Back in April 2021, the European Commission published its proposal for the Artificial Intelligence Regulation ("AI Regulation), which is currently making its way through the European legislative process. This draft AI Regulation seeks to harmonise rules on artificial intelligence by ensuring AI products are sufficiently safe and robust before they enter the EU market. The AI Regulation is intended to apply to what the EU terms "AI systems". The most recent iteration of this concept is defined (in summary) as all systems developed through machine learning approaches and logic, and knowledge-based approaches. This is a wide definition aimed to accommodate future developments in AI technology but extends to much of modern AI software. The broad scope of this definition is narrowed by the operational impact of the draft legislation, as the AI Regulation takes a'risk-based approach' to governing AI systems.

Tesla has applied for a set of patents that are set to significantly improve virtualization, recognition, and Full Self Driving overall. Tesla has worked tirelessly to improve full self-driving technology in the first two months of the year. Most recently, Tesla pushed its most significant improvement to employees, v11.3. Still, with new patented technology, the software is set to continue to improve dramatically this year. The two patents, focusing on virtualization and machine learning, appeared in the U.S. Patent Office database late last week.

Over the past ten years, Artificial Intelligence (AI) and Machine Learning (ML) have steadily crept into the Art Industry. From Deepfakes to DALL·E, the impact of these new technologies can be longer be ignored, and many communities are now on the edge of a reckoning. On one side, the potential for modern AIs to generate and edit both images and videos is opening new job opportunities for millions; but on the other is also threatening a sudden and disruptive change across many industries. The purpose of this long article is to serve as an introduction to the complex topic of AI Art: from the technologies that are powering this revolution, to the ethical and legal issues they have unleashed. While this is still an ongoing conversation, I hope it will serve as a primer for anyone interested in better understanding these phenomena--especially journalists who are keen to learn more about the benefits, changes and challenges that that AI will inevitably bring into our own lives.

While a picture might speak a thousand words, it only takes a few words in a text box to generate a picture these days, one that might even be considered top notch artwork. Artificial intelligence (AI) is to thank for this, or perhaps to blame. While artificial intelligence has long produced art, recent tools such as DALL-E 2, Midjourney, and Stable Diffusion, have given rise to an AI generated art boom that allows even the most uncreative among us to produce intricate, abstract, or lifelike pieces by merely entering a few words into a text box. For some, the potential and possibilities of these AI tools to democratise craftsmanship and make creativity more accessible to everyone fills them with excitement, for others it fills them with dread and a moral panic about real artists being replaced by machines, an angle that is often pushed by the news media. Dillah Zakbah, creative director and partner at BBH, says that while much has been written in the press from a position of AI replacing human talent, not much has been looked at or said about it from the point of view of using it as a tool.

If you read my work you probably know that I publish my articles first and foremost in my AI newsletter, The Algorithmic Bridge. What you may not know is that every Sunday I publish a special column I call "what you may have missed," where I review everything that has happened during the week with analyses that help you make sense of the news. Semafor reported two weeks ago that, if everything goes according to the plan, Microsoft will close a $10B investment deal with OpenAI before the end of January (Satya Nadella, Microsoft's CEO, announced the extended partnership officially on Monday). There's been some misinformation about the deal which implied that OpenAI execs weren't sure about the company's long-term viability. Leo L'Orange, who writes The Neuron, explains that "once $92 billion in profit plus $13 billion in initial investment are repaid to Microsoft and once the other venture investors earn $150 billion, all of the equity reverts back to OpenAI."

With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms - TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. We observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain from the popular dogma of ``one-size-fits-all''.

ChatGPT has gained huge attention and discussion worldwide, with responsible AI being a crucial topic of discussion. One key question is how we can ensure that AI systems, like ChatGPT, are developed and adopted in a responsible way? Responsible AI is the practice of developing, deploying, and maintaining AI systems in a way that benefits the humans, society, and environment, while minimising the risk of negative consequences. To solve the challenge of responsible AI, many AI ethics principles have been released recently by governments, organisations, and enterprises [1]. A principle-based approach provides technology-neutral and context-independent guidance while allowing contextspecific interpretations for implementing responsible AI. However, those principles are too abstract and high-level for practitioners to use in practice. For example, it is a very challenging and complex task to operationalise the the human-centered value principle regarding how it can be designed for, implemented and monitored throughout the entire lifecycle of AI systems. In addition, the existing work mainly focuses on algorithm-level solutions for a subset of mathematics-amenable AI ethics principles (such as privacy and fairness). However, responsible AI issues can happen at any stage of the development lifecycle crosscutting various AI and non-AI components of systems beyond AI algorithms and models.

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications which are ranked as High Risk by the European Commission in the A.I. act, as for instance for online job candidate recommendation. When used in the European Union, commercial AI systems for this purpose will then be required to have to proper statistical properties with regard to potential discrimination they could engender. This motivated our contribution, where we present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural-network classification. Our stratey is model agnostic and can be used on any multi-class classification neural-network model. To anticipate the certification of recommendation systems using textual data, we then used it on the Bios dataset, for which the learning task consists in predicting the occupation of female and male individuals, based on their LinkedIn biography. Results show that it can reduce undesired algorithmic biases in this context to lower levels than a standard strategy.

ROOTS is a 1.6TB multilingual text corpus developed for the training of BLOOM, currently the largest language model explicitly accompanied by commensurate data governance efforts. In continuation of these efforts, we present the ROOTS Search Tool: a search engine over the entire ROOTS corpus offering both fuzzy and exact search capabilities. ROOTS is the largest corpus to date that can be investigated this way. The ROOTS Search Tool is open-sourced and available on Hugging Face Spaces. We describe our implementation and the possible use cases of our tool.

Today, creators of data-hungry deep neural networks (DNNs) scour the Internet for training fodder, leaving users with little control over or knowledge of when their data is appropriated for model training. To empower users to counteract unwanted data use, we design, implement and evaluate a practical system that enables users to detect if their data was used to train an DNN model. We show how users can create special data points we call isotopes, which introduce "spurious features" into DNNs during training. With only query access to a trained model and no knowledge of the model training process, or control of the data labels, a user can apply statistical hypothesis testing to detect if a model has learned the spurious features associated with their isotopes by training on the user's data. This effectively turns DNNs' vulnerability to memorization and spurious correlations into a tool for data provenance. Our results confirm efficacy in multiple settings, detecting and distinguishing between hundreds of isotopes with high accuracy. We further show that our system works on public ML-as-a-service platforms and larger models such as ImageNet, can use physical objects instead of digital marks, and remains generally robust against several adaptive countermeasures.