AI copilots represent a new generation of AI-powered systems designed to assist users, particularly knowledge workers and developers, in complex, context-rich tasks. As these systems become more embedded in daily workflows, personalization has emerged as a critical factor for improving usability, effectiveness, and user satisfaction. Central to this personalization is preference optimization: the system's ability to detect, interpret, and align with individual user preferences. While prior work in intelligent assistants and optimization algorithms is extensive, their intersection within AI copilots remains underexplored. This survey addresses that gap by examining how user preferences are operationalized in AI copilots. We investigate how preference signals are sourced, modeled across different interaction stages, and refined through feedback loops. Building on a comprehensive literature review, we define the concept of an AI copilot and introduce a taxonomy of preference optimization techniques across pre-, mid-, and post-interaction phases. Each technique is evaluated in terms of advantages, limitations, and design implications. By consolidating fragmented efforts across AI personalization, human-AI interaction, and language model adaptation, this work offers both a unified conceptual foundation and a practical design perspective for building user-aligned, persona-aware AI copilots that support end-to-end adaptability and deployment.

Creative coding platforms like Scratch have democratized programming for children, yet translating imaginative ideas into functional code remains a significant hurdle for many young learners. While AI copilots assist adult programmers, few tools target children in block-based environments. Building on prior research \cite{druga_how_2021,druga2023ai, druga2023scratch}, we present Cognimates Scratch Copilot: an AI-powered assistant integrated into a Scratch-like environment, providing real-time support for ideation, code generation, debugging, and asset creation. This paper details the system architecture and findings from an exploratory qualitative evaluation with 18 international children (ages 7--12). Our analysis reveals how the AI Copilot supported key creative coding processes, particularly aiding ideation and debugging. Crucially, it also highlights how children actively negotiated the use of AI, demonstrating strong agency by adapting or rejecting suggestions to maintain creative control. Interactions surfaced design tensions between providing helpful scaffolding and fostering independent problem-solving, as well as learning opportunities arising from navigating AI limitations and errors. Findings indicate Cognimates Scratch Copilot's potential to enhance creative self-efficacy and engagement. Based on these insights, we propose initial design guidelines for AI coding assistants that prioritize youth agency and critical interaction alongside supportive scaffolding.

Adopting AI copilots in professional workflows presents opportunities for enhanced productivity, efficiency, and decision making. In this paper, we present results from a six month trial of M365 Copilot conducted at our organisation in 2024. A qualitative interview study was carried out with 27 participants. The study explored user perceptions of M365 Copilot's effectiveness, productivity impact, evolving expectations, ethical concerns, and overall satisfaction. Initial enthusiasm for the tool was met with mixed post trial experiences. While some users found M365 Copilot beneficial for tasks such as email coaching, meeting summaries, and content retrieval, others reported unmet expectations in areas requiring deeper contextual understanding, reasoning, and integration with existing workflows. Ethical concerns were a recurring theme, with users highlighting issues related to data privacy, transparency, and AI bias. While M365 Copilot demonstrated value in specific operational areas, its broader impact remained constrained by usability limitations and the need for human oversight to validate AI generated outputs.

Building a successful AI copilot requires a systematic approach. This paper is divided into two sections, covering the design and evaluation of a copilot respectively. A case study of developing copilot templates for the retail domain by Microsoft is used to illustrate the role and importance of each aspect. The first section explores the key technical components of a copilot's architecture, including the LLM, plugins for knowledge retrieval and actions, orchestration, system prompts, and responsible AI guardrails. The second section discusses testing and evaluation as a principled way to promote desired outcomes and manage unintended consequences when using AI in a business context. We discuss how to measure and improve its quality and safety, through the lens of an end-to-end human-AI decision loop framework. By providing insights into the anatomy of a copilot and the critical aspects of testing and evaluation, this paper provides concrete evidence of how good design and evaluation practices are essential for building effective, human-centered AI assistants.

Microsoft's next huge Windows 11 feature update, code-named Windows 11 23H2, has a big addition: AI. Microsoft is readying for the era of the AI PC with the addition of Windows Copilot, powered by Bing Chat. And it will debut on Sept. 26. It's the closest thing to a theme that we've seen within a Windows 11 update in some time. AI will power Windows Copilot, of course, but also recommended files in File Explorer and Start as well as a designated AI-specific section within the Microsoft Store app.

At the Microsoft Inspire partner event today, the Windows maker announced pricing for its AI-infused Copilot for Microsoft 365. The suite of contextual artificial intelligence tools, the fruit of the company's OpenAI partnership, will cost $30 per user for business accounts. In addition, the company is launching Bing Chat Enterprise, a privacy-focused version of the AI chatbot with greater security and peace of mind for handling sensitive business data. Revealed in March, Microsoft 365 Copilot is the company's vision of the future of work. The GPT-4-powered suite of tools lets you generate Office content using natural-language text prompts.

To empower people to unlock the joy of discovery, feel the wonder of creation and better harness the world's knowledge, today we're improving how the world benefits from the web by reinventing the tools billions of people use every day, the search engine and the browser. Today, we're launching an all new, AI-powered Bing search engine and Edge browser, available in preview now at Bing.com, to deliver better search, more complete answers, a new chat experience and the ability to generate content. We think of these tools as an AI copilot for the web. "AI will fundamentally change every software category, starting with the largest category of all – search," said Satya Nadella, Chairman and CEO, Microsoft. "Today, we're launching Bing and Edge powered by AI copilot and chat, to help people get more from search and the web."

On July 13, Boston's Merlin Labs announced that it would be working with the US Air Force to add autonomy to the C-130J Super Hercules cargo transport plane. Merlin's technology is a kind of advanced auto-copilot, designed to take over the responsibilities of one crew member in flight while being supervised by a human pilot. If the technology delivers as promised, it will allow planes that normally fly with two human pilots to operate with just one, and could even allow single-seater planes to fly fully autonomously. The same day that Merlin announced its partnership with the Air Force, it also announced a second round of $105 million in funding, which combined with a first round means the company has $130 million of runway to develop its technologies. This funding, says Merlin Labs CEO Matthew George, will help the company continue to develop "the world's most capable, safest and flexible pilot, that will eventually enable very large aircraft to fly with reduced crew and small aircraft to fly totally uncrewed."

'this mirrors the way autonomous vehicles work,' explains CEO and co-founder, mael fabien. 'biped will, for example, warn a user about a bike 12 meters ahead on the user's trajectory, but ignore an object that is closer but with no collision risk.' 'I was both inspired by my research and by working next to the main ophthalmic hospital in lausanne,' continues fabien. 'every day I would encounter blind and visually impaired people and wondered if we could go beyond sticks and guide-dogs to help them.' 'our aim is to launch first in switzerland in Q2 and then the US in early 2023.

The race between two key chip makers to put self-driving cars on the streets is getting heated. And it's no less entertaining, involving the likes of BB8, a self-driving Nvidia car named after a droid in Star Wars, and the company's automotive supercomputer called Xavier, named after an X-men superhero. These two playfully named products are a big part of Nvidia's ambitious plans to put a fleet of self-driving cars on the streets by 2020. Nvidia is collaborating with Audi to develop these autonomous cars, which will be based on the Drive-PX computer. Nvidia's announcement comes just a day after Intel and BMW said they were putting 40 self-driving cars on the street by the end of this year.