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.

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.

As U.S. particle physicists contemplate their future, they find themselves victims of their own surprising success. Seven years ago, the often fractious community hammered out its current research road map and rallied around it. Thanks to that unity—and generous budgets—the Department of Energy (DOE), the field's main U.S. sponsor, has already started on almost every project on the list. So this week, as U.S. particle physicists start to drum up new ideas for the next decade in a yearlong Snowmass process—named for the Colorado ski resort where such planning exercises once took place—they have no single big project to push for (or against). And in some subfields, the next steps seem far less obvious than they were 10 years ago. “We have to be much more open minded about what particle physics and fundamental physics are,” says Young-Kee Kim of the University of Chicago, chair of the American Physical Society's division of particles and fields, which is sponsoring the planning exercise. Ten years ago, the U.S. particle physics community was in disarray. The high-energy frontier had passed to CERN, the European particle physics laboratory near Geneva, where in 2012 the world's biggest atom smasher, the Large Hadron Collider (LHC), blasted out the long-sought Higgs boson, the last piece in particle physicists' standard model. Some physicists wanted the United States to build a huge experiment to fire elusive particles called neutrinos long distances through Earth to study how they “oscillate”—morph from one of their three types to another—as they zip along. Others wanted the country to help push for the next big collider. Those tensions came to a head during the last Snowmass effort in 2013, and the subsequent deliberations of the Particle Physics Project Prioritization Panel (P5), which wrote the road map. U.S. researchers agreed to build the neutrino experiment, but make it bigger and better by inviting international partners. They also decided to continue to participate fully in the LHC, and to pursue a variety of smaller projects at home (see table, below). The next collider would have to wait. Most important, DOE officials warned, the squabbling and backstabbing had to stop. In fact, physicists recall, the 2013 process had an informal motto: “Bickering scientists get nothing.” ![Figure][1] CREDIT: PARTICLE PHYSICS PROJECT PRIORITIZATION PANEL REPORT (2014) Physicists have just started to build the current plan's centerpiece. The Long-Baseline Neutrino Facility (LBNF) at Fermi National Accelerator Laboratory (Fermilab) in Illinois will shoot the particles through 1300 kilometers of rock to the Deep Underground Neutrino Experiment (DUNE) in South Dakota, a detector filled with 40,000 tons of frigid liquid argon. LBNF/DUNE, which should come online in 2026, aims to be the definitive study of neutrino oscillations and whether they differ between neutrinos and antineutrinos, which could help explain how the universe generated more matter than antimatter. “The angst in the neutrino community is a lot lower than it was last time,” says Kate Scholberg, a neutrino physicist at Duke University. “The DUNE program will be going on at least into the 2030s.” However, researchers are already thinking of upgrades to the $2.6 billion experiment, she notes. In other areas, the future looks less certain. The last time around, for example, scientists had a pretty clear path forward in their search for particles of dark matter—the so-far-unidentified stuff that appears to pervade the galaxies and bind them with its gravity. Researchers had built small underground detectors that searched for the signal of weakly interacting massive particles (WIMPs), the leading dark matter candidate, bumping into atomic nuclei. The obvious plan was to expand the detectors to the ton scale. Now, two multi-ton WIMP detectors are under construction. But so far WIMPs haven't shown up, and scaling up that technology further “is probably not going to work very well anymore,” says Marcelle Soares-Santos, a physicist at the University of Michigan, Ann Arbor. “So we need to think a little bit more out of the box.” Researchers are now contemplating a hunt for other types of dark matter particles, using new detectors that exploit quantum mechanical effects to achieve exquisite levels of sensitivity. A perennial question for the field is what the next great particle collider will be. The obvious need is for one that fires electrons into positrons to crank out copious Higgs bosons and study their properties in detail, says Meenakshi Narain, a physicist at Brown University. But possible designs vary. Physicists in Japan are discussing such a Higgs factory in the form of a 30-kilometer-long linear electron-positron collider. Meanwhile, CERN has begun a study of an 80- to 100-kilometer circular collider. China has plans for a similar circular collider. However, Vladimir Shiltsev, an accelerator physicist at Fermilab, says those aren't the only potential options. “The real picture is much murkier.” Snowmass organizers have received at least 16 different proposals for colliders, including one that would smash together muons—heavier, unstable cousins of electrons—and another that would use photons. Snowmass participants should consider all options, Shiltsev says. Joe Lykken, Fermilab's deputy director for research, suggests physicists could even push for DOE to support a massive experiment that has nothing to do with particles: a next-generation detector of gravitational waves, spacetime ripples set off when massive objects such as black holes collide. Their discovery in 2015 by the Laser Interferometer Gravitational-Wave Observatory (LIGO) opened a new window on the universe. LIGO consists of two L-shaped optical instruments with arms 4 kilometers long in Louisiana and Washington; it was built by the National Science Foundation. The next generation of ground-based detectors could be 10 times as big, and might better fit DOE, which specializes in scientific megaprojects, Lykken says. “It starts to sound like the kind of thing that the DOE would be interested in and maybe required for,” he says. During the coming year, Snowmass participants will air the more than 2000 ideas researchers have already proffered in two-page summaries. Then, a new P5 will formulate a new plan. Whatever ideas scientists come up with, to execute their new plan they'll have to maintain the harmony that in recent years has made their planning process an exemplar to other fields. “Being unified is the new norm for us,” quips Jim Siegrist, DOE's associate director for high energy physics. “So we have to continue to keep a lid on divisiveness and that'll be a challenge.” [1]: pending:yes

What should I work on first? What can wait until later? Which projects should I prioritize and which tasks are not worth my time? These are challenging questions that many people face every day. People's intuitive strategy is to prioritize their immediate experience over the long-term consequences. This leads to procrastination and the neglect of important long-term projects in favor of seemingly urgent tasks that are less important. Optimal gamification strives to help people overcome these problems by incentivizing each task by a number of points that communicates how valuable it is in the long-run. Unfortunately, computing the optimal number of points with standard dynamic programming methods quickly becomes intractable as the number of a person's projects and the number of tasks required by each project increase. Here, we introduce and evaluate a scalable method for identifying which tasks are most important in the long run and incentivizing each task according to its long-term value. Our method makes it possible to create to-do list gamification apps that can handle the size and complexity of people's to-do lists in the real world.