Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Elvis Presley once said, “Ambition is a dream with a V8 engine.” Brendan Greene, the creator of PlayerUnknown’s Battlegrounds (PUBG), has a lot of ambition. His battle royale game, inspired by the Japanese film Battle Royale (2000), has sold more than 80 million copies. And one of Greene’s ambitions is doing something important like that again in video games. And so he just announced that his PlayerUnknown Productions is resurfacing after years of development with a three-game plan to bring on the next generation of survival games. And it’s ambitious. I talked to Greene, who is known as PlayerUnknown, about it in an exclusive interview. It’s down at the bottom of this introduction and I hope you like it. At the end, I asked him about ambition. Greene got the idea from the movie that he could stage a battle where 100 people would compete with each other. With each player eliminated, the battle space would get smaller until the last two were battling it out in a very small circle. The last one standing was the winner. Greene first created a “mod” called DayZ in the Arma universe. Then he teamed up with South Korea’s Krafton to make PUBG. The game debuted in 2017, disrupted shooter games like Call of Duty. On the strength of PUBG’s 80 million in sales, Krafton went public and Greene became wealthy from that. That gave him the money to work on something even more ambitious. Brendan Greene is the creator of PUBG and he is on to his next survival project. I had a front row seat to this plan. Greene went off on his own to create a new startup, PlayerUnknown Productions, in 2021 to make a gaming survival world that was a lot like a metaverse. Then he gave me a scoop on his ambitions. Without anything to show me except a screenshot at the time, Greene said was creating a world called Prologue that had a huge amount of terrain — about 100 square kilometers. That world, bigger than just about any existing game world, would be a test where players would drop into the world and try to survive until they exited the world in a given spot. It would be different every time they dropped into it. Now Greene has released a video that describes his intentions more concretely. Prologue now has a real preview in the video and the world looks very realistic, with trees and grasses swaying in the wind. And it’s still a huge world, fashioned with machine learning and AI tools. The aim is to release it sometime in the middle of next year as a single-player game for people to try to survive. AI will generate the terrain of Prologue. The challenge is that the open-world of Prologue will be an emergent place, where anything can happen and the weather will get progressively worse. It may seem simple to get to the exit point on the map, but it’s likely going to be hell getting there. Then there will be something else. The company will do a shadow drop of the company’s free tech demo, called Preface: Undiscovered World, showcasing its in-house game engine called Melba. Preface will be able to generate terrain for an Earth-size virtual world, using very little in the way of computing resources. This demo aims to provide users with an early look at the innovative technology that will power the subsequent titles in the series, and eventually a third game called Project Artemis. Project Artemis is the large-scale end goal project of the series. As described in the past, Greene sees this as an Earth-size world where players can drop in and create their own gaming experiences in different sections of the world. We don’t use the word metaverse so much anymore, but that’s what it seems like to me. The journey to get there could take another five or ten years. In the video, Greene said he embarked on Prologue three years ago and “then life happened” and it has taken three years to get it into a solid and breakthrough shape. Now the company can start sharing it and getting feedback “to make it into really something different.” In our interview, Greene said that the team started pulling together when Laurent Gorga joined as CTO. About a year ago, Gorga started putting in motion a process that enabled the team to make a lot more process. While they were making the tech, the team would now create frequent builds to test the tech on a granular level. They started making enough progress so that they started scheduling the timelines for Prologue and Preface. And they talked about it in a video stream on December 6, during the PC Gaming Show. It made a lot of jaws drop. Prologue is expected to drop into early access on the second quarter of 2025. Here’s a view of Preface, another test of technology from PlayerUnknown Productions. “When I started this I was trying to make a larger open world experience than most people made, and we tried to provide a couple of years and we found a way to do that,” Greene said. “We essentially reinvented how you create these worlds using machine learning technology, using natural earth data to generate” the terrain. Now the company is ready to test this terrain, which will form the basis for the larger worlds. He said the team broke the journey into three stages. The first job was to fill out the terrain of the world. The second was to fill that terrain with lots of interaction when scaling up. And then third, the goal was to pull a bunch of those players onto the world, Greene said. The company will keep enhancing Prologue with its current game engine and then it will move it over to the next version of its game

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More For the 10th day of OpenAI’s “12 Days of Shipmas” event, the company decided to go a bit old-school by launching a phone number for people to call and speak with ChatGPT. U.S.-based users can call 1-800-ChatGPT (1-800-242-8478) on any device that can make calls — including a rotary phone — as OpenAI demonstrated in its live stream. If you have an international number, you can message ChatGPT using WhatsApp. It might seem odd that ChatGPT, which has a web version, mobile apps on iOS and Android, and desktop applications for MacOS and Windows, will need a phone number to ask ChatGPT a question. But OpenAI explained that the feature is ideal for those without consistent data connections.  Deploying AI wherever it can OpenAI said the feature enables wider access to ChatGPT. After all, calling a phone number is usually free and accessible for people who may not have unlimited data or who are not close to a Wi-Fi connection.  However, anyone who wants to call ChatGPT can only do so for 15 minutes a month.  This isn’t the first time a tech company has utilized calling or texting to expand its user base. In countries like the Philippines, Facebook offers a way for people to use SMS to post on the social media platform. This proved a boon in expanding Facebook’s reach in the Philippines, where most people have a cellphone, although not necessarily a smartphone. Filipinos are now some of the largest users of the social network.  Not that ChatGPT is hurting for users. It is still one of the most used AI platforms out there, even with more competition in the market. OpenAI said its business offerings, ChatGPT Enterprise, Team and Edu alone, logged more than 1 million users as of September. A month earlier, ChatGPT itself reached 200 million users. OpenAI offers ChatGPT for free, but it gives its more advanced models and other powerful features to subscribers at the Plus, Teams, Pro, Enterprise and Edu levels.  “This is an experimental way to talk to ChatGPT, so availability and limits may change,” the company said. “For a fuller experience with more tools, higher limits and more personalization, existing users should continue using ChatGPT directly through their accounts.” The drawbacks Calling ChatGPT feels like using OpenAI’s Voice and Advanced Voice Model. I called ChatGPT and asked for ideas on what to do during a layover in Tokyo. As expected, it gave me some suggestions.  The difference though, is I did not get a readout of my conversation with ChatGPT. If I use Advanced Voice Mode, anything we discussed is transposed into a written chat that I can look back at when I log into my account.  I also highly doubt any developers will call ChatGPT and ask it to code anything.  But the feature is interesting and it does work. Not bad for something OpenAI developers created just a few weeks ago for a Hack Week.  source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In its latest push to redefine the AI landscape, Google has announced Gemini 2.0 Flash Thinking, a multimodal reasoning model capable of tackling complex problems with both speed and transparency. In a post on the social network X, Google CEO Sundar Pichai wrote that it was: “Our most thoughtful model yet:)” And on the developer documentation, Google explains, “Thinking Mode is capable of stronger reasoning capabilities in its responses than the base Gemini 2.0 Flash model,” which was previously Google’s latest and greatest, released only eight days ago. The new model supports just 32,000 tokens of input (about 50-60 pages worth of text) and can produce 8,000 tokens per output response. In a side panel on Google AI Studio, the company claims it is best for “multimodal understanding, reasoning” and “coding.” Full details of the model’s training process, architecture, licensing, and costs have yet to be released. Right now, it shows zero cost per token in the Google AI Studio. Accessible and more transparent reasoning Unlike competitor reasoning models o1 and o1 mini from OpenAI, Gemini 2.0 enables users to access its step-by-step reasoning through a dropdown menu, offering clearer, more transparent insight into how the model arrives at its conclusions. By allowing users to see how decisions are made, Gemini 2.0 addresses longstanding concerns about AI functioning as a “black box,” and brings this model — licensing terms still unclear — to parity with other open-source models fielded by competitors. My early simple tests of the model showed it correctly and speedily (within one to three seconds) answered some questions that have been notoriously tricky for other AI models, such as counting the number of Rs in the word “Strawberry.” (See screenshot above). In another test, when comparing two decimal numbers (9.9 and 9.11), the model systematically broke the problem into smaller steps, from analyzing whole numbers to comparing decimal places. These results are backed up by independent third-party analysis from LM Arena, which named Gemini 2.0 Flash Thinking the number one performing model across all LLM categories. Native support for image uploads and analysis In a further improvement over the rival OpenAI o1 family, Gemini 2.0 Flash Thinking is designed to process images from the jump. o1 launched as a text-only model, but has since expanded to include image and file upload analysis. Both models can also only return text, at this time. Gemini 2.0 Flash Thinking also does not currently support grounding with Google Search, or integration with other Google apps and external third-party tools, according to the developer documentation. Gemini 2.0 Flash Thinking’s multimodal capability expands its potential use cases, enabling it to tackle scenarios that combine different types of data. For example, in one test, the model solved a puzzle that required analyzing textual and visual elements, demonstrating its versatility in integrating and reasoning across formats. Developers can leverage these features via Google AI Studio and Vertex AI, where the model is available for experimentation. As the AI landscape grows increasingly competitive, Gemini 2.0 Flash Thinking could mark the beginning of a new era for problem-solving models. Its ability to handle diverse data types, offer visible reasoning, and perform at scale positions it as a serious contender in the reasoning AI market, rivaling OpenAI’s o1 family and beyond. source

By Jared Foretek ( December 20, 2024, 9:02 PM EST) — Tech startup Xockets defended its monopoly and patent infringement suit against Nvidia and Microsoft Thursday, telling a Texas federal judge that the tech behemoths’ motion to dismiss is part of the “standard game plan” when a smaller patent holder alleges infringement by the industry’s top players…. 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 The messaging app where millions of office workers share memes and coordinate projects is quietly transforming into something far more ambitious: A platform where AI agents work alongside humans as digital coworkers. As part of Salesforce’s sweeping AI initiative announced Tuesday, Slack is evolving from a communication tool into what company officials call a “work operating system” — one where AI agents can attend your meetings, summarize your conversations, create presentations and even negotiate with other AI agents on your behalf. “We’ve been on a journey to become what we call a work operating system — one that simplifies the complexity of all the systems you use daily,” Slack CPO Rob Seaman, who oversees the company’s AI integration, said in an interview with VentureBeat. “While messaging and human interaction remain at its core, the system now provides access to automation and all the apps you need to do your job, plus AI agents, which we believe will be crucial players in the workplace.” The new digital workplace: AI Agents as your always-on colleagues The transformation is already visible at companies like Accenture, where client executives are using AI agents within Slack to dramatically reduce time spent on administrative tasks. These agents can prepare for meetings, summarize discussions and even draft proposals — all within the familiar Slack interface where employees already spend their workday. “To ensure these AI agents are both widely adopted and continuously improving, it’s critical to integrate them where people are already working,” Seaman explained. Inside Slack’s game-changing AI integration: What you need to know Unlike traditional chatbots or AI assistants that require users to visit separate websites or apps, Slack’s AI agents will be integrated directly into existing workflows. They appear in channels alongside human colleagues and can be called upon through natural conversation. The system is designed to be accessible to non-technical users. “There’s actually no code involved,” said Seaman. “From an end user perspective, there really is no technical work for you.” But the implications for workplace dynamics are profound. In demonstrations, AI agents showed they could independently schedule meetings, analyze documents, create visualizations and even collaborate with other AI agents on complex tasks. For example, during Tuesday’s presentation, an AI agent helped an Accenture executive returning from vacation quickly get up to speed on client activities, prepare for upcoming meetings and draft a proposal — tasks that would typically take hours of human effort. Building trust and control: How Slack’s AI safeguards protect your data While Salesforce will provide template agents for common business tasks, Seaman expects most organizations will customize their AI workers for specific needs. “We’ll provide ready-to-use templates that will meet about 80% of most needs, but we expect organizations will customize their AI agents for specific purposes,” he said. “We’ve seen this pattern with Slack already — companies tend to significantly customize the platform to fit their needs.” The company has built extensive safety measures into the system. “The agents execute on your behalf with no greater permissions than what you have,” Seaman explained. “They don’t have God permissions or admin permissions… we don’t create any holes for the AI to see things that it should not be able to.” The human-AI partnership: Redefining workplace collaboration For many employees, the prospect of AI colleagues raises concerns about job displacement. But Salesforce executives frame it as an augmentation of human capabilities rather than a replacement. “It’s not about handing over the data,” said Claire Cheng, VP of machine learning and engineering at Salesforce. “It’s about unlocking the full potential in the data to enable Agentforce to deeply understand your business and your customer and empower the agents to take more effective actions.” Looking ahead, Salesforce envisions even more sophisticated collaboration between human and AI workers. Future versions will enable multiple AI agents to work together on complex tasks, with specialized agents handling different aspects of projects. “Right now there is a human evoking different agents,” Silvio Savarese, who leads Salesforce’s AI research, told VentureBeat. “The future will have an orchestrator agent which will be calling out different specialized agents that will be talking, working together, performing tasks.” This vision of the workplace — where humans and AI agents collaborate seamlessly through platforms like Slack — represents a fundamental shift in how office work gets done. And while the full implications remain to be seen, one thing is clear: The office chat app where you’ve been sharing cat GIFs is about to become much more powerful. “We’re at the beginning of the beginning,” said Marc Benioff, Salesforce’s chief executive. “When you’re at the beginning of the beginning, you see these little things, and then you try to extrapolate what this is going to be. This is an incredible moment.” source

為了向東區居民傳遞關懷，東區民政事務處（民政處）聯同東區關愛隊於冬至期間舉辦了名為「冬至送暖•年年有『魚』東區關愛大行動」的活動。活動旨在讓長者及基層家庭在冬至這個重要中國傳統節日感受到社區的溫暖和關愛。 冬至當日（12月21日），民政及青年事務局局長麥美娟女士（圖左），連同漁農自然護理署（漁護署）署長麥堅明先生及東區民政事務專員兼東區關愛隊總指揮陳尚文先生探訪了明華大廈的長者及基層家庭，對他們表達關懷。活動中，麥局長走訪長者家庭，了解他們的日常生活，並親手向他們派發禮品包。禮品包內除了有日常生活用品外，還有一條由漁護署飼養的優質鮮魚，更有豉油和蠔油以供烹調，讓長者的冬至大餐更加美味可口，感受節日的喜悅。 正如活動口號：「冬至送『魚』到鄰里，東區關愛陪住你！」，麥局長希望透過此次關愛大行動，讓每位居民，特別是許多獨居長者，都能享受到豐富的冬至飯之餘，也感受到政府、社區的關懷與東區關愛隊的互助精神。 民政處亦於12月20日連同35隊東區關愛隊舉行了這次行動的啟動禮。陳專員表示，此次活動的一大亮點是取得漁護署的支持，贊助超過1 000條由東龍洲魚類養殖區現代化海產養殖示範場飼養的優質鮮魚。早前東區區議會參觀該示範場，並提議把鮮魚透過東區關愛隊派送給東區的長者和基層市民。漁護署不僅提供鮮魚，還負責魚類的加工、冷凍和包裝，以便義工向市民派發。 民政處也邀請了東區各界關愛及發展基金會贊助豐富的禮物包與鮮魚一同送出，讓活動內容更完整，傳遞更多關懷，達致「東區關愛傳滿屋」，使更多市民感受到冬至的節日氛圍。 此次關愛大行動中，漁護署還安排義工隊與東區關愛隊一同探訪區內居民、長者戶及安老院，不僅體現政府部門的團結精神、對市民的關愛，更促進社區之間的聯繫和凝聚力，強調互助精神的重要性。 LinkedIn Email Facebook Twitter WhatsApp The post 東區關愛隊「冬至送暖•年年有『魚』東區關愛大行動」：民青局局長麥美娟親訪明華大廈 appeared first on VeriMedia. source

Intel’s interim co-CEO claims retailers are seeing high returns of Arm PCs, but analyst says enterprise purchasers know better than to buy without checking for compatibility with corporate applications. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A startup founded by former Meta AI researchers has developed a lightweight AI model that can evaluate other AI systems as effectively as much larger models, while providing detailed explanations for its decisions. Patronus AI today released Glider, an open-source 3.8 billion-parameter language model that outperforms OpenAI’s GPT-4o-mini on several key benchmarks for judging AI outputs. The model is designed to serve as an automated evaluator that can assess AI systems’ responses across hundreds of different criteria while explaining its reasoning. “Everything we do at Patronus is focused on bringing powerful and reliable AI evaluation to developers and anyone using language models or developing new LM systems,” said Anand Kannappan, CEO and cofounder of Patronus AI, in an exclusive interview with VentureBeat. Small but mighty: How Glider matches GPT-4’s performance The development represents a significant breakthrough in AI evaluation technology. Most companies currently rely on large proprietary models like GPT-4 to evaluate their AI systems, a process that can be expensive and opaque. Glider is not only more cost-effective due to its smaller size, but also provides detailed explanations for its judgments through bullet-point reasoning and highlighted text spans showing exactly what influenced its decisions. “Currently we have many LLMs serving as judges, but we don’t know which one is best for our task,” explained Darshan Deshpande, research engineer at Patronus AI who led the project. “In this paper, we demonstrate several advances: We’ve trained a model that can run on-device, uses just 3.8 billion parameters, and provides high-quality reasoning chains.” Real-time evaluation: Speed meets accuracy The new model demonstrates that smaller language models can match or exceed the capabilities of much larger ones for specialized tasks. Glider achieves comparable performance to models 17 times its size while running with just one second of latency. This makes it practical for real-time applications where companies need to evaluate AI outputs as they’re being generated. A key innovation is Glider’s ability to evaluate multiple aspects of AI outputs simultaneously. The model can assess factors like accuracy, safety, coherence and tone all at once, rather than requiring separate evaluation passes. It also retains strong multilingual capabilities despite being trained primarily on English data. “When you’re dealing with real-time environments, you need latency to be as low as possible,” Kannappan explained. “This model typically responds in under a second, especially when used through our product.” Privacy first: On-device AI evaluation becomes reality For companies developing AI systems, Glider offers several practical advantages. Its small size means it can run directly on consumer hardware, addressing privacy concerns about sending data to external APIs. Its open-source nature allows organizations to deploy it on their own infrastructure while customizing it for their specific needs. The model was trained on 183 different evaluation metrics across 685 domains, from basic factors like accuracy and coherence to more nuanced aspects like creativity and ethical considerations. This broad training helps it generalize to many different types of evaluation tasks. “Customers need on-device models because they can’t send their private data to OpenAI or Anthropic,” Deshpande explained. “We also want to demonstrate that small language models can be effective evaluators.” The release comes at a time when companies are increasingly focused on ensuring responsible AI development through robust evaluation and oversight. Glider’s ability to provide detailed explanations for its judgments could help organizations better understand and improve their AI systems’ behaviors. The future of AI evaluation: Smaller, faster, smarter Patronus AI, founded by machine learning experts from Meta AI and Meta Reality Labs, has positioned itself as a leader in AI evaluation technology. The company offers a platform for automated testing and security of large language models, with Glider its latest advance in making sophisticated AI evaluation more accessible. The company plans to publish detailed technical research about Glider on arxiv.org today, demonstrating its performance across various benchmarks. Early testing shows it achieving state-of-the-art results on several standard metrics while providing more transparent explanations than existing solutions do. “We’re in the early innings,” said Kannappan. “Over time, we expect more developers and companies will push the boundaries in these areas.” The development of Glider suggests that the future of AI systems may not necessarily require ever-larger models, but rather more specialized and efficient ones optimized for specific tasks. Its success in matching larger models’ performance while providing better explainability could influence how companies approach AI evaluation and development going forward. source

Customer satisfaction is the highest priority in a contact center. If you want to provide top-notch customer service, you need well-trained, competent customer support agents. However, with burnout rates averaging between 30%-45% in the call center industry, hiring and retaining top talent can be a major challenge. This means that the hiring process is the most critical step in building a strong and effective call center team. And that’s where a pre-hiring assessment comes in. A pre-hiring assessment is an invaluable tool in identifying the right candidates, reducing turnover rates, and ensuring long-term success for your call center. It allows contact center hiring managers to identify and analyze the specific skills and behaviors that are compatible with the call center’s demands, such as excellent communication skills, problem-solving abilities, and customer empathy. 1 RingCentral RingEx Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Hosted PBX, Managed PBX, Remote User Ability, and more 2 Talkroute Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Call Management/Monitoring, Call Routing, Mobile Capabilities, and more 13-point contact center pre-hiring assessment Our pre-hiring assessment for contact centers covers a broad range of skills and traits you should assess when considering candidates to join your team. Here’s a high level overview: Remote readiness. Typing speed. Customer service experience. Problem-solving abilities. Technical proficiency. Communication skills. Multi-tasking abilities. Stress management capacities. Attention to detail. Empathy and understanding. Sales skills. Conflict resolution skills. Knowledge retention. I’ll walk through each point, why I think it’s important for candidates to demonstrate, and strategies I use to assess candidates for each skill or capability. 1. Remote readiness If you are 100% on-site, all the time, and you know that contact center agents will never work from home — you can skip this point in the pre-hiring assessment. For everyone else, you need to ensure that any candidate who plans to work from home (part or all of the time) has an internet connection that can support professional-grade communications. I would have them take a free VoIP speed test at a bare minimum, to measure their ability to make calls reliably. If you are hiring for a virtual contact center where the expectation is fully-remote work, you should also try to ensure that the employee has a quiet place to work where calls and video conferences will not be disturbed. Many first-time remote workers haven’t thought through the reality of working from home with kids, roommates, pets, neighbors, an upcoming move, etc. It’s common to provide equipment to new remote hires, but if you aren’t you will need to ensure that candidates have reliable VoIP headset to ensure hands-free, crystal clear conversations with customers. SEE: Learn whether or not virtual contact centers are viable for companies that handle a lot of volume.  2. Typing speed Typing speed is a critical skill across all contact center positions. Agents need to be able to type fast so they can: Respond quickly and accurately to customer queries. Document and transcribe conversations. Listen while typing to accurately record client notes. Accurately input error-free customer information. The most common way to assess a potential hire for typing speed is by assigning a typing test. These tests are readily available and often free, making it easy to integrate into the hiring process. These tests measure the words per minute (WPM) of an individual. A good rule of thumb for call centers is to aim for a minimum of 30 words per minute with an accuracy rate of at least 95%. If a test results in a slow typing speed or a high amount of errors, this may lead to inaccurate information being documented or a slower response time, which can negatively impact the customer experience. In a multichannel contact center, it’s completely normal for a single agent to be on the phone while answering questions via multiple live chats — verifying a candidate’s ability to type quickly and accurately is a must. SEE: Discover how a multichannel call center solution can replace numerous apps.  3. Customer service experience Previous experience can provide valuable glimpses into how a candidate may perform since there are a handful of universal aspects to customer service roles, like of these include: Adhering to scripts while maintaining a friendly tone. Superior listening skills to understand customer needs and concerns. Resilience and patience to handle difficult customer interactions. Having your candidate submit and elaborate on their prior customer service experience can help determine which specific skills they possess. Role-play scenarios help test the abilities of new hires, which involve recreating a real-life call that the future agent may encounter. Keep an eye out for listening skills, relational candor, and a friendly tone while maintaining compliance with the call script. You can also keep track of the agent’s ability to solve the theoretical problem you’re roleplaying and take note of how long it takes for them to resolve the issue. The better suited candidates are for providing quality service, the easier it will be for you to improve contact center customer experience. 4. Problem-solving abilities Employees must know how to think on their feet — and there is always a lot to know. You’ll want to to be hiring from a pool of people who can: Resolve complex customer issues swiftly. Work through unexpected technical glitches. Handle multiple problems simultaneously. You can assess a candidate’s problem-solving abilities with situational judgment tests or role-play scenarios that mimic real-life challenges. For example, you can provide a scenario where the agent must handle an irate customer while also dealing with a technical issue on their end. Some helpful metrics to keep an eye out for include the ability to respond quickly to unexpected issues, an overall positive demeanor in the face of conflict, and a capacity to generate creative solutions. The best call center software or contact center solution

“Nowhere does the EDPB seem to look at whether something is actually personal data for the AI model provider. It always presumes that it is, and only looks at whether anonymization has taken place and is sufficient,” Craddock wrote. “If insufficient, the SA would be in a position to consider that the controller has failed to meet its accountability obligations under Article 5(2) GDPR.” And in a comment on LinkedIn that mostly supported the standards group’s efforts, Patrick Rankine, the CIO of UK AI vendor Aiphoria, said that IT leaders should stop complaining and up their AI game. “For AI developers, this means that claims of anonymity should be substantiated with evidence, including the implementation of technical and organizational measures to prevent re-identification,” he wrote, noting that he agrees 100% with this sentiment. “This is not that hard, and tech companies need to stop being so lazy and looking for excuses. They want to do great things building tech, but then can’t be bothered treating the data they need for their great tech respectfully or responsibly.” source