringing on a patent held by Sonos. According to Law360, a California federal jury ordered the fine after determining that Google infringed on a patent Sonos holds relating to grouping speakers so they can play audio at the same time, something the company has been doing for years. US District Judge William Alsup had already determined that early version of products like the Chromecast Audio and Google Home infringed on Sonos' patent; the question was whether more recent, revamped products were also infringing on the patent. The jury found in favor of Sonos, but decided a second patent -- one that relates to controlling devices via a smartphone or other device -- wasn't violated. They said that Sonos hadn't convincingly shown that the Google Home app infringed on that particular patent.

Elizabeth Holmes convinced investors and patients that she had a prototype of a microsampling machine that could run a wide range of relatively accurate tests using a fraction of the volume of blood usually required. She lied; the Edison and miniLab devices didn't work. Worse still, the company was aware they didn't work, but continued to give patients inaccurate information about their health, including telling healthy pregnant women that they were having miscarriages and producing false positives on cancer and HIV screenings. But Holmes, who has to report to prison by May 30, was convicted of defrauding investors; she wasn't convicted of defrauding patients. This is because the principles of ethics for disclosure to investors, and the legal mechanisms used to take action against fraudsters like Holmes, are well developed.

As the artificial intelligence frenzy builds, a sudden consensus has formed. While there's a very real question whether this is like closing the barn door after the robotic horses have fled, not only government types but also people who build AI systems are suggesting that some new laws might be helpful in stopping the technology from going bad. The idea is to keep the algorithms in the loyal-partner-to-humanity lane, with no access to the I-am-your-overlord lane. Though since the dawn of ChatGPT many in the technology world have suggested that legal guardrails might be a good idea, the most emphatic plea came from AI's most influential avatar of the moment, OpenAI CEO Sam Altman. "I think if this technology goes wrong, it can go quite wrong," he said in a much anticipated appearance before a US Senate Judiciary subcommittee earlier this month. "We want to work with the government to prevent that from happening."

Texas residents share how familiar they are with artificial intelligence on a scale from one to 10 and detailed how much they use it each day. The "Godfather of A.I.," Geoffrey Hinton, quit Google out of fear that his former employer intends to deploy artificial intelligence in ways that will harm human beings. "It is hard to see how you can prevent the bad actors from using it for bad things," Hinton recently told The New York Times. But stomping out the door does nothing to atone for his own actions, and it certainly does nothing to protect conservatives – who are the primary target of A.I. programmers – from being canceled. Here are five things to know as the battle over A.I. turns hot: Elon Musk recently revealed that Google co-founder Larry Page and other Silicon Valley leaders want AI to establish a "digital god" that "would understand everything in the world.

To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.

With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised setup, we notice a very high variance in quality among summary candidates from these models while only one candidate is kept as the summary output. In this paper, we propose to re-rank summary candidates in an unsupervised manner, aiming to close the performance gap between unsupervised and supervised models. Our approach improves the unsupervised PEGASUS by up to 7.27% and ChatGPT by up to 6.86% relative mean ROUGE across four widely-adopted summarization benchmarks ; and achieves relative gains of 7.51% (up to 23.73% from XSum to WikiHow) averaged over 30 zero-shot transfer setups (finetuning on a dataset, evaluating on another).

The rapid advancements in artificial intelligence (AI) have led to the development of powerful tools and platforms, such as ChatGPT, which have shown great promise in a wide range of applications, from language understanding to decision-making support. As AI systems continue to evolve and become more sophisticated, there is a growing interest in harnessing their potential for personalized support, assistance, and enhancement through human-AI interactions. One promising approach is developing AI systems that can share sensory experiences with humans, thereby fostering a stronger bond between the two entities. In this paper, we propose a novel concept called Symbiotic Artificial Intelligence with Shared Sensory Experiences (SAISSE), which aims to establish a mutually beneficial relationship between AI systems and human users through shared sensory experiences. By integrating multiple sensory input channels and processing human experiences, SAISSE enables AI systems, such as multimodal ChatGPT, to learn from and adapt to individual users, providing personalized support, assistance, and enhancement. The SAISSE concept represents a paradigm shift in AI-human interaction, where AI systems not only process and analyze data but also actively participate in shared sensory experiences, leading to more empathetic, responsive, and adaptive AI solutions. By fostering a stronger bond between humans and AI systems, SAISSE has the potential to revolutionize the way we interact with technology, paving the way for seamless integration of AI in our daily lives and enabling us to unlock new opportunities for human growth, development, and well-being.

Cryptocurrency forensics became standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations. On the other hand, it can have far-reaching consequences for the rights of those affected. Argumentation schemes could remedy this untenable situation by rendering underlying premises transparent. Additionally, they can aid in critically evaluating the probative value of any results obtained by cryptocurrency deanonymisation techniques. In the argumentation theory and AI community, argumentation schemes are influential as they state implicit premises for different types of arguments. Through their critical questions, they aid the argumentation participants in critically evaluating arguments. We specialise the notion of argumentation schemes to legal reasoning about cryptocurrency deanonymisation. Furthermore, we demonstrate the applicability of the resulting schemes through an exemplary real-world case. Ultimately, we envision that using our schemes in legal practice can solidify the evidential value of blockchain investigations as well as uncover and help address uncertainty in underlying premises - thus contributing to protect the rights of those affected by cryptocurrency forensics.

Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of minds consist of large language models (LLMs) and other NN-based experts communicating through a natural language interface. In doing so, they overcome the limitations of single LLMs, improving multimodal zero-shot reasoning. In these natural language-based societies of mind (NLSOMs), new agents -- all communicating through the same universal symbolic language -- are easily added in a modular fashion. To demonstrate the power of NLSOMs, we assemble and experiment with several of them (having up to 129 members), leveraging mindstorms in them to solve some practical AI tasks: visual question answering, image captioning, text-to-image synthesis, 3D generation, egocentric retrieval, embodied AI, and general language-based task solving. We view this as a starting point towards much larger NLSOMs with billions of agents-some of which may be humans. And with this emergence of great societies of heterogeneous minds, many new research questions have suddenly become paramount to the future of artificial intelligence. What should be the social structure of an NLSOM? What would be the (dis)advantages of having a monarchical rather than a democratic structure? How can principles of NN economies be used to maximize the total reward of a reinforcement learning NLSOM? In this work, we identify, discuss, and try to answer some of these questions.

Retrieval augmentation can aid language models (LMs) in knowledge-intensive tasks by supplying them with external information. Prior works on retrieval augmentation usually jointly fine-tune the retriever and the LM, making them closely coupled. In this paper, we explore the scheme of generic retrieval plug-in: the retriever is to assist target LMs that may not be known beforehand or are unable to be fine-tuned together. To retrieve useful documents for unseen target LMs, we propose augmentation-adapted retriever (AAR), which learns LM's preferences obtained from a known source LM. Experiments on the MMLU and PopQA datasets demonstrate that our AAR trained with a small source LM is able to significantly improve the zero-shot generalization of larger target LMs ranging from 250M Flan-T5 to 175B InstructGPT. Further analysis indicates that the preferences of different LMs overlap, enabling AAR trained with a single source LM to serve as a generic plug-in for various target LMs. Our code is open-sourced at https://github.com/OpenMatch/Augmentation-Adapted-Retriever.