Also see Ray Kurzweil, The Singularity Is Near: Where Humans Transcend Biology (London: Penguin Books, 2006).

Developing policy informed by science and technology is now more complex than ever. Policymakers must address supply chains, climate change, inequality, technological breakthroughs, misinformation and more. Using artificial intelligence (AI) to mine the literature could put policymaking on a sounder footing. Advanced big-data and natural-language-processing models enable decision makers to look beyond conventional indicators and expert discussions. Millions of scientific articles, patents and market reports can be readily analysed to identify megatrends or fading topics, and to provide predictive opportunities (see go.nature.com/31snkp5). Machine learning can create maps of national competencies and centres of excellence of science and technology.

Further, we discuss only open academic research with entertaining wit and knowledge while making others feel reproducible published results, hence we will not address heard. The breadth of possible conversation topics and lack much of the considerable work that has been put into building of a well-defined objective make it challenging to define a commercial systems, where methods, data and results roadmap towards training a good conversational agent, or are not in the public domain. Finally, given that we focus on chatbot. Despite recent progress across the board (Adiwardana open-domain conversation, we do not focus on specific goaloriented et al., 2020; Roller et al., 2020), conversational agents techniques; we also do not cover spoken dialogue in are still incapable of carrying an open-domain conversation this work, focusing on text and image input/output only. For that remains interesting, consistent, accurate, and reliably more general recent surveys, see Gao et al. (2019); Jurafsky well-behaved (e.g., not offensive) while navigating a variety and Martin (2019); Huang, Zhu, and Gao (2020). of topics. Traditional task-oriented dialogue systems rely on slotfilling and structured modules (e.g., Young et al. (2013); Gao et al. (2019); Jurafsky and Martin (2019)).

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.

Humans learn all their life long. They accumulate knowledge from a sequence of learning experiences and remember the essential concepts without forgetting what they have learned previously. Artificial neural networks struggle to learn similarly. They often rely on data rigorously preprocessed to learn solutions to specific problems such as classification or regression. In particular, they forget their past learning experiences if trained on new ones. Therefore, artificial neural networks are often inept to deal with real-life settings such as an autonomous-robot that has to learn on-line to adapt to new situations and overcome new problems without forgetting its past learning-experiences. Continual learning (CL) is a branch of machine learning addressing this type of problem. Continual algorithms are designed to accumulate and improve knowledge in a curriculum of learning-experiences without forgetting. In this thesis, we propose to explore continual algorithms with replay processes. Replay processes gather together rehearsal methods and generative replay methods. Generative Replay consists of regenerating past learning experiences with a generative model to remember them. Rehearsal consists of saving a core-set of samples from past learning experiences to rehearse them later. The replay processes make possible a compromise between optimizing the current learning objective and the past ones enabling learning without forgetting in sequences of tasks settings. We show that they are very promising methods for continual learning. Notably, they enable the re-evaluation of past data with new knowledge and the confrontation of data from different learning-experiences. We demonstrate their ability to learn continually through unsupervised learning, supervised learning and reinforcement learning tasks.

It took a global pandemic and stay-at-home orders for 1.5 billion people worldwide, but something is finally occurring to us: The future we thought we expected may not be the one we get. We know that things will change; how they'll change is a mystery. To envision a future altered by coronavirus, Quartz asked dozens of experts for their best predictions on how the world will be different in five years. Below is an answer from Zia Khan, the senior vice president of innovation at The Rockefeller Foundation, a private foundation that seeks to promote humanity's wellbeing. Many of his professional experiences--as a management consultant, serving on the World Economic Forum Advisory Council for Social Innovation--have helped show him how to use data and technology to positively transform people's lives.

A US university's claim it can use facial recognition to "predict criminality" has renewed debate over racial bias in technology. Harrisburg University researchers said their software "can predict if someone is a criminal, based solely on a picture of their face". The software "is intended to help law enforcement prevent crime", it said. But 1,700 academics have signed an open letter demanding the research remains unpublished. One Harrisburg research member, a former police officer, wrote: "Identifying the criminality of [a] person from their facial image will enable a significant advantage for law-enforcement agencies and other intelligence agencies to prevent crime from occurring."

These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and development is geared towards researchers and practitioners alike who are making decisions on behalf of their organizations in considering the societal impacts of AI-enabled solutions. We cover a wide set of areas in this report spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over the past quarter surfacing signal from the noise so that you are equipped with the right tools and knowledge to confidently tread this complex yet consequential domain.

A da Vinci surgical robot system performs heart surgery in 2017 at a hospital in Hefei, China.Credit: Shutterstock In 2006, China highlighted the importance of robotics in its 15-year plan for science and technology. In 2011, the central government fleshed out these ambitions in its 12th five-year plan, specifying that robots should be used to support society in a wide range of roles, from helping emergency services during natural disasters and firefighting, to performing complex surgery and aiding in medical rehabilitation. Guang-Zhong Yang, head of the Institute of Medical Robotics at Shanghai Jiao Tong University, says that China's robotics research output has been growing steadily for two decades, driven by three major factors: "The clinical utilization of robotics; increased funding levels driven by national planning needs; and advances in engineering in areas such as precision mechatronics, medical imaging, artificial intelligence and new materials for making robots." Yang points out that funding levels for medical robotics from the National Natural Science Foundation of China and the Ministry of Science and Technology began to increase more sharply in 2011 compared to the previous decade. The accompanying rises in research output are closely related to the introduction of specialized robotics equipment in medical-research facilities, says Yao Li, a research scientist at Stanford Robotics Laboratory in California and founder of the company Borns Medical Robotics, based in both Chengdu, China, and Silicon Valley, California.

COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has caused more than 7.4 million cases and over 418,000 deaths. The highly dynamic and rapidly evolving situation with COVID-19 has made it difficult to access accurate, on-demand information regarding the disease. Online communities, forums, and social media provide potential venues to search for relevant questions and answers, or post questions and seek answers from other members. However, due to the nature of such sites, there are always a limited number of relevant questions and responses to search from, and posted questions are rarely answered immediately. With the advancements in the field of natural language processing, particularly in the domain of language models, it has become possible to design chatbots that can automatically answer consumer questions. However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data. In this paper, we propose to apply a language model for automatically answering questions related to COVID-19 and qualitatively evaluate the generated responses. We utilized the GPT-2 language model and applied transfer learning to retrain it on the COVID-19 Open Research Dataset (CORD-19) corpus. In order to improve the quality of the generated responses, we applied 4 different approaches, namely tf-idf, BERT, BioBERT, and USE to filter and retain relevant sentences in the responses. In the performance evaluation step, we asked two medical experts to rate the responses. We found that BERT and BioBERT, on average, outperform both tf-idf and USE in relevance-based sentence filtering tasks. Additionally, based on the chatbot, we created a user-friendly interactive web application to be hosted online.