Founded by The Allstate Corporation in 2016, Arity is a data and analytics company focused on improving transportation. We collect and analyze enormous amounts of data, using predictive analytics to build solutions with a single goal in mind: to make transportation smarter, safer and more useful for everyone. At the heart of that mission are the people that work here--the dreamers, doers and difference-makers that call this place home. As part of that team, your work will showcase both your intelligence and your creativity as you tackle real problems and put your talents towards transforming transportation. That's because at Arity, we believe work and life shouldn't be at odds with one another.

The World Intellectual Property Office (WIPO) held its third "Conversation on Intellectual Property and Artificial Intelligence" on November 4, 2020, to discuss its revised issues paper on Intellectual Property Policy and Artificial Intelligence. Public bodies in the United States, United Kingdom, and European Union have each recently published reports on the interrelationship of AI on IP policy. In October 2020, the United States Patent and Trademark Office (USPTO) published a report, Public Views on Artificial Intelligence and Intellectual Property Policy, on two formal requests for comments, and the European Parliament published a report on intellectual property rights for the development of AI technologies. In September 2020, the UK's Intellectual Property Office (UKIPO) published a call for views on the policy considerations and future relationship between AI and IP. Courts in each jurisdiction have so far rejected the suggestion that AI has its own legal personality.

In this paper, we consider a novel framework of positive-unlabeled data in which as positive data survival times are observed for subjects who have events during the observation time as positive data and as unlabeled data censoring times are observed but whether the event occurs or not are unknown for some subjects. We consider two cases: (1) when censoring time is observed in positive data, and (2) when it is not observed. For both cases, we developed parametric models, nonparametric models, and machine learning models and the estimation strategies for these models. Simulation studies show that under this data setup, traditional survival analysis may yield severely biased results, while the proposed estimation method can provide valid results.

Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab research to real world clinical practice. This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. We discuss early achievements in development of explainable AI for validation of known disease criteria, exploration of new potential biomarkers, as well as methods for the subsequent correction of AI models. Various explanation methods like visual, textual, post-hoc, ante-hoc, local and global have been thoroughly and critically analyzed. Subsequently, we also highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool and provide recommendations for the direction of future research.

This course has two main sections: one focused on effective business coaching, and the second focused on developing a successful business case. Two separate courses that already have more than 2000 students registered together. The section on The Key Stages of Coaching will involve learners in the process of discovery, goal setting, action planning, and follow-up that distinguishes coaching from other development methods. After completing the second section, you will be able to build an effective business case. You will understand what makes a business case, how to prepare one and how to design business cases to persuade decision makers.

Artificial Intelligence and machine learning have been hot topics in 2020 as AI and ML technologies increasingly find their way into everything from advanced quantum computing systems and leading-edge medical diagnostic systems to consumer electronics and "smart" personal assistants. Revenue generated by AI hardware, software and services is expected to reach $156.5 billion worldwide this year, according to market researcher IDC, up 12.3 percent from 2019. But it can be easy to lose sight of the forest for the trees when it comes to trends in the development and use of AI and ML technologies. As we approach the end of a turbulent 2020, here's a big-picture look at five key AI and machine learning trends– not just in the types of applications they are finding their way into, but also in how they are being developed and the ways they are being used. Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated – such as legacy business processes – should be automated.

Artificial Intelligence is transforming work, organisations, industries and society. Despite the many potential benefits of this general-purpose technology, there are significant challenges and risks, ranging from privacy, security, ethics, transparency and regulation. The prioritization of ethical, legal, and policy considerations in the development and management of AI systems to ensure responsible design, production and use of trustworthy AI requires integration of engineering, policy, law and ethics approaches. This special issue is the result of collaboration between the EU Horizon 2020 projects HumaneAI-Net, TAILOR and AI4EU. This special issue welcomes submissions from a wide variety of disciplines, including computer science, statistics, law, social sciences, the humanities, and education.

Several seminal ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, widespread criticism has pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach and the many shortcomings associated with it. This paper proposes a different approach. It defines four basic AI virtues, namely justice, honesty, responsibility and care, all of which represent specific motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or the many hidden psychological forces that impair ethical decision making and that are hitherto completely disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development.

Artificial intelligence (AI) has both surpassed and replaced humans in many fields. Will AI overpower humanity in the near or distant future? Will AI control humans and replace governments? Or will AI remain a tool that humans will use to improve their performances? Research on brain-computer interface (BCI) has begun and suggests that we implant chips or connect devices to our brain to increase computing power.

On 1st January 2019, we (Fabin Rasheed and I) had introduced to the world, a side project we've been working on for months. An artificial poet-artist, who doesn't physically exist in this world but writes a poem, draws an abstract art based on the poem and finally color the art based on emotion. We called "her" Auria Kathi -- an anagram for "AI Haiku Art". Auria has an artificial face along with her artificial poetry and art. Everything about Auria was built using artificial neural networks.