artificial intelligence
The future of artificial intelligence.
Recursive Superintelligence Startup Raises 650 Million Dollars to Automate AI Self Improvement
Read Time 6 minutes Tags AI Research Recursive Self Improvement AI Startups Autonomous AI Safety Notable researchers from Google Meta and OpenAI have launched a six month old company called Recursive Superintelligence to pursue the goal of building AI systems that can improve themselves with little or no human help The company has raised more than 650 million dollars from venture capital firms including GV Greycroft Nvidia and AMD and is now valued at more than 4 billion dollars despite having fewer than 30 employees The founders include Richard Socher who is also chief executive of You com and previously head of AI research at Salesforce and co founders Josh Tobin Jeff Clune and Tim Shi from OpenAI and Yuandong Tian from Meta The team also hired Peter Norvig who spent 25 years as director of research at Google and co wrote the standard AI textbook Artificial Intelligence A Modern Approach What recursive self improvement means One Definition and technical basis Recursion in computer science refers to a function that feeds itself After a recursive procedure generates information it uses that information to generate something else and so on In AI this means an AI system writes code that improves its own architecture training process or data pipeline without direct human engineering A veteran researcher Richard Socher said AI is code And now AI can code The ingredients are there The idea is to push more and more work onto machines including the generation of new ideas that drive AI development forward Two Current state of capability Companies like Anthropic and OpenAI released new AI systems late last year that were particularly good at writing computer code In recent months the technology has rapidly remade the way Silicon Valley engineers build test and modify new software applications If an artificial intelligence system can write code it can help accelerate the development of things as varied as word processors and social media apps OpenAI has said it is now building an automated AI researcher By the fall the company hopes to have a system that can do the work of a less experienced researcher said Sam Altman Similar efforts are underway at other leading companies Three Research focus on open endedness Many of the Recursive Superintelligence founders specialize in a kind of AI development called open endedness This involves building software systems that can run for days months or even years in pursuit of goals set by the researchers The aim is to create systems that explore large solution spaces without constant human guidance Company structure and challenges One Talent and funding density Recursive Superintelligence has fewer than 30 employees but is valued at more than 4 billion dollars The high valuation reflects both the seniority of the founders and investor belief that automating AI research could unlock compounding returns in capability The company operates offices in San Francisco and London Two Timeline and scope Dr Socher said his start up would need years to build the kind of technology that he and his co founders envisioned The company hopes to eventually apply the technology to other fields such as drug discovery and other kinds of biological research The founders acknowledge that current technology is a long way from the point where humans can be removed from the loop Humans must still generate the new ideas that drive AI development forward Three Competitive landscape Recursive Superintelligence is not alone in this pursuit The company should not be confused with Ricursive Intelligence which is pursuing a similar goal and is also valued at 4 billion dollars The prominent AI start ups Anthropic and OpenAI are also chasing recursive self improvement which has been an obsession among Silicon Valley technologists for decades Technical and safety considerations One Potential for capability acceleration If self improvement loops work even partially they could accelerate AI capability faster than linear scaling of compute and data This would shorten timelines to advanced systems and increase pressure on evaluation and safety processes Two Alignment and control risk Automated AI research raises the question of how to ensure that self modifying systems remain aligned with human intent A system that can rewrite its own training objective or architecture introduces new failure modes that are hard to audit or contain Three Evaluation bottleneck Current evaluation methods rely on human designed benchmarks and red teaming If AI begins to generate its own research agenda it may outpace human ability to assess risk This is why many researchers argue that interpretability and monitoring infrastructure must advance in parallel with capability Market and industry implications One Talent consolidation The involvement of researchers from OpenAI Meta and Google shows continued movement of senior talent into well funded startups focused on frontier research This concentrates expertise but also accelerates knowledge transfer outside large labs Two Investment thesis Investors are betting that the first group to automate AI research will gain a compounding advantage in both capability and cost The 650 million dollar raise signals confidence that the problem is now tractable with current models and tooling Three Application expansion The founders plan to apply the technology beyond code generation to fields like drug discovery and biological research If the approach works it could shorten development cycles in domains where experimentation is expensive and slow What to watch next One Benchmarks for autonomous research The field needs clear benchmarks for what counts as an autonomous AI researcher OpenAI target of a system that can do the work of a less experienced researcher by fall 2026 will be a test case Two Safety protocols As capability increases the need for secure model storage controlled access and robust monitoring becomes more urgent Any recursive system that modifies itself must have guardrails that cannot be easily removed by the system itself Three Regulatory response Governments are watching this area closely because recursive self improvement could change the pace of technological change faster than existing policy cycles The US China protocol discussed earlier this week is an early signal that states see AI autonomy as a diplomatic issue For researchers the challenge is to prove that recursive loops can produce real gains without losing control For investors the bet is that this is the next step after code generation models For the rest of the field it raises the question of how fast capability can move once humans are partially out of the loop Do you think recursive self improvement is closer than most people expect and should safety research get more funding than capability right now Share your view in the comments
By Behind the Tech5 months ago in Futurism
AI Did Not Crack Bitcoin But Helped User Recover 400000 Dollars By Finding Old Wallet File
Read Time 6 minutes Tags Bitcoin Crypto Recovery AI Claude Wallet Security Data Forensics A Bitcoin holder known as Cprkrn on X went viral on May 13 2026 after claiming he recovered around 400000 dollars in BTC from a wallet locked for more than a decade with help from Anthropic AI chatbot Claude The user said Claude helped him regain access to 5 Bitcoin after years of failed attempts At the time of writing Bitcoin was trading at around 79600 dollars putting the 5 BTC recovery at roughly 398000 dollars Holy fcking sht omg Claude just cracked this sht he wrote in the viral post thanking Anthropic and CEO Dario Amodei He even said he plans on naming his child after Amodei What actually happened One Lost password and old files The wallet had been locked since the user college days He said he originally bought the crypto when it was worth around 250 dollars per coin before losing access after changing the wallet password while high The password as later revealed was lol420fuckthePOLICE The user said he had tried for years to recover the funds claiming he ran through trillions of possible password combinations Two Claude role was data forensics not brute force The breakthrough came when he uploaded files from his old college computer into Claude Rather than simply guessing the password Claude helped dig through the old files and identify an older wallet dat file that appeared to predate the password change The user also reportedly had an old mnemonic phrase which helped unlock the wallet once the correct file was found I tried like 7 trillion passwords lmfao he wrote Found this old pneumonic a few weeks ago that ended up being the old password before I changed it Thought I was screwed Last ditch effort dumped my whole college computer into Claude It found an OLD wallet file that the pneumonic Three Tool context BTCRecover is a known wallet recovery tool designed for cases where users already know most of a wallet password or seed but need help testing variations Its documentation says it supports Bitcoin Core wallet recovery among several other wallet types Claude contribution was to locate the correct wallet dat file among a large set of old files and connect it to the mnemonic the user had found Why this is not a break of Bitcoin security One Cryptography remains intact Claude did not break Bitcoin elliptic curve cryptography or bypass the wallet encryption The system only works if you have the correct private key or seed phrase In this case the user already had the seed phrase from an earlier version of the wallet The problem was finding which wallet dat file matched that seed phrase Two Value of context aware search Large language models excel at parsing unstructured data across thousands of files and matching patterns A human would take days to manually inspect an old college hard drive for wallet files wallet backups and text fragments that look like seed phrases Claude automated that process by reading the files understanding file types and surfacing the relevant one Three Limits and risk This only works if the user has some existing credential material or an older backup If the wallet was truly lost with no seed phrase and no backup file AI cannot recover it The story spread quickly because it looks like AI hacking but it is closer to advanced data recovery and organization Broader implications for crypto and AI One Rise of AI assisted recovery Services that combine AI with wallet recovery tools are likely to grow Many users lose access due to forgotten passwords or misplaced backups not due to lost keys AI can reduce the time and cost of sifting through old drives email archives and cloud backups to find fragments of credential data Two Security practices need updating The case highlights why users should store backups in multiple secure locations and avoid changing passwords in an impaired state It also shows why old hard drives are valuable targets If AI can parse them quickly so can attackers Three Responsible disclosure Anthropic has not commented publicly on the case but the incident will raise questions about how much file access AI assistants should have and whether uploading entire drives creates privacy risk Users need to understand that sending data to a cloud AI means that data leaves their device What this means for users with lost crypto One Check for old backups Before trying brute force methods search for wallet dat files keystore files txt files with seed phrases and old email exports AI can help but only if the material exists Two Use dedicated tools BTCRecover and similar tools are designed for password variation testing If you know part of the password or seed they can automate the combinatorics faster than manual methods Three Secure recovered funds Once access is restored move funds to a new wallet with a secure backup process Hardware wallets and offline storage reduce future risk The story resonated because it mixes crypto nostalgia AI capability and a happy ending The reality is more mundane AI did not break math it found a file The distinction matters because it sets realistic expectations for what AI can and cannot do in crypto recovery Do you think AI assistants should be allowed to scan entire local drives for recovery purposes or is the privacy risk too high Share your view in the comments
By Behind the Tech5 months ago in Futurism
US Signals Willingness for AI Safety Talks With China Citing Lead in Model Development
Read Time 6 minutes Tags AI Geopolitics US China AI Safety Export Controls Nvidia Anthropic US Treasury Secretary Scott Bessent told CNBC on May 14 2026 that Washington can hold discussions with Beijing on artificial intelligence because we are in the lead Bessent spoke from Beijing during President Donald Trump two day meeting with Chinese President Xi Jinping and said the two countries would establish a protocol for AI safety adding that they were having a wholesome dialogue because the US remains ahead in the race to develop frontier models The statement marks a shift toward formal US China engagement on AI risk at a time when both countries are racing to scale large language models and deploy autonomous agents The protocol is framed around best practices to ensure non state actors do not get a hold of these models What was announced at the Trump Xi meeting One AI safety protocol framework Bessent said the two AI superpowers are gonna start talking We re gonna set up a protocol in terms of how do we go forward with best practices for AI to make sure non state actors don t get a hold of these models The scope was not detailed but the language mirrors prior discussions on model security access controls and red teaming standards for frontier systems The reason we are able to have wholesome discussions with the Chinese on AI is because we are in the lead he added I do not think we would be having the same discussions if they were this far ahead of us This framing positions US export controls and model lead as leverage for negotiating safety norms Two Chip export policy remains fluid Bessent was asked about a Reuters report that Washington had cleared sales of Nvidia H200 AI chips to several major Chinese technology firms He said there had been a lot of back and forth on the matter Nvidia CEO Jensen Huang joined Trump delegation to China as a late addition signaling that chip supply remains a live negotiation point US policy has restricted sales of advanced semiconductors to China since 2022 with periodic adjustments based on diplomatic and commercial calculations Three Taiwan and broader security context Beijing readout said Xi emphasized that Taiwan is the most important issue for bilateral relations and warned against mishandling the issue Bessent told CNBC that Trump would say more on Taiwan in the coming days The AI talks are happening alongside unresolved trade and security disputes which means any protocol will likely be narrow and technical rather than comprehensive Context on US China AI competition One US lead in frontier models Bessent claim of lead reflects current benchmark results where US labs OpenAI Google DeepMind Anthropic maintain advantage on general capability and reasoning Anthropic recent Mythos AI model has alarmed Washington due to reported cyberattack capabilities and the company decision to release it only to select business partners The US lead is not permanent and depends on access to advanced chips talent and compute infrastructure Two China response and domestic push China has prioritized domestic chip development and model training to reduce dependence on US technology Firms like Baidu Alibaba DeepSeek and ByteDance have released models with improving performance The gap is narrowing in some areas but US restrictions on high end GPUs continue to constrain training at the frontier scale Three Multilateral AI governance efforts The bilateral protocol fits into a broader pattern of states seeking norms for AI safety without slowing deployment The UK AI Safety Summit 2023 and UN AI advisory body work have pushed for risk based frameworks but enforcement remains limited A US China protocol could set a floor for model security and information sharing but will face skepticism given strategic rivalry Implications for industry and policy One Near term impact on model releases Bessent said he anticipates a big step function jump in upcoming large language model releases from Google Gemini and OpenAI If true this would widen the capability gap and strengthen US negotiating position It also raises stakes for safety testing and responsible deployment practices Two Corporate exposure Nvidia and other chip firms remain exposed to policy shifts Any clearance for H200 sales to China would be significant for revenue but politically sensitive The presence of Jensen Huang in Beijing signals that industry is seeking clarity on what is permissible Three Risk of leakage and misuse The stated goal of the protocol is to prevent non state actors from accessing advanced models This points to shared concern about model weights leaking and being used for cyberattacks or bioweapon assistance Both sides have an interest in avoiding catastrophic misuse even if they compete on commercial deployment What to watch next One Concrete measures The protocol needs to specify what counts as best practice Is it secure model storage mandatory red teaming watermarking for synthetic content or restrictions on fine tuning The absence of detail means the announcement is a starting point not a binding agreement Two Verification and trust Any agreement requires a way to verify compliance US and China have limited trust on technology issues so monitoring will be a sticking point One likely path is third party audits and information sharing on incidents rather than direct inspection Three Link to export controls If the US sees the protocol as working it may keep current chip restrictions in place If talks stall restrictions could tighten The connection between safety dialogue and trade policy makes this a high stakes negotiation For companies building frontier models the message is that safety is becoming a diplomatic issue not just a product feature For governments the test is whether a narrow technical agreement can survive wider strategic friction Do you think a US China AI safety protocol can work without addressing export controls on advanced chips Share your view in the comments
By Behind the Tech5 months ago in Futurism
NASA's next-generation Mars helicopter blades smash through the sound barrier.
It has long seemed nearly impossible to fly on Mars because, in many respects, it is. Aircraft find it difficult to stay in the air due to the planet's extremely thin atmosphere. Spinning blades that are already working close to their limitations must have every bit of lift extracted from them.
By Francis Dami5 months ago in Futurism
I Tried Different Google Lens Alternatives and Preferred Chance AI. AI-Generated.
Visual search tools have become a part of everyday life. Whether it’s identifying a plant, finding a product online, translating text from an image, or learning about something you see in the real world, people now expect AI to understand images almost instantly. For a long time, Google Lens has been one of the most popular options for this. But recently, I started exploring different Google Lens alternatives to see if there were tools that felt more interactive, smarter, or simply easier to use in daily situations.
By Maheep Makkar5 months ago in Futurism
AI Is Penetrating CAPTCHA. AI-Generated.
For many years, CAPTCHA has been regarded as the cheapest and most widely used human-verification tool on the internet. Distorted text, image selection, and behavioral verification were all designed with a clear purpose: keep bots outside the system while allowing real users to remain inside it. The problem is that this boundary is no longer as reliable as it once was. CAPTCHA is still widely deployed, but it is increasingly unable to bear a core security function. What has really changed is not just the capability of attack tools, but the underlying assumptions on which the entire security model was built.
By Daniel Widjaja Kusuma5 months ago in Futurism
OpenEvidence Reaches 65 Percent of US Doctors as AI Quietly Reshapes Clinical Decision Making
Read Time 6 minutes Tags AI Healthcare OpenEvidence Clinical Decision Support Medical AI HIPAA Your doctor is probably using AI even if they have not told you about it Over the past two years medical providers across America have quietly embraced a new AI tool called OpenEvidence to help them make clinical decisions brush up on medical knowledge and even prepare for their licensing exams The service a sort of chatbot for doctors was used by about 65 percent of US doctors across almost 27 million clinical encounters in April alone the company told NBC News Everyone is using it said Dr Anupam Jena an internal medicine physician at Massachusetts General Hospital in Boston and a professor of healthcare policy at Harvard Its growth really has been exponential How OpenEvidence works and why it spread fast One Point of care search engine At its core OpenEvidence is an AI powered medical search engine that combs through vast databases of healthcare research to provide suggestions about clinical decisions or medication options and help highlight the latest evidence from a variety of medical fields The landing page calls it America Official Medical Knowledge Platform and presents users with a search bar which suggests entering queries like alternatives if metformin causes diarrhea or what are the latest advancements in gene therapy for Duchenne muscular dystrophy To answer user questions the system generates a summary of the relevant medical research providing links to the peer reviewed articles or medical guidelines that informed the answer Healthcare providers can access the tool through its website or via a stand alone mobile app Practitioners must sign up for an account and provide their unique healthcare ID number supplied by the US government Once registered the providers can ask unlimited medical questions for free Two Licensing deals with top journals OpenEvidence rapid reference to top tier medical research is its special sauce according to both doctors and the OpenEvidence team The company has inked partnerships and licensing agreements with the world most prestigious medical journals like NEJM and the Journal of the American Medical Association It has also struck agreements with specialized medical organizations like the National Comprehensive Cancer Network and the American Diabetes Association to provide the latest and most relevant treatment guidelines Three Adoption without mandate In an industry where technological change is often forced on hesitant doctors by medical administrators few services have seen such rapid adoption as OpenEvidence We did the hardest thing in the history of American health care said CEO Daniel Nadler We got the majority of American doctors to all voluntarily adopt a single technology platform America financiers are impressed The startup raised 700 million dollars in less than a year and is backed by Sequoia Capital Google Ventures Nvidia Andreessen Horowitz Thrive Capital and more Valued at 1 billion dollars in early 2025 OpenEvidence has surged to a valuation of 12 billion dollars in just over a year Benefits and risks cited by clinicians One Time savings and coverage of gaps Doctors across specialties states and clinic sizes report that OpenEvidence saves time A junior doctor at a New Hampshire hospital said that when he saw a patient potassium value plummet he checked OpenEvidence to make sure it was a normal side effect of a medication and not a new emergency After searching through peer reviewed medical publications OpenEvidence said it was a common side effect and provided several options to restore normal potassium levels Another doctor at the Indian Health Service medical center in rural Pine Ridge South Dakota used it to confirm that a CT scan was preferred to confirm a suspected spine fracture Sixty percent of all the searches are about how to make clinical decisions said Jena who is currently examining 90 million OpenEvidence queries submitted since 2024 as part of a new research project The physicians are asking For this particular patient or with this profile this condition maybe other comorbidities that they have what is the right treatment Two Accuracy and hallucination concerns Some experts worry about potential hallucinations or incomplete answers a lack of rigorous scientific studies on the tool patient impact and the potential for doctors critical thinking and evaluation skills to erode with increased OpenEvidence use and dependence An academic study released in December found that OpenEvidence accurately answered more complex medical questions less than 45 percent of the time That study has not yet been peer reviewed Several doctors noted that OpenEvidence sometimes drew overly strong conclusions from medical studies with small sample sizes though other clinicians noted even the system mistakes tended to err on the side of caution Emergency physician Dr John Rozehnal said that OpenEvidence mistakenly suggested injecting a particular medicine might damage a patient liver even though the risk was very low Weeks later he said OpenEvidence had improved its answer Three Privacy and HIPAA compliance OpenEvidence says it complies with HIPAA through a series of privacy protocols and protections In April of last year the company said that US covered entities can securely input protected health information PHI in accordance with HIPAA privacy and security standards However some health systems are not satisfied with the system overall privacy safeguards For example MaineHealth currently asks its doctors to refrain from entering PHI into OpenEvidence Some doctors use the platform on personal devices entering age sex and previous medical history but refraining from names or other personal identifiers Market and competitive landscape One Shift from UpToDate For years clinicians have turned to UpToDate to get recommendations for proper treatment and clinical decision making UpToDate consists of long form peer reviewed summaries of the latest research tricky to search for doctors with targeted questions about specific scenarios UpToDate has for so long been the dominant place where clinicians wanted to look things up at point of care said Dr Paul Sax of Brigham and Women Hospital But OpenEvidence search feature is far more flexible The process of searching for answers is frictionless UpToDate is now rushing to implement its own AI tool called Expert AI Company spokesperson Suzanne Moran said about 2000 hospitals and health systems have signed up for Expert AI as of April 30 We believe healthcare AI must prioritize patient safety transparency and freedom from advertising Moran added Two Funding and expansion OpenEvidence is part of a growing cottage industry of AI powered medical tools from AI scribes that record and transcribe doctors speech during patient appointments to competitors like Doximity or iatroX A recent survey by the American Medical Association found that over 80 percent of physician respondents currently use some form of AI Nadler and the OpenEvidence team aim to expand the service AI notetaking billing and visit integration functions in the coming years Three Regulation and evidence gap Few studies have rigorously examined how OpenEvidence affects patient outcomes largely due to how recently the tool has exploded in popularity Several researchers including Dr Hannah Galvin are aiming to fill this evidence gap and better understand how OpenEvidence is changing clinical care We want to make sure that we have explored and understood how these tools are performing for our population and that they are making decisions in a fair valid equitable and safe manner Galvin said What to watch next One Clinical outcomes data Without rigorous studies on patient impact the question remains whether faster answers translate to better care Mount Sinai announced a new enterprise partnership with OpenEvidence in March to directly link to the service from the hospital system main electronic health record portal for use by doctors nurses and pharmacists Two Dependence risk While more experienced doctors might have the ability to fall back on years of clinical expertise some worry that reliance on such a medical tool might lead to dependence and misplaced confidence among medical students and junior doctors My worry is that when we introduce a new tool any kind of tool that is doing part of your skills that you had trained up for a while beforehand you start losing those skills pretty quickly said a midcareer doctor in Missouri For patients the reality is that AI is already in the exam room even if it is invisible For clinicians the challenge is to use it as a tool not a replacement for judgment Do you think hospitals should mandate disclosure when AI is used in your clinical decision making Share your view in the comments
By Behind the Tech5 months ago in Futurism
Michalene Melges and the Future of Ethical AI in Robotics Leadership. AI-Generated.
Michalene Melges is a seasoned Project Manager in AI robotics, leading complex cross-functional teams and driving advances in intelligent automation. Her work reflects a broader shift in the robotics industry where success is no longer measured only by technical performance but also by responsibility, transparency, and long-term system impact. As artificial intelligence becomes more deeply integrated into real-world environments, professionals like Michalene Melges are helping guide how these systems are designed and managed to ensure they operate safely, fairly, and with accountability.
By Michalene Melges5 months ago in Futurism
AI Obsession Is Bleeding Into Home Life and Straining Relationships in Tech Households
Read Time 6 minutes Tags AI Culture Relationships Work Life Balance Tech Industry Burnout AI Adoption At 11 pm in Berkeley California the author is home alone with a 10 month old daughter while her husband is in Cambridge Massachusetts for a new AI job At 2 am he FaceTimes shouting JUST LOOK AT THIS SEE and points the camera at a laptop on a hotel bed This scene from WIRED article Meet the Sad Wives of AI captures a problem that is becoming common in households where one partner works in artificial intelligence The issue is not that he is working The issue is that AI work does not stop A new model drops every week benchmarks change constantly and if you are offline for two weeks you feel behind The work follows people home through Slack notifications late night debugging sessions and an inability to stop talking about the latest release Partners report feeling like they are competing with a chatbot for attention Children notice when a parent is physically present but mentally debugging an eval failure Reader comments show the pattern is widespread Commenter BAGEL WARRIOR writes OMG excellent article I believe this obsession is caused by the fear of not being able to have a job in the AI market Needless to say this is also causing the male loneliness epidemic I do imagine a world divide between AI wannabes and nature wannabes The comment points to fear driven behavior in a market that moves too fast to feel secure Commenter ELLIE W describes a partner building an intelligence platform for an industry that is slow to adopt new technology He has been working on this latest transition for over 3 years He demos his product to me every 3 months or so It is interesting but I still do not grasp what is under the hood He has in house not cloud versions he uses of different AI tools models The urgency is always present what can he achieve today in the way of a new feature or new workflow I am struggling I was laid off over 6 months ago and I feel constant panic because I am not working and already the gap is widening to where I may no longer be considered a good candidate for jobs Even the hobby photography is tainted with AI I wish I didn't feel that the tool is dumbing down most people The root causes are structural First the pace of change in AI makes it impossible to maintain boundaries The job requires constant context switching and exposure to new tools Second identity fusion occurs when work is framed as mission driven Stopping feels like abandoning something important Third cultural norms in the Bay Area normalize extreme behavior When everyone at dinner talks about GPU clusters and Claude Code it feels normal The result is attention scarcity at home Partners who are not in AI cannot easily engage with the content of the work It is abstract and moves fast That makes it hard to celebrate wins or commiserate losses together The outcome is parallel lives under one roof Commenter I HATE TESLA compares it to the 1960s and 1970s when Silicon Valley was just starting My father was an EE and applied physics professor at Stanford when Silicon Valley was still someplace down there where he went to consult This sounds like my parents typical dinner party The men couldn't help talking about what they were working on But they were really working on things developing new technology that led to patents and real world outcomes and they were making things not blindly destroying the environment or eliminating human jobs They would have been horrified if they'd known their intellectual fervor was going to come to this assininity It was also the 60s and 70s A lot of our moms were on Valium Some of them got divorced Sounds like you desperately need a revolution The difference now is speed and stakes AI cycles are faster and the work is framed as existential That raises the emotional temperature and makes it harder to disconnect What changes the dynamic requires action on three levels First couples need explicit boundaries no AI talk after 8 pm no laptops in the bedroom no work calls during family time It requires discipline because the work is designed to be urgent and continuous Second creating identity outside of AI gives both partners something to connect over that does not involve GPUs or evals It also reduces the risk of total burnout Third employers need to treat sustainability as a product feature not an afterthought That means capping on call rotations building better tooling to reduce toil and modeling healthy behavior at leadership level Some firms are experimenting with mandatory time off but cultural norms still reward visible grind The article resonates because it names a problem that many feel but few discuss publicly The AI boom has created wealth and progress but it has also created a new class of relationship stress that is not covered by standard work life balance advice Do you think AI companies should be required to report employee burnout metrics alongside model performance metrics
By Behind the Tech5 months ago in Futurism
Google Launches Gemini Intelligence on Android to Shift OS From System Software to Proactive Agent
Read Time 6 minutes Tags Android Gemini AI Agent Automation Chrome OS Intelligence Mobile AI Today Google is introducing Gemini Intelligence on Android which brings the best of Gemini to our most advanced devices It integrates premium hardware and innovative software to help you stay a step ahead by working proactively to get things done throughout your day all while keeping your data private and keeping you in control Gemini Intelligence features will roll out in waves starting with the latest Samsung Galaxy and Google Pixel phones this summer and will become available across your Android devices including your watch car glasses and laptops later this year Core capabilities and technical approach One Multi step task automation Gemini Intelligence helps you automate tedious tasks so you can focus on what matters Google spent months fine tuning multi step automation capabilities on the Galaxy S26 and Pixel 10 on popular food and rideshare apps to ensure every interaction feels seamless Soon devices with Gemini Intelligence will do all that and more Gemini will navigate tasks for you whether it is snagging a front row bike for your spin class or finding your class syllabus in Gmail then putting the books you need in your cart Gemini handles the logistics while you stay in the moment App automation is even more powerful when you add screen or image context Instead of manually switching between apps and copying data Gemini can turn visual context into instant action Imagine you have a long grocery list on your notes app Just long press the power button over the list and ask Gemini to build a shopping cart with all of the items for delivery Or if you see a travel brochure in a hotel lobby that piques your interest you can simply snap a photo of it and say Find a tour like this on Expedia for a group of six You can track the progress live via notifications as Gemini works in the background Most importantly you remain in control Gemini only acts on your command and stops the moment the task is complete Two Gemini in Chrome and intelligent autofill Starting in late June Android devices will be getting a smarter browsing assistant for the web Gemini in Chrome can help you research summarize and compare content across the web Chrome auto browse can take care of more mundane tasks on your behalf whether it is appointment booking or reserving a parking spot Autofill with Google is evolving from a basic convenience into something more intelligent and intuitive By using Gemini Personal Intelligence Android will automatically fill in even more of those tiny text fields across your apps including Chrome Connecting Gemini to Autofill with Google is strictly opt in meaning you choose if and when you want to connect to Gemini and you can always turn this connection on or off in your settings This addresses privacy concerns by keeping sensitive form data under user control Three Generative UI and voice to text Gboard on Android already lets you convert speech to text quickly and accurately but there is a catch the way we talk is not always the way we want to write To bridge that gap Google is introducing Rambler a new Gemini Intelligence feature designed for the way people actually speak With Rambler you can speak naturally and it will take the important parts then fit them all together into a concise message Rambler will clearly show you when you have enabled it to help convert your voice to text and audio is only used to transcribe in real time and is not stored or saved Rambler is built for the way our global community communicates in multiple languages at once Using Gemini advanced multi lingual model Rambler can seamlessly switch between languages in a single message With Create My Widget Google is taking the first step in generative UI with a hallmark of Android widgets You can build entirely custom widgets just by describing what you want using natural language For example if you are a meal prepper just ask Create My Widget to Suggest three high protein meal prep recipes every week and watch as it builds a custom dashboard you can add and resize right on your home screen Product and ecosystem implications One Shift from OS to intelligence system Google is framing Android as transitioning from an operating system into an intelligence system Your devices are becoming even more helpful with upgrades that will save you time This is a fundamental change in product strategy where the OS acts as an orchestrator of agents rather than a passive platform for apps The focus on device processing and opt in data connection aims to address privacy concerns that have limited adoption of similar features in the past Two Device and partner rollout Gemini Intelligence features will roll out in waves starting with the latest Samsung Galaxy and Google Pixel phones this summer and will become available across your Android devices including your watch car glasses and laptops later this year The partnership with Samsung is critical for scale since Galaxy devices represent the largest share of Android premium devices The phased rollout allows Google to refine reliability before expanding to wearables automotive and XR devices Three Competitive positioning Apple introduced similar proactive features with Apple Intelligence in 2024 but Google has an advantage in cross app automation due to Android open architecture and deeper integration with Google services Gemini in Chrome auto browse and Create My Widget are examples of features that are harder to replicate on closed platforms The risk is fragmentation if OEMs implement subsets of the feature set inconsistently Technical and privacy considerations One On device processing and data control Google emphasizes that Gemini Intelligence keeps your data private and keeping you in control The use of opt in connections for Autofill and real time transcription without storage for Rambler suggests a hybrid approach where sensitive tasks run locally and cloud processing is used for complex reasoning This hybrid model is necessary to balance latency cost and privacy Two Reliability and user control The product design stresses that Gemini only acts on your command and stops the moment the task is complete This is critical for trust since autonomous agents can cause errors or unintended actions if they operate without clear boundaries The live progress notifications provide transparency into what the agent is doing Three Developer ecosystem impact If Create My Widget and multi step automation APIs are opened to third party developers Android could see a wave of agent enabled apps that reduce the need to switch between apps directly This could shift app store dynamics from app downloads to agent skill downloads What to watch First reliability of multi step automation across popular apps If Gemini fails frequently on real world tasks user trust will erode Second developer adoption of generative UI APIs Third competitive response from Samsung One UI and other OEMs on how they implement Gemini Intelligence For users the promise is less time spent on digital busywork and more time on decisions that require judgment For developers the opportunity is to build agents that operate across the Android ecosystem rather than inside single apps Do you think on device AI agents will replace manual app navigation for most users in the next two years Share your view in the comments
By Behind the Tech5 months ago in Futurism
