AI strategist Lisa Palmer and privacy consultant Jodi Daniels discuss privacy concerns around the acquisition of biometric data. A significant expansion in Artificial intelligence (AI) facial recognition technology is increasingly being deployed to catch criminals, but experts express concern about the impact on personal privacy and data. According to the Allied Market Research data firm, the facial recognition industry, which was valued at $3.8 billion in 2020, will have grown to $16.7 billion by 2030. Lisa Palmer, an AI strategist, said it is important to understand that an individual's data largely feeds what happens from an AI perspective, especially within a generative framework. While there has been data recorded on citizens for decades, today's surveillance is different because of the quantity and quality of the data recorded as well as how it's being used, according to Palmer.

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a limited time is essential and difficult. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai and demonstrate its ability to answer challenging questions accurately in three different QA scenarios: asking from 1) GPT-4, 2) chatClimate, and 3) hybrid chatClimate. The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the hybrid chatClimate provided more accurate answers, highlighting the effectiveness of our solution. This approach can be easily scaled for chatbots in specific domains, enabling the delivery of reliable and accurate information.

Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs. A motivation is the need for transparency in safety-critical AI-based systems, as mandated in various domains like automated driving. However, to use the concept representations for safety-relevant purposes, like inspection or error retrieval, these must be of high quality and, in particular, stable. This paper focuses on two stability goals when working with concept representations in computer vision CNNs: stability of concept retrieval and of concept attribution. The guiding use-case is a post-hoc explainability framework for object detection (OD) CNNs, towards which existing concept analysis (CA) methods are successfully adapted. To address concept retrieval stability, we propose a novel metric that considers both concept separation and consistency, and is agnostic to layer and concept representation dimensionality. We then investigate impacts of concept abstraction level, number of concept training samples, CNN size, and concept representation dimensionality on stability. For concept attribution stability we explore the effect of gradient instability on gradient-based explainability methods. The results on various CNNs for classification and object detection yield the main findings that (1) the stability of concept retrieval can be enhanced through dimensionality reduction via data aggregation, and (2) in shallow layers where gradient instability is more pronounced, gradient smoothing techniques are advised. Finally, our approach provides valuable insights into selecting the appropriate layer and concept representation dimensionality, paving the way towards CA in safety-critical XAI applications.

Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data for which sharing and centralizing remains a challenge as the result of data and privacy regulations. The federated learning technique is well-suited to tackle these challenges. However, federated learning comes with a new set of open problems related to communication overhead, efficient parameter aggregation, client selection strategies and more. In this work, we address the step prior to the initiation of a federated network for model training, client recruitment. By intelligently recruiting clients, communication overhead and overall cost of training can be reduced without sacrificing predictive performance. Client recruitment aims at pre-excluding potential clients from partaking in the federation based on a set of criteria indicative of their eventual contributions to the federation. In this work, we propose a client recruitment approach using only the output distribution and sample size at the client site. We show how a subset of clients can be recruited without sacrificing model performance whilst, at the same time, significantly improving computation time. By applying the recruitment approach to the training of federated models for accurate patient Length of Stay prediction using data from 189 Intensive Care Units, we show how the models trained in federations made up from recruited clients significantly outperform federated models trained with the standard procedure in terms of predictive power and training time.

Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects.

Elon Musk may have to answer detailed questions regarding a fatal 2018 Tesla crash where Autopilot was involved. Judge Evette Pennypacker has ordered Musk to give a three-hour deposition in a lawsuit over the crash, which killed Apple engineer Walter Huang when his Model X plowed into a highway median south of San Francisco. Attorneys for Huang's family want to grill the tech CEO over statements he made about Autopilot's capabilities in the years before the incident. Most notably, the plaintiffs point to a 2016 Code Conference interview (shown below) where Musk maintained that Tesla cars with Autopilot could already drive with "greater safety than a person." They're also concerned about a 2016 self-driving demo video that engineers testified was staged to show features that weren't ready.

Of all its characters, Nintendo is best represented by Kirby, a cutesy pink blob named after an intellectual property litigator. In 1983, John Kirby convinced a judge that Donkey Kong was not a trademark infringement of Universal Pictures' King Kong. The win helped pave the way for the company's wild success in the video game industry. Now, it is Nintendo that doles out the legal claims to protect its IP. The latest fan fixed in the hot glare of Nintendo's Sauronic eye also happens to be one of the most well-known.

Twitter CEO Elon Musk provides insight on the consequences of developing artificial intelligence and the potential impact on elections on "Tucker Carlson Tonight." Congress is under increasing pressure from technology giants and others to find a way to regulate artificial intelligence, and a likely candidate for early action is a bill that both Republicans and Democrats supported in the last Congress under Democrat leadership. In 2022, the House Energy and Commerce Committee passed the American Data Privacy and Protection Act (ADPPA), a bill that's aimed at boosting data privacy rights but would also play a big role in regulating emerging AI systems. The ADPPA won almost unanimous support from both parties last year and continues to be supported by companies that are eager to build trust in their AI products, and they believe that a federal regulatory structure will help them get there. BSA/Software Alliance represents dozens of companies, including Microsoft, Okta, Salesforce and others, that build software and AI tools that companies use to run their businesses. BSA is working closely with the committee to get a version of that bill passed this year that it hopes can be approved in a full House vote.

A California judge has tentatively ordered Elon Musk to be interviewed under oath about whether he made certain statements regarding the capabilities of Tesla's Autopilot features after the company questioned the authenticity of the remarks, claiming Musk is a "target for deep fakes". The ruling came in a lawsuit against Tesla, filed by the family of Walter Huang who was killed in a car crash in 2018. Huang's family argues Tesla's partially automated driving software failed. The carmaker contends Huang was playing a video game on his phone before the crash and disregarded vehicle warnings. The attorneys for Huang's family sought to depose Musk regarding recorded statements from 2016 in which he allegedly said: "A Model S and Model X, at this point, can drive autonomously with greater safety than a person.

Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by any algorithm. Traditional DP formulations impose a uniform privacy requirement for all users, which is often inconsistent with real-world scenarios in which users dictate their privacy preferences individually. This work considers the problem of mean estimation under heterogeneous DP constraints, where each user can impose their own distinct privacy level. The algorithm we propose is shown to be minimax optimal when there are two groups of users with distinct privacy levels. Our results elicit an interesting saturation phenomenon that occurs as one group's privacy level is relaxed, while the other group's privacy level remains constant. Namely, after a certain point, further relaxing the privacy requirement of the former group does not improve the performance of the minimax optimal mean estimator. Thus, the central server can offer a certain degree of privacy without any sacrifice in performance.