Insurance Times' annual Tech and Innovation Awards were back last night (22 September 2022) at London's glamorous Royal Lancaster Hotel. Sponsored by Genasys, NIG, Carpenters Group and Sønr, the event showcased 22 different award categories recognising finalists that are going the extra mile to use technology to improve insurance processes and the customer experience. Opening the evening, Insurance Times editor Katie Scott said: "Just featuring on one of tonight's shortlists is an accolade in itself. "The same goes for the nominated categories – in particular Technology Champion of the Year. It's so inspiring to see how firms have really capitalised on accelerated technology and digital advancements made during the Covid-19 pandemic."

Modern NLP models are becoming better conversational agents than their predecessors. Recurrent Neural Networks (RNNs) and especially Long-Short Term Memory (LSTM) features allow the agent to better store and use information about semantic content, a trend that has become even more pronounced with the Transformer Models. Large Language Models (LLMs) such as GPT-3 by OpenAI have become known to be able to construct and follow a narrative, which enables the system to adopt personas on the go, adapt them and play along in conversational stories. However, practical experimentation with GPT-3 shows that there is a recurring problem with these modern NLP systems, namely that they can "get stuck" in the narrative so that further conversations, prompt executions or commands become futile. This is here referred to as the "Locked-In Problem" and is exemplified with an experimental case report, followed by practical and social concerns that are accompanied with this problem.

… is the first of its kind to be awarded at UTSA and will support Sooby’s research on utilizing artificial intelligence (AI) to study complex …

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. Beth Goss was recently appointed as the CEO of kid game company Outright Games. To do that, the former chief brand officer at 20th Century Fox had to relocate her family from the U.S. to London. That tells you the nature of the opportunity. And now she is running a kids' video game publisher behind the likes of Peppa Pig, Paw Patrol and Star Trek titles. Goss has held positions with some of the biggest entertainment companies in the world including Universal Pictures and Cartoon Network. Goss wants to transform the family-operated publisher into a global player in the video games market, which has traditionally operated exclusively in games for young children. Goss is applying lessons and industry practices from her traditional entertainment background into gaming.

"Is AI Coming For Commercial Art?" rendered by Stable Diffusion, prompted by Rob Salkowitz Earlier this summer, a piece generated by an AI text-to-image application won a prize in a state fair art competition, prying open a Pandora's Box of issues about the encroachment of technology into the domain of human creativity and the nature of art itself. As fascinating as those questions are, the rise of AI-based image tools like Dall-E, Midjourney and Stable Diffusion, which rapidly generate detailed and beautiful images based on text descriptions supplied by the user, pose a much more practical and immediate concern: They could very well hold a shiny, photorealistically-rendered dagger to the throats of hundreds of thousands of commercial artists working in the entertainment, videogame, advertising and publishing industries, according to a number of professionals who have worked with the technology. How impactful would this be to the global creative economy that runs on spectacular imagery? Think about the 10 minutes of credits at the end of every modern Hollywood blockbuster. Same with videogames, where commercial artists hone their skills for years to score plum jobs like concept artist and character designer.

Ramachandran quotes a variety of sources--Indian government officials, a motley bunch of academics and Indian-American author Rajiv Malhotra, who goes on to claim that Sanskrit should be credited with the last 20 years of development in Natural Language Processing (NLP), the technology behind prominent LLMs like GPT-3, DALL-E 2, etc. The claims are wide-ranging: Sanskrit is the most'scientific' language, and so the "best to programme computers, or code AI/ML"; it is the "language for future super computers", etc. One common source that everyone cites, and which Ramachandran explores in detail, is "Nasa". Yes, the same Nasa that sends rockets into space. The reference actually has a published source, a 1985 paper'Knowledge Representation in Sanskrit and Artificial Intelligence' by Nasa researcher Rick Briggs (bit.ly/3qrIjMr).

In the new papers, biologists at the University of Washington School of Medicine show that machine learning can be used to create protein molecules much more accurately and quickly than previously possible. The scientists hope this advance will lead to many new vaccines, treatments, tools for carbon capture, and sustainable biomaterials. "Proteins are fundamental across biology, but we know that all the proteins found in every plant, animal, and microbe make up far less than one percent of what is possible. With these new software tools, researchers should be able to find solutions to long-standing challenges in medicine, energy, and technology," said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine and recipient of a 2021 Breakthrough Prize in Life Sciences. Proteins are often referred to as the "building blocks of life" because they are essential for the structure and function of all living things.

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and labeling, (ii) experimentation to improve ML performance, (iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering -- how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders? We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance. Finally, we discuss interviewees' pain points and anti-patterns, with implications for tool design.

The field of software development is greatly influenced by technology. Software development is constantly evolving and uncertain. Software development trends are essential for businesses that want to be different. This article will focus on software trends for 2022 and beyond. You need to be able to anticipate and adapt to future trends and have in-depth knowledge in order to stay ahead of your competition.

We introduce the task of entity-centric query refinement. Given an input query whose answer is a (potentially large) collection of entities, the task output is a small set of query refinements meant to assist the user in efficient domain exploration and entity discovery. We propose a method to create a training dataset for this task. For a given input query, we use an existing knowledge base taxonomy as a source of candidate query refinements, and choose a final set of refinements from among these candidates using a search procedure designed to partition the set of entities answering the input query. We demonstrate that our approach identifies refinement sets which human annotators judge to be interesting, comprehensive, and non-redundant. In addition, we find that a text generation model trained on our newly-constructed dataset is able to offer refinements for novel queries not covered by an existing taxonomy. Our code and data are available at https://github.