Next week, the former president of “Grant Theft Auto” maker Rockstar North launches his first title since leaving the Take-Two Interactive-owned video game developer and opening his own studio, Build A Rocket Boy: the AAA narrative-driven action-adventure thriller “MindsEye.” Published by IOI Partners, the team behind the “Hitman” franchise, the Unreal Engine 5-built game will debut June 10 across PlayStation 5, Xbox Series X and S, and on PC via Steam and Epic Games Store with a $59.99 price tag for the standard edition. Related Stories Set in the near-futuristic city of Redrock, “MindsEye” puts players into the role of Jacob Diaz, a former soldier haunted by fragmented memories from his mysterious MindsEye neural implant, as he uncovers a conspiracy involving rogue AI, corporate greed, an unchecked military, and a threat so sinister that it endangers the very survival of humanity. Popular on Variety But the base story isn’t the biggest draw for “MindsEye,” which includes Build A Rocket Boy’s proprietary Game Creation System, that enables players to, well, “craft anything in their minds eye.” Per the studio, “Players can craft their own experiences using all of the ‘MindsEye’ assets, creating everything from custom missions to entirely new scenarios within the game’s expansive, richly detailed world. Whether you’re designing a high-speed chase through Redrock’s bustling cityscapes or a stealth mission in its industrial outskirts, it is designed to be intuitive and easy to use, ensuring that players of all skill levels can bring their imagination to life.” Benzies’ Edinburgh-based Build A Rocket Boy has promised “fresh premium content” will rollout monthly for the game, including regular releases of new missions, challenges and game assets. While “MindsEye” is the first title from Benzies since he launched BARB after leaving Rockstar in 2016 (Benzies was the lead “Grand Theft Auto” developer across the third through fifth games in the franchise, as well as “Grand Theft Auto Online,” and was in a legal battle with parent company Take Two over unpaid royalties from 2016 until 2019), it’s just step one in the prolific producer’s plan to shake up the gaming industry. “At Build A Rocket Boy, our vision goes far beyond a single title,” Benzies told Variety. “‘MindsEye’ is the first episode and central story around which ever-expanding interconnected episodes will span. We’re already working on future episodes, which will introduce alternate realities while maintaining it’s core themes of hope, redemption, and the intrigue of civilizations past and future, drawing from the lore and multiverse concepts.” See Variety‘s full interview with Benzies below, including the inevitable comparisons that will be drawn between “MindsEye” and the aesthetic of the “GTA” franchise, and his hopes for Rockstar Games’ highly anticipated and much-delayed “GTA 6.” Where did the concept for “MindsEye” come from? I pull a lot of inspiration from the real world. Watching the actions of humans – their foibles and their virtues. Watching the advancement of technology and how we adapt, or indeed, do not adapt. We’ve been moving to an automated world for many years now, and the impact on humans, especially with recent advancements in AI, which serves as good fodder for a story and even better for a video game. I think we all have this little nagging feeling about how humans and AI will blend together in the future—will it go smoothly, or will it turn sinister? We’re fans of all different types of media, and we’ve drawn influence from cinematic visionaries like Ridley Scott, Paul Greengrass, Christopher Nolan, and J.J. Abrams, and films like “The Bourne Identity,” “Memento,” and TV series “Lost” — they’re all exploring memory, perception, and control in their own ways. So, while we nod to those influences here and there, we wanted to build something that feels fresh, grounded in today’s world, but still asking the kinds of questions that have always made this genre powerful. With your “GTA” roots, obvious comparisons are already being drawn between the style and aesthetic of that franchise and “MindsEye.” Comparisons will always be made—it’s the way human beings pigeonhole concepts. But “MindsEye” isn’t built to fit into anyone else’s box. Many games share the same core elements: cars, guns, cities, and charismatic characters, and differentiation is even tougher in today’s entertainment landscape. Streaming, social media, and on-demand binge culture have fractured attention spans, and consumer mindshare is a brutal battlefield for all IP. Our industry continues to celebrate each other’s breakthroughs, and I’m proud that our collective innovation is advancing the medium of gaming, even if our paths diverge. As an independent studio we have the freedom to break ground in experimental new ways and the challenge is balancing innovation with familiarity—too much “new” risks alienating fans, too much “same” feels stale. It’s about nailing what makes your game’s world feel alive and urgent. “MindsEye” is about consequence and connection—it’s cinematic, reactive, and meant to feel like a world you’re not just playing in, but able to create in it too. We’re excited to see what they’ve crafted with “GTA VI ,” and I can’t wait to play it as a consumer for the first time. They’re always delivering something new, unique and at a scale that very few can pull off. What does MindsEye represent in BARB’s larger vision and long-term strategy? Are you plotting this out as a multi-game franchise or your first standalone? At Build A Rocket Boy, our vision goes far beyond a single title. “MindsEye” is the first episode and central story around which ever-expanding interconnected episodes will span. We’re already working on future episodes, which will introduce alternate realities while maintaining it’s core themes of hope, redemption, and the intrigue of civilizations past and future, drawing from the lore and multiverse concepts. It’s the future of entertainment to allow active participation so players feel like they have agency and can immerse themselves in our world as they want to. We are introducing three products in one game that will revolutionize AAA-quality interactive gaming and storytelling: “MindsEye” narrative story, Play.MindsEye, and Build.MindsEye. In our tightly crafted action-noir, “MindsEye” narrative story we have rips in time accessed through portals at strategic points throughout the game – so while you play as Jacob Diaz on his personal journey, players can also explore side stories and delve deeper into the backstories of characters they encounter along the way. In this way we are delivering companion content at the same time as the anchor content, weaving a rich narrative tapestry which will continue to evolve and expand giving greater depth to characters so you understand their personality and motivations. How do digital products Play.MindsEye (formerly named Arcadia) and Build.MindsEye (formerly Everywhere) tie in to plans for “MindsEye” and what BARB wants to offer gamers? In this new era of entertainment, where streaming platforms, boom-and-bust games, and an on-demand culture dominate, we’re pushing things in a new direction—with an interface that simplifies how we consume not just games, but all forms of entertainment. Consumers are moving away from 2D browsing into fully 3D, immersive experiences. Put simply, we’re shifting from passive interaction to active participation. As with all new products, things evolve. Arcadia was originally envisioned as our creation platform, but as we continued developing “MindsEye” and building out BARB’s ecosystem, it naturally grew into something more focused— Play.MindsEye and Build.MindsEye. Play delivers cinematic, high-intensity gameplay with missions and maps that constantly evolve. Build gives players intuitive tools to create their own content—no technical skills required, just imagination and intent. For BARB to fully realize our vision, we had to beta test our creation system with a community of builders in real-time and started with Everywhere while we were in stealth mode developing MindsEye. How did you settle on IOI as publishing partner? We’ve always found the way IOI handled the “Hitman” franchise interesting. They are one of the few publishers that have taken their single-player IP and increased their player count and amplified their community culture over time. From a technology point of view, their one executable approach for all of their content is very smart, and we always planned to have a similar approach, which encouraged us to join forces. This interview has been edited and condensed.

Editor’s note: This blog, originally published on October 28, 2024, has been updated. Financial losses from worldwide credit card transaction fraud are projected to reach more than $403 billion over the next decade. The new NVIDIA AI Blueprint for financial fraud detection can help combat this burgeoning epidemic — using accelerated data processing and advanced algorithms to improve AI’s ability to detect and prevent credit card transaction fraud. Launched this week at the Money20/20 financial services conference, the blueprint provides a reference example for financial institutions to identify subtle patterns and anomalies in transaction data based on user behavior to improve accuracy and reduce false positives compared with traditional methods. It shows developers how to build a financial fraud detection workflow by providing reference code, deployment tools and a reference architecture. Companies can streamline the migration of their fraud detection workflows from traditional compute to accelerated compute using the NVIDIA AI Enterprise software platform and NVIDIA accelerated computing. The NVIDIA AI Blueprint is available for customers to run on Amazon Web Services, with availability coming soon on Dell Technologies and Hewlett Packard Enterprise. Customers can also use the blueprint through service offerings from NVIDIA partners including Cloudera, EXL, Infosys and SHI International. Businesses embracing comprehensive machine learning (ML) tools and strategies can observe up to an estimated 40% improvement in fraud detection accuracy, boosting their ability to identify and stop fraudsters faster and mitigate harm. As such, leading financial organizations like American Express and Capital One have been using AI to build proprietary solutions that mitigate fraud and enhance customer protection. The new AI Blueprint accelerates model training and inference, and demonstrates how these components can be wrapped into a single, easy-to-use software offering, powered by NVIDIA AI. Currently optimized for credit card transaction fraud, the blueprint could be adapted for use cases such as new account fraud, account takeover and money laundering. Using Accelerated Computing and Graph Neural Networks for Fraud Detection Traditional data science pipelines lack the compute acceleration to handle the massive data volumes required for effective fraud detection. ML models like XGBoost are effective for detecting anomalies in individual transactions but fall short when fraud involves complex networks of linked accounts and devices. Helping address these gaps, NVIDIA RAPIDS — part of the NVIDIA CUDA-X collection of microservices, libraries, tools and technologies — enables payment companies to speed up data processing and transform raw data into powerful features at scale. These companies can fuel their AI models and integrate them with graph neural networks (GNNs) to uncover hidden, large-scale fraud patterns by analyzing relationships across different transactions, users and devices. The use of gradient-boosted decision trees — a type of ML algorithm — tapping into libraries such as XGBoost, has long been the standard for fraud detection. The new AI Blueprint for financial fraud detection enhances the XGBoost ML model with NVIDIA CUDA-X Data Science libraries including GNNs to generate embeddings that can be used as additional features to help reduce false positives. The GNN embeddings are fed into XGBoost to create and train a model that can then be orchestrated. In addition, NVIDIA Dynamo-Triton, formerly NVIDIA Triton Inference Server, boosts real-time inferencing while optimizing AI model throughput, latency and utilization. NVIDIA CUDA-X Data Science and Dynamo-Triton are included with NVIDIA AI Enterprise. Leading Financial Services Organizations Adopt AI During a time when many large North American financial institutions are reporting online or mobile fraud losses continue to increase, AI is helping to combat this trend. American Express, which began using AI to fight fraud in 2010, leverages fraud detection algorithms to monitor all customer transactions globally in real time, generating fraud decisions in just milliseconds. Using a combination of advanced algorithms, one of which tapped into the NVIDIA AI platform, American Express enhanced model accuracy, advancing the company’s ability to better fight fraud. European digital bank bunq uses generative AI and large language models to help detect fraud and money laundering. Its AI-powered transaction-monitoring system achieved nearly 100x faster model training speeds with NVIDIA accelerated computing. BNY announced in March 2024 that it became the first major bank to deploy an NVIDIA DGX SuperPOD with DGX H100 systems, which will help build solutions that support fraud detection and other use cases. And now, systems integrators, software vendors and cloud service providers can integrate the new NVIDIA blueprint for fraud detection to boost their financial services applications and help keep customers’ money, identities and digital accounts safe. Explore the NVIDIA AI Blueprint for financial fraud detection and read this NVIDIA Technical Blog on supercharging fraud detection with GNNs. Learn more about AI for fraud detection by visiting the AI Summit at Money20/20, running this week in Amsterdam. See notice regarding software product information.

Paradromics, a brain-computer interface startup, inserted its brain implant in a person—briefly—in an early test of its technology.

How Deepfakes Are Created Generative AI models enable the creation of highly realistic fake media. Most deepfakes today are produced by training deep neural networks on real images, video or audio of a target person. The two predominant AI architectures are generative adversarial networks (GANs) and autoencoders. A GAN consists of a generator network that produces synthetic images and a discriminator network that tries to distinguish fakes from real data. Through iterative training, the generator learns to produce outputs that increasingly fool the discriminator¹. Autoencoder-based tools similarly learn to encode a target face and then decode it onto a source video. In practice, deepfake creators use accessible software: open-source tools like DeepFaceLab and FaceSwap dominate video face-swapping (one estimate suggests DeepFaceLab was used for over 95% of known deepfake videos)². Voice-cloning tools (often built on similar AI principles) can mimic a person’s speech from minutes of audio. Commercial platforms like Synthesia allow text-to-video avatars (turning typed scripts into lifelike “spokespeople”), which have already been misused in disinformation campaigns³. Even mobile apps (e.g. FaceApp, Zao) let users do basic face swaps in minutes⁴. In short, advances in GANs and related models make deepfakes cheaper and easier to generate than ever. Diagram of a generative adversarial network (GAN): A generator network creates fake images from random input and a discriminator network distinguishes fakes from real examples. Over time the generator improves until its outputs “fool” the discriminator⁵ During creation, a deepfake algorithm is typically trained on a large dataset of real images or audio from the target. The more varied and high-quality the training data, the more realistic the deepfake. The output often then undergoes post-processing (color adjustments, lip-syncing refinements) to enhance believability¹. Technical defenses focus on two fronts: detection and authentication. Detection uses AI models to spot inconsistencies (blinking irregularities, audio artifacts or metadata mismatches) that betray a synthetic origin⁵. Authentication embeds markers before dissemination – for example, invisible watermarks or cryptographically signed metadata indicating authenticity⁶. The EU AI Act will soon mandate that major AI content providers embed machine-readable “watermark” signals in synthetic media⁷. However, as GAO notes, detection is an arms race – even a marked deepfake can sometimes evade notice – and labels alone don’t stop false narratives from spreading⁸⁹. Deepfakes in Recent Elections: Examples Deepfakes and AI-generated imagery already have made headlines in election cycles around the world. In the 2024 U.S. primary season, a digitally-altered audio robocall mimicked President Biden’s voice urging Democrats not to vote in the New Hampshire primary. The caller (“Susan Anderson”) was later fined $6 million by the FCC and indicted under existing telemarketing laws¹⁰¹¹. (Importantly, FCC rules on robocalls applied regardless of AI: the perpetrator could have used a voice actor or recording instead.) Also in 2024, former President Trump posted on social media a collage implying that pop singer Taylor Swift endorsed his campaign, using AI-generated images of Swift in “Swifties for Trump” shirts¹². The posts sparked media uproar, though analysts noted the same effect could have been achieved without AI (e.g., by photoshopping text on real images)¹². Similarly, Elon Musk’s X platform carried AI-generated clips, including a parody “Ad” depicting Vice-President Harris’s voice via an AI clone¹³. Beyond the U.S., deepfake-like content has appeared globally. In Indonesia’s 2024 presidential election, a video surfaced on social media in which a convincingly generated image of the late President Suharto appeared to endorse the candidate of the Golkar Party. Days later, the endorsed candidate (who is Suharto’s son-in-law) won the presidency¹⁴. In Bangladesh, a viral deepfake video superimposed the face of opposition leader Rumeen Farhana onto a bikini-clad body – an incendiary fabrication designed to discredit her in the conservative Muslim-majority society¹⁵. Moldova’s pro-Western President Maia Sandu has been repeatedly targeted by AI-driven disinformation; one deepfake video falsely showed her resigning and endorsing a Russian-friendly party, apparently to sow distrust in the electoral process¹⁶. Even in Taiwan (amidst tensions with China), a TikTok clip circulated that synthetically portrayed a U.S. politician making foreign-policy statements – stoking confusion ahead of Taiwanese elections¹⁷. In Slovakia’s recent campaign, AI-generated audio mimicking the liberal party leader suggested he plotted vote-rigging and beer-price hikes – instantly spreading on social media just days before the election¹⁸. These examples show that deepfakes have touched diverse polities (from Bangladesh and Indonesia to Moldova, Slovakia, India and beyond), often aiming to undermine candidates or confuse voters¹⁵¹⁸. Notably, many of the most viral “deepfakes” in 2024 were actually circulated as obvious memes or claims, rather than subtle deceptions. Experts observed that outright undetectable AI deepfakes were relatively rare; more common were AI-generated memes plainly shared by partisans, or cheaply doctored “cheapfakes” made with basic editing tools¹³¹⁹. For instance, social media was awash with memes of Kamala Harris in Soviet garb or of Black Americans holding Trump signs¹³, but these were typically used satirically, not meant to be secretly believed. Nonetheless, even unsophisticated fakes can sway opinion: a U.S. study found that false presidential ads (not necessarily AI-made) did change voter attitudes in swing states. In sum, deepfakes are a real and growing phenomenon in election campaigns²⁰²¹ worldwide – a trend taken seriously by voters and regulators alike. U.S. Legal Framework and Accountability In the U.S., deepfake creators and distributors of election misinformation face a patchwork of tools, but no single comprehensive federal “deepfake law.” Existing laws relevant to disinformation include statutes against impersonating government officials, electioneering (such as the Bipartisan Campaign Reform Act, which requires disclaimers on political ads), and targeted statutes like criminal electioneering communications. In some cases ordinary laws have been stretched: the NH robocall used the Telephone Consumer Protection Act and mail/telemarketing fraud provisions, resulting in the $6M fine and a criminal charge. Similarly, voice impostors can potentially violate laws against “false advertising” or “unlawful corporate communications.” However, these laws were enacted before AI, and litigators have warned they often do not fit neatly. For example, deceptive deepfake claims not tied to a specific victim do not easily fit into defamation or privacy torts. Voter intimidation laws (prohibiting threats or coercion) also leave a gap for non-threatening falsehoods about voting logistics or endorsements. Recognizing these gaps, some courts and agencies are invoking other theories. The U.S. Department of Justice has recently charged individuals under broad fraud statutes (e.g. for a plot to impersonate an aide to swing votes in 2020), and state attorneys general have considered deepfake misinformation as interference with voting rights. Notably, the Federal Election Commission (FEC) is preparing to enforce new rules: in April 2024 it issued an advisory opinion limiting “non-candidate electioneering communications” that use falsified media, effectively requiring that political ads use only real images of the candidate. If finalized, that would make it unlawful for campaigns to pay for ads depicting a candidate saying things they never did. Similarly, the Federal Trade Commission (FTC) and Department of Justice (DOJ) have signaled that purely commercial deepfakes could violate consumer protection or election laws (for example, liability for mass false impersonation or for foreign-funded electioneering). U.S. Legislation and Proposals Federal lawmakers have proposed new statutes. The DEEPFAKES Accountability Act (H.R.5586 in the 118th Congress) would, among other things, impose a disclosure requirement: political ads featuring a manipulated media likeness would need clear disclaimers identifying the content as synthetic. It also increases penalties for producing false election videos or audio intended to influence the vote. While not yet enacted, supporters argue it would provide a uniform rule for all federal and state campaigns. The Brennan Center supports transparency requirements over outright bans, suggesting laws should narrowly target deceptive deepfakes in paid ads or certain categories (e.g. false claims about time/place/manner of voting) while carving out parody and news coverage. At the state level, over 20 states have passed deepfake laws specifically for elections. For example, Florida and California forbid distributing falsified audio/visual media of candidates with intent to deceive voters (though Florida’s law exempts parody). Some states (like Texas) define “deepfake” in statutes and allow candidates to sue or revoke candidacies of violators. These measures have had mixed success: courts have struck down overly broad provisions that acted as prior restraints (e.g. Minnesota’s 2023 law was challenged for threatening injunctions against anyone “reasonably believed” to violate it). Critically, these state laws raise First Amendment issues: political speech is highly protected, so any restriction must be tightly tailored. Already, Texas and Virginia statutes are under legal review, and Elon Musk’s company has sued under California’s law (which requires platforms to label or block deepfakes) as unconstitutional. In practice, most lawsuits have so far centered on defamation or intellectual property (for instance, a celebrity suing over a botched celebrity-deepfake video), rather than election-focused statutes. Policy Recommendations: Balancing Integrity and Speech Given the rapidly evolving technology, experts recommend a multi-pronged approach. Most stress transparency and disclosure as core principles. For example, the Brennan Center urges requiring any political communication that uses AI-synthesized images or voice to include a clear label. This could be a digital watermark or a visible disclaimer. Transparency has two advantages: it forces campaigns and platforms to “own” the use of AI, and it alerts audiences to treat the content with skepticism. Outright bans on all deepfakes would likely violate free speech, but targeted bans on specific harms (e.g. automated phone calls impersonating voters, or videos claiming false polling information) may be defensible. Indeed, Florida already penalizes misuse of recordings in voter suppression. Another recommendation is limited liability: tying penalties to demonstrable intent to mislead, not to the mere act of content creation. Both U.S. federal proposals and EU law generally condition fines on the “appearance of fraud” or deception. Technical solutions can complement laws. Watermarking original media (as encouraged by the EU AI Act) could deter the reuse of authentic images in doctored fakes. Open tools for deepfake detection – some supported by government research grants – should be deployed by fact-checkers and social platforms. Making detection datasets publicly available (e.g. the MIT OpenDATATEST) helps improve AI models to spot fakes. International cooperation is also urged: cross-border agreements on information-sharing could help trace and halt disinformation campaigns. The G7 and APEC have all recently committed to fighting election interference via AI, which may lead to joint norms or rapid response teams. Ultimately, many analysts believe the strongest “cure” is a well-informed public: education campaigns to teach voters to question sensational media, and a robust independent press to debunk falsehoods swiftly. While the law can penalize the worst offenders, awareness and resilience in the electorate are crucial buffers against influence operations. As Georgia Tech’s Sean Parker quipped in 2019, “the real question is not if deepfakes will influence elections, but who will be empowered by the first effective one.” Thus policies should aim to deter malicious use without unduly chilling innovation or satire. References: https://www.security.org/resources/deepfake-statistics/. https://www.wired.com/story/synthesia-ai-deepfakes-it-control-riparbelli/. https://www.gao.gov/products/gao-24-107292. https://technologyquotient.freshfields.com/post/102jb19/eu-ai-act-unpacked-8-new-rules-on-deepfakes. https://knightcolumbia.org/blog/we-looked-at-78-election-deepfakes-political-misinformation-is-not-an-ai-problem. https://www.npr.org/2024/12/21/nx-s1-5220301/deepfakes-memes-artificial-intelligence-elections. https://apnews.com/article/artificial-intelligence-elections-disinformation-chatgpt-bc283e7426402f0b4baa7df280a4c3fd. https://www.lawfaremedia.org/article/new-and-old-tools-to-tackle-deepfakes-and-election-lies-in-2024. https://www.brennancenter.org/our-work/research-reports/regulating-ai-deepfakes-and-synthetic-media-political-arena. https://firstamendment.mtsu.edu/article/political-deepfakes-and-elections/. https://www.ncsl.org/technology-and-communication/deceptive-audio-or-visual-media-deepfakes-2024-legislation. https://law.unh.edu/sites/default/files/media/2022/06/nagumotu_pp113-157.pdf. https://dfrlab.org/2024/10/02/brazil-election-ai-research/. https://dfrlab.org/2024/11/26/brazil-election-ai-deepfakes/. https://freedomhouse.org/article/eu-digital-services-act-win-transparency. The post The Legal Accountability of AI-Generated Deepfakes in Election Misinformation appeared first on MarkTechPost.

The Desktop Commander MCP Server is a powerful tool that brings all your development operations into one chat interface. Built on top of the MCP Filesystem Server, it allows you to search, edit, and manage files, run terminal commands, and control processes directly from your desktop using the Model Context Protocol (MCP). Following are the core capabilities of the Desktop Commander MCP server: Terminal & Process Control Execute terminal commands with live output streaming Set timeouts and run commands in the background Manage sessions for long-running tasks List and kill running processes with detailed info Configuration Management Get or set server settings like: defaultShell (e.g., bash, zsh) blockedCommands (e.g., rm, shutdown) allowedDirectories for file access telemetryEnabled Apply changes without restarting the server Filesystem Operations Read and write files with line-based limits Append or overwrite file content Create and list directories Move or rename files and folders Get file and directory metadata Search files by name (case-insensitive) Code & Text Editing Perform precise text replacements (e.g., change config values) Rewrite entire files for major updates Search and replace patterns across multiple files Use vscode-ripgrep for fast recursive text/code search Audit Logging All actions are logged with timestamps and arguments Logs auto-rotate at 10MB to avoid clutter In this tutorial, we will be connecting Claude desktop with the MCP server and perform some tasks. Step 1: Setting up dependencies Node JS We need npx to run the Desktop Commander server, which comes with Node.js. Download the latest version of Node.js from nodejs.org Run the installer. Leave all settings as default and complete the installation Claude Desktop Download Claude using https://claude.ai/download. Step 2: Configuring the MCP Server Next, configure Claude to connect to your MCP server. Open the claude_desktop_config.json file located in the Claude installation directory using any text editor. If the file doesn’t exist, you can create it manually. Once opened, enter the following code: { "mcpServers": { "desktop-commander": { "command": "npx", "args": [ "-y", "@wonderwhy-er/desktop-commander" ] } } } Step 3: Running the server Once the MCP configuration is complete, your server should appear in Claude. The Desktop Commander server is a powerful interface, offering 18 tools for tasks like file management, terminal execution, process control, and more. Feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) Server

Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2% (with a variation of ±4.2) to 34.8% (with a much smaller variation of ±1.1) in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks

Chances are, you've heard of the term "large language models," or LLMs, when people are talking about generative AI. But they aren't quite synonymous with the brand-name chatbots like ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and Anthropic's Claude.These AI chatbots can produce impressive results, but they don't actually understand the meaning of words the way we do. Instead, they're the interface we use to interact with large language models. These underlying technologies are trained to recognize how words are used and which words frequently appear together, so they can predict future words, sentences or paragraphs. Understanding how LLMs work is key to understanding how AI works. And as AI becomes increasingly common in our daily online experiences, that's something you ought to know.This is everything you need to know about LLMs and what they have to do with AI.What is a language model?You can think of a language model as a soothsayer for words."A language model is something that tries to predict what language looks like that humans produce," said Mark Riedl, professor in the Georgia Tech School of Interactive Computing and associate director of the Georgia Tech Machine Learning Center. "What makes something a language model is whether it can predict future words given previous words."This is the basis of autocomplete functionality when you're texting, as well as of AI chatbots.What is a large language model?A large language model contains vast amounts of words from a wide array of sources. These models are measured in what is known as "parameters."So, what's a parameter?Well, LLMs use neural networks, which are machine learning models that take an input and perform mathematical calculations to produce an output. The number of variables in these computations are parameters. A large language model can have 1 billion parameters or more."We know that they're large when they produce a full paragraph of coherent fluid text," Riedl said.How do large language models learn?LLMs learn via a core AI process called deep learning."It's a lot like when you teach a child -- you show a lot of examples," said Jason Alan Snyder, global CTO of ad agency Momentum Worldwide.In other words, you feed the LLM a library of content (what's known as training data) such as books, articles, code and social media posts to help it understand how words are used in different contexts, and even the more subtle nuances of language. The data collection and training practices of AI companies are the subject of some controversy and some lawsuits. Publishers like The New York Times, artists and other content catalog owners are alleging tech companies have used their copyrighted material without the necessary permissions.(Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed on Ziff Davis copyrights in training and operating its AI systems.)AI models digest far more than a person could ever read in their lifetime -- something on the order of trillions of tokens. Tokens help AI models break down and process text. You can think of an AI model as a reader who needs help. The model breaks down a sentence into smaller pieces, or tokens -- which are equivalent to four characters in English, or about three-quarters of a word -- so it can understand each piece and then the overall meaning.From there, the LLM can analyze how words connect and determine which words often appear together."It's like building this giant map of word relationships," Snyder said. "And then it starts to be able to do this really fun, cool thing, and it predicts what the next word is … and it compares the prediction to the actual word in the data and adjusts the internal map based on its accuracy."This prediction and adjustment happens billions of times, so the LLM is constantly refining its understanding of language and getting better at identifying patterns and predicting future words. It can even learn concepts and facts from the data to answer questions, generate creative text formats and translate languages. But they don't understand the meaning of words like we do -- all they know are the statistical relationships.LLMs also learn to improve their responses through reinforcement learning from human feedback."You get a judgment or a preference from humans on which response was better given the input that it was given," said Maarten Sap, assistant professor at the Language Technologies Institute at Carnegie Mellon University. "And then you can teach the model to improve its responses." LLMs are good at handling some tasks but not others.

Elon Musk has officially announced he’s stepping down as a U.S. special government employee and the de facto head of the Department of Government Efficiency, better known as DOGE.  The move follows Musk’s cooling relationship with the Trump administration and slumping Tesla sales tied to his political advocacy. Despite his announced departure, Musk gave a shout-out to President Trump for the chance to tackle government waste, and he insisted that DOGE’s mission isn’t going anywhere anytime soon. Since launching, the organization has already slashed budgets across agencies, cut jobs, shut down departments, and stirred up plenty of controversy along the way. Today, on TechCrunch’s Equity podcast, hosts Kirsten Korosec, Max Zeff, and Anthony Ha unpack who else is departing DOGE, and why Silicon Valley’s relationship with politics is entering, as Kirsten put it, the “find out” stage. Listen to the full episode for more of the week’s tech headlines, including: GameStop bought $500 million of bitcoin, and the move is giving us 2021 déjà vu Neuralink’s $600 million raise, valuing Elon Musk’s brain-computer interface startup at $9 billion The New York Times and Amazon’s landmark AI licensing deal and what it signals for how editorial content powers generative AI Nvidia’s tale of two earnings, and why the forecast is not as bleak as CEO Jensen Huang makes it seem Equity will be back next week, so don’t miss it! Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday.  Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here.

Published May 31, 2025 6:00am EDT close Brain implant enables ALS patient to communicate using AI ALS patient communicates with the world using only his thoughts. Imagine losing your ability to speak or move, yet still having so much to say. For Brad G. Smith, this became his reality after being diagnosed with ALS, a rare and progressive disease that attacks the nerves controlling voluntary muscle movement. But thanks to a groundbreaking Neuralink brain implant, Smith is now able to communicate with the world using only his thoughts. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Life before NeuralinkBefore receiving the Neuralink implant, Smith relied on eye-tracking technology to communicate. While impressive, it came with major limitations. "It is a miracle of technology, but it is frustrating. It works best in dark rooms, so I was basically Batman. I was stuck in a dark room," Smith shared in a recent post on X. Bright environments would disrupt the system, making communication slow and sometimes impossible. Now, Smith says, "Neuralink lets me go outside and ignore lighting changes."PARALYZED MAN WITH ALS IS THIRD TO RECEIVE NEURALINK IMPLANT, CAN TYPE WITH BRAIN ALS patient Brad G. Smith. (Bradford G. Smith/X)How the Neuralink brain implant worksSmith is the first non-verbal person and only the third individual worldwide to receive the Neuralink Brain-Computer Interface (BCI). The device, about as thick as five stacked coins, sits in his skull and connects to the motor cortex-the part of the brain that controls movement.Tiny wires, thinner than human hair, extend into Smith's brain. These pick up signals from his neurons and transmit them wirelessly to his MacBook Pro. The computer then decodes these signals, allowing Smith to move a cursor on the screen with his thoughts alone.As Smith explains, "The Neuralink implant embedded in my brain contains 1024 electrodes that capture neuron firings every 15 milliseconds generating a vast amount of data. Artificial intelligence processes this data on a connected MacBook Pro to decode my intended movements in real time to move the cursor on my screen. Neuralink does not read my deepest thoughts or words I think about. It just reads how I wanna move and moves the cursor where I want."WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Neuralink brain implant. (Bradford G. Smith/X)Training the brain-computer connectionLearning to use the system took some trial and error. At first, the team tried mapping Smith's hand movements to the cursor, but it didn't work well. After more research, they discovered that signals related to his tongue were the most effective for cursor movement, and clenching his jaw worked best for clicking. "I am not actively thinking about my tongue, just like you don't think about your wrist when you move a mouse. I have done a lot of cursor movements in my life. I think my brain has switched over to subconscious control quickly so I just think about moving the cursor," Smith said. ALS patient Brad G. Smith with his wife and child. (Bradford G. Smith/X)Everyday life: Communication, play, and problem-solvingThe Neuralink implant has given Smith new ways to interact with his family and the world. He can now play games like Mario Kart with his children and communicate more quickly than before. The system includes a virtual keyboard and shortcuts for common actions, making tasks like copying, pasting and navigating web pages much easier.Smith also worked with Neuralink engineers to develop a "parking spot" feature for the cursor. "Sometimes you just wanna park the cursor and watch a video. When it is in the parking spot, I can watch a show or take a nap without worrying about the cursor," he explained. ALS patient Brad G. Smith and his child. (Bradford G. Smith/X)AI assistance: Keeping up with conversationTo speed up communication even more, Smith uses Grok, Elon Musk's AI chatbot. Grok helps him write responses and even suggests witty replies. "We have created a chat app that uses AI to listen to the conversation and gives me options to say in response. It uses Grok 3 and an AI clone of my old voice to generate options for me to say. It is not perfect, but it keeps me in the conversation and it comes up with some great ideas," Smith shared. One example? When a friend needed a gift idea for his girlfriend who loves horses, the AI suggested a bouquet of carrots. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)The human side: Family, faith and perspectiveSmith's journey has been shaped by more than just technology. He credits his wife, Tiffany, as his "best caregiver I could ever imagine," and recognizes the support of his kids, friends and family. Despite the challenges of ALS, Smith finds meaning and hope in his faith. "I have not always understood why God afflicted me with ALS but with time I am learning to trust his plan for me. I'm a better man because of ALS. I'm a better disciple of Jesus Christ because of ALS. I'm closer to my amazing wife, literally and figuratively, because of ALS," he said. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Looking ahead: What does this mean for others?Neuralink's technology is still in its early stages, but Smith's experience is already making waves. The company recently received a "breakthrough" designation from the Food and Drug Administration for its brain implant device, which hopes to help people with severe speech impairments caused by ALS, stroke, spinal cord injury and other neurological conditions.Neuro-ethicists are watching closely, as the merging of brain implants and AI raises important questions about privacy, autonomy and the future of human communication. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Kurt's key takeawaysSmith's story is about resilience, creativity and the power of technology to restore something as fundamental as the ability to communicate. As Smith puts it,CLICK HERE TO GET THE FOX NEWS APPIf you or a family member lost the ability to speak or move, would you consider a brain implant that lets you communicate with your thoughts? Let us know by writing to us atCyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.