Cuneflow's e-paper writing tablet uses AI to record and transcribe meetings Cuneflow's e-paper writing tablet uses AI to record and transcribe meetings It's designed to automate the busywork of business. I recently reviewed reMarkable's Paper Pure, an e-paper writing tablet designed to be used in business meetings. The company markets itself as an almost analog space to think, pushing against the use of AI and the distractions of the digital age. Think of Cuneflow, then, as a rebuke of that philosophy, as its namesake e-paper writing slate has a built-in voice recorder to transcribe and extract insights from those very same business meetings. Is that a big enough draw for you to plonk down your money when it launches on Kickstarter?

How to Use the New AI Features in OmniFocus, the Power User's To-Do List One of the Mac's most popular productivity apps is incorporating generative artificial intelligence in a way that keeps it offline, private, and customizable. A lot of apps are adding artificial intelligence to their products in the most in-your-face manner possible. Companies like Google, Microsoft, and Meta are all adding colorful buttons and pop-ups to their user interface, and barraging their customers with marketing emails, all of which are loudly begging users to try out the new AI features. It was refreshing, in that context, to talk to indie app makers Omni Group about their approach to AI. The Seattle-based company makes OmniFocus, a powerful task management application long loved by reviewers and enthusiasts for its extreme flexibility.

Automating the end-to-end scientific research process poses a fundamental challenge: it requires both evolving high-level plans that are novel and sound, and executing these plans correctly amidst dynamic and uncertain conditions. To address this bilevel challenge, we propose a novel Double-Loop Multi-Agent (DLMA) framework to solve the given research problem automatically. The leader loop, composed of professor agents, is responsible for evolving research plans. It employs an evolutionary algorithm through involvement, improvement, and integration meetings to iteratively generate and refine a pool of research proposals, exploring the solution space effectively. The follower loop, composed of doctoral student agents, is responsible for executing the best-evolved plan. It dynamically adjusts the plan during implementation via pre-hoc and post-hoc meetings, ensuring each step (e.g., drafting, coding) is well-supported by contextual and external observations. Extensive experiments on benchmarks like ACLAward and Laboratory show that DLMA generates research papers that achieve state-of-the-art scores in automated evaluation, significantly outperforming strong baselines. Ablation studies confirm the critical roles of both loops, with evolution driving novelty and execution ensuring soundness.

Steven Johnson has a reputation as a research software nerd. The author of 13 nonfiction books, he's constantly looking for digital tools to streamline his creative process. So when large language models – which power text-generating AI tools such as ChatGPT – started getting attention, he was most interested in what they could mean for organising information. In 2022, an article Johnson wrote about LLMs for the New York Times caught the eye of researchers at Google Labs, the tech company's experimental AI arm, who came to him with a proposition: would he help them develop the kind of digital research assistant he'd been dreaming of? The result is NotebookLM, a note-taking tool that uses AI to help organise, summarise and answer questions about any information you give it.

If you're struggling to stay on top of your tasks or keep track of your notes, maybe what you need are some new tools. I'm always looking for better ways to stay organized. When I find a new app that sounds promising, I pit it against my existing tools in a game of survival of fittest, leaving only the ones that work best for me. These are currently the five services I rely on the most for note-taking, bookmarking, and task management. As we head into the new year, perhaps they'll provide just the kind of fresh inspiration you're looking for.

Did you miss a session from GamesBeat Summit Next 2022? All sessions are now available for viewing in our on-demand library. Click here to start watching. With Google Assistant inside and conversational AI, these speakers can do a great range of things. Here's 10 worth trying, drawn from VentureBeat coverage over the course of the past year. Before getting into the more dynamic features Google Assistant provides through Home smart speakers, start with the most popular ways people use speakers with intelligent assistants.

Most people struggle with prioritizing work. While inexact heuristics have been developed over time, there is still no tractable principled algorithm for deciding which of the many possible tasks one should tackle in any given day, month, week, or year. Additionally, some people suffer from cognitive biases such as the present bias, leading to prioritization of their immediate experience over long-term consequences which manifests itself as procrastination and inefficient task prioritization. Our method utilizes optimal gamification to help people overcome these problems by incentivizing each task by a number of points that convey how valuable it is in the long-run. We extend the previous version of our optimal gamification method with added services for helping people decide which tasks should and should not be done when there is not enough time to do everything. To improve the efficiency and scalability of the to-do list solver, we designed a hierarchical procedure that tackles the problem from the top-level goals to fine-grained tasks. We test the accuracy of the incentivised to-do list by comparing the performance of the strategy with the points computed exactly using Value Iteration for a variety of case studies. These case studies were specifically designed to cover the corner cases to get an accurate judge of performance. Our method yielded the same performance as the exact method for all case studies. To demonstrate its functionality, we released an API that makes it easy to deploy our method in Web and app services. We assessed the scalability of our method by applying it to to-do lists with increasingly larger numbers of goals, sub-goals per goal, hierarchically nested levels of subgoals. We found that the method provided through our API is able to tackle fairly large to-do lists having a 576 tasks. This indicates that our method is suitable for real-world applications.

We made our kids learn coding, we started to learn languages, enrolled for online courses in Artificial Intelligence and Machine Learning.

Even before beginning to code, we need to have an "intents.json" This JSON file is accessed by the Voice Assistant and the response accordingly. Let's start coding by importing all the required libraries After importing all the required modules, we need to create an instance of the speaker and the recognizer so that the assistant can capture what we humans say and convert it into textual form and, the remaining code is explained by comments within the program. A list named "todo_list" is created to work on the list that the assistant maintains for us. Now, let's begin coding functions for each of the required tasks.

Back when the world seemed bright and ambitious--another century, it might have been--I managed to convince myself, despite a lot of evidence to the contrary, that what I really needed in my life was an assistant. This was December, the month when traditionally I can no longer outrun the clerical tasks that have stalked me since the middle of the year. I had weeks of crinkled receipts to expense: the year-end tax on negligence. I was halfway through the process of contesting the charge on a vaccine shot that my insurance company had refused to cover, and I had to transcribe hours of interviews before I could begin to write--the only use of my time which generates an income. As a moonless night wore on, filled with snacking and monsters, I futzed with the formulas in my sad expense spreadsheets and knew that these were hours of life I'd never get back.