We have intellectual property (IP) protection watermarks on media contents such as images, musics and etc. A watermark is like an identity given to your media content, e.g. This is to identify that the content is made by you and people who use your content should pay you some money. We can apply the same to DNN since the improvement of DNN is going to improve every year and a lot of companies start using DNN in their businesses. Lets say you have invested a lot of resources (e.g.

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Peter Kairouz and H. Brendan McMahan conceived, coordinated, and edited this work.

Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically assess explainable systems along five key dimensions: functional, operational, usability, safety and validation. In order to design a comprehensive and representative taxonomy and associated descriptors we surveyed the eXplainable Artificial Intelligence literature, extracting the criteria and desiderata that other authors have proposed or implicitly used in their research. The survey includes papers introducing new explainability algorithms to see what criteria are used to guide their development and how these algorithms are evaluated, as well as papers proposing such criteria from both computer science and social science perspectives. This novel framework allows to systematically compare and contrast explainability approaches, not just to better understand their capabilities but also to identify discrepancies between their theoretical qualities and properties of their implementations. We developed an operationalisation of the framework in the form of Explainability Fact Sheets, which enable researchers and practitioners alike to quickly grasp capabilities and limitations of a particular explainable method. When used as a Work Sheet, our taxonomy can guide the development of new explainability approaches by aiding in their critical evaluation along the five proposed dimensions.

Earlier this year, artificial intelligence yielded a practical insight: people like to drink coffee in the morning, so workplaces should find efficient ways to serve coffee. That raised a question that's surprisingly deep -- and can cost serious money to ignore: Is AI actually necessary for this problem? is a question that remains largely unasked in Silicon Valley today. We think it's worth asking. To be sure, modern data products owe a lot of their success to artificial intelligence. Well-considered AI unlocks entirely new types of data-driven insights and cuts the time and money needed for manual data analysis. But ill-considered AI can fail -- expensively.

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices – or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?

The adoption of new technologies such as artificial intelligence could lead to the UK legal sector shedding 13,000 jobs, according to a report by the Law Society of England and Wales. The report on the future shape of the legal workforce projected a 13,000 fall in the number of jobs by 2027, equivalent to a four per cent drop. The body said the number of legal secretaries is projected to fall by nearly two thirds and other office support staff by a quarter. Many major law firms have already axed support staff, particularly in expensive locations such as London. Magic Circle firm Freshfields Bruckhaus Deringer offered voluntary redundancy to all 180 of its secretaries in London in 2017, while both Ashurst and Baker McKenzie have made staff cuts in the City this year.

The European Commission provides a platform for online dispute resolution (ODR): https://ec.europa.eu/consumers/odr. Our e-mail address can be found above in the site notice. We are not willing or obliged to participate in dispute resolution proceedings before a consumer arbitration board. As service providers, we are liable for own contents of these websites according to Paragraph 7, Sect. 1 German Telemedia Act (TMG). However, according to Paragraphs 8 to 10 German Telemedia Act (TMG), service providers are not obligated to permanently monitor submitted or stored information or to search for evidences that indicate illegal activities.

Tata Consultancy Services (TCS) has published a new report titled Understanding the Dynamics of Artificial Intelligence in Intellectual Property. Designed to provide technologists, academicians, entrepreneurs, professionals and policymakers with insights on the frontiers in AI-related research and IP management and protection, the report was released today in partnership with the Confederation of Indian Industry (CII) at the 5th International Conference on IPR, organized by CII in collaboration with the Department for Promotion of Industry and Internal Trade, Government of India, and the Intellectual Property Office, India, said a press release from the company. The report captures trends in AI-related research globally, noting for example that while machine learning is the most common AI technique, mentioned in 89% of the patents filed, deep learning (DL) is the fastest-growing technique mentioned in patent filings. Another finding is that the highest number of patent families is in computer vision (49%), NLP (14%) and speech processing (13%). While it is no surprise that the US and China lead the world in AI-related patents, it comes as an eye-opener that China leads the world in deep learning, with the Chinese Academy of Sciences owning the largest DL-related patent portfolio and Baidu leading the pack among corporates globally.

Baidu, Inc. has filed the most AI-related patent applications in China, a recognition of the company's long-term commitment to driving technological advancement, a recent study from the research unit of China's Ministry of Industry and Information Technology (MIIT) has shown. Baidu filed a total of 5,712 AI-related patent applications as of October 2019, ranking No.1 in China for the second consecutive year. Baidu's patent applications were followed by Tencent (4,115), Microsoft (3,978), Inspur (3,755), and Huawei (3,656), according to the report issued by the China Industrial Control Systems Cyber Emergency Response Team, a research unit under the MIIT. "Baidu retained the top spot for AI patent applications in China because of our continuous research and investment in developing AI, as well as our strategic focus on patents," said Victor Liang, Vice President and General Counsel of Baidu. "In the future, we will continue to increase our investments into securing AI patents, especially for high-value and high-quality patents, to provide a solid foundation for Baidu's AI business and for our development of world-leading technology," he said.

Automated decision making comes of age.