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A freelance writer has spoken out about the indignity of being fired by ClickOut Media, only to see his name attached to subpar articles generated by an artificial intelligence system. Ben Touati, a Stockholm-based journalist, was let go from the company’s German operation in early 2026, but his name continued to appear under new articles published on various websites owned by ClickOut Media.

Touati told Press Gazette that he felt ‘slapped in the face’ when he discovered the AI-generated content bearing his name. The articles were riddled with errors and lacked the quality he had come to expect from his own work. He described them as ‘lazy, obviously slop’, adding that there was no sign of a human writer behind the text.

The situation is not an isolated incident for ClickOut Media. Earlier this year, the company faced controversy when it emerged that one of its articles on Videogamer had been written by a bogus AI journalist. Further investigation revealed that numerous other articles across its outlets were also generated by AI systems, some even featuring fake authors with AI-generated profile photos.

Touati first began working with ClickOut Media in early 2024 and was moved between different sites, including Techopedia, iGaming, and Esports Insider. He claimed to have resisted pressure from his managers to use AI for writing articles, saying that they would often remark on the impossibility of getting by without it. Employees were also shown a video demonstrating how to ‘humanize’ AI-written content.

Touati’s experience with ClickOut Media raises questions about the ethics of using AI-generated content and the importance of transparency in authorship. The company’s statement, which cited its use of AI-assisted content alongside human checks and edits, did little to address Touati’s concerns or provide clarity on why his name was used for subpar articles.

The incident highlights a broader issue within the publishing industry: the increasing reliance on AI-generated content without adequate safeguards in place. As more online publishers turn to AI tools, there is a growing need for clear guidelines and regulations to ensure that authorship remains transparent and accurate.

Touati was eventually able to achieve some recourse by filing a claim against ClickOut Media under the European Union’s General Data Protection Regulation. The company subsequently removed his name from the articles, replacing it with another writer’s name. However, the incident has left many wondering about the implications of AI-generated content on authorship and ethics in publishing.

The news is out: Tesla has begun dismantling the original Model S and Model X assembly line at its Fremont Factory, marking a significant shift in the company’s production strategy. The decommissioning process, which started just 46 days ago, will make way for the production of Optimus humanoid robots, with an ambitious goal of reaching 1 million units per year once fully ramped up.

The Model S entered production at Fremont in June 2012, followed by the Model X in 2015. Together they defined Tesla’s identity as a premium electric vehicle (EV) maker for over a decade. Production of both vehicles officially wound down around May 10, 2026, after Elon Musk announced the phase-out during Tesla’s Q4 2025 earnings call in January 2026.

The speed at which this changeover is happening is remarkable. Tesla says the decommissioning of the legacy assembly line took just 46 days – a pace that reflects how aggressively the company is prioritizing the transition to robot production. According to reporting from The Robot Report and Assembly Magazine, the full conversion of the space to Optimus production lines is expected to be completed in roughly four months from start to finish.

The Fremont site will host production of the third generation of Optimus, Tesla’s humanoid robot. Musk has publicly targeted 1 million units per year as annual capacity once the line is fully ramped up – a figure that would dwarf any humanoid robot program in existence. This target is ambitious for a product that hasn’t yet shipped commercially.

Tesla plans to begin external commercial sales of Optimus later in 2026, with a long-term price target of $20,000 to $30,000 per unit. Early-access units in 2026 are expected to be priced higher – reports suggest $50,000 to $80,000 – with broader volume availability slated for 2027. Initial output will be ‘extremely slow,’ according to Musk’s own words.

Each Gen 3 Optimus contains roughly 10,000 unique parts, and Tesla is standing up entirely new production processes for actuators, hands, and structural components that have no direct analogue in EV manufacturing. Limited production on the converted Fremont line is expected to begin in late July or August 2026.

The decommissioned space was specifically the legacy Model S/X general assembly line, which had been operating at a fraction of its potential volume for years as demand for the flagship sedans and SUVs tapered off relative to mass-market cars. For existing Model S and Model X owners, this doesn’t change service or parts support – Tesla has consistently maintained legacy vehicle service commitments even as it retires older product lines.

The Bigger Strategic Picture

Musk has told investors on multiple earnings calls that Optimus could eventually be worth more than Tesla’s entire automotive business. Whether or not that materializes, physically converting the birthplace of Tesla’s car business into a robot factory is the clearest signal yet that the company is acting on that thesis, not just talking about it.

The internal deployment of Gen 3 Optimus units at Fremont gives Tesla a rare feedback loop: the robots being built will help build the next generation of themselves. Over 1,000 Gen 3 Optimus units were already operational on Tesla’s own production floor as of early 2026, handling tasks like battery module assembly and EV pack loading.

The next question is how quickly Tesla can fill the empty floor space at Fremont with robot production. The company has publicly acknowledged that ramping up a brand-new product to seven-figure annual volumes will take years, not months – but the momentum is building.

Tech giant Apple has filed a lawsuit against AI firm OpenAI, accusing it of deliberately soliciting and stealing confidential information from current and former employees. The lawsuit alleges that OpenAI’s actions are part of a systematic effort to acquire sensitive data about unreleased technologies, processes, and products developed by Apple.

The complaint highlights the case of Chang Liu, a former senior electrical engineer at Apple who allegedly accessed Apple’s cloud file storage using a bug he discovered on his work-issued laptop. After leaving Apple and joining OpenAI, Liu reportedly celebrated the exploit in a message to a colleague still employed by Apple: ‘LOL, I found out I can access the [network storage], so funny.’

Liu is accused of accessing and downloading dozens of confidential files from Apple’s network while working on hardware projects for OpenAI. Many of these files were labeled as confidential, and it is alleged that Liu used this information to inform his work at OpenAI.

Tang Tan, an Apple veteran who worked on iPhone and Apple Watch development before joining OpenAI as chief hardware officer, is also named in the complaint. As part of his role at io Products – a company co-founded by Tan and Jony Ive (Apple’s former chief design officer) to serve as OpenAI’s dedicated hardware vehicle – it is alleged that Tan used internal codenames from Apple to elicit more information from potential job candidates who currently work for Apple.

Tan allegedly instructed these candidates to bring ‘actual parts’ (such as batteries, logic boards, and SIPs) for a ‘show and tell,’ suggesting that he was using this tactic to gather sensitive data. Furthermore, it is claimed that Tan circulated an internal offboarding document from Apple – either retained or obtained during his time at the company – to teach new OpenAI hires how to bypass exit security checks.

The lawsuit also accuses OpenAI of approaching trusted partners with confidential information about Apple’s technologies and products as they developed their own hardware device. One partner was allegedly shown a trade secret metal-finishing technique, which it is claimed was presented under false pretenses that the partner had permission from Apple to view this information.

Apple has accused OpenAI of misusing its internal codenames to gather sensitive data about unreleased technologies and products. The company claims that over 400 former employees are now employed by OpenAI – a significant loss for Apple, particularly given their current partnership with OpenAI on integrating ChatGPT into Apple’s products.

The lawsuit seeks to stop defendants from possessing or using Apple trade secrets, as well as the preservation and return of any materials taken. It also demands damages for losses caused by trade secret misappropriation and breach of contract. The case marks a significant escalation in tensions between two major players in the tech industry.

Financial technology company Revolut has integrated its standalone crypto exchange, Revolut X, with third-party artificial intelligence (AI) assistants. This move enables users to analyze markets, set alerts, and place trades using natural language prompts, marking a significant expansion of agentic trading capabilities in the cryptocurrency space.

The supported AI assistants include Claude, Gemini, OpenClaw, and Cursor. Users can link their accounts through a universal skill or command-line interface published on GitHub without requiring any coding expertise. This integration allows customers to request plain-text breakdowns of portfolio performance, pull real-time market data, set custom price alerts, place market and limit orders, and manage open positions directly within the Revolut X app.

Revolut’s head of product for crypto, Leonid Bashlykov, noted that AI agents provide faster workflows, smarter execution, and tighter integration with trading tools. However, users remain responsible for reviewing and approving all orders before execution, as Revolut does not endorse or guarantee third-party tools and is not liable for losses or erroneous trades stemming from AI errors.

Revolut has over 75 million retail customers and more than 16 million crypto users worldwide. The company’s cryptocurrency services are provided by Revolut Digital Assets Europe Ltd., licensed by the Cyprus Securities and Exchange Commission as a crypto asset service provider under MiCA.

The integration of AI assistants with Revolut X comes as several other crypto firms increasingly incorporate these tools into their trading workflows. For instance, Gemini has rolled out Agentic Trading, allowing users to connect Claude and ChatGPT directly to their accounts via the MCP open standard. Liquid also launched Co-Invest in May, bringing live trade execution into ChatGPT and Claude.

Revolut’s crypto exchange, Revolut X, was initially launched as a desktop-only platform for UK retail customers in May 2024 before expanding to 30 European markets that November and adding mobile app support for UK and EEA users in March 2025. The company’s move towards integrating AI assistants with its exchange highlights the growing importance of data analysis tools in cryptocurrency trading.

The use of natural language prompts allows users to describe complex trading ideas, such as a grid strategy on bitcoin over the last 90 days, and receive historical performance, risk metrics, and optimization results directly within the Revolut X app. This feature streamlines the trading process by providing users with real-time data analysis capabilities without requiring them to navigate multiple platforms or tools.

Revolut’s decision to integrate AI assistants with its exchange underscores the company’s commitment to leveraging cutting-edge technology in the cryptocurrency space. As more crypto firms explore agentic trading and AI-powered trading workflows, Revolut is positioning itself at the forefront of this trend by providing users with a comprehensive platform for analyzing markets and executing trades using natural language prompts.

Pension administrators in North America are getting a boost thanks to a partnership between Procentia and Wipro. The two firms have joined forces to build an administration platform that leverages generative-AI assistants to automate tasks and make work easier.

The new platform is designed for insurers and public pension plans across the continent, with a focus on pension risk transfer and AI-assisted member services. By integrating their technologies, Procentia’s IntelliPen platform will be paired with Wipro’s operations and consulting services through its Wipro Intelligence offering.

Wipro brings to the partnership a strong history of working with insurers, wealth managers, and retirement providers on modernizing core operations. Their AI-powered operations aim to improve efficiency, advance digital transformation, and reduce risk for pension plans.

Procentia’s IntelliPen is a system that quickly processes large volumes of data while offering self-service tools to administrators and retirees. This platform will serve as the foundation for the new partnership’s administration technology.

The companies have identified key areas where their combined offering can make an impact: commercial off-the-shelf administration platforms built for high-volume, complex plans; end-to-end support for pension risk transfer; generative-AI administration services that assist administrators in retrieving information and making decisions. These are just a few examples of how the partnership will help.

Wipro will handle the operational side of things, simplifying processes, automating effort, and consistently meeting service commitments. According to Jason Gopaul, Procentia’s chief executive officer for North America, clients will run on Procentia’s technology platform supported by Wipro’s work.

The partnership aims to ‘industrialize pension risk transfer operations’ as Ritesh Talapatra, senior vice-president of capital markets and insurance at Wipro, put it. This collaboration also strengthens their joint approach to the retirement and life insurance market.

Artificial intelligence performance is often measured across three key dimensions: accuracy, throughput, and interactivity. While high accuracy is essential, it’s equally important to consider the model’s ability to process large amounts of data quickly and respond promptly to user inputs. In practical systems, these three aspects are intertwined, making it crucial for developers to optimize them simultaneously.

Whistleblower Frances Haugen’s leaked documents from Meta in 2020 revealed a disturbing statistic: the company’s algorithms designed to detect terrorist content incorrectly deleted nonviolent Arabic-language content at an alarming rate of 77 percent. This was not an isolated incident, as Meta’s own transparency report later that year showed similar findings. Five years on, researchers in the region confirm that overzealous moderation remains a persistent problem, with few solutions in sight.

The issue is particularly pronounced in less-resourced languages, where automated systems are struggling to keep pace. A 2025 report from the Center for Democracy and Technology found that labeled datasets in certain languages and dialects, such as Maghrebi Arabic and Kiswahili, contain inconsistencies, bias, and inaccuracies due to a lack of annotators who speak these languages fluently. An investigation into ChatGPT’s outputs in several low-resource languages highlights the depth of this problem.

Language disparities are just one concern among many when it comes to automated moderation. The systemic suppression of content from Palestine is another example, as well as the repeated misclassification of LGBTQ+ content as adult or explicit material. These varied examples demonstrate the risks of relying too heavily on automated systems and highlight the need for stronger safeguards.

Automated systems can process vast amounts of content at a scale that humans cannot match, potentially enabling better moderation and alleviating the psychological load on moderators who must view disturbing content. However, these systems also reproduce existing biases, struggle to understand context, and often make mistakes that disproportionately affect vulnerable communities. As Rachel Griffin noted in 2023, ‘Perfectly accurate moderation is not only technically out of reach but intrinsically impossible.’

Despite the intrinsic flaws of automated systems, there are steps companies, policymakers, and civil society can take to ensure they operate in ways that respect human rights and minimize predictable harms. If companies continue to rely on automation for content moderation – as seems likely – then accountability must evolve alongside these technologies.

One way this evolution can begin is by committing to the Santa Clara Principles 2.0. These principles, first outlined in 2020 and re-launched in 2021 after international input, reflect the needs and expectations of the global community and specifically address automation. The Foundational Principle states that companies should integrate human rights and due process considerations at all stages of content moderation, publishing information on how this integration is made.

Companies should only use automated processes to identify or remove content when there is sufficient confidence in their quality and accuracy. Users must also have clear and accessible methods for obtaining support if they believe their content has been wrongfully removed. Drawing on the Santa Clara Principles 2.0, international human rights standards, and years of research documenting the shortcomings of automated moderation, we propose eight recommendations for policymakers thinking about regulation and companies deploying AI-assisted content moderation systems.

These recommendations include ensuring that automated technologies complement, rather than replace, human moderators. Companies must be transparent about when and how automation is used in content decisions, regularly auditing their systems for bias – particularly in low-resource languages and conflict zones. Users should have the ability to appeal moderation decisions, providing context if necessary, with appeals promptly evaluated by human moderators.

Companies should also assess the human rights impact of their moderation decisions, issuing public statements on the results. If they rely on third-party vendors, companies must carefully audit those vendors for compliance with these principles. Lawmakers and policymakers should avoid promoting legislation that effectively or explicitly mandates automated moderation systems, instead focusing on creating a regulatory framework that prioritizes transparency and accountability.

The design and oversight of content moderation systems must respond to the concerns of policymakers, civil society, independent researchers, and affected communities. This is not just a technical problem for engineers and product teams to solve; it requires a comprehensive approach that addresses the complex issues at play.

The University of Chicago Law School has announced a new policy to ban electronic devices from the classroom for first-year students as part of its strategy to address the rise of artificial intelligence. The move is set to take effect during the upcoming fall semester, with some limited exceptions allowed.

As part of a broader plan to adapt to the changing landscape of AI, the school will be piloting a coordinated approach to classroom and examination policies for the core 1L curriculum starting in the 2026-2027 academic year. This new policy prohibits students from using laptops, tablets, or phones during classes across all first-year sections.

The University of Chicago Law School prides itself on producing graduates who are well-prepared to be excellent lawyers. To ensure this remains true, the school has consistently innovated its curriculum over time. The current moment is no exception, with the introduction of this new policy marking a significant shift in the way students learn and interact during classes.

The ban on electronic devices aims to promote focused learning and minimize distractions in the classroom environment. While there will be some exceptions for specific purposes, such as accessibility or academic requirements, these instances are expected to be rare.

The memory shortage is having far-reaching consequences for the tech industry, and even Google isn’t immune. The company’s own research arm, Google DeepMind, is feeling the pinch due to a lack of available memory chips.

The issue lies in the supply chain, which is heavily constrained. According to Demis Hassabis, CEO of Google DeepMind, ‘the whole supply chain’ for memory chips is under strain. This means that companies are struggling to get their hands on the necessary components to power their AI systems.

Hassabis pointed out that even with its own Tensor Processing Units (TPUs), which it produces in-house and leases to external customers through its cloud, Google still has to navigate the competitive memory market. ‘It still comes down to a few suppliers of a few key components,’ he said.

The three main suppliers of memory chips are Samsung, Micron, and SK Hynix. These companies are struggling to meet demand from AI hyperscalers like Google without sacrificing their longtime electronics customers. The problem is further complicated by the fact that AI companies require a different type of memory chip than PC manufacturers do.

Large language model producers need high-bandwidth memory (HBM) chips, which are in short supply. This shortage has led to increased costs and prices for products across the industry. Companies like Google are having to get creative with their spending on AI infrastructure and chips, but it’s not clear whether this will be enough to meet demand.

Google itself is projecting capital expenditures of $175 billion to $185 billion for 2026. This significant investment in AI infrastructure and chips suggests that the company is taking steps to mitigate the effects of the memory shortage. However, even with its own resources, Google may not be able to escape the constraints imposed by the supply chain.

The issue has far-reaching implications for businesses looking to leverage AI tools for their operations. As Hassabis noted, ‘you need a lot of chips to be able to experiment on new ideas’ and drive innovation in the field. The memory shortage is creating an ‘AI choke point,’ where companies are struggling to access the necessary resources to power their research and development efforts.

The situation highlights the complexities of the tech industry’s supply chain and the challenges that come with scaling up AI systems. As demand for memory chips continues to outstrip supply, it remains to be seen whether the industry will find a way to address this issue or if companies like Google will have to adapt their strategies in response.

Muse Image, Meta’s artificial intelligence image generator, has a new feature that’s raising eyebrows. It lets anyone take public Instagram photos and use them in AI-generated images without asking the owner first.

The tool is part of Meta’s AI chatbot, which can understand detailed prompts and even edit generated images using sketches or annotations. This means any public photo on Instagram could be used to create derivative works based on someone else’s image – with no permission needed.

Meta says users under 18 or on private accounts won’t be affected by this feature. But critics are sounding off about the lack of consent required for using public photos in AI generators.

Some people have called it ‘a privacy landmine waiting to detonate.’ They’re highlighting the risks of allowing AI to use publicly available images without permission. It’s a concerning development, especially given how easily accessible these tools are becoming.

For those who want to opt out, there are some steps they can take. The easiest option is to switch their account from public to private – this will exclude it from being used in Muse Image. Alternatively, users can follow the instructions below to prevent others from using their public photos in AI generators.

To do that, go into Instagram settings and activity (the three little bars on the top right of your profile). Scroll down to ‘sharing and reuse’ – there should be a section labeled ‘Allow people to create with and reuse your content.’ This includes two separate toggles for posts and reels. Toggle off both options to prevent others from using your public photos in AI generators.

Meta hasn’t provided any information on how users can opt out of this feature beyond putting their account private or following these steps. That’s left many feeling uneasy about the lack of transparency around user consent – and what it means for their online security.

Travel agents know all too well the challenges of building multicountry itineraries by hand. It’s a slow, messy, and inconsistent process that can be frustrating for both agents and clients. Vio Travel has been working to address this problem over the past few years, combining direct supplier contracts across 15 destinations with a platform that lets agents design and price trips in real-time.

The company recently became the first destination management company (DMC) in Asia to be certified under the Preferred by Nature Standard for Sustainable Travel Activities. This certification is recognized by the Global Sustainable Tourism Council and was achieved after an independent audit found Vio’s Indonesia operations 100% compliant across responsible management, people, nature, and climate.

We spoke with co-CEOs Dominik Schaufler and Michael Lynden-Bell to learn more about their platform and its capabilities. They explained that the goal is not to replace human judgment but rather to automate as much of the process as possible, freeing up agents to focus on what matters most – providing exceptional travel experiences.

For Schaufler, the starting point was recognizing that too many tour operators are still building itineraries manually. He noted that a significant number of tour operators are doing this the slow way and that Vio set out to remove that complexity. By giving agents access to local suppliers directly contracted across 15 destinations, they can deliver proposals faster, impress clients sooner, and close deals at a higher rate.

Lynden-Bell framed the change in behavior as friction removal rather than a rip-and-replace of existing relationships. He explained that Vio’s platform allows agents to build and price itineraries in minutes instead of waiting days for quotes or requesting availability. The pitch itself, he said, isn’t about technology but convenience, speed, and consistency.

When asked what the platform is doing differently from a traditional DMC’s product team, Lynden-Bell pushed back on the idea that software replaces people. He noted that the real advantage of Vio’s platform is its ability to process thousands of variables simultaneously and do it instantly – something technology can’t replicate with human judgment.

The speed at which agents can build and price itineraries is one of the most significant benefits of Vio’s platform. Lynden-Bell explained that there’s no queue, allowing an agent in London to build a 12-day multidestination itinerary without waiting for Bangkok to open. However, he was clear about the limits – technology can’t replace human judgment on non-standard situations.

Schaufler put a number on the automation ceiling, stating that technically they could automate 99% of the booking journey and are close to achieving it. But the 1% that matters most is human judgment. He emphasized that no algorithm captures earned instinct and that Vio’s goal is to protect this layer rather than shrink it.

When asked about getting hundreds of suppliers online, Schaufler noted that contracting directly with local suppliers across 15 destinations sounds clean on a slide but was actually a complex process. Many operators work with limited resources and aren’t built for speed – the opposite of what Vio Travel stands for.

The real bottleneck wasn’t willingness; it was infrastructure. Suppliers had to navigate technology literacy and bandwidth issues, which made getting pricing standardized and expectations aligned challenging. What moved the needle was having an in-destination team on the ground who could guide suppliers step-by-step.

Lynden-Bell emphasized that Vio’s recent Preferred by Nature certification wasn’t a marketing move but rather a reflection of their commitment to sustainability. The company applies this filter to every supplier decision, and if they don’t meet the bar, they don’t qualify as a partner. This is a firm line – not a purity test with no way back in.

Schaufler added that Vio doesn’t walk away from suppliers who genuinely want to improve but rather works with them to implement concrete sustainability initiatives. He emphasized that growth alone isn’t the objective; they want to build a business that’s scalable, profitable, and recognized for consistently delivering high-quality travel experiences.

When asked about competing with companies like Booking.com and Klook, Lynden-Bell noted that Vio doesn’t see itself as directly competing with them. They solve different problems – Vio focuses on multiday, multicountry travel across Asia Pacific involving complex logistics. This is where an agent needs white-label proposals, direct supplier relationships, and someone to call when something goes wrong.

Lynden-Bell was candid about the tech layer not being hard to defend but acknowledged that replicating the relationships, product curation, and operational infrastructure across 15 destinations overnight would be much harder. He noted that technology can help monitor quality, collect feedback, and improve processes but delivering exceptional travel experiences still depends on people.

The hardest part of Vio’s platform is encoding nuance – things like a change in a local operator’s team or a quality issue they’ve flagged but haven’t fully resolved. Lynden-Bell emphasized that technology can help see problems faster but doesn’t fix them on its own.

Looking ahead, Schaufler said Vio is building toward a world where any agent with a fair knowledge of a destination can design a complete, tailored trip for their client in minutes – no friction, no ceilings, and no waiting. He noted that curation stays human even as the process gets faster, and the quality and curation still come from exceptional handpicked products.

Lynden-Bell pointed to fixed-price packages, wholesale distribution, groups, and API-driven products as areas of opportunity for Vio’s growth. However, he emphasized that their priority is building a sustainable business with strong foundations, great people, and lasting partnerships – not just focusing on growth at any cost.

Attention is an essential component of the Transformer architecture, a fundamental building block in many modern deep learning models. However, attention operations can be computationally expensive and memory-intensive, making them a prime target for optimization. In this article, we’ll delve into PyTorch’s implementation of attention and explore various techniques to improve its performance.

Researchers are exploring the potential of neuro-symbolic artificial intelligence in medicine, where long-standing limitations in data-driven inference and explicit clinical knowledge have hindered progress. This new approach aims to integrate these two aspects to improve safety, transparency, and accountability in biomedical AI systems.

A key challenge in biomedical AI is the need for more accurate models of human biology and disease. Current approaches often rely on large datasets and machine learning algorithms, but these can be prone to errors and biases. Neuro-symbolic AI seeks to address this by combining symbolic reasoning with data-driven inference, allowing for a more nuanced understanding of complex biological systems.

The concept of neuro-symbolic AI has been explored in various studies published over the past few years. One notable example is a 2025 paper on personalized sepsis treatments that used a graph-centric architecture integrating clinical knowledge and patient data to improve treatment outcomes. Another study from 2023 demonstrated the potential of neurosymbolic AI for enhanced reasoning in small language models.

Researchers have been contributing to this field, presenting their work at conferences such as IEEE/ACM International Workshop on Neuro-Symbolic Software Engineering (NSE) and Neural-Symbolic Learning and Reasoning. Their papers cover a range of topics, including metacognitive AI frameworks and neurosymbolic approaches for enhanced reasoning in small language models.

The development of neuro-symbolic AI has received funding from various agencies, including the National Natural Science Foundation of China, which provided grant number T2525004 to support one study. Researchers involved have also received backing from other institutions and organizations.

Recent research suggests that this new approach shows promise in improving safety, transparency, and accountability in biomedical AI systems. By integrating data-driven inference with explicit clinical knowledge, neuro-symbolic AI can provide more accurate models of human biology and disease, potentially leading to better treatment outcomes and improved patient care.

The development of neuro-symbolic AI is a collaborative effort involving researchers from various institutions around the world. The authors of this paper have worked together to review and edit their work, ensuring that it meets high standards of quality and accuracy. Their research has been published in prominent journals such as Nature Biomedical Engineering.

German telecommunications giant Deutsche Telekom is undergoing a fundamental transformation, leveraging artificial intelligence (AI) to revolutionize customer service, network operations, and employee workflows. The company’s ambitious goal: to become one of the world’s first AI-native telcos.

With over 300 million customers across Europe and the United States, managing vast customer service operations and complex network infrastructure is a daunting task. However, Deutsche Telekom sees an opportunity beyond productivity gains with accelerated generative AI capabilities.

The leadership team views AI as a fundamental transformation of how decisions are made, how customer journeys are designed, and how telecommunications services are delivered. Rather than treating AI as another software rollout, the company aims to redesign the work itself.

We spoke with Jonathan Abrahamson, Chief Product & Digital Officer at Deutsche Telekom, about their journey towards becoming an AI-native telco. He emphasizes that this transformation combines top-down leadership with broad employee adoption and experimentation.

The first phase focused on empowering employees with ChatGPT Enterprise, encouraging them to experiment with the tool in various ways. The result was rapid adoption, as employees quickly grasped the potential of AI in their personal lives and applied it to their work.

Deutsche Telekom began redesigning critical customer-facing workflows simultaneously, starting with customer care. Abrahamson believes that AI-powered customer service is still in its early stages but holds significant medium- and long-term potential.

As these systems gain more context, learn from every interaction, and eliminate common frustrations like handoffs and wait times, they can ultimately outperform traditional support models in certain scenarios. This thinking extends beyond customer service into the core communications experiences customers use daily.

Working closely with OpenAI and other companies, Deutsche Telekom is bringing AI directly into customer interactions through capabilities such as live translation, in-call assistants, and post-call summaries without requiring new applications from customers.

Beyond customer interactions, AI is increasingly embedded into network operations. The company uses AI with various partners to optimize mobile network performance in real-time, adjusting resources dynamically as demand shifts throughout the day.

One of Deutsche Telekom’s most ambitious initiatives focuses on the future of voice communications. For decades, telco providers focused on connecting people, but Abrahamson believes AI creates an opportunity to fundamentally reinvent the voice experience itself.

Using several models, Deutsche Telekom is exploring capabilities including real-time translation, intelligent call assistance, and automated summarization. These innovations move AI out of standalone applications and into communication channels customers use daily.

The company sees this as part of a broader mission to democratize access to AI, generating real added value for people and businesses without requiring specialized devices or technical expertise.

Deutsche Telekom’s transformation combines several key strategies: treating AI transformation as an operating-model redesign rather than technology deployment; making leaders accountable for driving process change; focusing on redesigning workflows rather than simply adding AI to existing work;

Broad employee experimentation is also crucial, balancing top-down direction with bottom-up adoption. The company builds toward AI-native operations one business process at a time, starting with high-volume customer interactions where AI can improve both experience and efficiency.

Data protection, sovereignty, and security are always kept in mind to maintain customer trust. Employees have access to AI tools early on to accelerate learning and adoption. Core workflows that can be redesigned rather than simply automated are identified for this purpose.

The next phase is focused on bringing AI directly into the communications experiences customers use every day through capabilities such as real-time translation, intelligent call assistance, and automated summarization.

With over 300 million customers, Deutsche Telekom sees an opportunity to make AI accessible through the networks people already rely on. For them, becoming AI-native is not a future vision but a transformation reshaping telecommunications today.

A team of researchers has made a major breakthrough in developing an AI-powered navigation system that lets robots move through complex spaces using just one camera. The model, called Robostral Navigate, performed exceptionally well on tests with unknown environments and did better than systems that used multiple sensors while being more efficient. This advance is a significant step forward in creating a unified approach to artificial intelligence for robotics.