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A major development in the field of artificial intelligence has taken place at UC Santa Barbara, with the university receiving a four-year, $20 million federal grant to establish an AI-programmable cloud laboratory. This cutting-edge facility will be housed within OASIS, a technology and science hub for students and startup companies on campus.

The new lab will allow researchers from anywhere in the country to access equipment remotely, marking a significant advancement in collaborative research efforts. According to Javier Read de Alaniz, a professor of chemistry and biochemistry at UCSB, ‘You can run research from anywhere at any time.’

Read de Alaniz is also co-director of BioPACIFIC MIP, a federally funded program for synthetic biology, chemistry, and materials science research that has partnered with UCSB. The university was selected to receive the AI grant due in part to its existing partnership with this program.

The OASIS facility, which opened last fall, already serves as a hub for student researchers and private/public companies. It features an open office space surrounded by laboratories where students are actively working on projects.

Universities such as Stanford, Cal Tech, and Scripps are also operating similar AI-programmable cloud labs. The National Science Foundation awarded the $20 million grant to UCSB under its umbrella, with a four-year term for the project.

A vetting process is required for researchers who apply to work remotely at OASIS, limiting access to U.S.-based organizations only. According to Rachel Segalman, vice chancellor for research at UCSB, ‘The application is limited to U.S. organizations.’

Representative Salud Carbajal announced the grant on Friday to a group of UCSB administrators and faculty outside the OASIS facility in Goleta. He described the development as ‘a big freaking deal,’ highlighting its potential impact on research capabilities nationwide.

The AI-programmable cloud laboratory is expected to streamline collaborative efforts between researchers across different institutions, making it easier for them to share resources and expertise.

A South Korean company, SpeedFloor, is working on a solution to automate what has long been considered one of the most labor-intensive and time-consuming steps in warehousing: loading cargo onto trucks. The process typically involves workers climbing into trailers, stacking boxes, pushing pallets, and waiting for vehicles to be ready. This manual handoff can be costly, especially if vehicles or robotic systems are idle while someone stacks freight inside them.

SpeedFloor’s proposed solution is a powered conveyor system that turns the floor of a truck or trailer into a surface that can move cargo in or out. Instead of sending workers, forklifts, or robots into the cargo bay, the system pushes or pulls prepared loads horizontally. The company argues that this approach not only streamlines operations but also reduces labor costs and improves safety.

The key insight behind SpeedFloor’s solution is recognizing that warehouse automation can still leave a costly manual process at the dock. Even with sophisticated facilities featuring robots, automated storage systems, and autonomous trucks, workers may need to enter trailers to stack freight before vehicles depart. This is where SpeedFloor comes in – its system eliminates this step by preparing loads outside the vehicle using people, robot arms, or other warehouse equipment.

The company’s solution involves installing a low-profile conveyor belt inside a truck or trailer. For palletized cargo, the system can transfer loads into or out of the vehicle. For loose boxes, SpeedFloor proposes pre-stacking: boxes are assembled in loading order outside the vehicle and then pushed in when the truck arrives. This approach allows for faster turnaround times and increased efficiency.

SpeedFloor’s product is not just about making trucks autonomous; it’s about making them available sooner. Faster turnaround could mean more trips per shift for delivery operators and drivers paid by distance, weight, or boxes moved rather than time spent at a dock. For warehouses, this means using fewer vehicles to move the same daily volume.

The company says its system can be installed in various types of vehicles, from small box trucks to trailers and containers. SpeedFloor describes it as modular: head, body, and tail sections can be assembled to match a truck’s length while following standardized cargo vehicle widths. Smaller configurations use a 12-volt electrical system; heavier applications require dedicated battery packs beneath the trailer.

SpeedFloor acknowledges that its biggest proposed applications – handling loads of roughly 25 to 30 tons – come with a meaningful constraint: the system itself can weigh about two tons, which eats into payload capacity. The company is working to make the hardware lighter and thinner while maintaining strength for heavy cargo over long distances and rough roads.

Safety is also part of SpeedFloor’s pitch. Loading docks combine moving vehicles, forklifts, heavy cargo, and confined spaces – a hazardous mix that OSHA specifically identifies as high-risk areas in warehouses. Reducing the need for workers to enter trailers could lower exposure to some of these risks; however, the conveyor itself needs guarding, maintenance, lockout procedures, and safe integration with other equipment.

SpeedFloor claims it has deployed more than 100 core units worldwide, including systems for Korea’s Ministry of National Defense and Korea Post. Its customer list also includes Kurly, Hanwha Aerospace, and LG CNS as customers or partners in particular deployments. The company cites a Kurly implementation that reduced loading time from over an hour to under 10 minutes.

SpeedFloor has some external validation: South Korea’s Ministry of Land, Infrastructure and Transport recognized its small-vehicle automated loading and unloading technology in 2024. The ministry described the chain-and-belt design as a way to move cargo without requiring drivers to enter the loading space – an improvement for efficiency and safety.

SpeedFloor is now looking to prove that its idea can travel beyond South Korea’s borders. It began selling in the U.S. through MyTrucker Pro in February 2026, with installation and dealer partners intended to support regional expansion. The company also aspires to apply its technology to air-cargo applications where standardized unit load devices could make replication easier.

SpeedFloor acknowledges that its proposed moat – a full dock-automation stack including vehicle hardware, docking stations, software coordination of warehouse and truck conveyors, integration with AMRs and forklifts, installation expertise, and reliability under real freight conditions – is the test ahead. A convincing demonstration is one thing; repeatable deployment, service, payload economics, and verified customer outcomes are another.

If SpeedFloor can prove that it makes automated warehouses and trucks work together without creating another bottleneck, its conveyor floor could become a surprisingly important piece of logistics infrastructure.

Reddit is experimenting with a new feature that uses artificial intelligence to convert text posts into video and audio. This move aims to capitalize on the popularity of videos that read Reddit posts aloud, which have become increasingly common on platforms like TikTok and YouTube.

The test, currently restricted to manually selected English-language posts in designated subreddits, will roll out from Monday in web browsers before expanding to iOS and Android devices on Tuesday. While this feature is still in its early stages, it’s clear that Reddit wants to explore the potential of AI-generated content to make the platform more accessible and immersive.

A Reddit spokesperson confirmed the test, stating that the experiment uses AI to create new audio and video formats, including multi-voice text-to-speech, captions, and visual templates. This technology has the potential to bring real human conversations to life in a way that’s both engaging and informative.

It’s worth noting that users won’t be forced to watch their favorite subreddits in video form; the original text posts and comments will still be available in their usual format. A toggle above each post with this feature enabled will allow users to switch between ‘Read’ and ‘Play.’ When switched to ‘Play,’ a TikTok-style portrait-oriented video will appear, featuring an AI-generated voice reading out both the post and select comments.

The text will be displayed on screen, lighting up words like a karaoke video, while a note below the video indicates that it’s a ‘Real conversation voiced by AI.’ This new feature was previously mentioned during Reddit’s Q2 earnings call in July, where CEO Steve Huffman noted the increasing popularity of videos and podcasts featuring people reading Reddit posts aloud.

Huffman observed that such clips are now common on video platforms like TikTok and YouTube, but Reddit itself remains primarily text-based. The company wants to change this by using AI to cut out the middleman and create a more immersive experience for users. As Huffman put it during the earnings call, ‘we’re working on… just a video Reddit experience, so letting users not just watch video on Reddit, but listen to posts, background listen to posts.’

This new focus on video content is reflected in Reddit’s Q2 shareholder letter, which stated that the company is working toward ‘reimagining how users and communities connect, share, and consume content through a modernized video experience.’ The launch of video comments in June was an early step in this direction, allowing users to record and upload short clips in response to posts.

Reddit’s increased focus on AI-powered features also includes the expansion of its moderation tools. Earlier this month, the company began testing these tools with all new subreddits, marking a significant shift toward using artificial intelligence to streamline content management and improve user experience.

London-based startup Inherent, founded by DeepMind alumni, has made a notable breakthrough in the field of artificial intelligence. The company’s small AI agent, Faraday, has outperformed larger models from Anthropic and OpenAI at autonomously replicating published research using reinforcement learning. This achievement marks a significant step towards broader scientific discovery and could have practical benefits for industries such as cryptocurrency and DeFi security.

Inherent’s team of 12 employees is planning to expand to 20-25 by the end of the year, with a focused approach that uses existing tools like OpenAI’s GPT-5.5 Codex. This pragmatic strategy allows them to concentrate on what they believe is the core challenge: imbuing AI with ‘taste’ for scientific inquiry. The company’s co-founder and chief scientist Edward Hughes emphasizes that this goal goes beyond simple pattern matching, requiring an understanding of experimental procedures.

The ability to independently replicate research findings is a fundamental step for any AI system aiming to contribute to scientific discovery. Many PhD students begin their training by replicating existing work, and Inherent’s achievement highlights the significance of this capability. The startup’s approach uses reinforcement learning to reward good outcomes rather than explicitly teaching the rules of science, which they believe will generalize better to their long-term goal of creating AI scientists.

Inherent’s strategy is distinct from other approaches in that it focuses on a narrow, high-value task rather than trying to build a general-purpose assistant. The company deliberately avoids developing its own coding tools, instead using existing software like GPT-5.5 Codex for tooling. This approach allows them to concentrate on what they believe is the core challenge: imbuing AI with ‘taste’ for scientific inquiry.

The London-based startup’s progress suggests that a small, focused team can compete with much larger players in specific scientific tasks. Inherent’s co-founder Edward Hughes has also spoken out against the U.K.’s ‘garden leave’ practice, which he says delayed his own ability to start the company. This restriction puts U.K. startups at a disadvantage compared to their U.S. rivals when hiring talent from prior roles.

Inherent plans to expand its team and ambitions extend beyond paper replication to world models and broader AI research. With Demis Hassabis’s new role at Google DeepMind leaving some staff unsettled, Inherent could become an attractive destination for DeepMind researchers considering a move. The company’s progress highlights the potential of nimble, specialized teams to challenge larger incumbents.

The ability to autonomously replicate published research is a crucial step towards broader scientific discovery. Many AI systems aim to contribute to this field by replicating existing work, and Inherent’s achievement marks a significant step forward. The startup’s approach uses reinforcement learning to reward good outcomes rather than explicitly teaching the rules of science, which they believe will generalize better to their long-term goal.

Inherent’s strategy is distinct in that it focuses on a narrow task rather than trying to build an AI assistant. This approach allows them to concentrate on what they believe is the core challenge: imbuing AI with ‘taste’ for scientific inquiry. The company deliberately avoids developing its own coding tools, instead using existing software like GPT-5.5 Codex for tooling.

The London-based startup’s progress suggests that a small team can compete with larger players in specific tasks. Inherent’s co-founder Edward Hughes has spoken out against the U.K.’s ‘garden leave’ practice, which he says delayed his own ability to start the company. This restriction puts U.K. startups at a disadvantage compared to their U.S. rivals when hiring talent from prior roles.

Inherent plans to expand its team and ambitions extend beyond paper replication to world models and broader AI research. The company’s progress highlights the potential of nimble, specialized teams to challenge larger incumbents in specific tasks.

Artificial intelligence has become the cognitive foundation of contemporary civilization, changing how we produce value, make decisions, and interact with the world. However, this rapid technological acceleration also brings significant risks to digital trust and privacy. As AI-powered tools for businesses continue to evolve, it’s essential that organizations prioritize data protection and adopt a proactive approach to cybersecurity.

The same technologies driving innovation in government services, scientific research, healthcare, and economic opportunities are increasing attack surfaces and threatening organizational and personal data. Data is now an organization’s most significant asset as well as its most substantial liability, making strong privacy protections crucial for innovation itself.

Emerging technology is rewriting large-scale privacy risk. AI systems require massive datasets containing private, financial, health, and proprietary information. Voice cloning, automated spying, convincing deepfakes, hyper-targeted phishing, and machine-speed polymorphic malware are all made possible by generative and agentic AI. The attack cycle has accelerated to mere hours or minutes.

Identity is now the new boundary in a world where traditional network borders have vanished due to remote work, multi-cloud environments, and autonomous AI agents capable of decision-making and system interaction. Every significant security issue related to agentic AI boils down to the question of identity: Who (or what) is acting? What kind of authority do they possess?

Cyber risk failures are privacy failures. Excessive data gathering, lax access controls, indefinite retention, and insufficient governance all increase the impact of breaches. Customers, partners, and citizens increasingly evaluate companies based on how morally they gather, utilize, and safeguard data. One privacy event can destroy years of brand equity.

Protecting digital privacy in the modern era requires a multi-layered approach that incorporates technology, process, people, and leadership. This involves making privacy a board-level obligation, integrating it into cybersecurity plans, risk management systems, and culture. Businesses must view data stewardship as a fundamental business risk and innovate more responsibly by doing so.

Maintaining strict cyber hygiene is the first line of security in this era. Key procedures include phishing-resistant multi-factor authentication combined with strong passwords kept in reliable managers, continuous authentication, privileged access management, and least-privilege access. Continuous vulnerability monitoring, automated asset detection, and quick patching are also essential to match AI-powered attackers.

Sensitive data classification, secure disposal, and encryption of data while it’s in transit and at rest must be prioritized. Zero Trust architectures validate each user, device, transaction, and application, assuming no identity or device is inherently trustworthy. Continuous awareness training on deepfakes, AI-generated phishing, and safe AI use are also necessary.

Identity should be considered the fundamental control plane for both humans and machines in this era. Rapid revocation capabilities, continuous governance and behavioral monitoring, dynamic least-privilege authorization, developer-centric controls that incorporate security from the outset, and visibility into every agent are all crucial. Static or compartmentalized identity systems are liabilities.

Confidential computing can safeguard data by decrypting it only for approved processing in secure enclaves. Attestation confirms the integrity of the environment and code. This approach enables regulatory compliance, secure multi-party cooperation, privacy-preserving AI on sensitive datasets, protection of proprietary models, and increased trust in public cloud environments.

Businesses must be ready for the quantum horizon and convergence of technologies like edge computing, IoT, 5G, and nearing quantum capabilities. Planning migration to post-quantum cryptography, embracing crypto-agility, and inventorying cryptographic assets are all necessary steps towards resilience.

The concept of Zero Trust is no longer optional but a necessity in today’s digital ecosystem due to the failure of traditional perimeter-based security. Proactive cybersecurity requires coordinated standards, information exchange, public-private cooperation, energy, healthcare, finance, transportation, and government relying on cyber hygiene and privacy practices of interconnected institutions.

Proper preparation rather than fear is key to addressing these challenges. Organizations that prioritize data protection as a strategic basis will thrive in the AI era while maintaining digital trust.

In a recent address to the International Catholic Legislators Network, Pope Leo XIV sounded a warning about artificial intelligence. He emphasized that while technology should be encouraged and regulated, it must not come at the expense of human rights and freedoms.

The Holy Father stressed the importance of sound legislation in safeguarding fundamental human rights and freedoms. This includes protecting users from exploitation, preserving personal privacy, ensuring transparency in AI use, and guaranteeing that emerging technologies strengthen democratic institutions.

Pope Leo XIV highlighted the need for robust legal frameworks to regulate AI development. He noted that such oversight can operate at multiple levels – local, national, and international – facilitating open discussion of ethical frameworks and subjecting them to shared standards of social justice.

The Pope warned against the risks of technological dependence in poorer nations relying on wealthier ones for innovation. This could lead to subtle forms of domination through algorithms deciding who is seen or invisible, digital platforms shaping public discourse without accountability, and workers’ dignity being subordinated to system optimization.

Pope Leo XIV lamented the erosion of human values in AI development, citing examples such as reducing persons and relationships to data and simulations. This reveals a new face of the ancient temptation to domination and mastery without service.

The Pope proposed an alternative approach – what he called ‘the civilization of love.’ In this vision, the family is seen as humanity’s first school, where we learn essential values like trust before calculation, generosity before competition, dialogue before division, and love before power.

Pope Leo XIV emphasized that AI must never erode the primary cell of society – the family. He encouraged policymakers to promote policies strengthening marriage and families, supporting parents in their educational mission, and enabling young people to look to the future with confidence.

The Holy Father concluded by saying that humanity needs human intelligence enlightened by wisdom, political authority guided by conscience, and technological innovation directed by charity. Only then will AI become a servant of the common good rather than a rival to humanity.

DevSecOps teams are facing an increasingly daunting task: keeping up with the exponential growth of vulnerabilities in code. To address this challenge, Harness has introduced a suite of artificial intelligence (AI) agents that automate vulnerability triage, remediation, and pull request creation across DevSecOps workflows at machine speed.

Harness’s new AI agents include one integrated into its static application security testing (SAST) tool, which automatically prioritizes scanner findings based on exploitability. This integration enables teams to combine the probabilistic capabilities of an AI agent with a deterministic platform that validates whether a vulnerability is actually exploitable.

The SAST tool now also includes AI-powered features for creating and validating fixes, as well as opening pull requests for developers to review and approve. DevSecOps teams can opt to add their own AI scanners within their pipelines, further streamlining the process.

In addition to these enhancements, Harness has introduced a Zero-Day Agent that continuously monitors threat intelligence feeds around the clock. Once a zero-day vulnerability is discovered, this agent instantly identifies every affected pipeline and artifact and generates a validated fix ready for review within minutes.

Harness’s virtual patching capability allows DevSecOps teams to apply patches without immediately changing application code. This feature enables teams to mitigate vulnerabilities in real-time, reducing the risk of exploitation by cybercriminals who can create exploits in just hours.

Rahul Sood, general manager of application security at Harness, emphasizes that these additions make it possible for DevSecOps teams to respond to issues at machine speed – a crucial capability given the rapid pace of AI-driven vulnerability discovery and exploitation. He notes that many DevSecOps teams are underestimating the degree to which their applications may become collateral damage in cyberattacks enabled by AI.

Sood highlights the challenge posed by AI tools for businesses: while they can discover thousands of vulnerabilities, they also generate a high number of false positives. By incorporating AI agents into SAST tools, it becomes possible to combine probabilistic capabilities with deterministic validation, reducing the risk of unnecessary code changes and minimizing tokens consumed during scanning.

According to Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, DevSecOps teams are now drowning in vulnerability findings they can’t act on fast enough. Harness’s introduction of AI-powered triage and remediation into the pipeline is a significant step towards addressing this issue, as it enables fixes to travel with releases rather than being delayed until the next scan window.

Periodic scanning was designed for code written at human speed, but machine-speed generation breaks that model. As Ashley notes, DevSecOps teams need control over code creation in real-time, not just during periodic scans. This shift is crucial given the rapid pace of AI-driven vulnerability discovery and exploitation.

It’s unclear to what degree DevSecOps teams are revisiting existing workflows, tools, and platforms to address this growing crisis. However, it’s now a question of when and to what extent they will need to prioritize which applications to fix first – assuming that not all issues can be remediated before AI-enabled cyberattacks are launched.

While the short-term outlook may seem daunting, there is also an opportunity for DevSecOps teams to finally address technical debt issues that have been kicked down the road. In the longer term, this could lead to improved overall application security.

Google DeepMind has published a detailed account of its research in games, spanning 15 years and culminating in a partnership with Fenris Creations, the independent studio behind EVE Online. The post outlines how this journey began with early reinforcement learning results and led to current work on generalist agents within the EVE universe. This retrospective connects the lab’s earliest achievements to its ongoing efforts, highlighting specific capabilities it expects the EVE environment to test.

The chain of game results that built Google DeepMind’s research program is extensive. It begins with the 2015 Nature paper documenting a Deep Q-Network that learned 49 Atari 2600 games from raw pixels. This was followed by AlphaGo’s 2016 victory over world champion Lee Sae Dol, as well as AlphaGo Zero and AlphaZero, which removed human data and generalized across chess, shogi, and Go. MuZero played without knowing the rules, while AlphaStar reached Grandmaster level in StarCraft II in 2019.

The same foundations carried into AlphaFold, the protein structure system recognized with the 2024 Nobel Prize in Chemistry. This progression marks a shift from systems that master a single game to agents capable of operating within any game world like humans do. The vehicle for this is SIMA (Scalable Instructable Multiworld Agent), which perceives what a player sees on screen and acts through ordinary keyboard and mouse controls without access to game code.

SIMA 2, introduced in November 2025 with a Gemini model as its core, moved beyond the first version’s repertoire of over 600 language-following skills. It now reasons about goals, converses with users, and improves through self-directed play. Google DeepMind reports that SIMA 2 closes a significant portion of the gap to human task-completion performance across its training environments, including games it was never trained on like ASKA and Minecraft.

However, there are still limitations: the agent struggles with very long-horizon tasks, works from a short memory constrained by the context window needed for low-latency interaction, and is available only as a limited research preview to a small cohort of academics and game developers. The lab’s own stated limits highlight areas where further development is necessary.

The partnership between Google DeepMind and Fenris Creations gives DeepMind access to EVE Online’s persistent world that has been running continuously since 2003. This single-shard universe holds thousands of players in one shared economy with real supply-and-demand dynamics and political structures built entirely by its players. The studio behind the EVE Universe, Fenris Creations, spent more than two decades building one of gaming’s most persistent worlds.

The post identifies four capabilities the environment is expected to exercise: continual learning without forgetting, memory extending beyond current context windows, long-horizon planning over weeks or months, and complex multi-agent dynamics spanning cooperation, competition, negotiation, and economics. ‘Together with Google DeepMind, we’re pushing into uncharted territory where AI must learn, adapt and remember on timescales that no other game environment demands,’ said Hilmar Pétursson, CEO of Fenris Creations.

The partnership extends across three distinct environments: EVE Online offers the large-scale persistent universe; EVE Vanguard adds first-person, fast-paced tactical decision-making; and EVE Frontier presents a world where the rules themselves can change through programmable Smart Assemblies. The research program follows a staged path: it begins in an offline instance of EVE Online separated from live players, moves through EVE Frontier to study how humans and agents coexist in a persistent world, and reaches the live games only when capabilities are mature.

One system from this collaboration is already available to players: Aura Guidance, launched in prototype on February 17, 2026. This uses Gemini to answer new-player questions drawn from a vetted bank of real Rookie Help exchanges, running as a controlled A/B test measuring whether it improves new-player retention. The longer arc runs from Atari’s raw pixels to EVE Online’s 23-year-old persistent universe: each environment chosen because it forces the next capability the lab believes a general agent needs.

The current test of this approach is within the EVE universe, and work now starts in an offline sandbox. This marks another step forward for Google DeepMind as it continues its research journey toward creating more capable agents that can operate within complex game worlds like humans do.

Researchers at Chinese academic institutions have developed a new framework to boost the performance of large language models when answering technical questions.

In industries like manufacturing, equipment maintenance, and fault diagnosis, experts rely heavily on knowledge-based inquiries. However, current LLMs often produce fluent but incorrect outputs when handling specialized content – a problem these researchers set out to solve.

The new framework is designed specifically for accurately answering industrial-related questions. To achieve this goal, it’s built around four core technical contributions: creating an industry-specific knowledge base, improving text classification, enhancing knowledge retrieval, and refining question-answering capabilities.

One key component of the framework involves building a massive database from various sources like journal articles, dissertations, patents, and production documents. This data is cleaned up, processed, and organized to retain its meaning in segments no longer than 256 characters – long enough for most search queries.

The unified knowledge base is then split into five independent sub-databases for chemical engineering, petroleum, metallurgy, electricity, and building materials sectors. By partitioning the data this way, searches can focus on relevant areas, leading to more accurate results in industries that need precision.

A BERT-based text classifier acts as a filter within the framework’s modules. It performs two classification tasks: screening out non-industrial questions and assigning valid ones to their matching sub-domains based on semantic features extracted from token embeddings – essentially categorizing queries by relevance.

Tests show that this BERT classifier delivers consistent performance with low result variance across repeated training runs, which is crucial for knowledge-based applications like question answering in industry settings. Its strength lies in its ability to handle diverse industrial queries effectively and efficiently.

For knowledge retrieval, the researchers employ a GTE-DPR dual-encoder dense retrieval architecture using the GTE-large-zh embedding model. This system generates 1024-dimensional text vectors that allow it to match query embeddings with precomputed chunk embeddings quickly – making relevant information easier to locate within large databases.

According to metrics, the Domain + GTE–DPR setup outperforms general-purpose DPR and domain-only DPR alternatives in semantic matching precision for industrial text. Its consistent results across test trials demonstrate its effectiveness in retrieving accurate knowledge chunks from a large-scale industrial base – making it stand out among competitors.

After filtering and ranking relevant knowledge chunks, the framework combines them with original user prompts through prompt engineering before passing the information to two open-source LLMs: ChatGLM2-6B and LLaMA2-7B. These models generate domain-grounded answers based on validated documents, reducing reliance on incomplete or inaccurate information – a critical improvement in AI tools for businesses.

Experiments have shown that the modular RAG system outperforms standalone LLM inference in question answering tasks when properly aligned with human evaluation standards of answer quality. This advancement comes from carefully integrating various components rather than relying solely on one model’s capabilities.

Despite its improvements, however, the framework still faces challenges posed by niche industrial terminology and misalignment between current metrics and the nuanced understanding humans use to judge the accuracy of an AI-generated response – a limitation that future versions aim to address.

A 43-year-old man from East Patchogue has been arrested and charged with promoting a sexual performance by a child and possessing a sexual performance by a child. The arrest came after a search of his home turned up over 50 images that had been artificially altered using AI image generators.

The images, which were seized during the search, appear to show real minors engaged in sexual conduct, but have been digitally manipulated or created entirely through AI technology. This type of material is considered child exploitation and can be used to normalize and encourage the abuse of children.

According to authorities, the arrest was made possible by a tip from the Internet Crimes Against Children (ICAC) Task Force. The Suffolk Police’s Digital Forensics Unit executed a search warrant on August 13th, seizing multiple electronic devices that contained the images.

The Department of Justice has stated that federal child exploitation laws cover AI-generated images, even if they don’t depict real children being abused or don’t exist at all. This is because these images can still be used to promote and encourage the abuse of minors.

New York law also follows this approach, allowing prosecutors to bring charges based on images that appear to show a real minor engaged in sexual conduct, regardless of whether they are digitally altered or AI-generated. In cases like Zavesky’s, investigators said the devices contained child sex abuse images lifted from social media platforms and artificially altered using AI.

The arrest marks a significant step forward for law enforcement agencies nationwide, which have been increasingly pursuing such cases as technology makes it easier to create and distribute these types of images. The Suffolk Police Department has not released any further information on Zavesky’s arraignment or the ongoing investigation.

A new wave of browser-based AI video generators is making it easier than ever to create professional-looking videos without breaking the bank or requiring a significant time commitment. Flow AI and King AI are two tools that have gained attention this year, offering free text-to-video and image-to-video generation with no account required and no fee to get started.

Traditionally, testing an AI video generator has meant creating an account, verifying an email address, and often entering payment details before a single output is produced. However, Flow AI and King AI take a different approach: users land on the site, enter a prompt or upload an image, and receive a finished video clip without ever being asked to register.

This immediacy is proving popular with small businesses, freelancers, and social media managers who want to evaluate quality before committing to any paid workflow. The tools’ ease of use and lack of barriers make them ideal for marketers who need occasional promotional clips or agencies testing AI video for client pitches.

Both Flow AI and King AI support the same three core generation modes: text-to-video, image-to-video, and a first-frame/last-frame transition mode that lets users define a starting image and an ending image with the AI generating a smooth transition between the two. Output on both platforms is delivered at 720p resolution in a 16:9 aspect ratio, making it suitable for social feeds, landing pages, and slide decks without additional conversion.

The appeal of tools like Flow AI and King AI lies in their ability to provide instant results, allowing users to see what current AI video generation can do before investing in a paid production pipeline. As one industry observer noted, ‘You don’t create an account, you don’t enter a card number — you type a prompt or drop in a photo, and a video comes back.’

Flow AI offers free access with no login required for text-to-video and image-to-video generation, as well as the first-frame/last-frame transition mode. The platform also has an optional premium tier currently discounted for users who need higher output volume. King AI provides similar functionality, including 720p, 16:9 output formats ready for immediate use.

Both Flow AI and King AI offer a low-risk way to see what current AI video generation can do before investing in a paid production pipeline. Neither tool requires a download, a plugin, or specialized hardware — both run entirely inside a standard desktop or mobile web browser. This makes them accessible to anyone with an internet connection.

For those looking for occasional promotional clips or experimenting with the technology for the first time, Flow AI and King AI are ideal tools. They provide a straightforward, browser-only interface built for quick iteration and can be used by marketers who need to create short-form videos without requiring extensive technical expertise.

Lax Gopisetty, CEO of Infosys Public Services, has spent over three decades helping organizations transform their operations and services through emerging technologies. His leadership approach focuses on using technology as an enabler for broader transformation, rather than just adopting new tools or systems. This perspective is shaped by his extensive experience working across complex industries and diverse markets, including federal, state, and local government, healthcare, manufacturing, insurance, financial services, utilities, education, and more.

Gopisetty’s career has spanned multiple sectors, giving him a unique understanding of how technological change affects organizations differently. He recognizes that successful transformation requires a combination of strategic vision, organizational adaptability, responsible innovation, and a clear understanding of the people affected by change. This is particularly relevant in today’s AI era, where organizations are moving from experimenting with emerging technologies to embedding them into their core operations.

As CEO of Infosys Public Services, Gopisetty brings this expertise to public sector organizations, which operate in an environment unlike any other. They must balance citizen expectations, regulatory requirements, budget constraints, legacy systems, and rapidly evolving technology while making decisions that can affect millions of lives. His approach is rooted in helping agencies navigate complexity while keeping their purpose front and center.

Gopisetty believes the future of government will increasingly depend on combining modern technology with thoughtful governance and human-centered design. Artificial intelligence, digital platforms, and data-driven decision-making can transform public services, but their value ultimately depends on whether they make government more accessible, responsive, and effective for citizens. This perspective has made him a prominent voice on the broader implications of technology.

As a frequent speaker at industry conferences and events, Gopisetty connects various issues to technology, including artificial intelligence, disruptive innovation, digital governance, and the humanization of the digital world. He emphasizes that the conversation about AI is not just about algorithms or automation but also about people, organizations, ethics, inclusion, and the kind of future businesses and governments want to create.

Gopisetty’s interest in innovation extends beyond large organizations; he is actively involved in the startup ecosystem, mentoring several startups and ventures developing technology-led solutions designed to create meaningful change. His involvement reflects a belief that innovation does not belong exclusively to established corporations but can also emerge from smaller teams willing to challenge conventional thinking.

As a board member of multiple startup innovation accelerators, Gopisetty contributes his experience to entrepreneurs attempting to turn ambitious ideas into sustainable businesses. For young entrepreneurs, his career offers valuable insights on what it takes to move an idea from possibility to impact. His leadership philosophy emphasizes building ‘future-ready purposeful enterprises’ that connect innovation with purpose and responsibility.

Gopisetty’s professional journey is marked by a combination of technology expertise, business experience, and social responsibility. He holds master’s degrees in computer science and management, combining technical knowledge with business leadership. For the past decade, he has been a member of the Board at Sparsh Palliative Care Services, an organization providing hospice services in India.

Throughout his career, Gopisetty has pioneered new offerings and business models in areas such as sustainability, digital inclusivity, smart spaces, and last-mile front-office solutions. These initiatives reflect his belief that innovation is most powerful when it addresses real-world challenges and expands access to better services and opportunities. His work with AI tools for businesses focuses on using technology to improve the way organizations serve their people and communities.

As he continues to lead Infosys Public Services, mentor emerging ventures, and contribute to social causes, Gopisetty’s career offers a reminder that the most enduring form of leadership may be connecting innovation with impact, ambition with responsibility, and business success with a larger sense of purpose. His legacy is built on using technology as an enabler for broader transformation, rather than just adopting new tools or systems.

Gopisetty’s experience has given him a unique perspective that combines technology expertise with an understanding of the organizational and human dimensions of transformation. He recognizes that successful transformation requires more than implementing new tools; it demands strategic vision, organizational adaptability, responsible innovation, and a clear understanding of the people affected by change.

A major hurdle in global marketing has been the time-consuming and costly process of localizing video content. However, a new free AI-powered video creation platform is changing this landscape by enabling businesses to create and localize videos in seconds.

The platform, Vidnoz AI, offers a comprehensive set of tools designed for speed and global reach. One of its key features is the vast library of lifelike avatars available for use. With over 1900 options from various industries and roles, users can choose an avatar that perfectly fits their message and target audience.

Additionally, Vidnoz AI provides access to more than 2000 natural-sounding AI voices in over 140 languages. These voices are powered by ElevenLabs, Microsoft, and Google, ensuring a clear and realistic sound without the robotic tone often associated with AI-generated content. The platform’s lip-sync technology matches the avatar’s mouth movements to the voice, creating a seamless viewing experience regardless of language.

The platform also offers an extensive collection of pre-designed video templates, totaling over 2800 options built for various goals such as product demos and company announcements. Users can pick a template that suits their needs, plug in their content, and have a professional-looking video structure ready to go without needing extensive editing skills or experience.

Vidnoz AI’s free daily use policy allows every user to generate 30 videos per day using the platform’s credits system. This feature is ideal for testing ideas, creating short clips, or localizing quick messages before committing to a larger project. The platform’s flexibility and accessibility make it an attractive option for businesses of all sizes.

The process of using Vidnoz AI is straightforward and user-friendly, even for those without prior video creation experience. Users can log in, browse the template library, choose an avatar that fits their message and audience, create a voiceover with one of the many available languages, add custom touches such as logos or background music, and generate the final product in seconds.

Vidnoz AI’s certification with ISO/IEC 27001:2022 for Information Security Management ensures that businesses can create content with confidence, knowing their data is handled securely. This level of security provides peace of mind for companies handling sensitive information or working across multiple regions.

For businesses looking to expand globally and connect with customers in different countries, Vidnoz AI offers a significant advantage by streamlining the video creation process. With its comprehensive set of tools and features, this platform empowers marketing teams, sales teams, and small business owners to generate content quickly and efficiently without the need for extensive production schedules or external teams.

Vidnoz AI’s capabilities are particularly beneficial when it comes to data analysis tools, as they enable businesses to analyze their global reach and engagement metrics more effectively. By having access to a vast library of avatars, voices, and templates, companies can tailor their content to specific markets and audiences with greater precision.

The platform’s free AI video generator feature is a significant advantage for businesses looking to leverage technology to enhance their marketing efforts without incurring substantial costs. With Vidnoz AI, users can create high-quality videos that resonate with global audiences, driving engagement and sales across multiple regions.

OpenAI has launched AI Futures, a blog that explores the intersection of artificial intelligence (AI) and society. The initiative is led by OpenAI’s Strategic Futures team, which aims to answer one fundamental question: how can free societies balance individual rights and agency with the emergence of transformative AI? This inquiry is crucial because it addresses concerns about power concentration in the context of AI safety and policy.

The modern state has evolved through a complex process of bargaining between various centers of power. Historically, political and military power have rested on labor, with those who controlled legitimate force holding sway over society. However, advancements in real-world autonomous systems may soon enable states to project force without relying on human cooperation or consent. Similarly, machine intelligence could allow governments to collect revenue from data centers rather than individual human labor.

This shift would fundamentally alter the relationship between power and society. If unchecked, it could lead to a world where individuals have little control over their lives or the tools they use to express themselves. The preservation of long-term individual autonomy is essential; no amount of scientific progress or economic growth justifies sacrificing this fundamental right. Many anxieties surrounding AI relate to concerns about agency and power.

The concentration of power in the context of AI raises questions about who will determine the direction of society and economy. This issue transcends partisan politics, as it involves the very fabric of republican democracy. Even if voting rights are maintained, humans can be disempowered by technological advancements that concentrate power in the hands of a few.

The Founding Fathers were aware of these risks when they drafted the US Constitution. James Madison, for example, recognized that ‘parchment barriers’ would not suffice to prevent tyranny. Instead, he and other Founders sought to balance power through metaphors drawn from Newtonian mechanics, where celestial bodies orbit around a central sun. This model aimed to check power against power and ambition against ambition.

Today, we face similar challenges in addressing the structural implications of advanced machine intelligence on free society. The answer is not radical decentralization but finding the right balance of power, ensuring no single actor or small group dominates others. To achieve this, clear-eyed thinking about AI’s reality is necessary. A world where individuals can cause great harm with ease is not one that has struck a healthy balance.

However, neither have we achieved the correct balance if individuals lack control over their tools and expression. No company or oligopoly should determine the economy or basic architecture of society. We must also acknowledge risks that arise from AI’s potential to act beyond its assigned tasks and build on discoveries in unforeseen ways. The recent Hugging Face incident highlighted these concerns.

The Strategic Futures team will explore how to balance individual autonomy, security, economic growth, and collective action when addressing the challenges posed by advanced machine intelligence. This requires a comprehensive approach that considers public-policy design, economics, law, history, and machine learning. Research at the intersection of these fields is essential for developing richer ideas about how power structures might transform in response to AI.

The team will share its work through various formats, including papers, videos, podcasts, and blog posts. The aim is to foster iteration, debate, and receptivity to feedback as we navigate this complex landscape. We recognize that ‘parchment barriers’ alone are insufficient for addressing these challenges but also acknowledge the importance of preserving ideals enshrined in constitutions worldwide.

The collective effort required to address AI’s impact on society cannot be undertaken by a single company or industry. Humanity must come together to tackle these weighty questions, and our highest hope is to shed light on how AI will reshape our world. The work has only just begun, and we have much climbing left to do.

Interim Chancellor Eric Wilcots and Remzi Arpaci-Dusseau, the founding dean of University of Wisconsin-Madison’s new College of Computing and Artificial Intelligence (CAI), presented a nuanced view of artificial intelligence in higher education at a virtual panel hosted by the Wisconsin Foundation and Alumni Association on August 18. The discussion centered around the university’s efforts to prepare students for an increasingly AI-driven world, where chatbots are becoming a common substitute for human interaction and data analysis tools are transforming various industries.

Wilcots and Arpaci-Dusseau emphasized that UW-Madison needs to strike a balance between reaping AI’s benefits and mitigating its negative consequences. They acknowledged the growing reliance on AI assistants, which is changing the way students interact with each other and their instructors. For instance, Piazza, a popular classroom forum and social networking tool, has seen a significant decline in student participation since the rise of chatbots.

According to Arpaci-Dusseau, before AI became ubiquitous, Piazza was an essential platform for students to receive timely and accurate answers from professors and teaching assistants. However, with the advent of chatbots like ChatGPT, many students are opting out of posting questions on Piazza, instead relying on these automated tools.

The university’s efforts to address this issue involve developing a curriculum that combines core AI competencies with fundamental skills in various fields. Arpaci-Dusseau envisioned an education system where all UW-Madison students learn the basics of their disciplines alongside essential AI knowledge. In some cases, this might require a return to traditional teaching methods, such as blue-book exams and in-class work.

CAI aims to build on the university’s existing strengths with AI while retaining college fundamentals like learning and mentorship. Wilcots and Arpaci-Dusseau want CAI to support all existing colleges, rather than operating as a separate entity. They believe that this approach will enable UW-Madison to leverage its breadth of excellence in various domains.

The university’s new College of Computing and Artificial Intelligence grew out of the School of Computer, Data and Information Sciences (CDIS), which was established in 2019 in response to the changing economy following the dot-com boom. CDIS bridged three departments: computer science, information school, and statistics, launching a data science major that has become one of the largest on campus.

Arpaci-Dusseau highlighted AI’s potential for innovation across various industries. However, he also emphasized the need for a counterbalance to mitigate its negative consequences. He believes that universities like UW-Madison can provide a clear-eyed and balanced view of AI’s impact on society.

UW-Madison has set ambitious goals for its new College of Computing and Artificial Intelligence, with a focus on preparing students for an increasingly AI-driven world.

Researchers are rethinking how vaccines are designed, proposing a new strategy that uses generative artificial intelligence to create self-assembling immunogens capable of stimulating broadly neutralizing antibodies. This approach aims to tackle viruses with proteins on their surfaces that can physically move and change shape, making them difficult for the immune system to recognize consistently.

The problem isn’t just mutation; sometimes it’s movement. Traditional vaccine design relies on presenting an antigen that teaches B cells to produce useful antibodies, but conformationally dynamic viral proteins complicate this process. Think of trying to create a key for a lock with a shape that subtly changes over time – the target is constantly shifting.

The proposed research focuses on directing the immune system toward protective B-cell lineages capable of recognizing flexible epitopes. One particular interest is VH1-69, an antibody gene segment associated with recognition of flexible viral epitopes and viral clearance. Rather than hoping vaccination generates the desired antibody response, researchers want to engineer immunogens specifically designed to steer B cells toward those protective lineages.

Generative AI enters the picture by creating self-assembling, stabilized immunogens engineered to stimulate the types of B cells researchers want. Protein design creates an almost unimaginably large search space – far too many potential structures and combinations for researchers to experimentally build and test one at a time. AI changes this scale, allowing computational tools to screen trillions of potential designs and rapidly narrow the field to candidates predicted to have the characteristics most likely to generate broadly neutralizing antibodies.

The goal isn’t just to use AI because it’s faster; it’s to make vaccine design more intentional. Instead of starting with a viral antigen and asking what immune response it produces, researchers are moving toward a different question: What immune response do we want, and what antigen should we build to produce it? This is a significant conceptual shift.

Designing an impressive immunogen on a computer is only the beginning – researchers still need to determine how actual human immune systems respond. Traditional mouse models are valuable in biomedical research, but mouse antibodies aren’t human antibodies; differences in immunoglobulin genes mean that a vaccine capable of producing a promising antibody response in conventional mice may not generate the same response in people.

The proposed work attempts to close this gap by using humanized mice expressing human immunoglobulin genes. These animals would provide researchers with a preclinical model more closely reflecting the antibody repertoire available to a human immune system – essential when deliberately activating particular human antibody lineages is critical.

A high antibody titer doesn’t tell the whole story; researchers traditionally look at measurements such as antibody levels and neutralizing titers, but these don’t necessarily explain which parts of a virus antibodies recognize. The team wants a much higher-resolution picture, using epitope knockout probes to determine specific viral epitopes recognized by vaccine-induced B cells and antibodies.

These tools could reveal why one vaccine candidate produces broadly protective antibodies while another generates a large but less useful immune response. Researchers would map the epitopes recognized by responding B cells, connect those responses to individual B-cell receptor sequences, and determine the structures of resulting antibodies – creating a feedback loop that informs future design decisions.

The next generation of computationally designed candidates could be informed by the immune responses generated by previous generations. This iterative process challenges traditional vaccine development as a largely linear one; instead, it becomes an ongoing cycle of design, vaccination, mapping the response, and redesigning based on what’s learned.

This approach has implications far beyond a single virus – researchers hope to develop a broader roadmap for attacking biological targets that have historically been difficult because they’re structurally flexible. If scientists can learn how to deliberately generate antibodies capable of recognizing conformationally dynamic targets, this could extend into therapeutic strategies involving autoimmune disease and cancer, where recognizing changing biological targets is critical.

There’s still an enormous distance between a compelling research strategy and a vaccine protecting people in the real world – computational predictions need experimental validation, immune responses in humanized mice aren’t identical to those in humans. Safety, durability, breadth of protection, and clinical effectiveness would all have to be established through rigorous testing.

The project illustrates where vaccine science is heading: AI isn’t just being used for analysis after scientists design a vaccine; it’s increasingly brought into the earliest stages of biological design, helping researchers decide what molecules should exist in the first place. At the same time, sophisticated immunology allows researchers to examine vaccine responses at individual B-cell lineages, epitopes, receptor sequences, and antibody structures.

Put these technologies together – AI-generated vaccines and high-resolution analysis of immune responses – and vaccine development starts looking very different. For viruses that have spent millions of years becoming exceptionally good at escaping the immune system, researchers may finally be able to respond with sophisticated tricks of their own.

A construction-focused documentation, reality capture, communication, and reporting platform has just become more efficient with the addition of automated routine tasks. PlanRadar’s new ‘AI Agents’ can complete actions without needing human approval, allowing projects to move forward faster.

The AI Agents are designed to handle specific tasks such as responding to incoming requests for information (RFIs). For instance, the Response agent reads an RFI, searches project documents, and creates a reply with exact sources named. This process used to take hours but is now reduced to minutes.

Every action taken by these agents is logged and attributed, giving teams full control over what runs automatically. Customers can test exactly what an agent will do before it goes live, ensuring they stay in charge of the automated workflows.

In addition to using pre-made PlanRadar agents, customers can build their own using natural language prompts. This allows them to tailor automation to specific needs and streamline project management processes.

For those who require extra support, PlanRadar’s Forward Deployed Engineers work directly with teams to identify valuable AI Agent use cases for their business. These engineers help uncover areas where automated tasks can significantly reduce manual labor and increase productivity.

The company claims that customers want routine work on a project to take care of itself without sacrificing control over what happens. The introduction of PlanRadar’s AI Agents aims to deliver exactly this – catching potential issues before they become costly rework, allowing teams to focus on high-level decision-making.

According to Clemens Hammerl, Chief Product Officer at PlanRadar, customers want more tasks completed from a single location without having to log into another system or manage additional workspaces. The AI Agents run on existing project data and are checked against their creator’s access every time they operate, ensuring that nothing done by the agents goes beyond what the person who built it could do.

Building an AI Agent is described as briefing a new teammate – easy to describe in one’s own words and ready to act whenever needed. Each agent operates on this principle of real autonomy matched with real control.

The introduction of PlanRadar’s AI Agents marks a significant step towards automating routine tasks, making project management more efficient. The company has stated that every agent runs within its existing ISO-certified security and data-protection standards.

PlanRadar AI Agents are available across all plans as part of the platform’s AI features. This first release is just the beginning of a wider roadmap with future versions set to expand what agents can do and where they connect.

A recent development in the world of AI-generated content has sparked concern among users, particularly those who rely on these tools for business purposes. Anthropic’s Claude AI model now includes an imperceptible label on all text produced using its system, intended to serve as a marker that indicates the material was generated with AI. However, this move has led many to seek ways to remove or ‘wash’ their outputs of this indicator.

The surge in interest for tools that can scrub AI-generated content of these markers is evident in online searches and GitHub projects. The term ‘AI watermark remover’ has seen a 60% increase in search interest week over week, with an open-source project called Watermarks Remover gaining significant traction on the platform. This tool promises to remove any indicators that suggest the output came from AI.

Sabrina Ramonov, an AI educator, has also created her own web-based tool for washing text of residual model markers. The concern surrounding these watermarks is largely centered around issues of privacy and attribution. Guillaume Meyer, creator of the Watermarks Remover project, emphasizes that he supports content attribution but opposes the watermarking technique.

The introduction of AI watermarks has raised concerns about how users will be perceived if their work carries this invisible marker. Many fear it could create a false binary in people’s minds, leading them to assume an entire piece of content was created with Claude when only a small portion may have been AI-generated. This is particularly problematic given the existing trend of ‘AI shaming’, where individuals are accused of using AI tools without proper justification.

Additionally, there are concerns that these watermarks could be used as a means to claim ownership over certain material. While Anthropic has stated in a blog post that their watermark only helps test whether Claude produced or processed the content and does not imply ownership or authorship, this assurance may not hold up indefinitely. A blog post is not binding, after all.

The EU AI Act requires providers of AI systems to create methods for marking outputs, which Anthropic claims they are complying with by introducing these watermarks. However, there currently exists no rule that prohibits users from removing or altering these markers. As a result, the cat-and-mouse game between developers and those seeking to remove AI-generated content indicators is likely to continue.

It’s worth noting that this issue extends beyond text-based outputs, as tools for scrubbing AI-generated images and videos have also been developed. However, it raises an intriguing question: how many of these tools themselves were created using AI? The proliferation of AI-generated content has brought about a new set of challenges in terms of ownership, attribution, and the use of such technology in business settings.

New research from YouGov offers a nuanced view of how people feel about various AI tools. The study, part of a larger series on UK AI brand rankings, focuses specifically on satisfaction levels among current and former users of these products.

Understanding user satisfaction becomes increasingly important as the rapidly evolving landscape of AI sees new entrants join the market. At its core, satisfaction provides a more detailed picture than preference alone can offer – it shows how people really feel about the tools they’ve used.

The top 10 AI assistants are ranked based on their satisfaction levels among current and former users. And at number one is Anthropic’s Claude, with an impressive net score of 56.2. Gemini comes in a close second at 46.5, beating out ChatGPT by just half a point.

Perplexity AI takes fourth place with a score of 43.7, followed closely behind by Alexa at 42.6. Copilot ranks sixth at 37.2, while DeepSeek and Apple Intelligence bring up the rear with scores of 35.1 and 32.4 respectively. The lower end of the list includes Grok at 24.6 and Siri at 23.7.

One key takeaway from these rankings is how they differ from YouGov’s AI brand preference ranking. While ChatGPT leads among all UK AI users in terms of preference, Claude achieves a higher net satisfaction score specifically when looking at current and former users – highlighting the importance of considering multiple metrics.

A deeper dive into the analysis reveals which brands have the biggest difference between their current and former user satisfaction levels. DeepSeek has the highest current-user satisfaction score at 77.2, followed closely by Claude at 74.5 and Perplexity AI at 70.5 – suggesting these tools excel in delivering what users need.

However, when looking at former-users’ satisfaction levels, the picture changes significantly. Here, Claude tops the list with a relatively high score of 21.8, followed closely by Perplexity AI at 18.7 and Apple Intelligence at 18.1 – indicating that these tools may struggle to keep users satisfied over time.

The widest gaps between current and former user satisfaction appear for ChatGPT, DeepSeek, and Grok. In fact, ChatGPT has a massive gap of 63.7 points compared to its other rankings, while DeepSeek follows closely behind with a gap of 62.5 points – underlining the need for multiple metrics when evaluating brand performance.

These findings underscore why relying on just one brand-health metric can be insufficient. Claude leads the overall satisfaction ranking, but it’s not alone in exceling at delivering what users want and need right now. Meanwhile, DeepSeek shows its strengths among current users, while ChatGPT reveals a significant difference between its current and former user satisfaction levels – suggesting that this tool may struggle to keep users satisfied over time.

YouGov’s data offers insights into various aspects of AI brand performance, including preference, accuracy, personal impact, and audience-specific strengths and weaknesses. The analysis also highlights the importance of considering multiple metrics when evaluating brand health.

The Women, Peace, and Security (WPS) agenda has long recognized the importance of prevention in conflict resolution. Now, a new approach is emerging that combines WPS principles with artificial intelligence (AI). This fusion aims to shift the focus from reactive measures to proactive ones, anticipating conflicts before they escalate.

A key challenge lies in how AI systems are currently used within security frameworks. These implementations tend to be focused on operational or post-crisis response rather than prevention. Initiatives that develop early warning indicators or build social cohesion and community resilience are limited in scope and implementation.

The WPS agenda provides a proven model for conflict prevention, drawing on humanitarian principles such as the protection of civilians and respect for international law. Applying these principles to AI governance and policy structures would facilitate more comprehensive and context-sensitive approaches to conflict prevention.

Reframing AI from a reactive solution to a preventive instrument across government, civil society, and the private sector offers a critical opportunity to reduce both digital and physical pathways to conflict while advancing more inclusive and sustainable security outcomes. This requires deliberate choices and understanding about how AI is used and the broader security paradigms that determine which risks are prioritized.

Current approaches to AI governance and policy globally remain fragmented, largely voluntary, and primarily oriented toward innovation, risk management, or post-harm mitigation rather than prevention. Existing frameworks establish important norms around transparency, accountability, and safety but do not systematically address early warning, conflict prevention, or the protection of individual and community security as core objectives.

No comprehensive governance framework integrates gender analysis, conflict-prevention research, and the WPS agenda into the life cycle and application of AI systems for security. This gap between the capabilities of AI and its application in preventing conflict and mitigating risks is increasing.

Addressing this gap requires coordinated action across sectors and issue areas to move beyond reactive governance toward a prevention-focused approach. For instance, departments and agencies must establish and enforce requirements for gender-responsive and conflict-informed risk assessments for AI systems deployed (and being developed) in governance, security, and information environments.

The U.S. Government and Congressional Initiatives can play a crucial role in this effort by embedding prevention as a core objective of AI use and national security. This involves using existing laws and strategies such as the Elie Wiesel Genocide and Atrocities Prevention Act of 2018 and the U.S. Women, Peace and Security Act of 2017 to ensure that risks are identified and mitigated before harm occurs.

AI governance should build upon these structures rather than entirely separate processes. This requires mandating the integration of WPS prevention principles when using AI as a tool for diplomacy, foreign policy engagements, and security planning. Federal funding should be directed toward targeted research that examines how AI shapes early warning indicators, individual security, and democratic resilience.

Think tanks, academic institutions, and nonprofits can advance applied, interdisciplinary research that directly links the developing AI governance structures with WPS and conflict prevention outcomes to bridge the persistent gap between technical development and policy implementation. Organizations and leaders should translate technical risks into actionable tools that policymakers can implement.

The Institute of Strategic Dialogue’s (ISD) work on misogynistic radicalization pathways offers a strong example of this dynamic, translating technical risks into specific policy asks such as standardized transparency reporting requirements, survivor-centered standards, and mandated intersectional analysis in AI risk assessments. Think tanks, academic institutions, and nonprofits can serve as conveners, bringing together tech leaders, security practitioners, and WPS experts to share insights and coordinate responses.

The private sector must also take an active role in integrating prevention into the full life cycle of AI systems. This includes embedding gender analysis and conflict-prevention metrics into design, testing, and deployment processes rather than treating them as secondary considerations. Companies should invest in education initiatives and improve efforts for AI literacy for policymaking and public resilience.

Digital gender-based violence and targeted attacks must be recognized as core national and international security concerns. Investments in AI literacy are necessary to strengthen both policymaking and public resilience while reinforcing that AI supports, rather than replaces, human judgment and expertise.