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Mango AI has introduced an innovative tool that allows users to animate still photos with dance moves, making it easier for teams to create short dance clips for social media campaigns. This new feature is designed to help marketing teams quickly respond to trending dance challenges by adding dance moves to existing photos of their brand representatives or campaign spokespeople.

The AI-powered dance generator takes a clear, front-facing photo as input and applies dance moves from either a reference video or pre-made dance templates. The tool ensures that the subject’s facial features, clothing, and overall appearance remain consistent throughout the animation process. This means teams can try out different dance styles on an authorized photo without having to film each move from scratch.

For social media marketing teams, joining trending dance challenges is a great way to engage with their audience and increase brand visibility. However, finding time to organize and film new dance performances can be challenging. Mango AI’s tool streamlines this process by allowing users to quickly add dance moves to existing photos, making it easier for brands to participate in these popular social media trends.

The AI dance generator is designed to work with clear, front-facing, full-body photos in JPG, JPEG, PNG, or WEBP format. Users can upload a photo and select from pre-made dance templates or use a custom reference video up to 30 seconds long and 50MB in size. The tool also offers a pet-focused workflow for animating animal photos.

Once generated, the dance video can be shared across various marketing channels, including Facebook, X, and campaign pages. Users can download it for later editing or sharing, and share it through a QR code or copied link. This flexibility makes it easy to integrate the animated dance clip into existing social media campaigns.

Mango AI’s CEO, Winston Zhang, notes that this tool gives brands room to compare different looks before committing to a final clip. The AI-generated image dance tool is part of Mango AI’s suite of animation products designed for marketing teams, schools, entertainers, and independent creators. These tools support campaign content, learning materials, entertainment projects, and everyday publishing across various skill levels.

The company behind the tool, Mango Animate, builds a range of video creation and photo animation software used by businesses and individuals alike. Their lineup includes AI-assisted media production tools that cater to different needs and skill levels.

A growing number of writers, marketers, and businesses are turning to artificial intelligence (AI) tools to streamline content creation. However, the resulting text can sometimes feel repetitive or unnatural compared to human writing. This is where AI humanizers come in – software designed to revise AI-assisted text so it sounds more natural while preserving its original meaning.

An AI humanizer analyzes AI-generated writing and restructures sentence patterns, varies clause lengths, and adjusts rhythm to create a more natural reading experience. Unlike simple synonym replacement tools, quality AI humanizers aim to make the writing feel less formulaic by varying sentence openings and replacing unnatural expressions with more conversational alternatives.

The process of humanization can involve several types of changes, including shortening unnecessarily long sentences, combining closely related ideas, and introducing more natural variation in language. This is particularly important for SEO content, where removing specific phrases can damage topical relevance – a useful AI humanizer must balance natural language with the keywords and information that the original writer intentionally included.

Writers often use AI to create initial drafts or research notes, but still want their final articles to sound closer to their normal writing style. Humanization can become one stage in this broader editing workflow rather than a replacement for editorial review. The appeal of AI humanizers is especially strong for people producing content at scale, where repeatedly editing similar AI patterns can become time-consuming.

AI detection systems analyze statistical and linguistic characteristics of text to estimate whether the writing resembles patterns commonly associated with language models. Two key concepts in this area are predictability and variation – AI-generated text can sometimes contain highly predictable word choices and relatively consistent sentence structures, while human writing shows more variation in sentence length, vocabulary, phrasing, and organization.

Understanding how AI detection actually works helps explain why simply changing a few words may not substantially alter the characteristics of a passage. Detection systems examine broader patterns rather than individual synonyms – this is why some tools can still produce unnatural-sounding text even after rewriting.

The results of AI detectors should be treated as signals rather than definitive proof of authorship. A responsible content workflow focuses on quality, accuracy, originality, and disclosure requirements rather than attempting to treat one detector rating as absolute evidence. This means that writers need to use multiple tools and techniques to ensure their final product meets the highest standards.

Humanizers can restructure sentences, modify transitions, introduce more natural variation, and do away with repetitive styles while maintaining the writer’s intended message. For instance, an AI draft might repeatedly use similar sentence structures across numerous paragraphs – a humanizer can restructure those sentences to create a more varied rhythm, making the content easier to read without altering its crucial argument.

However, even the strongest humanization workflows cannot automatically guarantee factual accuracy, sturdy reasoning, appropriate citations, or a useful angle. Those features require human judgment and evaluation – AI tools should be used as assistants rather than replacements for editorial review.

The value of gear that rewrite AI-drafted text content may depend more on enhancing quality rather than simply converting detectable patterns. Writers need tools that preserve meaning, improve clarity, and offer more manipulation over the final model. The most beneficial humanization technology will match clearly into the writing process rather than being treated as a magic button.

Ultimately, the strongest approach comes from combining AI assistance with thoughtful human modifying. Understanding how AI detection actually works and using humanization as part of a broader editorial workflow can help writers produce clearer, more natural, and more useful content in 2026.

A growing trend of using AI assistants to optimize cash returns has raised concerns about the potential for a bank run. According to Torsten Slok, chief economist at private equity firm Apollo, this could happen if everyone’s agent simultaneously moves their money from traditional banks to higher-yield accounts offered by fintech firms.

Slok’s theory is based on the idea that AI agents like Meta’s Muse can identify low-interest checking and savings accounts, which typically offer returns of 0.1% or 0.4% per year respectively. These agents then divert funds to more lucrative options, such as Adelphi’s high-yield accounts, which offer up to 5% annual interest for some accounts.

The problem arises when everyone’s AI agent performs this optimization simultaneously, causing a sudden and massive withdrawal of deposits from traditional banks. This could lead to a bank run, where banks lose the cheap deposits they rely on to fund loans. As Slok noted in an insights post, ‘If every household used AI agents to optimize the return on their cash balances, banks could lose a large share of the cheap deposits they rely on to make loans.’

Slok’s firm, Apollo, has significant interests in lending and credit markets. With its $849 billion credit arm competing directly with traditional banks, it is clear that Slok’s warning carries weight. Moreover, Apollo aims to manage an impressive $1.2 trillion in private credit by 2029.

Personal AI agents like Muse and Instinct have gained popularity recently, with users employing them for everyday tasks such as ordering groceries or buying movie tickets. However, this trend has also led to concerns about data security and misuse of sensitive information. For instance, Amazon blocked Muse from adding items to shopping carts due to its handling of account data.

The online retail giant raised concerns that the AI assistant doesn’t identify itself properly and scrapes account data without permission. This incident highlights the need for businesses to be cautious when dealing with AI assistants and ensure they comply with conditions of use. As more people turn to these tools, it is essential for companies like Amazon to establish clear guidelines for interacting with AI agents.

The rise of AI assistants has also led some businesses to block access from these agents due to concerns about data security and misuse. While this trend may seem alarming at first glance, it underscores the importance of responsible use of technology in optimizing financial returns.

The need for simplified reporting is plain to see. Companies want their data analysis tools to turn insights into action – and fast. A recent webinar on using Power Automate with Power BI highlighted the challenges in this area, but also showed that organizations are seeking solutions.

A significant number of participants said they’re not yet taking full advantage of Power Automate’s capabilities. While interest is high, only 17.9% reported using it all the time. The majority – 39.1% – are still investigating its potential, while others haven’t even heard of it or don’t know where to start.

Disconnected data is a major pain point for most respondents. It was cited by 41.5%, followed closely by manual data preparation and report creation at 32.3%. Smaller shares mentioned insights requiring manual action and reports not arriving quickly enough – together, these two top responses represent an astonishing 73.8% of votes.

The way companies approach reporting is often a key factor in its inefficiency. Teams must first collect, combine, prepare, and distribute information before anyone can even view the dashboard. Connecting Power BI with Power Automate can help organizations design workflows that respond to data changes and reduce repetitive handoffs.

Reporting efficiency tops the agenda for future priorities – by a long way. Financial reporting and dashboard distribution lead at 34.9%, followed closely by integrating data across systems and applications at 28.2%. AI-driven alerts, recommendations, and decision support are also high on the list, as well as approval workflows and business processes.

Organizations want to connect their information more effectively – it’s clear from the data. They’re looking for ways to improve reporting so that it reaches decision makers faster, rather than just providing static dashboards. Some companies are even interested in AI-driven alerts that can prompt a response.

To move from insight to action, organizations need to focus on three key areas. First, they should start by identifying a recurring process where manual preparation or distribution creates delays – this is often the reporting bottleneck. Second, they should connect systems with purpose: instead of just connecting applications, focus on data needed for specific decisions or workflows.

Third, building confidence before scaling is essential. Start with contained use cases, confirm governance and ownership, measure results, and then apply lessons to additional processes. This approach will help avoid over-automation and ensure that reporting workflows are truly responsive.

The need for smarter ways to connect data is clear – organizations want to reduce manual effort and turn insights into action. They’re not just looking for more reports; they want to use automation tools like Power Automate to make decision-making more efficient.

Disconnected systems or manual reporting processes can be a major hindrance to an organization’s efficiency. If your team is struggling with these issues, it may be time to explore how Microsoft Power BI and Power Automate can support connected, responsive workflows – start by discussing the challenges that matter most to you.

If automation tools like Power Automate are going to deliver real benefits, organizations need a comprehensive approach. This means connecting systems with purpose, building confidence before scaling up, and identifying reporting bottlenecks – all of which require careful planning and execution.

A major breakthrough in synthetic biology and AI-powered biosecurity has been achieved by Google DeepMind, the tech giant’s AI lab. The company has developed a new method to watermark AI-designed proteins, establishing a permanent proof of origin directly inside biological code. This innovation is made possible through SynthID Bio, a proof-of-concept technology published in the journal Nature.

The development comes at a time when rapid advancements are being made in biological machine learning. Systems like AlphaFold 3 and ProteinMPNN allow scientists to predict complex folding patterns and engineer functional binders with unprecedented accuracy. However, these generative capabilities have also heightened global biosecurity risks. Unfamiliar, machine-generated sequences can slip past conventional gene-synthesis filters, while unverified synthetic structural data risks contaminating open-access scientific repositories.

Google DeepMind Chief AI Scientist Demis Hassabis emphasized the importance of guarding against biological misuse in the era of frontier AI: ‘Biosecurity is one of the most urgent challenges for the AI era.’ He highlighted that bringing SynthID to biology so AI-generated proteins can be watermarked is a critical step, and the company is open-sourcing SynthID Bio tools for the research community to build on this work.

Pushmeet Kohli, Vice President of Science at Google DeepMind and Chief Scientist at Google Cloud, celebrated the practical synthesis of these molecules: ‘Very happy to announce that our team has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked.’

The SynthID Bio method operates as a modular toolkit tailored across two primary dimensions of structural biology. It subtly steers the selection of amino acid building blocks during sequence generation through its ‘sequence’ component, known as SynthIDBio-sequence. In laboratory validation, watermarked protein binders engineered via AlphaProteo and a customized version of ProteinMPNN exhibited binding affinities on par with unmarked variants.

The method also relies on a fine-tuned iteration of AlphaFold 3 to insert an imperceptible mathematical watermark across atomic coordinate arrays through its ‘structure’ component, SynthIDBio-structure. This leaves a dependable detection signal without warping the molecular conformation. The signature persists beyond the digital blueprint: it remains fully detectable on the physical, synthesized protein itself after wet-lab manufacturing.

The development of SynthID Bio marks a significant step in safeguarding the new era of AI-powered generative biology and strengthening global biosecurity. By enabling the watermarking of AI-generated proteins, researchers can now verify their origin and authenticity, reducing the risk of contamination or misuse.

British universal bank Barclays is expanding its strategic collaboration with Anthropic to integrate secure and enterprise-grade artificial intelligence (AI) systems across its global operations. This move marks a significant step towards upgrading the bank’s processes and improving client experience through the use of AI assistants.

As part of this rollout, Barclays aims to accelerate software development, modernize legacy systems, and enhance operational efficiency by extending Claude, Anthropic’s large language model, across the bank. The goal is for 50% of its developer population to adopt Claude Code by the end of 2026, with a majority of software engineers expected to use it in 2027.

The integration of AI across Barclays’ complex and highly regulated organization requires both capable technology and disciplined governance. Expanding its collaboration with Anthropic reflects a shared commitment to deploying AI responsibly, reliably, and effectively. This approach enables real outcomes such as providing faster support for customers.

Anne Marie Darling, Group Co-Chief Operating Officer at Barclays, emphasized the importance of measuring technology’s impact on clients, colleagues, and customers. ‘The true measure of any technology is the impact it has on our stakeholders,’ she said. Expanding Claude’s use across Barclays goes beyond adopting AI; it transforms how work gets done by reimagining processes, enhancing client experiences, and building a simpler organization within a secure environment.

Craig Bright, Group Co-Chief Operating Officer at Barclays, highlighted the transformative potential of increasingly capable AI systems in software engineering and cyber security. ‘We’re moving towards AI as an agentic capability embedded within how we build, test, secure, and operate technology,’ he said. At Barclays, Claude is being used to modernize legacy platforms, improve software quality, and allow technical experts to focus on complex challenges.

One example of the partnership’s success is the Colleague Knowledge Assistant, which helps UK employees find information more quickly when supporting their 20 million retail customers. This capability has been live since 2025 and uses Claude through a retrieval-augmented generation architecture. Over 16,000 colleagues have adopted it, handling over one million searches.

The result of this partnership is faster access to information and quicker support for clients. The Colleague Knowledge Assistant demonstrates the potential of AI assistants in streamlining processes and enhancing client experiences.

Within Barclays’ Global Markets business, Claude models are helping streamline how client inquiries are processed and managed. Teams use these models to classify, enrich, and determine the optimal processing route for incoming emails, prioritizing requests and ensuring they contain necessary information for operations colleagues to take action. The platform handles approximately 120,000 emails each day, reducing manual handling activity while enabling colleagues to identify relevant information more efficiently.

As Barclays continues to expand its use of AI, responsible deployment remains central to the partnership. The bank applies robust governance, security controls, and human oversight to ensure technology is deployed safely and effectively across the organization.

Paul Smith, Chief Commercial Officer at Anthropic, noted that this collaboration reflects a significant milestone for Anthropic in the UK. ‘Barclays serves millions of customers worldwide,’ he said. Their standard for AI deployment is high, which is why they are rolling out Claude with strong security and oversight.

The expanded partnership between Barclays and Anthropic marks a long-term commitment to using AI to improve productivity, strengthen resilience, and enhance client outcomes. By combining Anthropic’s frontier AI capabilities with Barclays’ focus on responsible innovation, the organizations aim to scale practical AI applications across one of the world’s largest financial institutions.

Redditors who moderate communities are now getting some much-needed assistance from Reddit itself. The platform has just rolled out an artificial intelligence-powered tool to help detect and filter out online harassment on its site.

This new feature, which uses data collected from moderator actions and enforcement teams, is designed to aid moderators in identifying potential harassing or objectionable content within their communities. When the AI flagging system spots a problematic post or comment, it will alert the mods so they can review it and take action if needed.

According to Reddit’s own words, the Large Language Model (LLM) behind this tool was trained on over 340 words of data from moderator actions and internal enforcement tools. This training allows the AI to accurately spot harassment even in cases where content might not directly break Reddit’s rules.

The new feature is part of a broader effort by Reddit to upgrade its moderation tools and support its moderators. In recent months, they’ve introduced several updates aimed at making users’ lives easier and addressing online safety concerns.

To turn on the harassment filter, mods will need to follow these steps: desktop users should go to their community’s About page, click ‘Mod Tools’, and then toggle the Harassment Filter switch. iOS and Android users can find it in their Mod Tools section too – just look for the Safety settings.

Redditors have two options when it comes to filtering out harassment: they can either opt for Low, which is less thorough but more accurate; or High, which sweeps up a lot of content but might catch some false positives. Reddit advises using High in communities with loads of harassing posts.

While automatic removals from administrators will still be happening as usual, the AI-powered filter gives moderators another tool to keep an eye on their community’s behavior and address any issues before they blow out of control.

Meta AI has released a new feature called Imagine Me, which lets users create custom AI-generated images based on their photos and instructions. To use it, you simply type in what you want to see - like ‘Imagine me as a superhero’ or ‘Picture me in ancient Egypt.’ The AI then uses this information to generate an image.

The process of making these personalized images is easy: just type your command into the chat interface. Meta says its system can edit photos quickly and easily, adding or removing objects with no fuss. It’s all part of a growing list of features that let users experiment with different looks and styles.

A new AI model called Llama 405B powers this feature, giving it a solid foundation for producing high-quality images. But here’s the fun bit: you can also use more playful language to get creative results - like ‘Change me into a royal image.’ The possibilities are endless, and users love being able to try out new ideas.

The Imagine Me tool is part of an ongoing effort by Meta AI to make creativity with AI accessible and enjoyable. One way they’re doing this is by letting people generate silly images on the fly - think 3D stickers or cartoon versions of themselves. It’s a great way for users to experiment without needing any special skills.

This feature is just one part of an update that includes more languages supported and availability in 22 countries worldwide, including Argentina, Chile, Colombia, Ecuador, Mexico, Peru, and Cameroon. Users can now access these features from multiple places around the globe.

Looking ahead, Meta plans to keep updating its AI technology every two weeks with new tools and capabilities. Next month will bring an ‘Edit with AI’ button that lets users fine-tune their photos even further - a clear sign of how the company is committed to continually improving its service.

A fake image of Bianca Motley Broom, the mayor of College Park, Georgia, kissing a man who was not her husband in a bar has gone viral on Facebook. The fact that it was fabricated is spreading even faster, but only after thousands had seen it.

The ease with which this image was created highlights the growing threat of deepfakes and misinformation driven by artificial intelligence. Six years ago, when Motley Broom first took office in the Atlanta suburb, a political enemy would have needed some skill with image-editing software to forge an image of a politician in a compromising position.

Today, all that is required is a computer and a prompt for an AI image generator. This has significant implications for election integrity, as voters are increasingly concerned about the spread of fake images and videos on social media.

A poll taken by Rad Campaign and Women Who Tech found that 71% of respondents worry about AI-generated deepfakes and disinformation affecting elections. Two in three respondents also expressed concern about AI being used to sexualize women.

The findings show a growing awareness among Americans about the potential risks associated with AI, particularly when it comes to election interference. The Trump administration has already demonstrated its willingness to use AI imagery, including digitally altering an image of Nekima Levy Armstrong to make her appear distressed and darker-skinned.

A picture of her calmly being escorted by a DHS agent was altered so that she appeared to be crying, and her skin had been darkened. Kaelan Dorr, the White House deputy communications director, later described the image as a ‘meme’.

Fake images of Zohran Mamdani, New York’s mayor, also began circulating from a parody account on X in February, falsely depicting him and his mother in the company of Jeffrey Epstein. Using Google’s SynthID tool, Reuters debunked the images, but not before other accounts had recirculated them as authentic.

Despite significant apprehension around the potential economic consequences of AI, concern about deepfake images being used to sexualize women or children, or to influence elections, outstrips concern about AI affecting jobs by a ratio of three to one. The public is deeply concerned, according to Allyson Kapin, founder of Rad Campaign and co-founder of Women Who Tech.

Raphael Warnock, a Georgia Democratic senator, has spoken out about the issue in a video posted on Facebook. He noted that deepfake images had been circulating online falsely depicting him with health problems, with an armful of grandchildren he does not have – and with a full head of hair, which he said was funny if problematic.

As these tools become increasingly powerful, it will get harder and harder to distinguish fact from fiction, Warnock warned. There are already bad actors out there who are doing everything they can to divide us and confuse us. This has huge implications for our democracy.

What if hours before an election, someone created a page with AI-generated images, fake stories about the candidates – how will voters know what’s true and what’s not? Warnock is among a group of Senate Democrats who have been war-gaming political responses to possible election interference scenarios.

Chief among those scenarios: disinformation driven by fabricated images or videos circulated through social media. The outgoing US senator John Kennedy, a Louisiana Republican, offered a bill last week requiring AI firms to install a ‘kill switch’ for rogue AI.

Senator Rand Paul of Kentucky blocked Kennedy’s bill, describing the effort as premature without additional industry input. Hakeem Jeffries, the Democratic House minority leader, said in an interview that government should move ‘urgently’ on AI regulations broadly, calling for ‘guardrails to protect the American people from bad actors and bad behavior’.

Jeffries noted that Republicans attempted to pass legislation pre-empting states from regulating AI last year as part of the omnibus One Big Beautiful Bill. The amendment failed on a 99-1 vote in the Senate.

Some states have legislation like Tennessee’s Ensuring Likeness Voice and Image Security Act – the Elvis Act, which protects politicians and residents alike from AI voice cloning and digital likeness replication using the state’s robust right-of-publicity protections that are a product of Nashville’s recording industry.

Clear disclaimers are legally required in most states when a political campaign uses AI to manipulate the speeches, appearances or actions of officials or candidates. Georgia is not one of them.

While legislators in Georgia began debating deepfake protections in bills proposed in 2024 and 2025, they have not yet enacted legislation. Motley Broom said she believed she was targeted by political opponents who objected to her role as a past president of the Georgia Municipal Association.

I have worked for my city to try to be the best advocate I can be on behalf of College Park,

Dungeon Fighter Online has become embroiled in a controversy surrounding the use of artificial intelligence (AI) in creating event artwork. The issue centers on the ‘Magical Boy’ events, which were launched following maintenance on October 1. These events feature illustrations that have sparked debate among players regarding their authenticity and artistic style.

The Magical Boy Photo Legendary Card, one of the rewards for participating in the ‘HOT DEBUT! Magical Boys’ event, has been at the forefront of this controversy. Four cards – Captain Thunder Honey, Macho Pink Heart Breaker, Dark Devil Foxy, and Big Bear Milky Way – were revealed as part of this event. However, it was not until players began scrutinizing these images that concerns about AI-generated artwork arose.

A notice on the event page explicitly states that ‘Some images were created by utilizing AI generation and retouching.’ While this disclosure has fueled debate over how extensively AI was used in creating these illustrations, specific production details remain unclear. The extent to which AI contributed to image generation versus retouching is not specified, leaving room for interpretation.

Players have pointed out the detailed rendering on the cards as evidence that they may be AI-generated. Given the event page’s acknowledgment of AI use, the focus has shifted from whether AI was used at all to how it was utilized in the creation process. This nuance underscores the complexity of this controversy.

Some players have also drawn comparisons between the artwork and Hirohiko Araki’s manga ‘JoJo’s Bizarre Adventure’, citing similarities in pose, shading, and facial expressions. However, no confirmation has been made regarding whether the actual production process referenced a specific work or artist.

In response to player concerns, Nexon has announced that it will modify the event images. An official notice on its website states that ‘Some awkward image resources’ from the ‘HOT DEBUT! Magical Boys - Magical Boy Photo Legendary Card Album’ will be updated. Additionally, Nexon expressed apologies for any inconvenience caused by the insufficient polish of AI illustrations and pledged to reinforce inspection and retouching processes to prevent similar quality issues in the future.

A man in Santa Ana, California, is facing potential charges after he admitted to creating a hoax involving an image of a highly venomous snake. The incident began when the individual called 911 and reported seeing a Gaboon Viper in his backyard, prompting a response from local authorities and reptile experts.

The police department’s social media accounts were used to issue an alert about the potential presence of the deadly snake, along with a photo provided by the man who made the report. However, it was later determined that the image had been generated using artificial intelligence (AI).

According to Natalie Garcia, a spokesperson for the Santa Ana Police Department, the individual eventually confessed to creating the AI-generated image after being interviewed again by detectives at his workplace. The motive behind this action remains unclear.

The police department’s initial response was cautious due to concerns about the legitimacy of the image. Although they don’t have dedicated resources to verify images, they ran the photo through an AI generator and received conflicting results from different tools. This led them to decide that verifying the presence or absence of the snake in person was necessary.

A Gaboon Viper is a highly venomous snake native to sub-Saharan Africa, characterized by its heavy weight, which causes it to be primarily a ground dweller rather than an arboreal species. It has a calm nature and rarely bites humans, but can still cause fatal injuries if provoked.

The man’s actions have caused significant disruption in the community, prompting concerns about public safety and the potential consequences of spreading false information through emergency services. The police department is now planning to cite him for falsifying an emergency report and may work with the District Attorney’s office to press charges against him.

Garcia stated that the individual eventually provided a reason for creating the hoax but declined to share this information, citing concerns about giving further attention to his actions. The incident highlights the challenges faced by law enforcement in verifying the authenticity of images shared through various channels and underscores the importance of being cautious when dealing with potentially sensitive or misleading information.

The Santa Ana Police Department’s response demonstrates their commitment to thoroughly investigating incidents like these while also acknowledging the limitations they face in terms of resources for image verification. The incident serves as a reminder about the potential consequences of spreading false information, particularly through emergency services.

A highly venomous snake sighting in Santa Ana, California, turned out to be a hoax after police revealed that the image was an AI-generated fake. The incident highlights the challenges of detecting AI-generated images and the limitations of current verification tools.

The initial report on September 28 claimed that a Gaboon viper had been spotted in a neighborhood, prompting officers and reptile experts to canvass the area. However, Santa Ana police had doubts about the image even as they investigated further.

According to Natalie Garcia, a spokesperson for the department, officials attempted to verify whether the image was generated by artificial intelligence while also pointing out apparent problems with the image’s authenticity. The snake in question appeared to be positioned awkwardly in the tree, and what seemed like an ear on the animal raised suspicions that it might not be real.

Despite these red flags, police still couldn’t definitively establish whether the report was false until Tuesday, September 29. At this point, detectives went back to the workplace of the man who reported the snake sighting to ask for additional information. It was during this interview that he admitted fabricating the report and image.

Garcia declined to provide details about the explanation given by the man for his actions but stated that he attempted to justify what he had done and appeared remorseful. However, investigators were no longer certain which of his statements they could trust due to inconsistencies in his account.

The initial report included details that seemed to lend credibility to the sighting, such as the man’s dogs barking prompting him to go outside and investigate, and his son claiming to have seen the snake as well. Neighbors also reported seeing the man appear pale after describing what had happened, which added to their suspicions about the authenticity of the report.

Initially, police did not plan to seek charges against the man for making a false report of an emergency. However, after he admitted fabricating the image and report, detectives decided to pursue charges in this regard.

On Wednesday, Garcia declined to reveal what tool the department used to try and verify whether the snake image was real or generated by AI. Prior to confirming that it was indeed a hoax, Santa Ana police stated they had run the image through various AI generators but were unable to confirm its authenticity using these tools.

Interestingly, OpenAI’s photo verification tool did show that the photo was either generated or enhanced by AI when submitted for verification by this publication. This incident raises questions about the reliability of such tools and their limitations in detecting AI-generated content.

The accuracy of generative AI verification tools is a topic of discussion among experts. Dr. Karen North, professor of digital social media at USC’s Annenberg School, notes that these tools are not entirely accurate. According to her, they can produce both false negatives and false positives when flagging AI content.

North herself has tested AI detection in essays and found instances where some content was detected as 100% AI even though it was written fully by a human. These inaccurate readings occur when the tool detects an algorithmic pattern similar to that of AI, even if no AI was used.

The limitations of these tools are due to how they work: looking for patterns in content generated by AI and detecting whether what’s created looks like ‘the most obvious, mundane, or likely version’ as North puts it. This can lead to false positives when the tool mistakenly identifies human-generated content as AI-created.

OpenAI claimed that false positives are rare in their photo detection software but acknowledged limitations with its SynthID watermark. Stripping metadata or degrading the watermark could result in a false negative read, and OpenAI stated that its verification tool only works on content created using OpenAI products.

The reliability of AI detectors is crucial for businesses and organizations looking to verify authenticity online. However, North advises people not just rely on these tools but also trust their instincts when encountering suspicious images or text. She suggests relying on the uncanny valley theory – that viewers will get an eerie feeling if something looks unnatural or off.

In the case of the Santa Ana snake hoax, OpenAI’s photo verification tool did flag the image as potentially AI-generated. However, this incident highlights the need for more accurate and reliable tools to detect AI-created content.

A new tool has been developed to address the growing concern of biosecurity in synthetic biology. SynthID Bio, a family of watermarking methods, is designed to strengthen biosecurity and scientific integrity by embedding an imperceptible signature directly into biological code. This approach ensures that watermarks are verifiable not just on digital models but also on synthesized physical proteins themselves – all while preserving their biological function in laboratory testing.

SynthID Bio builds upon the success of SynthID, a tried-and-tested watermarking tool developed by Google DeepMind. The new method adapts its approach depending on the type of data, subtly guiding the choice of amino acids for sequences and adjusting atomic coordinates for predicted 3D structures to create a reliable signal for detection.

In experiments, these adjustments did not compromise the protein’s biological function, which is essential for effectively treating disease and advancing scientific research. The researchers verified their approach by using AlphaProteo, a binder design method, alongside a SynthID Bio-enabled version of ProteinMPNN, a commonly used protein sequence generation method.

The team tested watermarked designs on three target proteins – VEGF-A, the SARS-CoV-2 spike protein RBD, and PD-L1. The results showed that watermarked designs matched the hit rate, binding affinity, and natural sequence diversity of unwatermarked versions, successfully creating the first-ever watermarked and biologically functional protein binders.

SynthID Bio also fine-tunes a small part of AlphaFold 3’s diffusion network to build the ability to watermark directly into the model’s weights. This ensures that predicted 3D coordinates inherently carry a detectable signature regardless of who runs the model, preserving AlphaFold 3 prediction accuracy while offering near-perfect detectability.

Biosecurity relies on layered defenses – think of it like a ‘Swiss cheese’ defense model, where multiple independent safety measures work in tandem to cover each other’s blind spots. SynthID Bio serves as an important verification layer embedded in the biological design itself, linking designs to the model developer and empowering developers to lead on safety.

SynthID Bio can also be paired with provenance metadata approaches or central repositories of AI-generated biological data to better identify and track AI-generated proteins. The team is researching how to apply watermarking to more complex biological objects, such as genomes, in ongoing work with the Hie lab at Stanford University and Arc Institute.

The extension of this work to watermark DNA of bacteriophages is a collaboration between Google DeepMind and the Hie lab at Stanford and Arc Institute. Key contributions were made by Jeremy Ratcliff, Aleks Petrov, Alexander I. Cowen-Rivers, David Stutz, Elisa L. H. Wong, Victor Martin Palacios, Francesca Pietra, Alfred Piccioni, Tristan Oliver Kwan, Tor Lattimore, Sumanth Dathathri, and Pushmeet Kohli from Google DeepMind.

SynthID Bio has the potential to address some of the biosecurity risks associated with genome design. The team is committed to responsible innovation and open-sourcing the code and in vitro data, releasing the weights to the research community so they can build on this work.

The development of SynthID Bio was initiated by Pushmeet Kohli, led by Alexander I. Cowen-Rivers and David Stutz, with key engineering and research contributions from Guillermo Ortiz-Jimenez, Jeremy Ratcliff, Vinicius Zambaldi, Lindsay Willmore, Josh Abramson, Harshnira Patani, Christina Kouridi, Florian Stimberg, Mel Vecerik, Alex Chu, Sukhdeep Singh, Sumanth Dathathri, Eliseo Papa, Valentin De Bortoli, Arnaud Doucet, Jue Wang, and Sven Gowal.

SynthID Bio can help maintain the integrity of databases such as the Protein Data Bank, UniProt, and GenBank. These databases play a vital role in scientific advancements but mislabeled entries can have an outsized negative impact in biosecurity decision-making.

The team is also researching how to apply watermarking to more complex biological objects, such as genomes, in ongoing work with the Hie lab at Stanford University and Arc Institute. This collaboration has the potential to address some of the biosecurity risks associated with genome design.

SynthID Bio can provide an automated verification signal, proving that an order originated from a trusted model with built-in safeguards. This is especially vital for DNA synthesis screening, which sits on the frontlines of biosecurity. Converting digital protein designs into physical molecules requires placing an order with DNA synthesis providers who screen requests against databases of known threats.

SynthID Bio can help streamline screening by providing an automated verification signal that proves an order originated from a trusted model with built-in safeguards. This is especially vital for DNA synthesis companies, which have an important role in helping AI-driven innovation scale responsibly.

The development of SynthID Bio has the potential to address some of the biosecurity risks associated with genome design and synthetic biology. The team is committed to responsible innovation and open-sourcing the code and in vitro data, releasing the weights to the research community so they can build on this work.

Whitbread, the UK’s largest hotel chain and owner of Premier Inn, is embracing artificial intelligence (AI) to streamline its operations. The company has partnered closely with Microsoft to implement responsibly governed AI that can improve productivity and automate business processes, freeing up colleagues to focus on higher-value tasks such as serving guests.

Domenica Sabou, Senior Location Strategy and Data Manager at Whitbread, has been experimenting with Microsoft 365 Copilot to build an AI agent that assesses the potential success of new hotel locations. The agent combines factors like driving distances and local amenities to score them, providing a data-driven starting point for strategic business recommendations rather than relying on instinct or partial evidence.

Sabou emphasizes that while the AI provides valuable insights, human judgment is still essential in making decisions. ‘The judgement is still coming from you,’ she says. ‘You have to do that. And you’re just learning constantly about how to prompt, what not to prompt: the best way of doing things.’ This approach highlights the importance of balancing AI-driven data analysis with human critical thinking and oversight.

The project showcases Whitbread’s efforts to leverage agentic AI technology from Microsoft to automate data-heavy tasks. By using Copilot, Sabou has been able to build an agent that can assist her in making informed decisions about new hotel locations. This is just the beginning of Whitbread’s plans to scale its use of this technology.

Whitbread has established an AI Enablement Office and implemented a governance process for colleagues who want to develop agents beyond their own work and share them with others. The company considers factors like safety, ownership, and cost before allowing useful experiments to become shared services. This approach is crucial in ensuring the responsible adoption of AI across the organization.

Mark Smith, Whitbread’s Group CTO, sees this governance as a foundation for broader adoption. ‘When we get to the end of this, everybody will have the opportunity to build an agent,’ he says. The ambition is to drive more efficiency into their business while preserving frontline roles and ensuring that humans remain involved in critical decision-making.

The company’s agentic ambitions are built on a solid Copilot foundation, which has helped spread AI literacy throughout Whitbread. They began with a pilot involving 270 support-centre colleagues at the head office, amplified by a ‘Digital Heroes’ programme between September 2025 and March 2026. This initiative provided access to Copilot alongside training, practical sessions, and a community where people could share their experiences.

The Digital Heroes programme was designed to be broad, bringing together individuals from various functions such as commercial, finance, property, procurement, people, technology, and more. Smith notes that while the initial response to the new tool was positive, it wasn’t enough on its own. ‘We’re a budget hotel business; we need to justify every pound that we spend,’ he says.

Whitbread gathered feedback throughout the pilot, with colleagues reporting an average time savings of 24 minutes per day as a result of using Copilot. As a consequence, the company is now extending Copilot to 1,650 people across its support centre and multi-site hotel managers. The next phase focuses on fostering sustained use.

Emma Cluett, Head of Delivery Transformation in Whitbread’s Group Transformation function, has used Copilot to help her team build internal capability. Her group manages large programmes for the company, handling tasks like business cases, executive presentations, meeting organization, and stakeholder alignment. Cluett emphasizes that AI can never replace human oversight: ‘The decisions will never go away from that kind of human sense-check.’

However, through Copilot, her team has been able to avoid extra costs by leveraging the tool as an additional workforce support system. ‘We’ve been able to keep our team number quite static because we’ve used Copilot,’ she says.

Sabou also reports increased efficiency due to using Copilot, estimating that it has saved her a considerable amount of time over a year of experimentation. She describes her interaction style with the AI as ‘a backwards and forwards conversation between you and the AI.’ Sabou notes that asking questions is key in getting the most out of Copilot.

The Digital Heroes programme plays a crucial role in spreading confidence among colleagues when it comes to using Copilot. Christopher Adams, Business Change Manager in Technology and Data, and Harprit Takkar, Technical Product Specialist for Frontline and AI, have led group sessions, drop-ins, written guidance, and short recap videos alongside formal training.

Adams notes that the content must constantly change due to the rapid evolution of technology: ‘The material we used six months ago is out of date already.’ He and Takkar focus on providing colleagues with a platform for asking questions as well as demonstrating new features. This approach also enables the company to discover valuable ideas from those closest to the work.

During the pilot, colleagues developed over 60 role-specific use cases across eight functions. Some tasks were straightforward, such as summarizing meetings or finding information, while others used Copilot to research complex questions that previously required external advice.

As Whitbread continues to roll out Copilot for more people, Smith will be monitoring whether colleagues keep discovering new ways to utilize the tool and share their experiences. ‘I want to see more people grabbing me in the coffee shop to say it’s been fantastic and they’ve learned lots,’ he says.

Ultimately, the goal of AI is to accelerate business operations while driving value for guests and shareholders. Smith emphasizes that Whitbread aims to get its employees working better-quality tasks in a more productive manner.

Hp has launched a mobile workstation that challenges the idea that serious ai models can only run in cloud environments. The hp zbook ultra g3a is built around amd’s ryzen ai max pro processor and packs up to 192 gb of unified memory, with a significant portion – 160 gb – allocatable to the gpu.

A shift towards running large-scale ai models locally on laptops or desktops could have far-reaching implications for design teams. They might be able to work more efficiently without relying entirely on cloud-based solutions. The conversation around ai in architecture, engineering, and construction has long centered on cloud-based options.

David mcafee from amd spoke with hp about the new workstation during its launch event. One key topic was how a local ai model would compete with other resource-intensive applications running at the same time. McAfee noted that performance can be affected when multiple tasks vie for resources, but he assured us that this isn’t insurmountable.

The zbook ultra g3a uses lpddr memory efficiently, but it cannot be upgraded later, which may become an issue as ai requirements continue to shift. Companies need to carefully consider their hardware needs when planning for the future and expect those needs to change over time. This mobile workstation is a testing ground for how businesses will approach hardware procurement.

McAfee’s conversation with hp also covered amd’s work on professional drivers for radeon gpus and ai-assisted coding. The company is developing drivers that can take advantage of its rocm platform, making it easier to port applications built for nvidia’s cuda to amd’s architecture. This could be a game-changer for businesses looking to adopt ai tools.

AMD envisions a future where users have multiple options for compute depending on their specific needs and workflows. They’re exploring everything from high-end desktops to cloud-based solutions, with the hp zbook ultra g3a sitting somewhere in between. The company sees this as an opportunity to provide more flexibility and choice for customers.

The discussion around bringing ai on-device also touched on data analysis tools and how they can be used by companies that don’t necessarily need a full-fledged local model running all the time. Llm models, in particular, could serve as judges or decision-makers within larger workflows, allowing businesses to get insights without having to set up complex cloud-based solutions.

Microsoft has announced a comprehensive set of new features across its Fabric platform, Power BI, OneLake, and Azure SQL at the Fabric + SQL European Community Conference in Barcelona. The company’s goal is to make data, business context, and artificial intelligence work together seamlessly to fuel the agentic enterprise.

The conference drew over 5,000 attendees from 71 countries, with Arun Ulag, executive vice president of Azure Data, kicking off the event by highlighting Fabric’s impressive growth: it boasts over 40,000 customers, 2.6 million active users, and certifications in this area are the fastest growing within the Microsoft ecosystem.

SQL continues to be a core technology for enterprise workloads, used by over 90% of Fortune 500 companies. However, to get the most out of AI, Microsoft wants its customers’ data platforms to evolve toward a unified platform that integrates business context and artificial intelligence.

The key word at FabCon is ‘context.’ Organizations need more than just AI models and agents; they require reliable data, shared business definitions, operational knowledge, security, and governance. At the center of this effort is Fabric IQ, a shared intelligence layer that connects OneLake data, Power BI semantic model metrics, and context from ontologies and real-time intelligence.

One of the most significant announcements made at the conference is the integration of Fabric IQ with Microsoft Copilot. The intention is for Microsoft’s assistant to work not only with generic information but also with a shared business view built on each organization’s data and knowledge.

Fabric IQ is now generally available in Copilot Chat and Cowork, with no additional token costs, while its integration with the new Code experience will be coming soon through the Frontier program. For Power BI users, the business definitions, metrics, and insights embedded in their semantic models can also be used directly as the basis for Copilot’s responses and actions.

This move marks a significant shift in the traditional role of data platforms. It’s no longer just about storing, integrating, and analyzing information; the goal is to transform data into a business context that is understandable to both people and AI agents.

The evolution of Power BI has also been highlighted at the conference. Microsoft has announced the ability to create agentic applications directly from Power BI Desktop. Users can start with a trusted semantic model, describe the application they need using natural language, generate it, preview it, modify it, and publish it to Fabric.

This conceptual leap is significant because it enables users to move from analyzing information to creating actionable applications based on that same data. Microsoft aims to enable users to automate tasks by leveraging AI capabilities in their business processes.

The company has also expanded Fabric applications to facilitate the transition from prototype to production. Developers can connect applications with Fabric data stores, SQL databases, lakehouses, and Power BI semantic models without duplicating data and while maintaining existing permissions and business logic.

New features include a TypeScript backend, Secret Store, PostgreSQL support, private access by default, and built-in usage and performance metrics. Additionally, Microsoft has introduced the Fabric data engineering agent to automate complex tasks such as migrations, lakehouse modernization, optimization, ETL processes, and data preparation.

The operations agent incorporates root cause analysis and recommendations for resolving incidents. Furthermore, it has expanded Fabric IQ to include capabilities for generating ontologies from semantic models, OneLake data, and context documents.

Organizations can now define rules in natural language and reuse Power BI metrics, providing people with a common business meaning and thereby improving the accuracy of their responses. Microsoft also includes on-demand billing, which enables customers to pay for computing capacity used, and zero provisioning of Fabric and OneLake.

In terms of database management, Database Hub offers a centralized control plane for SQL Server, Azure SQL, PostgreSQL, Cosmos DB, and SQL Database on Fabric. Database agents for SQL and PostgreSQL are also coming soon.

Finally, Microsoft has announced the general availability of SQL Server on Azure On-Premise and its offline operations, strengthening support for on-premises, edge, and sovereign environments.

The announcements outline a significant evolution of Microsoft’s data platform, which aims to enhance real-world efficiency in the age of agents. To achieve this, Microsoft is building an architecture around Fabric where OneLake connects data; Power BI provides semantic models and metrics; Fabric IQ incorporates business context; Copilot and agents utilize that knowledge; and observability, governance, and database tools provide the operational layer necessary to keep the entire system running smoothly.

Microsoft’s vision for a unified platform marks a paradigm shift in how organizations approach AI. Transformation through AI will not depend so much on the number of agents an organization is able to create as on its ability to understand the business and collaborate to achieve results.

Claude’s official incident report has been resolved, and services are operating normally since recovery. The issue affected multiple platforms, including claude.ai, Claude Console, the Claude API, Claude Code, and Claude Cowork.

The impact was felt from 07:00 PT to 07:59 PT, with users experiencing failed requests, errors loading or sending conversations, and being asked to sign in again. However, retrying may have succeeded in some cases.

Claude’s team initially reported elevated error rates affecting claude.ai, including the desktop and mobile apps, as well as Claude Code and Cowork. Some requests to the Claude API were also returning errors.

The investigation into the issue began with reports of degraded performance affecting multiple services, including platform.claude.com, the Claude API, Claude Code, and Cowork. The team provided regular updates on their progress in resolving the issue.

A mitigation was applied, which led to a significant drop in error rates. However, some sign-in attempts, account actions, and sessions for Claude Code and Cowork were still failing at this point.

The errors that began at 14:00 UTC were mitigated at 14:36 UTC, with existing conversations mostly working again. A second issue prevented many users from signing in, starting new chats, voice conversations, and Claude Code or Cowork sessions, making purchases, and uploading files.

Users who were signed in were advised not to sign out, as this would likely cause further issues. The team worked on a fix and provided regular updates within 30-minute intervals.

As of 14:59 UTC, most services had recovered, with signing in, starting new chats, voice conversations, Claude Code and Cowork sessions, purchases, and file uploads working again. However, some messages sent between 14:00 and 14:59 UTC may not have been saved.

The issue was fully resolved by the time all services were operating normally at 14:59 UTC. The team continued to monitor the situation closely.

Google has filed two appeals with the European Union’s General Court in Luxembourg, challenging orders that would require it to grant rival artificial intelligence (AI) assistants access to certain features of its Android operating system. The move comes as part of a broader effort by the EU to promote competition and fairness in the digital market.

The EU’s Digital Markets Act (DMA) requires Google to open up eleven key features on Android, including microphone, camera, screen contents, and wake-word detection, to competing AI assistants such as ChatGPT and Perplexity. This would allow these rivals to function more seamlessly within the operating system, potentially giving them an edge over Google’s own Gemini assistant.

The orders also require Google to share anonymized search data with rival search engines and AI chatbots from January 2027 onwards. However, Google has argued that this would compromise user privacy, as it would involve sharing sensitive information without adequate safeguards in place.

Oliver Bethell, Google’s senior director for competition, described the potential consequences of mandating such a move as ‘irreversible harm to user privacy.’ The company’s public statement echoed these concerns, stating that the orders would force it to share people’s private search history without sufficient anonymization and weaken vital security protections on Android.

The EU Commission has responded by pointing out that it has developed specific safeguards for anonymizing search data, which are designed to strip identifying signals before any query reaches a competitor. However, Google remains opposed to these measures, arguing that they do not go far enough in protecting user privacy.

One of the key issues at stake is parity between Google’s Gemini assistant and its rivals. Currently, Gemini has privileged access to certain features on Android, such as being able to listen for wake words with the screen off or read what’s on the screen. The DMA aims to erase this kind of gap by requiring Google to grant similar access to competing AI assistants.

The case is significant because it marks a shift in focus from earlier EU actions against Google’s search and Android business, which were primarily concerned with distribution deals and default placement. This time around, the Commission is tackling raw system access – the underlying wiring that makes an assistant feel native rather than bolted on.

Google’s appeal does not pause the Commission’s order, as EU law requires that deadlines be met unless a court explicitly grants interim relief. The January 2027 deadline for sharing anonymized search data and the August 2027 deadline for opening up Android features to rival AI assistants remain in place, pending the outcome of Google’s appeal.

DuckDuckGo, a company known for its no-AI search page, has already sided with Brussels on this issue. A spokesperson for DuckDuckGo stated that the anonymization rules proposed by the Commission are sound and that Google’s appeal would only delay the inevitable – not change the outcome.

The spike in traffic to DuckDuckGo’s No AI search page following Google’s latest AI-heavy Search announcements is a telling indicator of growing demand for search products that make AI optional. While this does not yet threaten Google’s dominance, it highlights an emerging trend where users are seeking more control over their online experience and the data they share.

The Android order has significant implications for how people find and use AI on their devices. With roughly seven out of ten smartphones worldwide running on Android, granting rival AI assistants access to key features could fundamentally reshape the competitive landscape in this space.

If OpenAI, Perplexity or Anthropic can gain similar hooks into the operating system as Gemini currently enjoys, it would effectively level the playing field for default AI assistant on a phone. This is what makes Google’s appeal so critical – if they succeed, their Android advantage in AI could be preserved; but if they fail, it would become just another feature to compete on.

While Google’s privacy concerns are not entirely unfounded, some observers believe that its objections may also serve as a delay tactic rather than a genuine safety concern. The Commission has built safeguards into the anonymization scheme specifically designed to address these worries, including allowing Google to assess whether a given recipient poses a security risk before handing over data.

The case now enters the General Court’s process, which typically takes well over a year to produce a ruling. If Google loses this appeal, it can still seek further review with the European Court of Justice after that. Meanwhile, the clock set by the Commission remains ticking – Android 18 is due to ship with those eleven features opened up by next August unless Google convinces the court to freeze the order while the underlying case plays out.

A new class at the University of Utah aims to equip students with the skills to navigate the complex and rapidly evolving landscape of artificial intelligence. The course, which is part of the university’s One-U Responsible AI initiative, will explore the ethics surrounding AI design, regulation, and use. As AI becomes increasingly integrated into various aspects of life, concerns about its impact on society are growing, and this class arrives at a critical time to address these issues.

The University of Utah’s new AI Ethics class is co-developed by associate professor of Philosophy C. Thi Nguyen and Kahlert School of Computing professor Jeff Philips. The course will delve into various topics, including fairness and discrimination in automated prediction, authorship and ownership in generative media, environmental costs, replacement of work, and impacts on human flourishing. Students will engage with case studies, papers by AI ethicists, and participate in discussions to develop a deeper understanding of the ethical considerations surrounding AI development.

Nguyen emphasizes that superintelligent evil AI is only one of many concerns related to AI. He notes that job displacement, bias baked into systems, and systemic bias in AI hirings are all pressing issues that require attention. The class will address these topics through a combination of theoretical discussions and practical applications, enabling students to develop responsible approaches to AI development.

The course is designed for computer science majors who have taken CS 1410 or CS 1420, as well as philosophy majors without prerequisites. It’s also available as an elective for any undergraduate student and will be required for students in the new AI Major starting in Fall semester of 2026. The class aims to bridge the gap between technical expertise and philosophical considerations, recognizing that ethics is not just a secondary concern but an integral part of responsible AI development.

One of the inspirations behind this course was Harvard’s Embedded EthiCS initiative, which grew out of students’ requests for more focus on the moral implications of technology. The University of Utah has adopted a similar approach by co-developing materials and teaching with both philosophers and computer scientists. This ‘aggressive interdisciplinarity’ aims to make ethics an integral part of AI development, rather than treating it as an afterthought.

Nguyen notes that students are already asking pressing questions about the impact of AI on society. They want to know if relying too heavily on automation will lead to a loss of moral and social skills. The class will explore these concerns through real-world examples, such as how machine learning was used to predict successful student outcomes in universities but ended up being biased towards certain groups.

Another example discussed in the course is the COMPAS case, where a machine learning risk assessment tool used in the U.S. criminal justice system was found to be racist despite not being intentionally designed that way. Nguyen emphasizes that these issues are not just theoretical concerns but real-world problems that require attention and solutions. The class will tackle the concept of value-ladenness – technologies and methods that appear neutral on the surface but make very value-laden, politically charged decisions due to their design.

The course also addresses moral and social deskilling: what happens when people rely too heavily on automation and lose essential skills like critical thinking and hearing criticism. Nguyen uses ChatGPT as an example of how AI can be used in therapy settings but notes that it lacks the ability to challenge or critique, which is a crucial aspect of human interaction.

Nguyen believes that focusing solely on the dangers of superintelligent AI distracts from more pressing concerns like job displacement and social erosion. He hopes that this class will inspire students to design AI for good by incorporating ethics into code development rather than relying solely on regulations or international agreements.

The University of Utah’s new AI Ethics class is a timely response to growing concerns about the impact of artificial intelligence on society. By equipping students with the skills to navigate these complex issues, the course aims to inspire goodness and sensitivity in AI development.

Dodge AI Inc., a startup, has secured $2.65 million in seed funding from Accel and the Google AI Futures Fund, along with participation from Schema Ventures, New Build Ventures, Antler, and several angel investors from SAP SE’s ecosystem. This investment aims to disrupt the ‘enterprise firefighting’ industry, which is valued at around $600 billion. The industry has emerged specifically to maintain corporate software platforms such as SAP and Salesforce, which are incredibly complex and require extensive upkeep to ensure they function properly.

Enterprise software installations form the backbone of most large enterprises today. However, every platform is unique, with organizations adapting them over time through thousands of custom rules, company-specific configurations, and workarounds. This results in many companies hiring large systems integrators like Accenture Plc or IBM Corp., which assign teams of 20 to 50 people to handle support tickets, upgrades, and everyday operations.

Dodge AI co-founder and Chief Executive Rebhav Bharadwaj acknowledges that this model has not failed but is inefficient. Over the years, hundreds of fixes may go undocumented while customizations pile up, leading to technical debt compounding over time. According to Bharadwaj, companies often become reliant on systems integrators they hired to help them understand and operate their own software.

The startup’s solution involves developing a ‘control plane’ for enterprise software maintenance that is controlled by artificial intelligence agents. These agents can be overlaid across various platforms like SAP, Salesforce, or Microsoft Dynamics to analyze the deployment, grasp customizations, and configurations. The goal is to automate maintenance going forward.

Bharadwaj claims that Dodge AI’s control plane can identify system failures’ root causes, recommend fixes, and handle maintenance issues faster than human teams. Most importantly, it documents all changes, exceptions, and custom logic implemented, helping organizations understand their IT infrastructure. This approach aims to continuously improve and heal mission-critical systems so incidents are resolved quickly, technical debt is understood, and knowledge trapped in maintenance work becomes the foundation for future transformation.

At its core, Dodge AI understands enterprise software installations by creating an ‘exception intelligence context graph,’ which connects business processes, ERP configurations, and legacy systems. Bharadwaj emphasizes that these exceptions explain why separate warehouses may allocate inventory differently or one pricing rule overrides another. The startup’s agents work to capture and document all this logic, building a centralized source of truth guiding their maintenance work.

Dodge AI has already convinced several large enterprises to drop human consultants in favor of its AI agents. Although it doesn’t name these customers, the company is handling hundreds of queries per hour, resolving issues that would normally take hours or days to fix within minutes. For instance, it helped one customer identify a warehouse loading failure caused by an incorrect Goods Receipt Note across multiple systems and fixed the problem in mere minutes.

In another example, Dodge AI optimized a SAP inventory planning process for one of its customers, making it 132 times faster than before. Moving forward, the startup wants to do more than just help enterprises maintain their software; it also aims to accelerate digital transformations by understanding custom logic and preventing critical systems from being broken during modernization efforts.

Accel investor Prayank Swaroop notes that application maintenance is one of the largest yet least modernized segments in the enterprise technology industry. He believes Dodge AI’s approach addresses this issue, building a context layer needed before agents can safely operate in production.