Reading Time: 4 minutes The marketing landscape is undergoing a dramatic transformation driven by artificial intelligence. AI has evolved beyond a mere marketing assistant that expedites tasks. With AI Agents, marketers can now delegate entire tasks, from initiation to completion. Despite the current hype surrounding AI, its impact on the future is undeniable and likely to be permanent. Amara’s Law perfectly understands the current situation. What is Amara’s Law? The law says that we often overestimate a technology’s short-run impact and underestimate its long-run impact. This is very similar to Gartner’s Hype Cycle. With so much hype around AI, it can be very overwhelming. So, it’s critical to look beyond all the hype and figure out real-world AI uses and how to fit those solutions into your business workflows. Rise of AI Agents “AI Agents” has to be the buzzword for 2025. You might have seen people talking about it on Twitter, LinkedIn, YouTube, and other platforms. There are a lot of opinions; some say it’s just hype, and a few say it’s the next big thing. So it comes down to: But is it worth all the hype? AI agents are intelligent, autonomous systems that understand natural language and leverage business context to reason through complex tasks and take action on your behalf, while operating within set guardrails. If we look at Customer Engagement specifically, there are tons of practical use cases. Its usage ranges from campaign creation to onboarding to analytics/optimization. 1. Campaign Creation  If we specifically look at campaign creation, the AI agents can do everything from – Choosing an audience segment Crafting campaign content Deciding the best channel and time to send The current way we’re segmenting users with filters works, but it’s not super easy to learn. It takes time to create segments using filters, and you need to know your events and attributes pretty well. This means marketers need to seek additional support. However, using AI agents, you can effortlessly create targeted customer segments in seconds using natural language. Creating a segment is as easy as typing a prompt like “Identify customers who abandoned the credit card application process”. That’s it. Basically, you can use AI agents to turn ideas for a customer segment into reality with just one instruction. It’s super quick to get things done this way, and you don’t need to learn anything complicated or leverage additional tools. 2. Customer Journey Orchestration  Building adequate multi-touch flows for customer experiences is often time-consuming and complex, involving intricate logic and a steep learning curve. AI Agents addresses this by using AI to translate customer journey ideas into optimized flows instantly. This AI-driven approach dramatically reduces build time, enabling marketers to brainstorm and ideate with AI, focusing on strategy and creativity. Customers can create and refine flows using natural language and receive tailored recommendations. 3. Analytics & Optimization  AI Agents can also assist in – Data Querying – Ask in natural language to create appropriate charts for the data. Data Monitoring – AI detects an anomaly and conducts RCA and regression models to uncover the “why” for the movement of your metrics. Data Interpretation – AI explains what’s happening in a chart/data. 4. Decisioning Agents  Marketers face the challenge of moving from “best fit for a segment” to “true 1:1 fit for an individual”. This requires optimizing interactions with the right message and incentive, for each customer, in the right channel, at the right time and frequency – something that’s not humanly possible at scale. Decision-making agents address this by leveraging Reinforcement Learning and Generative AI to enhance user context. They optimize delivery by determining the best message copy, send time, and channel. Incrementality testing can measure the impact of decision-making agents, providing transparency in decision-making. This enables marketers to achieve more effective and personalized customer experiences. Benefits of AI Agents Implementing AI agents for customer engagement has multiple benefits. The benefits of AI agents extend beyond simple automation, creating value through enhanced efficiency, cost optimization, real-time audience insights, and elevated customer experiences. Here are a few – Improved Productivity: AI agents handle routine inquiries and tasks simultaneously, freeing human agents to focus on complex issues. They operate in real-time to provide the latest customer insights. This seamless workflow integration allows businesses to accomplish more with existing resources. Reduced Costs: By automating repetitive tasks, brands can significantly lower operational expenses while maintaining a great customer experience. Improved Decision Making: AI agents analyze vast amounts of customer data to identify patterns and trends that might otherwise go unnoticed. They provide consistent, data-driven recommendations free from human biases or fatigue. Real-time insights allow brands to anticipate customer needs and proactively engage with them. Impact of Generative AI on Customer Engagement  Generative AI continues to improve, driving growth in Customer Engagement across various segments. The number of martech software products has grown by a whopping 27.8% in the last 12 months, the highest in the last few years. If we look back, adoption curves used to be measured in years. But with generative AI, adoption is growing in months or even weeks. One significant challenge is accurately gauging the real-time adoption of Generative AI. It’s like the Heisenberg uncertainty principle in quantum mechanics. This difficulty is akin to the Heisenberg uncertainty principle in quantum mechanics. We can all agree that Generative AI has many useful use cases. However, multiple reports suggest marketers leverage it mainly for brainstorming campaign copy ideas and for writing the copy itself. That’s why we launched Merlin AI Copywriter and Designer last year. With Merlin AI, we have successfully merged the advantages of Generative AI with the world of Customer Engagement. You can read more about it here.  Wrapping It Up  AI is transforming Customer Engagement beyond automation with intelligent AI agents handling complex marketing tasks autonomously. Despite the hype around AI agents and generative AI, they offer real benefits like improved productivity, cost savings, better decisions, and enhanced customer experience through streamlined processes and insightful analytics. Leveraging Generative AI and AI agents is now critical for the sustained success and growth of consumer brands. The post AI in Customer Engagement – AI Agents, Gen AI, Future and More appeared first on MoEngage.

Three years ago, as the pandemic caused chaos for companies big and small, Colgate-Palmolive’s chief supply chain officer Luciano Sieber orchestrated a “logistics blitz.”  The result gave Sieber a better understanding of how Colgate-Palmolive moves its products around the world. But it stuck Sieber with another problem: too much data.  About a year ago, Sieber says he found a solution to that problem with Uber Freight. The ride-hailing service’s long-running logistics and analytics arm has been developing new ways to wrangle large amounts of data by using artificial intelligence. Colgate-Palmolive became one of the first companies to use one of its newest products, a a logistics-focused LLM Uber Freight calls Inisights AI.  Now, Uber Freight is more formally launching a suite of AI features to shippers around the world as part of its existing supply chain software. That includes an expansion of Insights AI, which Uber Freight quietly launched in 2023, as well as more than 30 AI agents built to “execute key logistics tasks throughout the freight lifecycle.”  Uber Freight is not alone in trying to tame unruly supply chains with modern artificial intelligence tools. Flexport announced its own suite of AI tools in February, and there are myriad startups trying to help companies wrangle data, reduce inventory stockpiles, and better predict supply and demand. But Uber Freight is betting its AI solutions can make an immediate impact on the bottom line of both its blue-chip customers and the nearly 10,000 other shippers it works with. That’s largely because of the knowledge base and relationships it has established in the eight years since it was created to match long-haul truckers with shippers. “Supply chain is inherently a data-rich problem. It’s complex, it’s nuanced, and AI can serve a fundamental role in shaping it and accelerating it,” Uber Freight founder Lior Ron said in an interview with TechCrunch.  Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW ‘We’ve been building towards this moment’ Uber Freight began as a more straightforward brokerage business model when it launched in 2017. But the Uber subsidiary has steadily evolved over the years into more of a service provider to companies that ship goods around the world.  Many modern companies are trying to find ways to incorporate artificial intelligence (often to mixed results); it should come as no surprise that Uber Freight is putting the technology front and center. After all, both Ron’s undergraduate work and his master thesis were centered around AI – way back “in the dark ages when it was called ‘neural networks,’” he joked. Ron continued to work with machine learning technology when he was running Google Maps from 2007 until 2016. It was there, he said, that he saw “the potential of digitizing the physical universe.”  “That sort of led me to the foundational belief, nine years ago, that supply chain is fundamentally a data-first, technology-first challenge that could be accelerated with data connectivity, and over time, AI,” he said. “We’ve been building towards this moment, I think, since I started Uber Freight.” Ron said Uber Freight has used machine learning in its work since the beginning. But it was around two years ago that the team started trying to work with more advanced generative AI capabilities.  That “hasn’t been an easy road,” Ron said. Uber Freight’s initial attempts at building a sort of “co-pilot for logistics” were riddled with hallucinations and returned accurate answers only around 60% to 70% of the time. Now that technology has been “battle tested” and is “driving real business outcomes,” with an accuracy rate of 98%, according to Ron. The company says the Insights AI model has been trained on internal and external data related to the $20 billion worth of freight that it helps move every year. It also leverages multiple undisclosed AI models “providing optimal combinations of price, precision and performance,” according to Uber Freight. Ron said this AI push creates new ways for customers to work with the data related to their supply chain. They can ask Insights AI to quickly pull up, say, the worst-performing origin points for particular shipments. Or they can ask to be shown “all shipments to CVS in 2023.” Ron stressed that the queries can be far more complex than this, too, and the model always keeps up.  Insights AI is presented to customers much like other popular LLM interfaces; it will also show its work and make clear where all the data is coming from, just like other reasoning models. All of this lets a customer “gain insights on your network much faster, at close to 100% accuracy instantly, versus formulating what you want to know, sending it to some analysts, and waiting for two weeks for the PowerPoint presentation to come back to have a discussion,” Ron said. ‘What do you want to know?‘ Uber Freight works with a lot of Fortune 500 companies, but it found a particularly willing partner in Colgate-Palmolive to trial Insights AI and its other new tools. The conglomerate already makes a suite of AI models available to all of its employees, according to Sieber. It also makes those workers take a mandatory training on AI ethics that was developed in-house. “I think it’s great, because it turns the conversation from fear into, ‘how that makes me more efficient, and how [do] I become a better professional and deliver more by having access and using those new technologies,’” Sieber said.  For instance, Sieber said his company has used Insights AI to easily identify carriers who are accepting fewer shipments than they’re contractually obligated to to move. From there, they can work out why those levels are low, and either come up with a solution to get the carrier back in compliance or drop them in favor of another. This was previously a challenge to solve in real time, Sieber said, because companies like Colgate-Palmolive work with thousands of carriers. Each of those might work with different systems and workflows, and all of that resulting information was never really centrally managed.  The next step with AI, both Sieber and Ron said, has been finding ways to create more proactive solutions. Ron said this is another place Uber Freight can flex its data strengths. “We know the facilities, we know the lanes, we know the prices,” he said. “What do you want to know?” These more proactive integrations come in the form of alerts that tell a customer like Colgate-Palmolive they’re overpaying on certain routes, or that there are faster options available for a particular shipment.  Any single suggestion like that may only save a few hundred, or perhaps a few thousand dollars. But aggregated over an entire network, it could make a big difference. That’s why, when asked, Sieber was quick to answer that Colgate-Palmolive’s chief financial officer is the executive who’s most pleased with what Uber Freight’s enabled. “He loves to see logistics costs coming down,” Sieber laughed.

The Procurement Analyst is responsible for supporting procurement and finance teams in negotiating contracts and enhancing Paymentology’s purchasing processes. This role plays a key part in improving Paymentology’s commercial position and financial performance by identifying cost-saving opportunities, ensuring compliance with internal and external procurement policies, and implementing structured purchasing procedures.Contract Negotiation and Procurement Management:Assist in the negotiation of new contracts and contract renewals, ensuring optimal commercial terms for Paymentology.Identify cost-saving opportunities by analysing procurement data and monitoring expenditure.Maintain accurate and up-to-date records of supplier agreements, procurement activities, and savings opportunities.Financial and Data Analysis:Monitor and analyse procurement data to track expenditure trends and highlight efficiency opportunities.Develop dashboards to visualise spend, assess supplier performance, and identify key cost-reduction strategies.Compliance and Vendor Onboarding:Work closely with legal, compliance, and cybersecurity teams to onboard vendors in accordance with Paymentology’s internal policies, PCI regulations, and client requirements.Ensure that all procurement processes align with regulatory standards and risk management protocols.Cross-Functional Collaboration:Collaborate with internal stakeholders to proactively manage purchases and secure the best commercial deals.Support the rollout and continuous improvement of procurement processes, including the implementation of a third-party risk management system and purchase order process within the current ERP system.Outcome: A structured, data-driven procurement process that enhances financial performance, mitigates risk, and ensures Paymentology secures the best value in supplier agreements.What it takes to succeed:Strong analytical skills with the ability to interpret and compare complex, multi-layered commercial proposals.Excellent interpersonal skills, with the ability to engage and influence internal and external stakeholders.Proficiency in Microsoft Office, particularly Excel, for procurement data analysis and reporting.High attention to detail, with the ability to manage multiple concurrent tasks in a fast-paced environment.Experience in technology procurement, particularly in SaaS-based agreements.Understanding of legal contract review and negotiation.Familiarity with ERP systems is preferred.Years of Experience2–3 years of experience in a procurement or commercial-based role.Proven track record of contract negotiation, supplier management, and cost-saving initiatives.Experience in technology and SaaS procurement is highly desirable.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote All Other Remote JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot

Reading Time: 7 minutes The way brands look at data has changed over the past few years. While in the last decade, brands may have focused on data collection (the more data, the merrier), over the passage of time, marketers are dawning upon the realization that unless data is actionable, it’s not really usable. Marketers have struggled for years to draw actionable insight from fragmented customer data that exists in silos across countless tools.  Rather than adding yet another tool to their tech stack to mitigate this silo problem, marketers are re-evaluating their entire tech stack. As a result, forward-thinking brands realize that to unlock truly personalized, real-time customer experiences requires tapping into the single source of truth: their data warehouse. Let’s explore why this shift is happening and how a CDEP like MoEngage is the perfect partner in this new paradigm. From Passive to Active: The Evolution of The Modern Data Warehouses Data warehouses aren’t new. Traditionally, many businesses relied on on-premise warehouses, which were powerful but often rigid, expensive to scale, and sometimes siloed.  The game-changer has been the advent of cloud data warehouses (such as Snowflake, Google BigQuery, Databricks, Amazon Redshift, and Azure Synapse). Cloud data warehouses offer consumer brands the following advantages: Scalability: Easily handle vast and growing datasets without massive upfront infrastructure investment. Flexibility: Integrate structured and semi-structured data from teams like sales, customer support, finance, marketing, etc. Performance: Process complex queries rapidly, enabling faster insights. Accessibility: Democratize data access for various teams (with appropriate governance). Gone are the days when data warehouses were merely passive repositories of data. Today, they often contain the most complete, up-to-date, and trustworthy business view. Benefits of the Warehouse-First Approach for Campaigns Real-time data: Data warehouses often provide real-time data, enabling marketers to understand the pulse of the customer and make decisions based on timely and accurate information rather than the slightly-dated information that ends up in a CDP (customer data platform). Real-time analytics: A warehouse-first approach helps marketers to understand the current state of the business (as in ‘what is happening right now’. This real-time analytics also helps them spot deviations in key performance indicators (KPIs), detect problems proactively, and make faster decisions. Real-time segmentation: Traditional segmentation relies on slightly stale data (hours or days old). By automatically moving customers into or out of segments based on their latest actions/changes in attributes, marketers can personalize the customer experience in real time, thereby becoming more relevant and timely. Data consistency and quality: Loading data into a warehouse involves data cleansing, validation, standardization, and transformation, which improves the quality of data in the warehouse. Speed, scalability, and cost-effectiveness: Data warehouses offer scalable storage and processing power, allowing businesses to adapt to their evolving needs rapidly. Compared to storing and processing data within multiple tools, data warehouses are a more cost-effective way for businesses to manage massive amounts of diverse data. Faster time-to-value (Potentially): Once set up, activating new data points or segments can be quicker if the data already resides in the warehouse. This also means that campaigns can be launched faster vs. when data needs to be copied into a CDP before being sent to an email platform or an SMS automation tool. Improved data governance: By consolidating all data into a central repository, data warehouses ensure that data governance policies, standards, and procedures are standardized across the board.   Benefits of a Warehouse-First Approach For B2C Consumer Brands CDEP and the Warehouse-First Approach Forward-thinking consumer brands have realized that customer data management and customer engagement cannot exist in silos.  This is how a data-driven marketer is thinking in 2025: I have all the information about my customer consolidated in a data warehouse. The data combines customers’ past purchase history, product interactions, customer support history, marketing-related data, and many other pivotal data points. I want to use this data warehouse information directly to drive contextual, relevant, and personalized experiences through my campaigns in real-time. I don’t want to spend time copying the data into another tool and then into a marketing automation or a customer engagement platform. Any data latency will prevent me from providing real-time personalized experiences, and will negatively impact my engagement, retention, and monetization campaigns.   For consumer brands that need to respond to the pulse of the customer in real-time, the martech stack needs to be warehouse-native.  This is where a CDEP (Customer Data and Engagement Platform) fits in like a glove. What is a CDEP? A customer data and engagement platform (CDEP) is an integrated, all-in-one platform that combines robust data management capabilities with best-in-class engagement, AI, and analytical capabilities.  A CDEP can: Unify data to build a single customer profile with real-time data ingestion Segment your customers based on behavior, demographics, or propensity Hyper-personalize communications in real-time, at scale Automate campaigns via advanced AI functionalities Analyze campaign performance and customer behavior to unlock deep customer insights What Makes MoEngage the Perfect Warehouse-Native CDEP? A CDEP helps consumer brands leverage warehouse as the central system without creating new data silos.  A warehouse-native CDEP like MoEngage minimizes data duplication and data movement, leverages the warehouse’s processing power, enhances security, and can lower costs in the long run. To evaluate why a CDEP (Customer Data and Engagement Platform) like MoEngage shines in a warehouse-first marketing approach, we need to understand these three pillars: 1) Capabilities of a CDEP Data Ingestion, Unification, and Management Capabilities Instead of extensive data storage, a warehouse-native CDEP connects directly to your data warehouse. It allows marketers to build segments using rich data in situ within the warehouse environment, leveraging the warehouse’s processing power and data freshness. Side note: A CDEP can also double down as a data management platform if you do not have a data warehouse or do not wish to connect it with your warehouse.  Data Ingestion: A CDEP comes equipped with flexible and robust data management capabilities that can capture a complete view of customer interactions. Sources of data ingestion include event streaming, batch data uploads, platform integrations (via connectors and APIs), and data warehouse integrations (Snowflake, BigQuery, RedShift, etc). Data Storage: A CDEP would leverage your existing data warehouse as the primary, persistent storage location for comprehensive customer data. Data Unification: CDEPs can unify data from various sources into a 360-degree customer view for each individual, resolving identity across devices and channels. CDEPs do this by ingesting and recognizing identifiers (email, phone, user ID, device ID etc) and applying deterministic matching rules to stitch identifiers and associated data together. Data Governance, Privacy & Security: CDEPs have best-in-class data governance, privacy and security features like Data Access Management, Data Encryption, PII Masking and Tokenization, and Data Retention Policies. These measures ensure that data remains protected at all costs. Integrated Engagement Capabilities A CDEP seamlessly combines data processing with built-in omnichannel campaign orchestration and delivery across multiple channels. These best-in-class engagement capabilities are further augmented with AI capabilities that can help consumer brands improve the customer experience. Omnichannel Messaging Capabilities With over 11 channels, including email, SMS, push notification, WhatsApp, etc., a CDEP like MoEngage can help your brand build and optimize personalized customer journeys across channels and devices. AI Capabilities Generative and predictive AI capabilities offered by a CDEP can easily learn from the warehouse data and use AI agents to suggest the ideal customer journeys, decisions that help brands optimize campaigns, and generate segments. Segmentational Capabilities A CDEP can generate actionable segments in real time based on demographics and attributes, behavioral and transactional data, and propensity. A CDEP uses fresh and comprehensive data in the warehouse, allowing marketers to build customer segments based on demographics, attributes, behavior, transactions, or propensity scores. Analytical Capabilities After unifying data from different sources, a CDEP can help marketers make sense of the data through the power of its analytical capabilities. Be it identifying trends and patterns in customer behavior, segmenting the audience, predicting the likelihood of certain actions in the future, or informing future strategy, CDEP’s robust analytical capabilities can help marketers with actionable insights in real time. Reporting and Dashboards: Built-in data visualization capabilities can help marketing, growth, and product teams across consumer brands track key business metrics like campaign engagement rates, conversion rates, churn rates, DAUs, and MAUs. Behavioral Analytics: Features like funnel, RFM, and cohort analysis can help consumer brands analyze behavioral trends of customers. 2) Scalability, Reliability, and Elasticity Reliability offered by a CDEP means that the platform is consistently available without any downtimes or disruptions. A reliable CDEP ensures high availability, fast response times, and stable performance for brands like yours. A reliable CDEP is built on elastic infrastructure, which means that the platform’s capabilities will be available to you even during periods of extensive and high usage by other customers. 3) Customer Support  If you’re planning to migrate to a warehouse-native CDEP, you might have some concerns about the amount of effort, developer bandwidth, friction, and migration costs that you could incur while making the switch. MoUpgrade program by MoEngage accelerates migration timelines and delivers faster time-to-value (TTV) in weeks instead of months or years.  The program allows for a smooth migration to a warehouse-native CDEP without any disruptions to ongoing campaigns, data collection cadences, or your brand’s existing data pipelines. Conclusion: This shift towards warehouse-first marketing marks a pivotal change, moving beyond fragmented data collection to leveraging the data warehouse as the single source of truth. A warehouse-native CDEP like MoEngage emerges as the ideal solution. By integrating data management, engagement, and analytics directly with the warehouse, CDEPs empower brands to deliver truly contextual, real-time experiences, driving better results and faster time-to-value in 2025 and beyond. Talk to our team to understand how a CDEP can help your brand better harness the power of your data warehouse.   The post Warehouse-First Marketing: Why Brands in 2025 Are Choosing a CDEP appeared first on MoEngage.

Dark Outlaw Games is a new PlayStation studio born out of the ashes of Deviation Games and led by developer Jason Blundell, best known for Call of Duty: Black Ops’Suggested ReadingNintendo Switch 2 Price Is Set at $450 for Now, But Could Go Higher Share SubtitlesOffEnglishSuggested ReadingNintendo Switch 2 Price Is Set at $450 for Now, But Could Go Higher Share SubtitlesOffEnglishNintendo Switch 2 Price Is Set at $450 for Now, But Could Go HigherFirst spotted by The Game Post, the listing reads, “We are seeking a Senior Concept Artist to join our LA studio, reporting directly to our Art Director. The ideal candidate is proactive, with strong skills in digital illustration, 3D modeling, character design, graphic design, experience with generative AI tools, and passionate about making games. Help us create our next AAA title—one that will truly stand apart from the rest!”In the qualifications section, the listing points to a requirement for “advanced expertise in using generative AI tools such as Stable Diffusion, Midjourney, ChatGPT, or similar platforms.” In the duties section, the listing says one of the concept artist’s jobs will be to “Refine and polish 2D artwork created by both human artists and generative AI tools.”Read More: The new job listing at the Sony-owned studio comes after a recent GDC 2025 survey found that companies are moving ahead with AI tools whether their employees agree with using them or not. Concerns about the technology range from the ethics of using tools trained on stealing other people’s work to worries about continued layoffs as companies seize on the hype around generative AI to cut workforces. Anecdotally at least, artists and writers appear to have been particularly hard hit in the most recent waves of mass game industry layoffs. “I suspect there will be a dual demand in gaming: one for AI-driven innovative experiences and another for handcrafted, thoughtful content,” PlayStation Studios head Hermen Hulst told the BBC in December. “Striking the right balance between leveraging AI and preserving the human touch will be crucial.” It was revealed earlier this year that Sony had at least experimented with turning its Horizon Zero Dawn hero Aloy into an AI-powered chatbot. Meanwhile, finished games that incorporate AI-generated assets have been forced to disclose the practice on storefronts like Steam. Call of Duty is one of the franchises frequently dunked on by fans for making use of what appear to be AI-generated cosmetics, player cards, and marketing materials. .

uflypro - stock.adobe.com News Ireland sets out chip plan Ireland has a long history of attracting high-tech firms. Building on the EU Chip Act, it now aims to be a leader in semiconductors By Cliff Saran, Managing Editor Published: 20 May 2025 14:45 The Irish government has unveiled a semiconductor strategy in alignment with the European Chip Act. Silicon island: A national semiconductor strategy sets out a clear roadmap to grow Ireland’s semiconductor sector by creating high-value jobs, attracting major investment and deepening the country’s leadership in cutting-edge technology as a key player in Europe’s semiconductor future. The Irish government aims to become a leader in the semiconductor industry by building on its track record in technology development, capitalising on existing strengths and taking proactive, strategic actions to address emerging challenges. In 2023, the European Union (EU) set out plans to secure the supply of semiconductors and drive chip development across the region. The EU Chip Act commits €43bn of policy-driven investment until 2030, funding a number of initiatives including next-generation technologies and providing access across Europe to design tools and pilot lines for the prototyping, testing and experimentation of cutting-edge chips. Ireland – which is home to over 130 semiconductor companies, directly employing 20,000 people and generating €13.5bn in annual exports – is building on the EU Chip Act. The Irish strategy aims to foster the further growth of Ireland’s semiconductor ecosystem over the coming decade in support of the European Chips Act and Digital Decade ambitions through three strategic strands: strengthening the existing ecosystem, ensuring a robust national talent pipeline, and seizing industry opportunities. Key deliverables include publishing a comprehensive mapping of Ireland’s semiconductor ecosystem, fostering connections between research and innovation clusters, and engaging the education system to meet growing talent demands. Read more semiconductor stories EU looks to ramp up sovereign tech as Trump trade war begins: Trump’s trade war is now looking at the EU, with tariffs on steel and aluminium imports. Could US tech be in the firing line? The challenges of securing a UK semiconductor supply chain: The UK’s national semiconductor strategy aims to establish the country's credentials as a leader in the sector. We look at how it stacks up. Speaking at the launch, minister for enterprise, tourism and employment Peter Burke said: “Ireland already has a strong semiconductor base. But with the right support, I believe we could do far more. By 2040, Ireland could support up to 34,500 new semiconductor roles.” The overall goal of the Irish chip strategy is to ensure there is a secure supply chain for semiconductors in Europe by building on Ireland’s already well-established semiconductor footprint. Integrated device manufacturers including Intel, Analog Devices and Infineon have facilities in Ireland. Fabless manufacturers including Apple, AMD and IBM also have sites in Ireland, as do firms like ARM, AutoCAD and Siemens, which provide chip design, and design and testing software for chipmakers. Photolithography equipment manufacturer ASML also has an Irish facility. The multi-faceted strategy involves securing major industrial investments, with ambitious plans to establish a leading-edge chip fabrication facility, two trailing-edge foundries and an advanced packaging facility. Ireland also aims to develop next-generation sites with the infrastructure needed to support large-scale manufacturing and support startups and spinouts with access to finance. The Irish government also aims to enhance research and development, by supporting both indigenous innovation and multinational collaboration and fostering an open ecosystem based on collaboration. Overall, it wants to ensure Ireland is regarded as a hub of excellence in semiconductor design, manufacturing and research. Ireland has established a national competence centre for semiconductors under the European Chips Act. It has also collaborated with Analog Devices and 14 EU member states as part of the EU’s €8.0bn Important Projects of Common European Interests initiative to support microelectronics and communications. In The Current Issue: UK critical systems at risk from ‘digital divide’ created by AI threats UK at risk of Russian cyber and physical attacks as Ukraine seeks peace deal Standard Chartered grounds AI ambitions in data governance Download Current Issue Starburst chews into the fruits of agentic – CW Developer Network Calm settles over digital identity market - for now... (Hark, is that Big Tech on the horizon?) – Computer Weekly Editors Blog View All Blogs

NVIDIA and Taiwan’s manufacturing ecosystem, including Delta Electronics, Foxconn, TSMC and Wistron, are showcasing this week at COMPUTEX in Taipei the crucial role digital twins play in accelerating industrial AI. These electronics, semiconductor and robotics manufacturing leaders are using Universal Scene Description (OpenUSD) and NVIDIA Omniverse libraries and blueprints to develop physically based digital twins. This is transforming factory planning by unlocking new operational efficiencies and accelerating the development, testing and validation of autonomous robots and robotic fleets. Many of these manufacturers are also extending the digitalization of their factories to the real world, using the NVIDIA AI Blueprint for video search and summarization (VSS) — now generally available and part of the NVIDIA Metropolis platform — to deploy video analytics AI agents into their operations and drive additional automation and optimizations in defect detection and other operations. Taiwan Manufacturers Optimize Planning and Operations With Simulation and AI Agents  Taiwan’s leading electronics and semiconductor manufacturers are using digital twins, physically based simulation and AI agents to optimize existing operations and vastly accelerate the planning and commissioning of new factories. Foxconn is leading the way. At its Taiwan facilities, Foxconn engineers rely on the Fii Digital Twin platform, developed with OpenUSD, Siemens and Omniverse technologies, to design and simulate robot work cells, assembly lines and entire factory layouts. These digital twins connect to material control systems and use Autodesk Flexsim, NVIDIA cuOpt and NVIDIA Isaac Sim to enable engineers to simulate and dynamically optimize the flow of materials, equipment, autonomous mobile robots (AMRs), automated guided vehicles, and other robots and humans. By developing a standard digital twin model for their factories, Foxconn can quickly migrate and easily reconfigure its designs and plans for new factory deployments. Foxconn is using the NVIDIA Isaac GR00T N1 model, the NVIDIA Isaac GR00T-Mimic blueprint for synthetic manipulation motion generation and NVIDIA Isaac Lab to train industrial manipulator arms and humanoid robots for performing complex tasks such as screw-tightening, pick and place, assembly and cable insertion. Foxconn robotics developers use the Mega NVIDIA Omniverse Blueprint to simulate and test large robotic fleets comprising AMRs, manipulators and humanoid robots before deploying them in facilities. To accelerate analysis and decision-making, Foxconn engineers use their digital twin platform to conduct thermal assessments of POD rooms across different scenarios. By connecting their digital twins to the Cadence Reality Digital Twin Platform and integrating NVIDIA PhysicsNeMo frameworks, teams can conduct thermal simulations 150x faster, reduce thermal risks and identify energy-saving opportunities. Using the Omniverse Blueprint for AI factory digital twins, Foxconn can simulate and test GB200 Grace Blackwell Superchips in liquid-cooled PODs to replicate the conditions of an AI factory. Credit: Foxconn The company is also deploying video analytics AI agents using the VSS blueprint from NVIDIA Metropolis for real-time video analysis and insights in live production scenarios. TSMC is collaborating with an AI-powered digital twins startup to optimize the planning and construction of its new fabs. TSMC taps into an AI engine and applications built with Omniverse libraries to transform traditional 2D computer-aided designs into rich, interactive 3D layouts of their complex facilities, including specialized areas like clean rooms. Credit: TSMC Visualizing these optimized layouts in a digital twin allows planning teams to proactively identify and resolve equipment collisions, understand system interdependencies, and assess impacts on space and operational key performance indicators. This AI-driven approach is enhanced by NVIDIA cuOpt for optimization and reinforcement learning with NVIDIA Isaac Lab, enabling the generation of intricate, multilevel piping systems in seconds — a task that traditionally requires substantial time and effort. This enables engineers to virtually validate complex pipe routing and drastically reduce design revisions, ultimately streamlining the entire fab development process. TSMC also uses vision language models and vision foundation models to improve automated defect classification workflows — boosting efficiency to classify wafer product defects for engineers to pinpoint potential root causes for the issues. Beyond the use of digital twins and vision AI, TSMC also taps into NVIDIA CUDA-X software libraries and NVIDIA GPUs to accelerate its entire semiconductor chip design workflow — from lithography with NVIDIA cuLitho to semiconductor process simulation. Wistron teams drive operational efficiencies, optimize layout planning of their plants, and train robots and workers with the Wistron Digital Twin (WiDT) platform. The platform is powered by software from Autodesk, Cadence and Microsoft and taps into NVIDIA AI and Omniverse libraries. By connecting the WiDT platform to generative AI tools and real-time data from surface mount technology machines and shopfloor control systems, operations teams can visualize real-time dashboards to quickly diagnose and improve machine and plant performance. Wistron robotics developers use the platform, and its integration with NVIDIA Isaac Sim, to simulate and test robotic arms. With a simulation-first approach, teams reduced the time needed for each arm to assemble parts on the production line by 12 seconds. Credit: Wistron The Wistron digital twin platform also uses the VSS blueprint to create and curate training videos for teaching workers how to perform and manage complex tasks and scenarios. The platform uses NVIDIA Cosmos Tokenizer to help teams analyze and break down worker actions on the production line and improve standard operating procedures. This approach is enabling Wistron to accelerate onboarding, improve worker productivity and ensure safety. Wiwynn uses AI-enabled digital twins built with Omniverse technologies to optimize factory layouts, simulate production, integrate cobots and enhance quality control through improved inspection and analysis. These solutions have driven significant manufacturing and logistics innovation and efficiencies. Pegatron’s PEGAVERSE and PEGAAi platforms equip engineers and factory managers with digital twins that support many use cases, including factory planning, predictive maintenance, process optimization, resource planning, remote monitoring and quality control. Teams also use the platforms to build visual AI agents to help workers perfect complex assembly tasks. These AI agents, developed with the NVIDIA AI Blueprint for VSS and NVIDIA Metropolis, have enabled Pegatron to augment assembly processes, reduce labor costs by 7% and decrease assembly line defect rates by 67%. Kenmec and MetAI are using Omniverse technologies and the Mega NVIDIA Omniverse Blueprint to build physically accurate digital twins for simulating, testing and deploying warehouse automation solutions. Together, the teams virtualized the entire Chief Smart Logistics Center, creating a full-fidelity simulation environment that brings together physical dynamics, real-time controller logic, AI-driven testing and optimization — all within a simulated environment. GIGABYTE operations teams are using digital twins developed with Omniverse libraries and connected to live IoT data from the manufacturing floor to improve operational monitoring of production systems. By visually flagging anomalies, including equipment issues and delays, the digital twins help teams quickly identify issues, conduct root cause analysis and take corrective actions. Quanta Cloud Technology engineering, operations and logistics teams collaborate using digital twin solutions built with Omniverse to accelerate factory planning. Digital twins provide these cross-functional teams with access to the latest design data, enabling them to provide immediate feedback on proposed layouts, which leads to optimized workflows and improved space utilization. Teams can further extend collaboration sessions to external customers and suppliers so they can remotely contribute to design reviews and validation. Credit: Quanta Manufacturers Embrace Digital Twins to Accelerate Robotics Development In addition to creating the future in manufacturing, Taiwan manufacturers are using digital twins, powered by Omniverse libraries and blueprints, to develop the next wave of AI-enabled robots. Delta Electronics is using Isaac Sim to optimize electronic component production and to simulate, train and validate its entire range of industrial robots — from AMRs to industrial manipulators. Credit: Delta Electronics The company is transforming its expertise into a service by designing a cyber-physical integrated classroom to be launched soon in Taiwan, where customers learn to use the DIATwin platform to simulate and integrate Delta’s industrial equipment and robots to ensure a more effective implementation into their own production lines. Credit: Techman Robot Techman Robot is advancing intelligent automation at Volkswagen’s Transparent Factory. Using Isaac Sim, Techman’s AI Cobots learn to operate on GESSbot AMRs in physically accurate simulations to perform real-time assembly, inspection and adaptive manipulation tasks with precision. By simulating robot behavior and workflows virtually, Techman Robot has reduced the time to program robots by 70% and improved robot productivity by 20%. Credit: Foxlink Foxlink is using the Isaac GR00T N1 model to add generalized intelligence and autonomy to its industrial robots used in manufacturing facilities. Solomon’s AI vision solution, powered by NVIDIA Isaac Manipulator CUDA-X acceleration libraries, is helping Inventec significantly accelerate its robotic server inspection process by boosting complex motion planning speed by up to 8x and reducing errors by 50%. Kudan is integrating its Visual SLAM technology with Isaac Perceptor CUDA-X acceleration libraries into NexAIoT’s AMR, NexMOV-2. This integration uses advanced 3D perception and navigation, enabling them to navigate complex, unstructured environments such as manufacturing, logistics and healthcare facilities with greater precision and reliability. MSI is powering its industrial robots with the NVIDIA Jetson AGX Orin module to perform a variety of tasks, from pick-and-place and material handling to delivering payloads inside large warehouses and facilities. Credit: Adata In healthcare, Adata and Advantech are jointly using Isaac Sim, Isaac Perceptor and Jetson Orin to develop AMRs for disinfecting hospitals. This collaboration has reduced deployment time by 70% and made the disinfection process 3x faster. Ubitus is also using the Isaac platform to train G1 humanoid robots to deliver medical checkup materials and specimens, helping alleviate labor shortages in hospitals. Learn more by watching the COMPUTEX keynote from NVIDIA founder and CEO Jensen Huang and attending sessions at NVIDIA GTC Taipei, running through May 22. See notice regarding software product information. Featured image courtesy of Quanta (top left), Wistron (top right), Foxconn (bottom left), Pegatron (bottom right).

May 20, 2025Ravie LakshmananCloud Security / Vulnerability Cybersecurity researchers have discovered risky default identity and access management (IAM) roles impacting Amazon Web Services that could open the door for attackers to escalate privileges, manipulate other AWS services, and, in some cases, even fully compromise AWS accounts. "These roles, often created automatically or recommended during setup, grant overly broad permissions, such as full S3 access," Aqua researchers Yakir Kadkoda and Ofek Itach said in an analysis. "These default roles silently introduce attack paths that allow privilege escalation, cross-service access, and even potential account compromise." The cloud security firm said it identified security issues in default IAM roles created by AWS services like SageMaker, Glue, EMR, and Lightsail. A similar flaw has also been unearthed in a popular open-source framework called Ray, which automatically creates a default IAM role (ray-autoscaler-v1) with the AmazonS3FullAccess policy. What's concerning about these IAM roles is that while they are intended for something specific, they could be abused to perform administrative actions and break isolation boundaries between services, effectively allowing an attacker who has a foothold in the environment to move laterally across services. These attacks go beyond bucket monopoly attacks, which revolve around a scenario where a threat actor could take advantage of predictable S3 bucket naming patterns to set up buckets in unused AWS regions and ultimately gain control over the contents of the bucket when a legitimate customer starts using services like CloudFormation, Glue, EMR, SageMaker, ServiceCatalog, and CodeStar. "In this case, an attacker who gains access to a default service role with AmazonS3FullAccess doesn't even need to guess bucket names remotely," the researchers explained. "They can use their existing privileges to search the account for buckets used by other services using the naming patterns, modify assets like CloudFormation templates, EMR scripts, and SageMaker resources, and move laterally across services within the same AWS account." Put differently, an IAM role within an AWS account with AmazonS3FullAccess permissions has read/write access to every S3 bucket and modifies various AWS services, effectively turning the role into a powerful method for lateral movement and privilege escalation. Some of the identified services with the permissive policy are listed below - Amazon SageMaker AI, which creates a default execution role named AmazonSageMaker-ExecutionRole-<Date&Time> when setting up a SageMaker Domain that comes with a custom policy equivalent to AmazonS3FullAccess AWS Glue, which creates a default AWSGlueServiceRole role with the AmazonS3FullAccess policy Amazon EMR, which creates a default AmazonEMRStudio_RuntimeRole_<Epoch-time> role that's assigned the AmazonS3FullAccess policy In a hypothetical attack scenario, a threat actor could upload a malicious machine learning model to Hugging Face that, when imported into SageMaker, can result in the execution of arbitrary code, which could then be used to seize control of other AWS services like Glue by injecting a backdoor capable of stealing IAM credentials of the Glue job. The adversary could then escalate their privileges within the account, ultimately breaching the entire AWS environment by looking for buckets used by CloudFormation and injecting a malicious template to escalate privileges further. In response to the disclosure, AWS has addressed the issues by modifying the AmazonS3FullAccess policy for default service roles. "Default service roles must be tightly scoped and strictly limited to the specific resources and actions they require," the researchers said. "Organizations should proactively audit and update existing roles to minimize risk, rather than relying on default configurations." The findings come as Varonis detailed a vulnerability in a utility used for mounting Azure Storage that comes preinstalled on Microsoft Azure AI and High-Performance Computing (HPC) workloads and allows an unprivileged user on a Linux machine with this utility installed to escalate their privileges to root. "It involves a classic privilege escalation method involving a SUID binary that is part of the installation of AZNFS-mount, a utility for mounting Azure Storage Account NFS endpoints," security researcher Tal Peleg said. "For example, a user could elevate permissions to root and use those permissions to mount additional Azure Storage containers, install malware or ransomware on the machine, and attempt to move laterally in the network or cloud environments." The flaw, which affects all versions of the utility up to 2.0.10, has been addressed in version 2.0.11 released on January 30, 2025. Found this article interesting? Follow us on Twitter  and LinkedIn to read more exclusive content we post. SHARE    

Google just wrapped up its big keynote at I/O 2025. As expected, it was full of AI-related announcements, ranging from updates across Google’s image and video generation models to new features in Search and Gmail.But there were some surprises, too, like a new AI filmmaking app and an update to Project Starline. If you didn’t catch the event live, you can check out everything you missed in the roundup below.Google’s AI Mode for Search is coming to everyoneGoogle has announced that it’s rolling out AI Mode, a new tab that lets you search the web using the company’s Gemini AI chatbot, to all users in the US starting this week.Google will test new features in AI Mode this summer, such as deep search and a way to generate charts for finance and sports queries. It’s also rolling out the ability to shop in AI Mode in the “coming months.”Project Starline is now Google BeamImage: GoogleProject Starline, which began as a 3D video chat booth, is taking a big step forward. It’s becoming Google Beam and will soon launch inside an HP-branded device with a light field display and six cameras to create a 3D image of the person you’re chatting with on a video call.Companies like Deloitte, Duolingo, and Salesforce have already said that they will add HP’s Google Beam devices to their offices.Imagen and Veo are getting some big upgradesGoogle has announced Imagen 4, the latest version of its AI text-to-image generator, which the company says is better at generating text and offers the ability to export images in more formats, like square and landscape. Its next-gen AI video generator, Veo 3, will let you generate video and sound together, while Veo 2 now comes with tools like camera controls and object removal.Google launches an AI filmmaking appGIF: GoogleIn addition to updating its AI models, Google is launching a new AI filmmaking app called Flow. The tool uses Veo, Imagen, and Gemini to create eight-second AI-generated video clips based on text prompts and / or images. It also comes with scene-builder tools to stitch clips together and create longer AI videos.Gemini 2.5 Pro adds an “enhanced” reasoning modeThe experimental Deep Think mode is meant for complex queries related to math and coding. It’s capable of considering “multiple hypotheses before responding” and will only be available to trusted testers first.Google has also made its Gemini 2.5 Flash model available to everyone on its Gemini app and is bringing improvements to the cost-efficient model in Google AI Studio ahead of a wider rollout.Xreal shows off its Project Aura prototypeXreal and Google are teaming up on Project Aura, a new pair of smart glasses that use the Android XR platform for mixed-reality devices. We don’t know much about the glasses just yet, but they’ll come with Gemini integration and a large field-of-view, along with what appears to be built-in cameras and microphones.Google is also partnering with Samsung, Gentle Monster, and Warby Parker to create other Android XR smart glasses, as well.Google’s experimental AI assistant is getting more proactive Project Astra could already use your phone’s camera to “see” the objects around you, but the latest prototype will let it complete tasks on your behalf, even if you don’t explicitly ask it to. The model can choose to speak based on what it’s seeing, such as pointing out a mistake on your homework.Gemini is coming to ChromeGoogle is building its AI assistant into Chrome. Starting on May 21st, Google AI Pro and Ultra subscribers will be able to select the Gemini button in Chrome to clarify or summarize information across webpages and navigate sites on their behalf. The feature can work with up to two tabs for now, but Google plans on adding support for more later this year.Google’s new AI Ultra plan costs $250 per monthGoogle is rolling out a new “AI Ultra” subscription that offers access to the company’s most advanced AI models and higher usage limits across apps like Gemini, NotebookLM, Flow, and more. The subscription also includes early access to Gemini in Chrome and Project Mariner, which can now complete up to 10 tasks at once.Search Live will let you discuss what’s on your camera in real-timeSpeaking of Project Astra, Google is launching Search Live, a feature that incorporates capabilities from the AI assistant. By selecting the new “Live” icon in AI Mode or Lens, you can talk back and forth with Search while showing what’s on your camera.After making Gemini Live’s screensharing feature free for all Android users last month, Google has announced that iOS users will be able to access it for free, as well.Image: GoogleGoogle has revealed Stitch, a new AI-powered tool that can generate interfaces using selected themes and a description. You can also incorporate wireframes, rough sketches, and screenshots of other UI designs to guide Stitch’s output. The experiment is currently available on Google Labs.Google Meet adds AI speech translationImage: GoogleGoogle Meet is launching a new feature that translates your speech into your conversation partner’s preferred language in near real-time. The feature only supports English and Spanish for now. It’s rolling out in beta to Google AI Pro and Ultra subscribers.Gmail’s smart replies will soon pull info from your inboxImage: GoogleGmail’s smart reply feature, which uses AI to suggest replies to your emails, will now use information from your inbox and Google Drive to prewrite responses that sound more like you. The feature will also take your recipient’s tone into account, allowing it to suggest more formal responses in a conversation with your boss, for example.Gmail’s upgraded smart replies will be available in English on the web, iOS, and Android when it launches through Google Labs in July.Image: GoogleGoogle is testing a new feature that lets you upload a full-length photo of yourself to see how shirts, pants, dresses, or skirts might look on you. It uses an AI model that “understands the human body and nuances of clothing.”Google will also soon let you shop in AI Mode, as well as use an “agentic checkout” feature that can purchase products on your behalf.Google Chrome will soon help you update compromised passwordsIf Chrome detects that your password’s been compromised, Google says the browser will soon be able to “generate a strong replacement” and automatically update it on supported websites. The feature launches later this year, and Google says that it will always ask for consent before changing your passwords.See More:

Google just wrapped up its big keynote at I/O 2025. As expected, it was full of AI-related announcements, ranging from updates across Google’s image and video generation models to new features in Search and Gmail.But there were some surprises, too, like a new AI filmmaking app and an update to Project Starline. If you didn’t catch the event live, you can check out everything you missed in the roundup below.Google’s AI Mode for Search is coming to everyoneGoogle has announced that it’s rolling out AI Mode, a new tab that lets you search the web using the company’s Gemini AI chatbot, to all users in the US starting this week.Google will test new features in AI Mode this summer, such as deep search and a way to generate charts for finance and sports queries. It’s also rolling out the ability to shop in AI Mode in the “coming months.”Project Starline is now Google BeamImage: GoogleProject Starline, which began as a 3D video chat booth, is taking a big step forward. It’s becoming Google Beam and will soon launch inside an HP-branded device with a light field display and six cameras to create a 3D image of the person you’re chatting with on a video call.Companies like Deloitte, Duolingo, and Salesforce have already said that they will add HP’s Google Beam devices to their offices.Imagen and Veo are getting some big upgradesGoogle has announced Imagen 4, the latest version of its AI text-to-image generator, which the company says is better at generating text and offers the ability to export images in more formats, like square and landscape. Its next-gen AI video generator, Veo 3, will let you generate video and sound together, while Veo 2 now comes with tools like camera controls and object removal.Google launches an AI filmmaking appGIF: GoogleIn addition to updating its AI models, Google is launching a new AI filmmaking app called Flow. The tool uses Veo, Imagen, and Gemini to create eight-second AI-generated video clips based on text prompts and / or images. It also comes with scene-builder tools to stitch clips together and create longer AI videos.Gemini 2.5 Pro adds an “enhanced” reasoning modeThe experimental Deep Think mode is meant for complex queries related to math and coding. It’s capable of considering “multiple hypotheses before responding” and will only be available to trusted testers first.Google has also made its Gemini 2.5 Flash model available to everyone on its Gemini app and is bringing improvements to the cost-efficient model in Google AI Studio ahead of a wider rollout.Xreal shows off its Project Aura prototypeXreal and Google are teaming up on Project Aura, a new pair of smart glasses that use the Android XR platform for mixed-reality devices. We don’t know much about the glasses just yet, but they’ll come with Gemini integration and a large field-of-view, along with what appears to be built-in cameras and microphones.Google is also partnering with Samsung, Gentle Monster, and Warby Parker to create other Android XR smart glasses, as well.Google’s experimental AI assistant is getting more proactive Project Astra could already use your phone’s camera to “see” the objects around you, but the latest prototype will let it complete tasks on your behalf, even if you don’t explicitly ask it to. The model can choose to speak based on what it’s seeing, such as pointing out a mistake on your homework.Gemini is coming to ChromeGoogle is building its AI assistant into Chrome. Starting Wednesday, Google AI Pro and Ultra subscribers will be able to select the Gemini button in Chrome to clarify or summarize information across webpages and navigate sites on your behalf. The feature can work with up to two tabs for now, but Google plans on adding support for more later this year.Google’s new AI Ultra plan costs $250/monthGoogle is rolling out a new “AI Ultra” subscription that offers access to the company’s most advanced AI models and higher usage limits across apps like Gemini, NotebookLM, Flow, and more. The subscription also includes early access to Gemini in Chrome and Project Mariner, which can now complete up to 10 tasks at once.Search Live will let you discuss what’s on your camera in real-timeSpeaking of Project Astra, Google is launching Search Live, a feature that incorporates capabilities from the AI assistant. By selecting the new “Live” icon in AI Mode or Lens, you can talk back and forth with Search while showing what’s on your camera.After making Gemini Live’s screensharing feature free for all Android users last month, Google has announced that users on iOS will be able to access it for free as well.Image: GoogleGoogle has revealed Stitch, a new AI-powered tool that can generate user interfaces using selected themes and a description. You can also incorporate wireframes, rough sketches, and screenshots of other UI designs to guide Stitch’s output. The experiment is currently available on Google Labs.Google Meet adds AI speech translationImage: GoogleGoogle Meet is launching a new feature that translates your speech into your conversation partner’s preferred language in near real-time. The feature only supports English and Spanish for now. It’s rolling out in beta to Google AI Pro and Ultra subscribers.Gmail’s smart replies will soon pull info from your inboxImage: GoogleGmail’s smart reply feature, which uses AI to suggest replies to your emails, will now use information from your inbox and Google Drive to pre-write responses that sound more like you. The feature will also take your recipient’s tone into account, allowing it to suggest more formal responses in a conversation with your boss, for example.Gmail’s upgraded smart replies will be available in English on the web, iOS, and Android when it launches through Google Labs in July.Image: GoogleGoogle is testing a new “try-it-on” feature that lets you upload a full-length photo of yourself to see how shirts, pants, dresses, or skirts might look on you. The feature uses an AI model that “understands the human body and nuances of clothing.”Google will also soon let you shop in AI Mode, as well as use an “agentic checkout” feature that can purchase products on your behalf.Google Chrome will soon help you update compromised passwordsIf Chrome detects that your password’s been compromised, Google says the browser will soon be able to “generate a strong replacement” and automatically update it on supported websites. The feature launches later this year, and Google says that it will always ask for consent before changing your passwords.See More: