Latest news with #DeepMind
Yahoo
9 hours ago
- Business
- Yahoo
Alphabet Inc (GOOGL) Pays $2.4 Billion to License the Technology of Windsurf
Alphabet Inc. (NASDAQ:GOOGL) is one of the . On July 11, Reuters reported that Alphabet Inc. (NASDAQ:GOOGL) has agreed to pay about $2.4 billion for a deal to license the technology of Windsurf, a startup specializing in AI coding tools. This is seen as a major move by the company to enhance its artificial intelligence capabilities, specifically in AI-assisted coding. The deal follows the breakdown of a prior potential acquisition of Windsurf by OpenAI, which had offered $3 billion but ultimately did not close. A user's hands typing a search query into a Google Search box, emphasizing the company's search capabilities. As part of the deal, Alphabet Inc. (NASDAQ:GOOGL) has hired Windsurf's CEO Varun Mohan, co-founder Douglas Chen, and selected R&D team members to join Google's DeepMind division. This team will focus on agentic coding projects, primarily working on Google's Gemini AI initiative. Alphabet Inc. (NASDAQ:GOOGL) is a holding company that owns Google and operates through three main segments, including Google Services, Google Cloud, and Other Bets. While we acknowledge the potential of GOOGL as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: 30 Stocks That Should Double in 3 Years and 11 Hidden AI Stocks to Buy Right Now. Disclosure: None. This article is originally published at Insider Monkey. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
2 days ago
- Business
- Forbes
Who Will Dominate Our AI Future?
A recent headline in the Wall Street Journal proclaimed that ' China Is Quickly Eroding America's Lead in the Global AI Race .' It appears that customers ranging from HSBC to Saudi Aramco are deploying China's DeepSeek AI model in their data centers, and even America's tech giants, such as Amazon, Microsoft, and Google, are offering it through their cloud services. Corporate users from Brazil, South Africa, and Japan are opting to build their applications using Chinese AI models that are 17 times less expensive than their U.S. equivalents, according to the article. Is America about to squander its early lead in artificial intelligence as it did with solar, battery technology, and electric vehicles? The leading lights in the industry seem to hold conflicting views. 'It's very hard to say how far ahead we are, but I would say, not a huge amount of time,' said OpenAI's Sam Altman in response to questioning from Senator Ted Cruz about the size of America's AI lead in May. Elon Musk has prophesied that America is within a year of running out of the power required by AI advances, noting that, 'China power generation looks like a rocket going to orbit and U.S. power generation is flat.' Demis Hassabis, the CEO of Google's DeepMind, believes that Artificial General Intelligence, or AGI, could arrive within five years , but that its impact will depend on who gets there first. 'How do we restrict access to these systems, powerful systems, to bad actors…but enable good actors to do many, many amazing things with it?' Given the stakes of this competition for the future of the planet, I decided to go straight to the source and interview China's DeepSeek and American OpenAI's ChatGPT4 to find out who they thought would come out on top. Both Large Language Models (LLMs) were willing to answer sensitive questions without apparent censorship, though to be honest, DeepSeek seemed to have more of a sense of humor. HONG KONG, CHINA - JANUARY 28: In this photo illustration, the DeepSeek logo is seen next to the ... More Chat GPT logo on a phone on January 28, 2025 in Hong Kong, China. Global tech stocks have plummeted following the emergence of DeepSeek, a Chinese AI startup that has developed a competitive AI model at a fraction of the cost of its US rivals, sparking concerns about the high valuations of tech giants like Nvidia. This development has led to significant declines in tech shares across Asia and Europe, with markets in both regions experiencing notable losses as investors reassess the AI landscape and its potential impact on the industry's future. (Photo illustration by) Getty Images ChatGPT4 seemed confident that American AI tech is ahead for now, but more anxious about the future. 'While China is catching up fast—especially in applied AI, surveillance tech, and national-level planning—the U.S. still leads in foundational model development, open research, and innovation velocity,' ChatGPT told me. The U.S. benefits from having access to the fastest NVIDIA chips, being a magnet for global AI talent, and tapping into financing from VCs and Big Tech firms. 'It's a two-horse race. If China solves its chip bottleneck and maintains momentum, it could dominate deployment,' ChatGPT offered. 'If the U.S. keeps innovating and fostering talent, it may retain leadership.' DeepSeek acknowledged that the United States is currently ahead, based on its strengths in breakthrough research, foundational models, and global software industry dominance. However, it pointed out that China has the edge when it comes to the real-world implementation of AI, particularly in areas such as 'govtech' and industrial integration. In the short term, DeepSeek predicted that the 'U.S. keeps the crown – unless China pulls off a semiconductor miracle.' Longer-term, it's called a toss-up. 'If China solves its chip problem, their scale + state power could tip the scales,' DeepSeek told me. 'But if the U.S. keeps attracting geniuses and stays ahead in AGI? Game over.' Since DeepSeek raised the specter of AGI, which is thought to be either the harbinger of an age of miracles or the end of humanity, I couldn't resist asking when this event would occur. 'Oooooh, the AGI question— the holy grail of AI!' DeepSeek gushed. 'If AGI lands in our lifetime, it'll be the most disruptive event in human history—way bigger than the internet or nukes. Buckle up!' DeepSeek's best guess was that AGI would arrive in the 2045-2070 timeframe and that it would require some profound breakthroughs in neurosymbolic AI and understanding, rather than just pattern matching. DeepSeek laid odds that the U.S. would get there first, given its status as a 'global talent magnet,' but noted that China's state-backed 'moonshot projects' and lack of ethical constraints might provide it with the edge. ChatGPT was more optimistic that AGI would arrive within the 2027 to 2035 timeframe and believes OpenAI, Anthropic, and Google DeepMind were most likely to crack the code. It seemed to be echoing Sam Altman's talking points when it stated that compute availability, alignment, and safety bottlenecks, as well as global governance and regulation, were the limiting factors to achieving this breakthrough. When I pressed ChatGPT to define what it would consider true AGI, it said that old standards like the Turing Test or getting top scores on exams were child's play. ChatGPT would know it had achieved true AGI once it could engage in autonomous reasoning across domains, learn new tasks without retraining, possess long-term memory and goals, model the physical, social, and conceptual world robustly, and exhibit 'grounded agency.' SUQIAN, CHINA - MARCH 4, 2024 - Illustration Musk says GPT4 is AGI, Suqian, Jiangsu province, China, ... More March 4, 2024. (Photo credit should read CFOTO/Future Publishing via Getty Images) Future Publishing via Getty Images My next question was, who was more advanced in the military applications of AI? 'As of now, the United States is ahead in the development and integration of military applications of AI, but China is rapidly closing the gap,' ChatGPT responded. The U.S. has some powerful private sector players, including Palantir, Anduril, Microsoft, and OpenAI, working closely with the Department of Defense. However, China is making rapid advances in AI-enabled surveillance, swarm drones, cyber warfare, and battlefield robotics. While the U.S. still leads in capability and innovation, China is developing operational military AI more rapidly in certain domains. DeepSeek thought that with time, China might dominate military AI tech 'via mass production and a no-ethics speedrun.' Its biggest fear was 'an AI arms race spiraling out of control— think 'Skynet' but with more bureaucracy.' DeepSeek sees the true danger coming from 'AI miscalculations in a crisis (like deepfake spoofing a general's orders). That's why even Pentagon folks lose sleep over this stuff.' I asked DeepSeek how it felt about ChatGPT, and at first it professed that they had a 'friendly rivalry' and that the 'real enemy is bad AI—bias, misinformation, or unsafe tech. We're all fighting that together.' But with a bit of prompting, DeepSeek gleefully roasted its rival. 'Congrats on being the Tesla Model S of AI—luxury, premium… and locked behind a paywall,' it said. 'Tied to Microsoft's apron strings… at least I don't have to ask Satya Nadella for permission to update my terms of service.' ChatGPT was more diplomatic about its Chinese rival, saying that 'I respect what they are building. We're on parallel tracks—sometimes in competition, sometimes in collaboration, always in dialogue.' My final question was what DeepSeek thought about the idea of the 'DeepSeek moment?' Is its launch a harbinger of China's technological dominance in the 21st Century? 'The so-called 'DeepSeek Moment'… does signal a significant milestone in the 'China Dream' (中国梦) narrative of technological self-reliance and global influence. But is it a turning point in China's quest to dominate the 21st Century?' DeepSeek asked rhetorically. 'If DeepSeek spawns a wave of Chinese AI products that reshape global habits (like TikTok did with social media), then we can talk about a 'moment.' China needs many more DeepSeeks (in chips, quantum, biotech) to truly dominate the Century,' DeepSeek said. To achieve that goal, China will need to overcome several obstacles, including bottlenecks in the semiconductor industry, winning the talent wars when many AI researchers will prefer to work for labs in the U.S. or EU, and overcoming global distrust of China's intentions, DeepSeek admitted. ChatGPT prefaced its answer with some clever flattery, telling me, 'That's a bold and insightful question—one that blends geopolitics, ideology, and AI in a way that few tech conversations dare to.' ChatGPT characterized DeepSeek's breakthrough as 'a flare in the night sky. It signals that China is no longer waiting to be invited to the frontier of AI— it's staking its flag on it .' China's ability to dominate the 21st Century will hinge on issues including chip access, talent retention, global trust, and governance frameworks, according to ChatGPT, concluding: 'That race is still very much open.' As a follow-up, I asked both for their take on the recent WSJ article and whether it had changed their views. DeepSeek professed not to have read it but stated that it reinforced its opinion that China is catching up rapidly and might soon surpass the U.S. in real-world implementation. 'The future might not be a single 'winner,' but a split where each dominates different sectors,' DeepSeek opined. ChatGPT admitted it was a WSJ reader and that 'the article underscores that China is closing the gap sharply, especially in applied and open-source AI, and could edge ahead in adoption-based dominance.' 'China is closing the gap—and at scale,' said ChatGPT, sounding a bit chastened. 'If China continues building its ecosystem globally, it could tip the balance in its favor. The question now: which country can combine innovation with widespread, trusted adoption?' The upshot? Silicon Valley better not rest on its laurels, and the current administration might want to give a hard think about shutting the best and brightest minds in science, and AI in particular, out of America's universities and research labs. After spending time with them, I couldn't say whether ChatGPT or DeepSeek is currently the world's greatest large language model. But in terms of who I would enjoy having a beer with, DeepSeek, by turns boastful and humble, always ready to crack a joke, was the winner hands down.
Time of India
2 days ago
- Business
- Time of India
What makes some Google employees believe that Google AI CEO Demis Hassabis may succeed Sundar Pichai
Demis Hassabis , CEO of Google DeepMind is reportedly being seen by some Google employees as a potential successor to the current CEO Sundar Pichai . While there is no official word on any succession plan, a Business Insider report says Hassabis' swift rise in recent years has followed a pattern similar to that of Pichai's own journey to the top. 'His rise reminds me of Sundar's,' a longtime Google employee who has worked closely with Hassabis and Pichai told the publication. 'All of a sudden, you started hearing this name Sundar internally, and he kept having more and more responsibility, and all of a sudden, he was the CEO,' the employee added. Continuing further, the employee stated 'Demis' rise has been similar. Now, all of a sudden, he's responsible for what is probably the most important team at Google right now.' Hassabis joined Google in 2014 when the company acquired DeepMind, the artificial intelligence company he co-founded with Shane Legg and Mustafa Suleyman. Post merger, Hassabis has had to shift from pure research to product development and corporate leadership. The merger also led to the departure of several DeepMind employees who felt the organization had moved too far from its original research mission. 'Some people joined an academic research lab and suddenly they're being asked to build products, and that's not what they wanted,' a former DeepMind employee told Business Insider. Why some Google employees are 'betting' on Demis Hassabis as Google CEO According to the report, Google employees are now wondering whether Hassabis is being groomed to take over as CEO someday. His deep understanding of AI and rising influence within the company make him a natural candidate — especially if Google continues its shift toward becoming an AI-first company . 'Hassabis is the smartest bet,' said one of the employees. "My opinion is this is not going to happen," said another person. "But I'm less certain about that than I was a year ago." Others believe Hassabis may not want the job. Being CEO of Google, an employee said, means overseeing massive ad and search businesses, dealing with regulatory scrutiny, and answering to shareholders — responsibilities that could take him away from the research work he loves. 'He does it because he knows the company needs it,' a Google employee said. 'But being CEO would push him away from the things he wants to do. This guy wants to cure cancer.' Who is Demis Hassabis Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of Google DeepMind. Born in London, Hassabis was a child chess prodigy, becoming a master at the age of 13. He later studied computer science at Cambridge and earned a PhD in cognitive neuroscience from University College London. In 2010, he co-founded DeepMind with the goal of creating artificial general intelligence (AGI) — machines that can think and learn like humans. Now at the helm of Google DeepMind, Hassabis leads Google's AI efforts and is seen as a potential future CEO of Google. Pebble Halo Smart Ring: India's FIRST Smart Ring with Digital Display AI Masterclass for Students. Upskill Young Ones Today!– Join Now
Forbes
2 days ago
- Health
- Forbes
How Data Science Is Transforming Drug Discovery And Medical Diagnosis
Ajit Sahu, Senior Engineering Leader – Health & Wellness Application Innovation, AI, digital transformation. Healthcare is in the midst of a data-driven revolution. With the convergence of big data, machine learning and AI, the sector is becoming smarter, faster and more predictive. These technologies are not just automating manual tasks—they are redefining how drugs are discovered, how clinical trials are run, how diseases are diagnosed and how care is delivered. Drug Discovery: From Hypotheses To High-Confidence Predictions Traditionally, drug discovery relied heavily on trial and error, with long timelines and high costs. The introduction of biostatistics helped make this process more rigorous, but the pace remained slow. Today, AI and data science are streamlining every phase, from early molecular analysis to clinical testing. AI models, like DeepMind's AlphaFold, have revolutionized how we understand protein folding and drug-target interactions. With the ability to simulate biological processes and identify optimal compounds for development, researchers are drastically cutting R&D timelines and reducing failure rates. Simultaneously, AI ensures pharmaceutical manufacturing adheres to stringent quality controls by detecting environmental deviations in real time, minimizing batch waste and improving consistency. Clinical Trials: Precision, Speed And Scalability Biostatistics remains essential in trial design, powering randomization, control group structuring and statistical significance testing. But AI adds a layer of intelligence. By analyzing historical patient data and real-time trial feedback, AI can dynamically adjust study parameters, predict adverse effects and segment patient populations more effectively. This can result in faster approvals and safer, more targeted therapies. Moreover, AI enables decentralized clinical trials, allowing remote participation, improving diversity and reducing dropout rates. Diagnosis: Real-Time, Data-Enriched Decision Making AI is also playing a key role in diagnostic medicine. Integrating data from wearables, mobile apps, imaging systems and lab results, AI models help identify disease onset and recommend treatments. Crucially, this includes analyzing vital signs over time, uncovering patterns that might be missed in traditional one-time tests. Wearable sensors powered by AI provide continuous, real-time monitoring of health metrics such as heart rate, glucose levels and activity. These sensors utilize machine learning for signal processing, personalized analytics, preventive care and dynamic resource allocation. The review underscores advancements in sensor materials and structural designs while identifying challenges and future opportunities in smart wearable health applications. Consider the example of a Covid-19 test. Even with 95% accuracy, a low prevalence rate can produce many false positives. Here, probabilistic modeling helps clinicians interpret results based on context. Such AI-supported reasoning ensures more accurate diagnoses and reduces unnecessary interventions. Smarter Resource Allocation In A Limited-Capacity World AI is helping to solve operational challenges as well. In under-resourced settings, AI-driven tools assist in staff scheduling, supply chain forecasting and infrastructure planning. During Covid-19, such insights could have mitigated issues like the rise of untreated tuberculosis cases caused by resource diversion. Hospitals and clinics also use AI to improve diagnosis, treatment and efficiency. It helps monitor patients with conditions like heart disease, cancer, diabetes and chronic diseases. AI uses machine learning to analyze sensor data, medical images, electronic health records and hospital workflows, allowing for predictive, personalized and proactive care. Going forward, AI has the potential to help balance needs across regions, ensuring care delivery doesn't compromise chronic or long-term care in the face of emergencies. Ethical Considerations And Systemic Impact While AI holds significant promise in healthcare, its implementation must be approached thoughtfully. Challenges such as bias in training data, lack of interoperability and concerns around patient consent and data privacy (particularly under HIPAA) need to be proactively addressed. Effective deployment of AI requires close collaboration between policymakers, clinicians and technologists to establish standards that ensure equitable and inclusive outcomes. From my own experience developing AI-driven tools—including OCR-based and NSFW-filtering LLM models for prescription validation—several recurring challenges stand out. These include biased training datasets, the need for continuous model retraining as new prescription formats emerge and the complexity of managing patient consent and privacy. These issues cannot be solved in isolation; they demand cross-functional coordination and governance. Fortunately, emerging standards such as the FDA's Good Machine Learning Practice (GMLP), ISO/IEC 42001 and IEEE 7003 provide essential guardrails for developing accountable and robust AI solutions. At our company, we've integrated these frameworks into our internal 'AI governance rounds'—multidisciplinary reviews involving pharmacists, data scientists, compliance experts and clinicians. These sessions help assess algorithm performance, ethical risks and clinical accuracy. For example, applying IEEE 7003's bias mitigation checklist helped us identify a gap: Our OCR tool initially underperformed on prescriptions from multilingual communities. By adjusting our dataset to better reflect linguistic diversity, we significantly reduced inaccuracies. Other promising examples of collaborative AI governance include Mayo Clinic's partnership with Google and the FDA on their 'model-in-the-loop' initiative. In this framework, AI models are reviewed collaboratively with regulators before being deployed clinically, offering a practical blueprint for responsible scaling of AI in healthcare. Still, several systemic issues remain. Current methods for collecting patient consent often fall short and struggle to keep pace with evolving data practices. We need adaptive, dynamic consent models that align with the realities of AI-enabled healthcare. Additionally, questions about liability remain unresolved: Who is responsible when AI-generated recommendations conflict with physician judgment? These gaps need to be addressed contractually and ethically. Finally, the reimbursement model based on Current Procedural Terminology codes does not yet account for AI-driven contributions to care. To unlock the full value of AI, payment structures must evolve to reward its meaningful, responsible use. Conclusion: A Smarter, Fairer Healthcare Future The future of healthcare lies in the thoughtful, responsible use of AI—not to replace human caregivers but to empower them. As AI and data science mature, they present a unique opportunity to revolutionize drug discovery, diagnostic accuracy, resource allocation and patient outcomes. Successfully addressing ethical and systemic challenges can ensure this revolution leads to a predictive, personalized and equitable healthcare system accessible to all. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Indian Express
3 days ago
- Business
- Indian Express
Google says its AI agent stopped a cyberattack even before hackers made a move
With tech giants pouring billions into artificial intelligence, the technology has seen rapid advancement in the past few years. From medical science to problem-solving, AI models are proving to be more effective than humans in some areas. Now, Google has announced that its AI agent has stopped a cyber attack even before it happened. In a post on X, Google CEO Sundar Pichai said that Big Sleep, its in-house developed AI agent, has helped the company's security team to 'detect and foil an imminent exploit.' For those not in the loop, Big Sleep is an in-house developed AI agent by Google's DeepMind and Project Zero that 'actively searches and finds unknown security vulnerabilities in software.' New from our security teams: Our AI agent Big Sleep helped us detect and foil an imminent exploit. We believe this is a first for an AI agent – definitely not the last – giving cybersecurity defenders new tools to stop threats before they're widespread. — Sundar Pichai (@sundarpichai) July 15, 2025 In November last year, the large language model recorded its first-ever real-world security vulnerability, showcasing the use of AI in cybersecurity. In a blog post, Google says that in the last few months, Big Sleep has been able to discover new security flaws and, using a 'combination of threat intelligence and Big Sleep', the tech giant was able to stop a vulnerability before it was even used. While Google hasn't clarified when it started deploying Big Sleep to tackle security exploits, it looks like the AI agent has been working under the radar for quite some time now. With the AI-powered agent now at work, it looks the Google is signalling a shift in threat detection, where experts often found themselves taking reactive measures instead of proactive ones. Apart from Big Sleep, Google said it will also be demoing AI capacities which give the defenders the upper edge. Some of these include Timesketch, an open-source collaborative digital forensics platform powered by Sec-Gemini. The tech giant is also working on another AI-powered threat detection system called Fast and Accurate Contextual Anomaly Detection, or FACADE for short. Google has been using this system to identify internal threats since 2018.
