This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.

In this dissertation I would like to guide the reader to the research on dialogue but more precisely the research I have conducted during my career since my PhD thesis. Starting from modular architectures with machine learning/deep learning and reinforcement learning to end-to-end deep neural networks. Besides my work as research associate, I also present the work I have supervised in the last years. I review briefly the state of the art and highlight the open research problems on conversational agents. Afterwards, I present my contribution to Task-Oriented Dialogues (TOD), both as research associate and as the industrial supervisor of CIFRE theses. I discuss conversational QA. Particularly, I present the work of two PhD candidates Thibault Cordier and Sebastien Montella; as well as the work of the young researcher Quentin Brabant. Finally, I present the scientific project, where I discuss about Large Language Models (LLMs) for Task-Oriented Dialogue and Multimodal Task-Oriented Dialogue.

Is it still worth doing computer networking research? What are relevant problems in this space given the supremacy of hyperscalers in deployed large networks? We take an unconventional approach to finding relevant research directions, by starting from Microsoft's plans to build a $100 billion datacenter for ML. Our goal is to understand what models could be trained in such a datacenter, as well as the high-level challenges one may encounter in doing so. We first examine the constraints imposed by cooling and power requirements for our target datacenter and find that it is infeasible to build in a single location. We use LLM scaling laws to determine that we could train models of 50T or 100T. Finally, we examine how distributed training might work for these models, and what the networking requirements are. We conclude that building the datacenter and training such models is technically possible, but this requires a novel NIC-based multipath transport along with a redesign of the entire training stack, outlining a research agenda for our community in the near future.

Holistic health expert and bestselling author Deepak Chopra shares a message of hope as his latest book, "Quantum Body," is published: "We have a common identity. Bestselling author and entrepreneur Deepak Chopra may be known the world over as a holistic health and wellness guru -- he's counseled scores of people to lead better, calmer and healthier lives over the decades -- but he also has a lighter side. In an on-camera interview this week just as his new book, "Optimum Body: The New Science of Living a Longer, Healthier, More Vital Life," was published, Chopra poked a little fun at himself when asked how he takes care of personal well-being. "Well, my family doesn't take me seriously, and that's good," he said. "And so," he said, "I don't take myself seriously." Chopra went on about how he personally stays healthy. "I sleep well," he said. And my only purpose is to serve the world. I don't have any personal ambition. And that means no stress, either."

The activity around creating a legit graphics processor for RISC-V chip designs, an emerging competitor to x86 and ARM, is gaining steam. Special interest groups at RISC-V next year will expand the focus on extensions for shaders and advanced matrix operations, which is important for artificial intelligence and machine learning, Mark Himelstein, chief technology officer at RISC-V, told The Register. "There is no reason why you could not integrate C-series -- which is the part that has ray tracing -- with RISC-V," David Harold, chief marketing officer at Imagination, told The Register. Andes Technology, which creates RISC-V chip designs, has verified that Imagination's GPUs work with RISC-V, and so has RIOS Lab, which has David Patterson, vice chair of the Board at RISC-V Foundation, on staff. The need for a GPU on RISC-V could be fundamental as the chip architecture gains importance, Shreyas Derashri, vice president of compute at Imagination, told The Register.

The Institute of Physics (IOP) is convening a new type of forum – four days of discussion, debate and knowledge sharing at the IOP's flagship King's Cross building. Members of IOP special interest groups, including leading thinkers in the physics community in the UK and Ireland, will host discussions, talks, meetings and panels to foster debate and exploration of key topics such as oceans and the climate, optics in quantum technologies and machine learning. IOP member-led special interest groups are at the heart of the physics community and enable you to take part in activities in your particular areas of interest, share and receive expert advice on developments in your sector, grow your network across disciplines, attend topical and inter-disciplinary sessions. A drinks reception will be held at the end of each day, where you can meet speakers and the organising committees.

Many organisations have already embarked on a Robotics Process Automation or Robotics chatbot initiative. However, many firms also experience delays and cost over-runs, and ultimately failed initiatives. The IITPSA invites you to attend an enlightening business breakfast where you will hear from industry experts about the use of these technologies in the South African context, and have an opportunity to network with other AI and Robotics enthusiasts from the local industry. Free to IITPSA members in good standing (paid up fees).

The problem of ranking is a multi-billion dollar problem. In this paper we present an overview of several production quality ranking systems. We show that due to conflicting goals of employing the most effective machine learning models and responding to users in real time, ranking systems have evolved into a system of systems, where each subsystem can be viewed as a component layer. We view these layers as being data processing, representation learning, candidate selection and online inference. Each layer employs different algorithms and tools, with every end-to-end ranking system spanning multiple architectures. Our goal is to familiarize the general audience with a working knowledge of ranking at scale, the tools and algorithms employed and the challenges introduced by adopting a layered approach.

Your smartphone knows your wedding anniversary is coming up, and you have a chat with it about how you might celebrate the event. Based on its deep understanding of you and your spouse, the device suggests a romantic weekend in Paris. It knows from your photos and calendar that you got engaged at a small bistro in the 9th arrondissement, and from your travel history it knows you favor a boutique hotel near Parc Monceau and your preferred airline is Air France. It creates an itinerary and presents it to you for your approval. After you've used your smartphone a few times to book trips, you trust the artificial intelligence (AI) that powers it so much that you authorize it to book everything -- and to negotiate on your behalf -- without even checking with you.

When a group of about 40 players first tested out a live game called the Machine Learning President at a private event in San Francisco this February, they were unaware that the game would end up memorialized in the pages of The New Yorker. But during a ski vacation in March, the Republican mega-donor Rebekah Mercer gathered her friends together to play several rounds of the game, which pits special interest groups, political candidates, and activist organizations against each other in a simulated presidential election, aided by cash and artificial intelligence. A lawyer for Mercer told The New Yorker that she owned a copy of the Machine Learning President but had not created it and that it did not reflect her family's views. It's not hard to draw comparisons between the rules of the game, with its reliance on big cash and tech capabilities, and the actions of the Mercer-backed Cambridge Analytica during the 2016 U.S. presidential election. But, as Mercer's lawyer stated, she had nothing to do with creating the game--in fact, it was conceptualized by one of her vocal critics.