Geoffrey Hinton, a British computer scientist, who has spent his entire career pushing the field of artificial intelligence forward (AI), is a pioneer of Artificial Intelligence and the 2018 Turing Award, winner. After graduating from the University of Cambridge in 1970 with a BA in experimental psychology, Mr. Hinton joined the graduate program in artificial intelligence at the University of Edinburgh, with neural networks as his focus. He is currently split between Google Brain (the division dedicated to artificial intelligence research) and the University of Toronto, where he is working to provide artificial intelligence based on deep learning with intuition. For over 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do -- using intuition rather than rules. I recently wrote an article about intuition, the ability to recognize similarities quickly.

Bots to resolve queries and AI solutions to help customers renew their policies automatically and even measure calorie intake... Giving a glimpse into insurance in the tech age, Girish Nayak of ICICI Lombard General Insurance Co Ltd says companies are tailoring their offerings for efficiency and personalised experiences. An app developed by the company is helping retail and corporate customers use features such as tele-consultation and homecare. And newly launched AI-based features are helping them understand their health vitals and calorie intake, Nayak, the company's chief, Customer Service, Operations and Technology, told PTI in an interview. He said ICICI Lombard is one of the first among the large insurance companies to move its core applications to the cloud and is looking at cloud for its transformative possibilities. Excerpts from an exclusive interview: Q: How is ICICI Lombard leveraging digital technology to push insurance policy sales, and drive growth? A: Big data and analytics are helping organisations like ours in understanding customer needs better.

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world. In this work, we present a new evaluation framework entitled Attributable to Identified Sources (AIS) for assessing the output of natural language generation models, when such output pertains to the external world. We first define AIS and introduce a two-stage annotation pipeline for allowing annotators to appropriately evaluate model output according to AIS guidelines. We empirically validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset) via human evaluation studies that suggest that AIS could serve as a common framework for measuring whether model-generated statements are supported by underlying sources. We release guidelines for the human evaluation studies.

Dr. Jian Pei is a Professor in the School of Computing Science at Simon Fraser University. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications, and transferring his research results to products and business practice. He is recognized as a Fellow of the Royal Society of Canada (Canada's national academy), the Canadian Academy of Engineering, the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is one of the most cited authors in data mining, database systems, and information retrieval.

New cars are in short supply, with delivery times stretching as long as a year because of the ongoing semiconductor shortage and other supply chain issues. This is affecting customers of all kinds, but none more than fleet buyers. The shortage means many fleets, from rental car companies to police departments, are not able to replace vehicles as often, resulting in a growing reliance on older vehicles, and increased resources devoted to maintaining them. Rather than simply replacing vehicles, which was often the strategy before because it was more cost-effective than making repairs or keeping up with ongoing maintenance, fleet managers need to invest more in inspections, monitoring and repairs. One way they can ensure that the vehicles they do deploy remain safe and in the best conditions possible is by using artificial intelligence solutions that increase driver accountability, examine a vehicle's performance, and determine ways to make sure that it remains up to the standards necessary in order to ensure safety and effectiveness -- as well as recommend ways to bring vehicles up to code, or identify issues before they cause problems.

The IJCAI distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers. The winners were selected by the associate programme committee chairs, the programme and general chairs, and the president of EurAI. Abstract: The metric distortion framework posits that n voters and m candidates are jointly embedded in a metric space such that voters rank candidates that are closer to them higher. A voting rule's purpose is to pick a candidate with minimum total distance to the voters, given only the rankings, but not the actual distances.

Software engineer Blake Lemoine worked with Google's Ethical AI team on Language Model for Dialog Applications (LaMDA), examining the large language model for bias on topics such as sexual orientation, gender, identity, ethnicity, and religion. Over the course of several months, Lemoine, who identifies as a Christian mystic, hypothesized that LaMDA was a living being, based on his spiritual beliefs. Lemoine published transcripts of his conversations with LaMDA and blogs about AI ethics surrounding LaMDA. In June, Google put Lemoine on administrative leave; last week, he was fired. In a statement, Google said Lemoine's claims that LaMDA is sentient are "wholly unfounded." "It's regrettable that despite lengthy engagement on this topic, Blake still chose to persistently violate clear employment and data security policies that include the need to safeguard product information," Google said in a statement.

Yunshi Zhao is a Machine Learning Engineer at Liftoff, a mobile app optimization platform for marketing and monetizing apps at scale. Her responsibilities range from researching and training models to deployment and monitoring models in production. She is also part of the diversity, equity, and inclusion (DEI) committee at Liftoff, focusing on representation in engineering. Before transitioning to startup life, she worked as a data scientist and aerospace engineer. Here, she talks about machine learning development, best practices, use cases, and ML in production.

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.

Abstract: Scientific machine learning (SciML) is the burgeoning field combining scientific knowledge with machine learning for data-efficient predictive modeling. We will introduce SciML as the key to effective learning in many engineering applications, such as improving the fidelity of climate models to accelerating clinical trials. This will lead us to the question on the frontier of SciML: what about stiffness? Stiffness is a pervasive quality throughout engineering systems and the most common cause of numerical difficulties in simulation. We will see that handling stiffness in learning, and thus real-world models, requires new training techniques.