When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. WhatsApp beta users can now craft their own AI chatbots - here's why you might want one Paul Hill Neowin @ziks_99 · Jun 3, 2025 05:10 EDT Since the end of 2022, tech companies, and even non-tech companies, have been clamoring to pile AI into their services. Despite what many people say about not liking AI, plenty of people are still using it every day, making it a key offering. Not only that, but for public companies like Meta, the inclusion of AI does very well with investors, so that’s another reason it’s being added. While the most common chatbot people talk about is ChatGPT, which is pretty faceless, there is demand for AI chatbots with a face, this is why people use tools like Character.ai and Replika. One of the only big tech firms that has gone down this route is Meta, which lets you create and share AI characters. To date, some of Meta’s apps, like Messenger, allow you to chat with these AI personas but you can’t do that yet in the stable version of WhatsApp. The company is now testing it with the Android Beta and when it’s ready, it should make a more seamless experience across Meta’s applications. Many of the popular bots that people use including ChatGPT, Gemini, and DeepSeek are faceless and offer the same tone out of the box. To be fair to Gemini, it does allow all users to create Gems now, and they actually offer a bit more flexibility than just creating characters to talk to like in Messenger. The chatbots in Messenger have the benefit of being in the Messenger app, which most people use and giving them a personality and making them feel like an “AI person” fits better in Messenger. Whether we really need these AI bots in Messenger is still up for debate. It’s quite a new feature and some people may find some good uses for them, but as mentioned, they don’t seem as flexible, or provide as detailed responses as custom bots made on Poe or Gemini Gems. They are definitely for having casual conversations with. WhatsApp's new AI chatbot creator We’ve known that the chatbot feature was coming to WhatsApp for a long time already. WhatsApp beta for Android 2.25.1.26, released in January, included the feature for some beta testers. With the latest WhatsApp beta for Android 2.25.18.4, it seems like WhatsApp is trialing the feature with members of the public, suggesting its release is imminent. Screenshots of the app, obtained by WABetaInfo show that you can describe your AI, select its personality, its traits, its image and more. The process seems to be the same as the process already available in Messenger. One of the nice things that Meta provides when creating these AI bots is templates and suggestions such as the attitude of the bot or the instructions for the bot. This is the same as in Messenger and allows you to get started chatting with your custom bots faster. In terms of sharing, you have the option to make the bots private, share them with friends (at least in the case of Messenger and presumably WhatsApp), or share them publicly. If you make something specific for your needs then the private option would be best, while bots with mass appeal could be set to public. Creating bots in WhatsApp is straightforward once you have access to the AI Studio. During the creation process you’ll need to name your AI, define its personality, choose a tone, design an avatar (some will be made for you with Meta’s AI), and create a catchy tagline to attract users if you ever set it to public. Much of the information will be pre-filled based on the initial details you provide about the AI’s role and personality. Some ideas for bots that you can create include a motivational coach, a travel recommendation AI, or a daily planner. While setting up these AI bots is easy to do, users may find their actual benefits limited. Besides the nagging feeling that you’re socializing with a clever bit of code, Meta seems to truncate the answers of these bots so they don’t rattle on, but depending on what you want them to do, you may need them to give a lengthy response, but they won’t. What personalized AI chatbots could offer If you are looking for an AI that chats to you conversationally like real people do, then this could be the feature you’re looking for. The fact that you can personalize bots with specific traits is something you can’t do as easily in apps like ChatGPT and Gemini and the fact that they have an avatar makes them more connectable too. Two of the defining features of Meta’s AI implementation is the ability to create custom AIs with a unique personality and to share them publicly. If you are having difficulty thinking of what a bot could be instructed to do, you can easily find community bots and interact with those instead and may find they provide some value. While these bots could be interesting for some people, they do carry the same risks as other AIs and that is that they can hallucinate. There was also a case in the UK where a man had been encouraged by his Replika to break into Buckingham Palace with a crossbow to kill the then head of state. Similar issues to this could result from Meta’s AI chatbots in time. Potential pitfalls While the feature is pretty interesting there are some things to be aware of. Firstly, the feature is still in beta on WhatsApp so you may run into issues and things could change once it’s finally released. Meta also states that it uses your interactions to improve its AI services, for this reason it is essential not to share personal information as Meta could read it. While Meta does limit the creation of bots that go against its standards, the company also warns that bots can output harmful content, so this could be dangerous for impressionable people who end up acting on what an AI has said with negative outcomes. What to watch for next It’s not clear when these AI chatbots will be available in the stable channel but given that a wider rollout is underway among beta users perhaps we are not too far off. For most people, this is not going to be a must-have feature, just a nice to have. We’ve been using WhatsApp to chat with friends for years, so clearly the app is just fine without the inclusion of AI, but when it’s available, people may be able to get more value out of the app. When the feature launches for all users, bots should be discoverable in the same way they are on Messenger where they’re categorized by category allowing users to begin chats easily. It remains to be seen how users will interact with this feature in the long-run. Last year, we reported that Meta was looking to give bots profiles on its social networks and this was met by somebacklash in our comments section. Tags Report a problem with article Follow @NeowinFeed

People Can Fly, the developer of Outriders and Gears of War: Judgement, announced Monday that its ending development on two of its upcoming games due to issues with its publisher and an inability to secure funding to continue development. As part of this decision, People Can Fly will be forced "significantly regroup" and "scale down [its] teams," the studio's CEO Sebastian Wojciechowksi shared in a statement on LinkedIn. The statement doesn't elaborate on how many staff will be impacted by the cuts, but does call out Project Gemini and Project Bifrost as the two games being cancelled. People Can Fly made the decision to shut down Gemini because the game's publisher failed to provide a publishing agreement and didn't communicate "its willingness to continue or terminate the Gemini project." Without that publishing deal or the funds to continue working on Bifrost — a self-published VR game — the studio was forced to cancel it, too. This isn't the first time People Can Fly has shut down a project or made cuts to its teams. In December 2024, the studio announced that it was ending development on a game called Project Victoria and also reducing the number of people working on Bifrost. In that same announcement, People Can Fly also revealed that Square Enix was publishing Gemini. People Can Fly last worked with Square Enix to publish Outriders, somewhat of a minor cult hit now, but not a commercial success at launch. Even with the cuts and cancelled games, the studio still has multiple upcoming projects in the works, including Project Delta, which People Can Fly is creating for Sony and Gears of War: E-Day, which the studio is co-developing with Xbox studio The Coalition.This article originally appeared on Engadget at https://www.engadget.com/gaming/people-can-fly-cancels-two-games-and-lays-off-developers-220310524.html?src=rss

I spent $125 to see if I could replicate the high quality of Google Veo 3 videos I was seeing online. Here's what I learned.

글로벌 라벨 및 RFID 솔루션 제조기업 에이버리 데니슨(Avery Dennison)은 문화 우선 AI 도입 전략으로 생산성과 재무 측면에서 상당한 성과를 거뒀다. ‘인간 참여(Human in the Loop, HITL)’를 강조한 전사적인 AI 활용 촉진 프로그램을 통해 2024년 한 해 동안 250명 이상의 임원과 1,000명 이상의 직원이 20개 이상의 AI 파일럿 프로젝트에 참여했다. 어떻게

People Can Fly, announced that the company has suspended development on two games -- Project Gemini and Project Bifrost.

Large Reasoning Models (LRMs), trained from LLMs using reinforcement learning (RL), demonstrated great performance in complex reasoning tasks, including mathematics, STEM, and coding. However, existing LRMs face challenges in completing various puzzle tasks that require purely logical reasoning skills, which are easy and obvious for humans. Current methods targeting puzzles focus only on designing benchmarks for evaluation, lacking the training methods and resources for modern LLMs to tackle this challenge. Current puzzle datasets lack diversity and scalability, covering limited puzzle types with little control over generation or difficulty. Moreover, due to the success of the “LLM+RLVR” paradigm, it has become crucial to obtain large, diverse, and challenging sets of verifiable puzzle prompts for training agents. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key method for improving models’ reasoning capabilities, removing the need for reward models by directly assigning rewards based on objectively verifiable answers. Puzzles are particularly well-suited for RLVR. However, most prior RLVR research has overlooked the puzzles’ potential for delivering effective reward signals. In puzzle reasoning of LLMs, existing benchmarks evaluate different types of reasoning, including abstract, deductive, and compositional reasoning. Few benchmarks support scalable generation and difficulty control but lack puzzle diversity. Moreover, the improvement of LLMs’ puzzle-solving abilities mainly falls into two categories: tool integration and RLVR. Researchers from ByteDance Seed, Fudan University, Tsinghua University, Nanjing University, and Shanghai Jiao Tong University have proposed Enigmata, the first comprehensive toolkit designed for improving LLMs with puzzle reasoning skills. It contains 36 tasks across seven categories, each featuring a generator that produces unlimited examples with controllable difficulty and a rule-based verifier for automatic evaluation. The researchers further developed Enigmata-Eval as a rigorous benchmark and created optimized multi-task RLVR strategies. Puzzle data from Enigmata enhances SoTA performance on advanced math and STEM reasoning tasks like AIME, BeyondAIME, and GPQA when trained on larger models like Seed1.5-Thinking. This shows the generalization benefits of Enigmata. The Enigmata-Data comprises 36 puzzle tasks organized into 7 primary categories, including Crypto, Arithmetic, Logic, Grid, Graph, Search, and Sequential Puzzle, making it the only dataset having multiple task categories with scalability, automatic verification, and public availability. The data construction follows a three-phase pipeline: Tasks Collection and Design, Auto-Generator and Verifier Development, and Sliding Difficulty Control. Moreover, the Enigmata-Eval is developed by systematically sampling from the broader dataset, aiming to extract 50 instances per difficulty level for each task. The final evaluation set contains 4,758 puzzle instances rather than the theoretical maximum of 5,400, due to inherent constraints, where some tasks generate fewer instances per difficulty level. The proposed model outperforms most public models on Enigmata-Eval with 32B parameters, showing the effectiveness of the dataset and training recipe. The model stands out on the challenging ARC-AGI benchmark, surpassing strong reasoning models such as Gemini 2.5 Pro, o3-mini, and o1. The Qwen2.5-32B-Enigmata shows outstanding performance in structured reasoning categories, outperforming in Crypto, Arithmetic, and Logic tasks, suggesting effective development of rule-based reasoning capabilities. The model shows competitive performance in search tasks that require strategic exploration and planning capabilities. Moreover, Crypto and Arithmetic tasks tend to provide the highest accuracy, while spatial and sequential tasks remain more difficult. In this paper, researchers introduced Enigmata, a comprehensive suite for equipping LLMs with advanced puzzle reasoning that integrates seamlessly with RL using verifiable rule-based rewards. The trained Enigmata-Model shows superior performance and robust generalization skills through RLVR training. Experiments reveal that when applied to larger models such as Seed1.5-Thinking (20B/200B parameters), synthetic puzzle data brings additional benefits in other domains, including mathematics and STEM reasoning over state-of-the-art models. Enigmata provides a solid foundation for the research community to advance reasoning model development, offering a unified framework that effectively bridges logical puzzle-solving with broader reasoning capabilities in LLMs. Check out the Paper, GitHub Page and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Multimodal Foundation Models Fall Short on Physical Reasoning: PHYX Benchmark Highlights Key Limitations in Visual and Symbolic IntegrationSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better AlignmentSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/NVIDIA AI Introduces AceReason-Nemotron for Advancing Math and Code Reasoning through Reinforcement Learning

Gmail will now automatically show you a summary card for lengthy email threads if you check a Google Workspace account on the iOS or the Android app. The company introduced AI summaries last year when it rolled out Gemini side panels for Docs, Sheets, Slides, Drive and Gmail. However, you'd have had to manually tap the "Summarize this email" option at the top of emails before if you wanted to see a thread's contents at a glance. Now, the summary will show up at the top of the email the moment you open a thread. Google's AI assistant will write up key points from multiple messages and include them in the summary. It will keep re-generating that summary and keeping it up to date as more replies come in. At the moment, however, the feature will only work for emails written in English. Also, your personalization smart features in Gmail, Chat and Meet, as well as smart features in Google Workspace, have to be switched on. As The Verge notes, Google didn't say whether it will also make auto-summaries available on non-Workspace accounts and Gmail on desktop. But you can always tap the "Summarize this email" option at the top whenever auto-summary isn't available for you.  In addition to summarizing emails, Gemini in Gmail can help you draft new emails and help you find information from within your inbox or from your Drive files. At I/O 2025, Google CEO Sundar Pichai introduced Personalized Smart Replies, an upcoming feature that can look at your past emails and files to draft a response containing relevant information in the tone you typically use when you write. This article originally appeared on Engadget at https://www.engadget.com/ai/the-gmail-app-can-automatically-summarize-those-long-email-threads-120023369.html?src=rss

Meta (Nasdaq:META) is restructuring its AI division into two distinct units, AI Products and AGI Foundations, marking its most significant internal overhaul as it races to compete with OpenAI and Google. The move, detailed in an internal memo from Chief Product Officer Chris Cox and reported by Axios, appoints Connor Hayes to lead AI product integration while Ahmad Al-Dahle and Amir Frenkel will co-direct long-term AGI research. The restructuring comes amid mounting crises, including the delayed Llama 4 Behemoth model and the departure of key Llama architects to competitors like Mistral AI. This marks Meta’s second major AI reorganization since CEO Mark Zuckerberg’s 2023 attempt to “turbocharge” generative AI efforts, which saw the company fall further behind rivals despite early promise. “Structural changes alone won’t solve Meta’s AI challenges,” said Amandeep Singh, practice director at QKS Group. “While the new AGI Foundations unit creates clarity, retaining elite talent requires a seamless pipeline from research to real-world deployment. Meta has struggled with fragmented pipelines and unclear priorities,” added Singh. Talent exodus and technical setbacks The restructuring follows talent losses that have exposed fundamental weaknesses in Meta’s AI strategy. Only three authors remain from the original 14-person Llama research team, according to Business Insider. Internal surveys cited by The Information reveal plummeting morale in Meta’s AI division, where employees cite resource constraints and sluggish progress. “Talent follows momentum, and right now, momentum lives where research decisions directly shape deployed capabilities,” noted Singh. This talent drain coincides with technical setbacks, most notably the underperforming Llama 4 model, which has struggled with reasoning and mathematical tasks. These combined challenges have left Meta playing catch-up in the race toward artificial general intelligence, despite its early open-source advantages. The company’s strategy, bolstered by initiatives such as Llama for Startups and the recent Llama API launch, aims to attract developers and differentiate it from competitors’ proprietary models. But analysts caution these initiatives alone may not be enough to win enterprise trust. The enterprise adoption dilemma While Llama’s cost advantages remain attractive to businesses, growing concerns about its governance controls and the looming copyright lawsuit over training data are giving enterprises pause. “Companies love Llama’s affordability but expanding safety gaps and legal risks are becoming hard to ignore,” Singh said. “For mission-critical applications, many will ultimately choose more reliable, if more expensive, options like GPT or Gemini.” These cost benefits can lose their appeal when weighed against operational risks. Meta’s reorganization attempts to mitigate these concerns through specialized teams, one deploying generative AI across products, another advancing AGI research, but analysts remain skeptical about whether structural changes alone can solve deeper issues. Unlike Microsoft’s turnkey OpenAI integration or Google’s enterprise-ready Vertex AI platform, Meta lacks both the sales infrastructure and compliance pedigree for regulated industries. As Singh argued, “Enterprise AI adoption hinges on proven compliance frameworks, operational reliability, and mature support systems. Meta still needs to build that trust at Fortune 500 scale.” Meta’s race to close the AGI gap “Meta’s AGI push focused on models with reasoning, multimedia, and voice capabilities aligns with the broader industry trend toward multimodal AI as a catalyst for enterprise transformation,” said Surjyadeb Goswami, research director for AI and Automation at IDC Asia Pacific. He noted that open-source models are critical to enabling cost-effective, transparent, and customizable deployments, especially as organizations deepen their GenAI investments. For Meta to truly capitalize on this opportunity and succeed in its AGI bid, it must rebuild trust especially for enterprise adoption. Singh highlighted the need for Linux-like community stewardship, OpenAI-level safety protocols, and robust enterprise tooling. “Balancing openness with responsibility is Meta’s real challenge, especially as models approach general-purpose cognitive capability.” Meta now needs to show this reorganization yields meaningful improvements in model performance, talent retention, and enterprise adoption to validate its new approach. “Meta’s open-source vision is bold, but execution is everything,” Singh concluded.

People Can Fly cancels two projects and confirms it will once again have to "scale down" its teams "We wish to express our deepest regret and sadness over how these events have unfolded." Image credit: People Can Fly/Square Enix News by Vikki Blake Contributor Published on June 1, 2025 People Can Fly has cancelled two projects and "scaled down" its teams. In a statement posted to LinkedIn this afternoon, CEO Sebastian Wojciechowski said it had been a "difficult decision" but attributed the suspension of Project Gemini and Bifrost to the publisher "not present[ing] us with a draft of the subsequent content rider to the Publishing Agreement", and the Group's cash flow, "which showed a lack of prospects", respectively. "Today we made a very difficult decision to suspend the development of project Gemini and project Bifrost - the relevant current reports have been released to the market," Wojciechowski wrote. "The suspension of the Gemini project is a consequence of the fact that the Publisher has not presented us with a draft of the subsequent content rider to the Publishing Agreement covering the terms and conditions of further milestone on project Gemini and the lack of communication from the Publisher as to its willingness to continue or terminate the Gemini project. "Project Bifrost was suspended due to the above and the analysis of the Group's cash flow, which showed a lack of prospects for securing organisational resources and funds necessary to continue the production and release of this project. "As a result, we have to significantly regroup as a studio and scale down our teams, which hurts the most," Wojciechowski added. "We wish to express our deepest regret and sadness over how these events have unfolded and our sincere gratitude for everyone's contribution up to this point." Wojciechowski did not specify which publisher he thought had failed to "communicate", but the studio has worked with several publishing partners, most recently Square Enix, Krafton, and Sony. It's also working on Gears of War: E-Day. In December, People Can Fly the Outriders developer announced its second round of layoffs in less than a year, this time affecting "more than 120" people. The move accompanies the cancellation or downsizing of several projects, with the studio blaming "external market pressures".

Microsoft and Google are taking distinctive approaches with AI agents in their productivity suites, and enterprises need to account for the differences when formulating digital labor strategies, analysts said. In recent months, both companies have announced a dizzying array of new agents aimed at extracting value from corporate documents and maximizing efficiency. The tech giants have dropped numerous hints about where they’re headed with AI agents in their respective office suites, Microsoft 365 and Google Workspace. Microsoft is reshaping its Copilot assistant as a series of tools to create, tap into, and act on insights at individual and organizational levels. The Microsoft 365 roadmap lists hundreds of specialized AI tools under development to automate work for functions such as HR and accounting. The company is also developing smaller AI models to carry out specific functions. Google is going the opposite way, with its large-language model Gemini at the heart of Workspace. Google offers tools that include Gems for workers to create simple custom agents that automate tasks such as customer service, and Agentspace in Google Cloud to build more complex custom agents for collaboration and workflow management. At the recent Google I/O developer conference, the company added real-time speech translation to Google Meet. “For both, the goal is to bring usable and practical productivity and efficiency capabilities to work tools,” said Liz Miller, vice president and principal analyst at Constellation Research. But the differing AI agent strategies are heavily rooted in each company’s philosophical approaches to productivity. Although Microsoft has long encouraged customers to move from its traditional “perpetual-license” Office suite to the Microsoft 365 subscription-based model, M365 notably retains the familiar desktop apps. Google Workspace, on the other hand, has always been cloud-based. Microsoft users are typically a bit more tethered to traditional enterprise work styles, while Google has always been the “cloud-first darling for smaller organizations that still crave real-time collaboration,” Miller said. When it comes to the generative AI models being integrated into the two office suites, “Google’s Gemini models are beating out the models being deployed by Microsoft,” Miller said. “But as Microsoft expands its model ‘inventory’ in use across M365, this could change.” Microsoft has an advantage, as many desktop users live in Outlook or Word. The intelligence Copilot can bring from CRM software is readily available, while that integration is more complex in the cloud-native Google Workspace. “Microsoft still has an edge in a foundational understanding of work and the capacity to extend Copilot connections across applications as expansive as the Office suite through to Dynamics, giving AI a greater opportunity to be present in the spaces and presentation layers where workers enjoy working,” Miller said. Microsoft’s Copilot Agents and Google’s Gems and Agentspace are in their early stages, but there have been positive developments, said J.P. Gownder, a vice president and principal analyst on Forrester’s Future of Work team. Microsoft recently adopted Google’s A2A protocol, which makes it easier for users of both productivity suites to collaborate and unlock value from stagnant data sitting on other platforms. “That should be a win for interoperability,” Gownder said. But most companies that are Microsoft shops have years or decades of digital assets that hold them back from considering Google, he said. For example, Excel macros, pivot tables, and customizations cannot be easily or automatically migrated to Google Sheets, he said. “As early as this market is, I don’t think it’s fair to rank either player — Microsoft or Google — as being the leader; both of them are constructing new ecosystems to support the growth of agentic AI,” Gownder said. Most Microsoft Office users have moved to M365, but AI is helping Google is making inroads into larger organizations, especially among enterprises that are newer and less oriented toward legacy Microsoft products, said Jack Gold, principal analyst at J. Gold Associates. Technologies like A2A blur the line between on-premises and cloud productivity. As a result, “Google Workspace is no longer perceived as inferior, as it had been in the past,” Gold said. And for budget-constrained enterprises, the value of AI agent features is not the only consideration. “There is also the cost equation at work here, as Google seems to have a much more transparent cost structure than Microsoft with all of its user classes and discounts,” Gold said. Microsoft does not include Copilot in its M365 subscriptions, which vary in price depending on the type of customer. The Copilot business subscriptions range from $30 per user per month for M365 Copilot to $200 per month for 25,000 messages for Copilot Studio, which is also available under a pay-as-you-go model. Google has flat subscription pricing for Workspace, starting at $14 per user per month for business plans with Gemini included.