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Geeky Gadgets
13 hours ago
- Business
- Geeky Gadgets
ChatGPT Agent : The Future of Work in One Versatile AI Tool
What if you could have a single AI tool that not only writes code, generates stunning visuals, and conducts deep research but also learns and improves with every task? Enter the ChatGPT Agent, a new innovation by OpenAI that redefines what it means to work smarter. Imagine consolidating multiple apps, tools, and platforms into one seamless interface that adapts to your needs while safeguarding your data. This isn't just another AI assistant—it's a versatile powerhouse designed to streamline workflows, amplify productivity, and tackle complex challenges across industries. But with such fantastic potential comes an equally pressing question: how do we balance innovation with responsibility? In this video, Prompt Engineering explores the ChatGPT Agent's innovative capabilities, from automating programming tasks to generating AI-driven visuals and conducting in-depth research. You'll discover how this all-in-one AI worker is reshaping professional landscapes, empowering users to achieve more while navigating the ethical and security considerations that come with advanced AI tools. Whether you're a developer, researcher, or creative professional, the ChatGPT Agent offers a glimpse into the future of work—one where technology doesn't just assist but actively collaborates. As we delve deeper, you'll see how this tool can unlock new possibilities while challenging us to rethink the way we approach productivity and innovation. Overview of ChatGPT Agent Key Features and Capabilities The ChatGPT Agent distinguishes itself as a multi-functional AI system that consolidates several advanced tools into a single, user-friendly interface. This integration eliminates the need for multiple specialized applications, streamlining workflows and improving efficiency. By using reinforcement learning, the agent continuously refines its capabilities, delivering increasingly precise and effective results. Some of its standout features include: Code execution: Automates programming tasks, including debugging, script optimization, and algorithm development. Automates programming tasks, including debugging, script optimization, and algorithm development. Image generation: Produces AI-driven visuals tailored for creative projects or professional use cases. Produces AI-driven visuals tailored for creative projects or professional use cases. Deep research: Conducts thorough investigations, making it invaluable for academic studies, market analysis, or professional inquiries. These tools operate within a secure sandbox environment, making sure that all activities remain isolated from external systems. This design allows you to explore the agent's potential while safeguarding sensitive data and maintaining control over its operations. Practical Applications and Performance The ChatGPT Agent excels in a variety of professional and technical domains, often outperforming earlier AI models in terms of speed and accuracy. Its ability to analyze large datasets, generate detailed reports, and assist in tasks like competitive programming or financial modeling makes it a valuable asset in industries such as data science, investment banking, and software development. Despite its impressive capabilities, the agent is not without limitations. While it handles structured and well-defined tasks effectively, it may struggle with more nuanced or creative challenges, such as editing highly complex spreadsheets or solving abstract problems. These limitations highlight the continued importance of human expertise in areas where AI tools are less proficient. By understanding these boundaries, you can better use the agent's strengths while compensating for its weaknesses. ChatGPT Agent Overview Watch this video on YouTube. Gain further expertise in ChatGPT Agent by checking out these recommendations. Security and Ethical Considerations Security is a foundational aspect of the ChatGPT Agent's design. Operating within a sandbox environment, the system ensures that tasks are executed securely and under your direct supervision. Before accessing sensitive data or performing critical actions, the agent requires your explicit consent, giving you full control over its operations. However, the use of such advanced AI tools also introduces potential risks. Granting the agent access to private accounts or confidential information could expose vulnerabilities, especially if proper safeguards are not in place. As a user, it is essential to weigh the convenience of the agent's capabilities against the potential risks to personal or organizational data. Vigilance, informed decision-making, and adherence to best practices are critical to mitigating these risks and making sure secure usage. Ethical considerations also play a significant role in the adoption of AI technologies like the ChatGPT Agent. Issues such as data privacy, bias in AI-generated outputs, and the potential misuse of its capabilities must be carefully addressed. By using the agent responsibly and within ethical guidelines, you can maximize its benefits while minimizing potential harm. Access and Future Developments The ChatGPT Agent is available to Pro, Plus, and Teams users, with varying levels of access depending on the subscription tier. Pro users, for instance, enjoy higher message limits, allowing more extensive interactions and greater flexibility. OpenAI has also announced plans to phase out the operator tool, replacing it with standalone features like deep research, which will further enhance the agent's functionality and usability. These developments reflect a broader effort to make advanced AI tools more accessible to a diverse range of users. However, they also underscore the importance of understanding the responsibilities that come with using such powerful technologies. As AI continues to evolve, staying informed about updates and best practices will be essential for using its full potential. AI's Role in Shaping the Future The ChatGPT Agent is part of a larger trend in artificial intelligence, where systems are increasingly designed for practical, real-world applications. Competing with tools like Google's Mariner, it exemplifies the rapid pace of innovation in the AI landscape. These advancements are transforming industries by allowing faster, more efficient workflows in areas such as programming, financial analysis, and creative content generation. However, the widespread adoption of AI technologies also presents challenges. Regulatory compliance, ethical considerations, and the need for user adaptation are critical factors that must be addressed to ensure responsible use. Successfully navigating these complexities will be key to unlocking the full potential of AI tools like the ChatGPT Agent while minimizing risks and unintended consequences. Balancing Innovation with Responsibility The ChatGPT Agent offers unparalleled convenience, empowering you to tackle complex tasks with greater efficiency and precision. Its integration of advanced tools into a single platform makes it a powerful resource for professionals and organizations alike. However, this convenience comes with responsibilities. As a user, you must remain vigilant about privacy, security, and ethical considerations to ensure that the agent is used effectively and responsibly. By understanding the agent's strengths and limitations, you can make informed decisions about how to incorporate it into your workflows. This balanced approach will enable you to harness the fantastic potential of AI while safeguarding against potential risks. The ChatGPT Agent represents a significant milestone in AI development, but its true value lies in how it is used to address real-world challenges and drive meaningful progress. Media Credit: Prompt Engineering Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
National Post
4 days ago
- Science
- National Post
Colby Cosh: The lifelike nature of artificial intelligence
Article content On Tuesday a Harvard artificial-intelligence researcher, Keyon Vafa, published a short tweet-thread about a new paper looking at how some types of high-performing intelligence algorithms are behaving under the hood. If you're interested in the implications of AI progress, this paper is instructive even if you don't fully understand it, and, yes, that is tantamount to a confession on my part. (And, as the old joke goes, if you're not interested in the implications of AI progress, rest assured that AI progress is interested in you.) Article content Article content For academics like Vafa and his colleagues, AI has a pervasive 'black box' issue that is part of why it inspires fear and confusion. We have learned how to make computers mimic intelligence quite convincingly, and sooner than almost anyone imagined, by applying previously unfathomable amounts of brute computing power. But our ability to understand how these thinking objects are thinking is often limited. Article content Article content If you don't understand anything at all about the Vafa paper, the thing to notice about it is that it is fundamentally experimental. The research approach is oddly like a biologist's, like somebody who studies wombats by rounding up a bunch of wombats and observing wombat behaviour. The team 'teaches' an AI algorithm to perform intellectual task X to near-perfection by giving it verbal instructions and data (in plain English), and then has to figure out 'How did it actually do that?' using statistical inference. It's the choice of task X that makes this paper most intriguing. Anybody educated enough to still be reading a newspaper probably knows the basics of how the human understanding of planetary orbits evolved. In classical antiquity, the prevailing assumption was that the planets orbited the Earth in circular paths. Well before the birth of Jesus, astronomers were already good at predicting the movements of the planets on the basis of this false model. The planets sometimes apparently move backwards in the sky, so an unmodified 'fixed Earth' + 'perfectly circular paths' model couldn't do the job on its own: to make accurate predictions, astronomers had to add other circular motions-within-motions, called 'deferents' and 'epicycles,' to the basic circular-orbit picture. Article content Article content Well, fast-forward a dozen centuries, and along come Copernicus asking 'What if Earth isn't at the centre after all?'; Kepler asking 'What if the orbits aren't circular, but elliptical?'; and Newton, who got to the bottom of the whole thing by introducing the higher-level abstraction of gravitational force. Bye-bye epicycles. Article content None of these intellectual steps, mind you, added anything to anyone's practical ability to predict planetary motions. Copernicus's model took generations to be accepted for this reason (along with the theological/metaphysical objections to the Earth not being at the centre of the universe): it wasn't ostensibly as sophisticated or as powerful as the old reliable geocentric model. But you can't get to Newton, who found that the planets and earthbound objects are governed by the same elegant and universal laws of motion, without Copernicus and Kepler. Article content Which, in 2025, raises the question: could a computer do what Newton did? Vafa's research group fed orbital data to AIs and found that they could correctly behave like ancient astronomers: make dependable extrapolations about the future movements of real planets, including the Earth. This raises the question whether the algorithms in question generate their successful orbital forecasts by somehow inferring the existence of Newtonian force-abstractions. We know that 'false,' overfitted models and heuristics can work for practical purposes, but we would like AIs to be automated Newtons if we are going to live with them. We would like AIs to discover new laws and scientific principles of very high generality and robustness that we filthy meatbags haven't noticed yet.
Yahoo
11-07-2025
- Health
- Yahoo
AI in health care could save lives and money − but change won't happen overnight
Imagine walking into your doctor's office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what's wrong. This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives. What's more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved. But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low. A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses. I'm a professor and researcher who studies AI and health care analytics. I'll try to explain why AI's growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI's widespread adoption by the medical industry. Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care. AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care. But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn't perfectly match the patient in front of them. As a result, AI doesn't always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations. Racial and ethnic bias is another issue. If data includes bias because it doesn't include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened. Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor's offices simply don't have the time, personnel, money or will to implement AI. Also, many cutting-edge AI systems operate as opaque 'black boxes.' They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification. But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings. There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records. For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient's data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards. Privacy concerns also extend to patients' trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care. The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises. Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they're safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations. Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time. Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help. Suffice to say that health care's transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI's potential to treat millions and save trillions awaits. This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Turgay Ayer, Georgia Institute of Technology Read more: Biased AI can be bad for your health – here's how to promote algorithmic fairness The White House's 'AI Bill of Rights' outlines five principles to make artificial intelligence safer, more transparent and less discriminatory What is an AI agent? A computer scientist explains the next wave of artificial intelligence tools Turgay Ayer owns shares in Value Analytics Labs, a healthcare technology company. He received funding from government agencies, including NSF, NIH, and CDC.
CTV News
10-07-2025
- Science
- CTV News
Concerns about wildfire impact on Lake Winnipeg
Winnipeg Watch A U of M researcher is raising concerns about the effects of wildfires on Lake Winnipeg. Joseph Bernacki reports.
Arab News
08-07-2025
- General
- Arab News
In Hiroshima, search for remains keeps war alive for lone volunteer
NINOSHIMA: Dozens of times a year, Rebun Kayo takes a ferry to a small island across from the port of Hiroshima in search of the remains of those killed by the atomic bomb 80 years ago. For the 47-year-old researcher, unearthing even the tiniest fragments on Ninoshima Island is a sobering reminder that the war is a reality that persists — buried, forgotten and unresolved. 'When we die, we are interred in places like temples or churches and bid farewell in a ceremony. That's the dignified way of being sent off,' said Kayo, a researcher at Hiroshima University's Center for Peace who spends his own time and money on the solo excavations. After the United States dropped the atomic bomb over Hiroshima on August 6, 1945, instantly killing about 78,000 people and injuring far more, Ninoshima, about 4 km (2.5 miles) from the hypocenter, became a field hospital. Within weeks, some 10,000 victims, both dead and alive, were ferried across the water. Many perished soon after, and when cremations could not keep up, people were buried in mass graves. While many remains were unearthed in the decades following the war, witness accounts suggested there were more burial grounds. The son of a resident informed Kayo about one area on the island's northwestern coast in 2014 and from there, he saved up funds and began digging four years later. NO CLOSURE In searing heat last weekend, Kayo cut through overgrown brush to return to the spot where he had left off three weeks before. After an hour and a half of digging, he carefully picked out two thumbnail-sized bone fragments from the dirt — additions to the roughly 100 he has unearthed so far. Every discovery brings home to him the cruelty of war. The pain was never as raw as when Kayo found pieces of a young child's jaw and tooth earlier this year, he said. 'That hit me really hard,' he said, his white, long-sleeve shirt soaked through with sweat. 'That child was killed by the bomb, knowing nothing about the world ... I couldn't come to terms with it for a while, and that feeling still lingers.' One day, he plans to take all the fragments to a Buddhist temple, where they can be enshrined. Kayo's drive for repeating the gruelling task year after year is partly personal. Born in Okinawa, where some of the bloodiest battles during World War Two were fought, Kayo himself has three relatives whose remains were never found. Volunteers still descend on Okinawa from all over Japan for excavations, and because the poison ivy in the forests there is prohibitive for him, Kayo returns the favor on Ninoshima instead. As long as traces of the dead keep turning up, the war's proximity is palpable for Kayo. 'People today who don't know about the war focus only on the recovery, and they move the conversation forward while forgetting about these people here,' he said. 'And in the end, you'll have people saying, 'even if you drop an atomic bomb, you can recover' ... There will always be people who try to justify it in a way that suits them.'
