Earlier in the week, American Airlines was forced to ground flights for up to an hour on Christmas Eve, one of the busiest travel days of the year. American asked US Federal Aviation Administration to issue a “nationwide groundstop” at 6:50 am Eastern Time on Tuesday, preventing any of its aircraft from flying. The advisory was lifted an hour later. Vendor technology issue The airline blamed the problems on a “vendor technology issue” in a message sent from its official X/Twitter account. The issue was in networking equipment managed by DXC Technologies, according to an American Airlines statement cited by several news outlets. Neither DXC nor American immediately responded to our requests for comment. American has been working with DXC to modernize mainframe systems using a devops approach, according to a blog post by its in-house technology team. In a separate modernization initiative, it also transformed its analytics tools, so it should be well placed to evaluate the consequences of Tuesday morning’s outage. source

By Christopher Cole ( December 20, 2024, 6:32 PM EST) — Rep. Richard Hudson, R-N.C., has been selected as the next chair of the House Energy and Commerce panel with telecom jurisdiction…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More One of Google’s latest experimental models, Gemini-Exp-1206, shows the potential to alleviate one of the most grueling aspects of any analyst’s job: getting their data and visualizations to sync up perfectly and provide a compelling narrative, without having to work all night. Investment analysts, junior bankers, and members of consulting teams aspiring for partnership positions take their roles knowing that long hours, weekends, and pulling the occasional all-nighter could give them an inside edge on a promotion. What burns so much of their time is getting advanced data analysis done while also creating visualizations that reinforce a compelling storyline. Making this more challenging is that every banking, fintech and consulting firm, like JP Morgan, McKinsey and PwC, has unique formats and conventions for data analysis and visualization. VentureBeat interviewed members of internal project teams whose employers had hired these firms and assigned them to the project. Employees working on consultant-led teams said producing visuals that condense and consolidate the massive amount of data is a persistent challenge. One said it was common for consultant teams to work overnight and do a minimum of three to four iterations of a presentation’s visualizations before settling on one and getting it ready for board-level updates. A compelling use case for test-driving Google’s latest model The process analysts rely on to create presentations that support a storyline with solid visualizations and graphics has so many manual steps and repetitions that it proved a compelling use case for testing Google’s latest model. In launching the model earlier in December, Google’s Patrick Kane wrote, “Whether you’re tackling complex coding challenges, solving mathematical problems for school or personal projects, or providing detailed, multistep instructions to craft a tailored business plan, Gemini-Exp-1206 will help you navigate complex tasks with greater ease.” Google noted the model’s improved performance in more complex tasks, including math reasoning, coding, and following a series of instructions. VentureBeat took Google’s Exp-1206 model for a thorough test drive this week. We created and tested over 50 Python scripts in an attempt to automate and integrate analysis and intuitive, easily understood visualizations that could simplify the complex data being analyzed. Given how hyperscalers are dominant in news cycles today, our specific goal was to create an analysis of a given technology market while also creating supporting tables and advanced graphics. Through over 50 different iterations of verified Python scripts, our findings included: The greater the complexity of a Python code request, the more the model “thinks” and tries to anticipate the desired result. Exp-1206 attempts to anticipate what’s needed from a given complex prompt and will vary what it produces by even the slightest nuance change in a prompt. We saw this in how the model would alternate between formats of table types placed directly above the spider graph of the hyperscaler market analysis we created for the test.   Forcing the model to attempt complex data analysis and visualization and produce an Excel file delivers a multi-tabbed spreadsheet. Without ever being asked for an Excel spreadsheet with multiple tabs, Exp-1206 created one. The primary tabular analysis requested was on one tab, visualizations on another, and an ancillary table on the third. Telling the model to iterate on the data and recommend the 10 visualizations it decides best fit the data delivers beneficial, insightful results. Aiming to reduce the time drain of having to create three or four iterations of slide decks before a board review, we forced the model to produce multiple concept iterations of images. These could be easily cleaned up and integrated into a presentation, saving many hours of manual work creating diagrams on slides. Pushing Exp-1206 toward complex, layered tasks VentureBeat’s goal was to see how far the model could be pushed in terms of complexity and layered tasks. Its performance in creating, running, editing and fine-tuning 50 different Python scripts showed how quickly the model attempts to pick up on nuances in code and react immediately. The model flexes and adapts based on prompt history. The result of running Python code created with Exp-1206 in Google Colab showed that the nuanced granularity extended into shading and translucency of layers in an eight-point spider graph that was designed to show how six hyperscaler competitors compare. The eight attributes we asked Exp-1206 to identify across all hyperscalers and to anchor the spider graph stayed consistent, while graphical representations varied. Battle of the hyperscalers We chose the following hyperscalers to compare in our test: Alibaba Cloud, Amazon Web Services (AWS), Digital Realty, Equinix, Google Cloud Platform (GCP), Huawei, IBM Cloud, Meta Platforms (Facebook), Microsoft Azure, NTT Global Data Centers, Oracle Cloud, and Tencent Cloud. Next, we wrote an 11-step prompt of over 450 words. The goal was to see how well Exp-1206 can handle sequential logic and not lose its place in a complex multistep process. (You can read the prompt in the appendix at the end of this article.) We next submitted the prompt in Google AI Studio, selecting the Gemini Experimental 1206 model, as shown in the figure below. Next, we copied the code into Google Colab and saved it into a Jupyter notebook (Hyperscaler Comparison – Gemini Experimental 1206.ipynb), then ran the Python script. The script ran flawlessly and created three files (denoted with the red arrows in the upper left). Hyperscaler comparative analysis and a graphic — in less than a minute The first series of instructions in the prompt asked Exp-1206 to create a Python script that would compare 12 different hyperscalers by their product name, unique features and differentiators, and data center locations. Below is how the Excel file that was requested in the script turned out. It took less than a minute to format the spreadsheet to shrink it to fit in the columns. The next series of commands asked for a table of the top six hyperscalers compared across the top of a page and the

By Christopher Cole ( December 20, 2024, 6:21 PM EST) — The end of 2024 portends a sea change in telecom policy, as voters usher in a second Donald Trump term and with it a newly named GOP chief of the Federal Communications Commission who has pushed for a 180-degree turn at the agency…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google’s release of Gemini 2.0 Flash this week, offering users a way to interact live with video of their surroundings, has set the stage for what could be a pivotal shift in how enterprises and consumers engage with technology. This release — alongside announcements from OpenAI, Microsoft, and others — is part of a transformative leap forward happening in the technology area called “multimodal AI.” The technology allows you to take video — or audio or images — that comes into your computer or phone, and ask questions about it. It also signals an intensification of the competitive race among Google and its chief rivals — OpenAI and Microsoft — for dominance in AI capabilities. But more importantly, it feels like it is defining the next era of interactive, agentic computing. This moment in AI feels to me like an “iPhone moment,” and by that I’m referring to 2007-2008 when Apple released an iPhone that, via a connection with the internet and slick user interface, transformed daily lives by giving people a powerful computer in their pocket. While OpenAI’s ChatGPT may have kicked off this latest AI moment with its powerful human-like chatbot in November 2022, Google’s release here at the end of 2024 feels like a major continuation of that moment — at a time when a lot of observers had been worried about a possible slowdown in improvements of AI technology.   Gemini 2.0 Flash: The catalyst of AI’s multimodal revolution Google’s Gemini 2.0 Flash offers groundbreaking functionality, allowing real-time interaction with video captured via a smartphone. Unlike prior staged demonstrations (e.g. Google’s Project Astra in May), this technology is now available to everyday users through Google’s AI Studio. I encourage you to try it yourself. I used it to view and interact with my surroundings — which for me this morning was my kitchen and dining room. You can see instantly how this offers breakthroughs for education and other use cases. You can see why content creator Jerrod Lew reacted on X yesterday with astonishment when he used Gemini 2.0 realtime AI to edit a video in Adobe Premiere Pro. “This is absolutely insane,” he said, after Google guided him within seconds on how to add a basic blur effect even though he was a novice user.  Sam Witteveen, a prominent AI developer and cofounder of Red Dragon AI, was given early access to test Gemini 2.0 Flash, and he highlighted that Gemini Flash’s speed — it is twice as fast as Google’s flagship until now, Gemini 1.5 Pro — and “insanely cheap” pricing make it not just a showcase for for developers to test new products with, but a practical tool for enterprises managing AI budgets. (To be clear, Google hasn’t actually announced pricing for Gemini 2.0 Flash yet. It is a free preview. But Witteveen is basing his assumptions on the precedent set by Google’s Gemini 1.5 series.) For developers, the live API of these multimodal live features offers significant potential, because they enable seamless integration into applications. That API is also available to use; a demo app is available. Here is the Google blog post for developers. Programmer Simon Willison called the streaming API next-level: “This stuff is straight out of science fiction: being able to have an audio conversation with a capable LLM about things that it can ‘see’ through your camera is one of those ‘we live in the future’ moments.” He noted the way you ask the API to enable a code execution mode, which lets the models write Python code, run it and consider the result as part of their response — all part of an agentic future. The technology is clearly a harbinger of new application ecosystems and user expectations. Imagine being able to analyze live video during a presentation, suggest edits, or troubleshoot in real time. Yes, the technology is cool for consumers, but it’s important for enterprise users and leaders to grasp as well. The new features are the foundation of an entirely new way of working and interacting with technology — suggesting coming productivity gains and creative workflows. The competitive landscape: A race to define the future Wednesday’s release of Google’s Gemini 2.0 Flash comes amid a flurry of releases by Google and by its major competitors, which are rushing to ship their latest technologies by the end of the year. They all promise to deliver consumer-ready multimodal capabilities — live video interaction, image generation, and voice synthesis — but some of them aren’t fully baked or even fully available.  One reason for the rush is that some of these companies offer their employees bonuses to deliver on key products before the end of the year. Another is bragging rights when they get new features out first. They can get major user traction by being first, as OpenAI showed in 2022, when its ChatGPT become the fastest growing consumer product in history. Even though Google had similar technology, it was not prepared for a public release and was left flat-footed. Observers have sharply criticized Google ever since for being too slow.  Here’s what the other companies have announced in the past few days, all helping introduce this new era of multimodal AI. OpenAI’s Advanced Voice Mode with Vision: Launched yesterday but still rolling out, it offers features like real-time video analysis and screen sharing. While promising, early access issues have limited its immediate impact. For example, I couldn’t access it yet even though I’m a Plus subscriber.  Microsoft’s Copilot Vision: Last week, Microsoft launched a similar technology in preview — only for a select group of its Pro users. Its browser-integrated design hints at enterprise applications but lacks the polish and accessibility of Gemini 2.0. Microsoft also released a fast, powerful Phi-4 model to boot. Anthropic’s Claude 3.5 Haiku: Anthropic, until now in a heated race for large language model (LLM) leadership with OpenAI, hasn’t delivered anything

The Biden administration implemented some of the harshest and most heavily criticized asylum restrictions yet in 2024 but also implemented measures to revamp temporary foreign worker programs and expand avenues for immigrants to change their status. Here, Law360 looks back at four of the biggest immigration policy developments of the year. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI’s latest o3 model has achieved a breakthrough that has surprised the AI research community. o3 scored an unprecedented 75.7% on the super-difficult ARC-AGI benchmark under standard compute conditions, with a high-compute version reaching 87.5%.  While the achievement in ARC-AGI is impressive, it does not yet prove that the code to artificial general intelligence (AGI) has been cracked. Abstract Reasoning Corpus The ARC-AGI benchmark is based on the Abstract Reasoning Corpus, which tests an AI system’s ability to adapt to novel tasks and demonstrate fluid intelligence. ARC is composed of a set of visual puzzles that require understanding of basic concepts such as objects, boundaries and spatial relationships. While humans can easily solve ARC puzzles with very few demonstrations, current AI systems struggle with them. ARC has long been considered one of the most challenging measures of AI.  Example of ARC puzzle (source: arcprize.org) ARC has been designed in a way that it can’t be cheated by training models on millions of examples in hopes of covering all possible combinations of puzzles.  The benchmark is composed of a public training set that contains 400 simple examples. The training set is complemented by a public evaluation set that contains 400 puzzles that are more challenging as a means to evaluate the generalizability of AI systems. The ARC-AGI Challenge contains private and semi-private test sets of 100 puzzles each, which are not shared with the public. They are used to evaluate candidate AI systems without running the risk of leaking the data to the public and contaminating future systems with prior knowledge. Furthermore, the competition sets limits on the amount of computation participants can use to ensure that the puzzles are not solved through brute-force methods. A breakthrough in solving novel tasks o1-preview and o1 scored a maximum of 32% on ARC-AGI. Another method developed by researcher Jeremy Berman used a hybrid approach, combining Claude 3.5 Sonnet with genetic algorithms and a code interpreter to achieve 53%, the highest score before o3. In a blog post, François Chollet, the creator of ARC, described o3’s performance as “a surprising and important step-function increase in AI capabilities, showing novel task adaptation ability never seen before in the GPT-family models.” It is important to note that using more compute on previous generations of models could not reach these results. For context, it took 4 years for models to progress from 0% with GPT-3 in 2020 to just 5% with GPT-4o in early 2024. While we don’t know much about o3’s architecture, we can be confident that it is not orders of magnitude larger than its predecessors. Performance of different models on ARC-AGI (source: arcprize.org) “This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs,” Chollet wrote. “o3 is a system capable of adapting to tasks it has never encountered before, arguably approaching human-level performance in the ARC-AGI domain.” It is worth noting that o3’s performance on ARC-AGI comes at a steep cost. On the low-compute configuration, it costs the model $17 to $20 and 33 million tokens to solve each puzzle, while on the high-compute budget, the model uses around 172X more compute and billions of tokens per problem. However, as the costs of inference continue to decrease, we can expect these figures to become more reasonable. A new paradigm in LLM reasoning? The key to solving novel problems is what Chollet and other scientists refer to as “program synthesis.” A thinking system should be able to develop small programs for solving very specific problems, then combine these programs to tackle more complex problems. Classic language models have absorbed a lot of knowledge and contain a rich set of internal programs. But they lack compositionality, which prevents them from figuring out puzzles that are beyond their training distribution. Unfortunately, there is very little information about how o3 works under the hood, and here, the opinions of scientists diverge. Chollet speculates that o3 uses a type of program synthesis that uses chain-of-thought (CoT) reasoning and a search mechanism combined with a reward model that evaluates and refines solutions as the model generates tokens. This is similar to what open source reasoning models have been exploring in the past few months.  Other scientists such as Nathan Lambert from the Allen Institute for AI suggest that “o1 and o3 can actually be just the forward passes from one language model.” On the day o3 was announced, Nat McAleese, a researcher at OpenAI, posted on X that o1 was “just an LLM trained with RL. o3 is powered by further scaling up RL beyond o1.” On the same day, Denny Zhou from Google DeepMind’s reasoning team called the combination of search and current reinforcement learning approaches a “dead end.”  “The most beautiful thing on LLM reasoning is that the thought process is generated in an autoregressive way, rather than relying on search (e.g. mcts) over the generation space, whether by a well-finetuned model or a carefully designed prompt,” he posted on X. While the details of how o3 reasons might seem trivial in comparison to the breakthrough on ARC-AGI, it can very well define the next paradigm shift in training LLMs. There is currently a debate on whether the laws of scaling LLMs through training data and compute have hit a wall. Whether test-time scaling depends on better training data or different inference architectures can determine the next path forward. Not AGI The name ARC-AGI is misleading and some have equated it to solving AGI. However, Chollet stresses that “ARC-AGI is not an acid test for AGI.”  “Passing ARC-AGI does not equate to achieving AGI, and, as a matter of fact, I don’t think o3 is AGI yet,” he writes. “o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence.” Moreover, he notes that o3 cannot autonomously learn these skills and

Banks are striving for digital innovation but regulatory constraints, data security and privacy concerns, integration challenges, and the high costs of enabling change prevent 70% from achieving their transformation goals. Considering the speed that technology is evolving, the alternative of standing still isn’t an option.  How can banks cross the threshold into a digital future to enhance experiences, improve operational efficiency, and stay ahead? Success is the result of strategy, skill, collaboration and, above all, the right platform foundation. It’s possible, and we’ll show you how. How one bank broke the mold  Access Bank is a leading full-service bank with more than 600 branches and 60 million customers worldwide. Last year, the organization successfully moved to a flexible, cloud-based foundation that unlocks AI-powered solutions like conversational IVR, voice biometrics, call back assist, and real-time speech analytics to™ accelerate process handling, create more personalized and proactive care, and enrich agent experiences. The company now supports open banking, a rapidly growing business model that involves the integration of open APIs to enhance experiences and foster innovation. The bank has built a new chatbot from scratch that includes 13 self-service workflows and the ability to seamlessly escalate to a human agent if needed. Gamification, enhanced reporting, and automated quality management have changed the game for supervisors in terms of agent retention, workforce management, and overall productivity. As a result, the bank’s Customer Satisfaction (CSAT) score increased from 53% to 64% and its Customer Effort Score (CES) improved from 68% to 75% from 2022 to 2024. But that’s just the beginning: their voice response time improved by 80%, case resolution improved by 20%, and abandoned calls dropped by 22%. Overall, the bank’s digital channel perception CSAT improved from 63% in 2022 to 80% in 2024. Want similar results? You need Avaya Experience Platform Avaya Experience Platform™ (AXP) empowers banks to accelerate transformation and sustain business growth with a unified digital platform designed to deliver exceptional experiences. It’s not just another standalone CX product – it’s a comprehensive cloud-based solution that adapts seamlessly to any IT environment. Whether you’re integrating on-premises and cloud-based services, combining private and public cloud solutions, or looking to expand, integrate, or consolidate, AXP is uniquely equipped to meet your needs. It excels in enabling innovation by enhancing and building upon existing investments. Here are five ways AXP helps banks unleash their innovative potential: Seamless digital access: Integrate and manage traditional and digital channels including voice, SMS, chat, AI-powered self-service, and virtual agents seamlessly to provide a consistent banking customer experience. Customer journey orchestration: Create a seamless customer journey that ensures a smooth and personalized experience across all touchpoints, regardless of how many systems you have or where they live (on-prem, private cloud, hybrid cloud, or public cloud).  Connected employee: Banking agents have more on their plate than ever and often lack the right tools for handling them, driving one of the highest turnover rates in the customer service industry.  AXP allows banks to streamline the agent experience with tools that unify communication channels and workflows to enhance efficiency and reduce workload.  Advanced fraud detection: It’s no secret that fraud banking incidents are rapidly increasing. AXP enables predictive analytics and real-time data for identifying fraudulent or potentially fraudulent activity faster, plus proactive notifications across multiple channels to contain mole hills before they become mountains.  Security biometrics: Imagine if your customers could be authenticated in seconds each time they contacted your bank, even for more complex processes like applying for a loan or credit card, with full regulatory and industry compliance. AXP makes this possible with a simple plug-in offered by one of Avaya’s third-party partners, Journey.The capability can also save millions by reducing interaction- and fraud-related costs. Three reasons to select AXP for your bank’s digital transformation  1. Unlock your full business potential with the most powerful AI platform See what your company can do with unlimited access to AI, from automation and self-service to virtual assistants and security biometrics. AI-enhanced workflow orchestration, advanced routing, seamless transfers, and more…the sky’s the limit. 2. Choose your own journey Innovate your way with full control over your transformation journey. Bridge capability gaps and step into the future of digital banking while optimizing existing strategic solutions that work well for your organization.   3. Empower your trusted solution with cloud capabilities Fortify your trusted foundation with the power of cloud. On average, AXP users boost customer conversion by 65% and CES by 40% with 5% higher EBITDA. Learn more about AXP for digital banking. source

Putting your goals in writing drastically increases your chances of achieving them. Goal setting templates make it easy to not only write them down, but also break them into actionable tasks and track progress towards reaching them. Whether it’s a personal goal, professional goal, team goal, or a combination of all three, these templates will help you get off to an organized start. monday.com: A quarterly objective goal setting template monday.com is a project management platform that’s suitable for teams of all sizes. It’s perfect for goal setting because the collaborative elements help keep everyone on the same page and working towards common outcomes. You can try it for free with up to two users, but most businesses will have to upgrade to a paid plan to get the most out of it. It can also work well for solo users — if that’s you, you’ll likely be able to use it for free for a while. However, its range of collaboration and team-based features may feel overwhelming. More on monday.com: monday.com review | monday.com vs Wrike | monday.com vs. Pipedrive. Get your team aligned to your quarterly goals with this free template for monday.com. Image: Monday.com This goal-setting template is great for executives, managers, and team leaders. It’s set up for quarterly goal planning, so you can define key objectives at a high level and align them with longer-term business goals. After identifying your goals, you can break them into smaller tasks and assign them to your team with due dates, priority levels, and real-time progress tracking. The software’s flexible nature makes it easy to organize tasks however you’d like, set up automated notifications, and group items however it makes sense. monday.com also has advanced features that help you track time, create automations to boost productivity, and even track your budget as it pertains to your team’s goals. Most of these features are on higher tiers though. While this template is built for quarterly goal planning and tracking, you can adapt it to longer or shorter time frames if you’d like. ClickUp: Goal setting templates for short and long-term planning ClickUp is a feature-rich project management solution that’s powerful enough to run an entire business. The free plan is great for personal use, but the platform can also accommodate collaborative goals on a team or company-wide level. ClickUp offers goal-setting templates for daily goals and quick wins, as well as big-picture goals for annual planning. You can use either, or potentially both of these templates depending on what you need. More on ClickUp: ClickUp Review | ClickUp vs Asana | ClickUp vs Notion. Template 1: A simple template for daily goals and habits This template works like a digital checklist you can reference on a daily basis. It’s perfect for things like daily meditations, drinking water, reading goals, daily journaling, and other quick wins or habits you’re working towards. You can also add other short-term goals that may take longer than a day if you’d like. ClickUp makes it easy to track and manage daily or short-term goals. Image: Clickup.com While this template was intended for personal use, it can be used just as well in a professional setting for developing soft skills, celebrating small wins, or encouraging steady growth. Out of the box, it lets you categorize your goals, making it easy to organize by different areas of your life. For example, you may have categories for fitness, family, friends, mental health, money, personal, and professional. These categories make it possible to filter, sort, and group your goals however you’d like. You can view all of them or focus on a specific area. You can also give each goal a due date, status, and comment to leave notes for yourself. Template 2: For tracking and managing long-term goals If you’re looking to plan and manage goals longer term, this template is built for just that. Instead of quick daily wins, it’s set up to track high-level goals you want to achieve throughout the year. Like the last template, this one is also built for personal use but can be adapted for use in a business setting as well. Track progress towards high-level goals with this free ClickUp template.  Image: Clickup.com The best part about this template is that it makes it easy to break up a single long-term goal into three or more attainable tasks that help you move in the right direction. Rather than staring at something that feels intangible and too far off to matter, you can focus on taking actions that’ll directly help you get closer over time. You’ll be able to prioritize goals, identify milestones, set due dates, and assign work to others if you’re using it as a team. If new goals or opportunities present themselves along the way, you can easily add them to your list at any time. It comes with a personal profile document where you can fill in your name, age, address, and other information. It’s a bit odd to have and you can delete it if you feel like it’s getting in your way. Alternatively, you can tweak the document for vision planning or other more relevant activities if you’d like. Wrike: A business goal setting template that focuses on OKRs Wrike is a rigid yet powerful solution for businesses. It’s easy to set up and use but has a wide range of automation, collaboration, and team-based features that make it flexible enough as you scale. It’s perfect for defining company goals and using OKRs (objectives and key results) to track progress and define success. Unlike other project management tools that only offer free versions for individuals or a couple of people, Wrike’s free plan supports an unlimited number of users. It’s limited on features but lets you onboard your entire team at no cost. More on Wrike: Wrike Review | Wrike vs Asana | Wrike vs Smartsheet. Wrike makes it easy to manage and track company goals. Image: Wrike.com This template is versatile enough for department

By Bryan Koenig ( December 23, 2024, 4:47 PM EST) — Google has countered the Justice Department’s proposed divestiture of the Chrome browser in a brief filed in D.C. federal court arguing the proper fix for its illegal search monopoly would be to allow Android phone makers and browser companies the ability to more readily pick rival engines…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source