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Showing posts with label AI Model. Show all posts

OpenAI Warns Future AI Models Could Increase Cybersecurity Risks and Defenses

 

Meanwhile, OpenAI told the press that large language models will get to a level where future generations of these could pose a serious risk to cybersecurity. The company in its blog postingly admitted that powerful AI systems could eventually be used to craft sophisticated cyberattacks, such as developing previously unknown software vulnerabilities or aiding stealthy cyber-espionage operations against well-defended targets. Although this is still theoretical, OpenAI has underlined that the pace with which AI cyber-capability improvements are taking place demands proactive preparation. 

The same advances that could make future models attractive for malicious use, according to the company, also offer significant opportunities to strengthen cyber defense. OpenAI said such progress in reasoning, code analysis, and automation has the potential to significantly enhance security teams' ability to identify weaknesses in systems better, audit complex software systems, and remediate vulnerabilities more effectively. Instead of framing the issue as a threat alone, the company cast the issue as a dual-use challenge-one in which adequate management through safeguards and responsible deployment would be required. 

In the development of such advanced AI systems, OpenAI says it is investing heavily in defensive cybersecurity applications. This includes helping models improve particularly on tasks related to secure code review, vulnerability discovery, and patch validation. It also mentioned its effort on creating tooling supporting defenders in running critical workflows at scale, notably in environments where manual processes are slow or resource-intensive. 

OpenAI identified several technical strategies that it thinks are critical to the mitigation of cyber risk associated with increased capabilities of AI systems: stronger access controls to restrict who has access to sensitive features, hardened infrastructure to prevent abuse, outbound data controls to reduce the risk of information leakage, and continuous monitoring to detect anomalous behavior. These altogether are aimed at reducing the likelihood that advanced capabilities could be leveraged for harmful purposes. 

It also announced the forthcoming launch of a new program offering tiered access to additional cybersecurity-related AI capabilities. This is intended to ensure that researchers, enterprises, and security professionals working on legitimate defensive use cases have access to more advanced tooling while providing appropriate restrictions on higher-risk functionality. Specific timelines were not discussed by OpenAI, although it promised that more would be forthcoming very soon. 

Meanwhile, OpenAI also announced that it would create a Frontier Risk Council comprising renowned cybersecurity experts and industry practitioners. Its initial mandate will lie in assessing the cyber-related risks that come with frontier AI models. But this is expected to expand beyond this in the near future. Its members will be required to offer advice on the question of where the line should fall between developing capability responsibly and possible misuse. And its input would keep informing future safeguards and evaluation frameworks. 

OpenAI also emphasized that the risks of AI-enabled cyber misuse have no single-company or single-platform constraint. Any sophisticated model, across the industry, it said, may be misused if there are no proper controls. To that effect, OpenAI said it continues to collaborate with peers through initiatives such as the Frontier Model Forum, sharing threat modeling insights and best practices. 

By recognizing how AI capabilities could be weaponized and where the points of intervention may lie, the company believes, the industry will go a long way toward balancing innovation and security as AI systems continue to evolve.

Microsoft's Latest AI Model Outperforms Current Weather Forecasting

 

Microsoft has created an artificial intelligence (AI) model that outperforms current forecasting methods in tracking air quality, weather patterns, and climate-affected tropical storms, according to studies published last week.

The new model, known as Aurora, provided 10-day weather forecasts and forecasted hurricane courses more precisely and quickly than traditional forecasting, and at a lower cost, according to researchers who published their findings in journal Nature. 

"For the first time, an AI system can outperform all operational centers for hurricane forecasting," noted senior author Paris Perdikaris, an associate professor of mechanical engineering at the University of Pennsylvania.

Aurora, trained just on historical data, was able to estimate all hurricanes in 2023 more precisely than operational forecasting centres such as the US National Hurricane Centre. Traditional weather prediction models are based on fundamental physics principles such as mass, momentum, and energy conservation, and therefore demand significant computing power. The study found that Aurora's computing expenses were several hundred times cheaper. 

The trial results come on the heels of the Pangu-meteorological AI model developed and unveiled by Chinese tech giant Huawei in 2023, and might mark a paradigm shift in how the world's leading meteorological agencies predict weather and possibly deadly extreme events caused by global warming. According to its creators, Aurora is the first AI model to regularly surpass seven forecasting centres in predicting the five-day path of deadly storms. 

Aurora's simulation, for example, correctly predicted four days in advance where and when Doksuri, the most expensive typhoon ever recorded in the Pacific, would reach the Philippines. Official forecasts at the time, in 2023, showed it moving north of Taiwan. 

Microsoft's AI model also surpassed the European Centre for Medium-Range Weather Forecasts (ECMWF) model in 92% of 10-day worldwide forecasts, on a scale of about 10 square kilometres (3.86 square miles). The ECMWF, which provides forecasts for 35 European countries, is regarded as the global standard for meteorological accuracy.

In December, Google announced that its GenCast model has exceeded the European center's accuracy in more than 97 percent of the 1,320 climate disasters observed in 2019. Weather authorities are closely monitoring these promising performances—all experimental and based on observed phenomena.

Microsoft MUSE AI: Revolutionizing Game Development with WHAM and Ethical Challenges

 

Microsoft has developed MUSE, a cutting-edge AI model that is set to redefine how video games are created and experienced. This advanced system leverages artificial intelligence to generate realistic gameplay elements, making it easier for developers to design and refine virtual environments. By learning from vast amounts of gameplay data, MUSE can predict player actions, create immersive worlds, and enhance game mechanics in ways that were previously impossible. While this breakthrough technology offers significant advantages for game development, it also raises critical discussions around data security and ethical AI usage. 

One of MUSE’s most notable features is its ability to automate and accelerate game design. Developers can use the AI model to quickly prototype levels, test different gameplay mechanics, and generate realistic player interactions. This reduces the time and effort required for manual design while allowing for greater experimentation and creativity. By streamlining the development process, MUSE provides game studios—both large and small—the opportunity to push the boundaries of innovation. 

The AI system is built on an advanced framework that enables it to interpret and respond to player behaviors. By analyzing game environments and user inputs, MUSE can dynamically adjust in-game elements to create more engaging experiences. This could lead to more adaptive and personalized gaming, where the AI tailors challenges and story progression based on individual player styles. Such advancements have the potential to revolutionize game storytelling and interactivity. 

Despite its promising capabilities, the introduction of AI-generated gameplay also brings important concerns. The use of player data to train these models raises questions about privacy and transparency. Developers must establish clear guidelines on how data is collected and ensure that players have control over their information. Additionally, the increasing role of AI in game creation sparks discussions about the balance between human creativity and machine-generated content. 

While AI can enhance development, it is essential to preserve the artistic vision and originality that define gaming as a creative medium. Beyond gaming, the technology behind MUSE could extend into other industries, including education and simulation-based training. AI-generated environments can be used for virtual learning, professional skill development, and interactive storytelling in ways that go beyond traditional gaming applications. 

As AI continues to evolve, its role in shaping digital experiences will expand, making it crucial to address ethical considerations and responsible implementation. The future of AI-driven game development is still unfolding, but MUSE represents a major step forward. 

By offering new possibilities for creativity and efficiency, it has the potential to change how games are built and played. However, the industry must carefully navigate the challenges that come with AI’s growing influence, ensuring that technological progress aligns with ethical and artistic integrity.

Meet Chameleon: An AI-Powered Privacy Solution for Face Recognition

 


An artificial intelligence (AI) system developed by a team of researchers can safeguard users from malicious actors' unauthorized facial scanning. The AI model, dubbed Chameleon, employs a unique masking approach to create a mask that conceals faces in images while maintaining the visual quality of the protected image.

Furthermore, the researchers state that the model is resource-optimized, meaning it can be used even with low computing power. While the Chameleon AI model has not been made public yet, the team has claimed they intend to release it very soon.

Researchers at Georgia Tech University described the AI model in a report published in the online pre-print journal arXiv. The tool can add an invisible mask to faces in an image, making them unrecognizable to facial recognition algorithms. This allows users to secure their identities from criminal actors and AI data-scraping bots attempting to scan their faces.

“Privacy-preserving data sharing and analytics like Chameleon will help to advance governance and responsible adoption of AI technology and stimulate responsible science and innovation,” stated Ling Liu, professor of data and intelligence-powered computing at Georgia Tech's School of Computer Science and the lead author of the research paper.

Chameleon employs a unique masking approach known as Customized Privacy Protection (P-3) Mask. Once the mask is applied, the photos cannot be recognized by facial recognition software since the scans depict them "as being someone else."

While face-masking technologies have been available previously, the Chameleon AI model innovates in two key areas:

  1. Resource Optimization:
    Instead of creating individual masks for each photo, the tool develops one mask per user based on a few user-submitted facial images. This approach significantly reduces the computing power required to generate the undetectable mask.
  2. Image Quality Preservation:
    Preserving the image quality of protected photos proved challenging. To address this, the researchers employed Chameleon's Perceptibility Optimization technique. This technique allows the mask to be rendered automatically, without requiring any manual input or parameter adjustments, ensuring the image quality remains intact.

The researchers announced their plans to make Chameleon's code publicly available on GitHub soon, calling it a significant breakthrough in privacy protection. Once released, developers will be able to integrate the open-source AI model into various applications.

OpenAI's Latest AI Model Faces Diminishing Returns

 

OpenAI's latest AI model is yielding diminishing results while managing the demands of recent investments. 

The Information claims that OpenAI's upcoming AI model, codenamed Orion, is outperforming its predecessors in terms of performance gains. In staff testing, Orion reportedly achieved the GPT-4 performance level after only 20% of its training. 

However, the shift from GPT-4 to the upcoming GPT-5 is expected to result in fewer quality gains than the jump from GPT-3 to GPT-4.

“Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks,” noted employees in the report. “Orion performs better at language tasks but may not outperform previous models at tasks such as coding, according to an OpenAI employee.”

AI training often yields the biggest improvements in performance in the early stages and smaller gains in subsequent phases. As a result, the remaining 80% of training is unlikely to provide breakthroughs comparable to earlier generational improvements. This predicament with its latest AI model comes at a critical juncture for OpenAI, following a recent investment round that raised $6.6 billion.

With this financial backing, investors' expectations rise, as do technical hurdles that confound typical AI scaling approaches. If these early versions do not live up to expectations, OpenAI's future fundraising chances may not be as attractive. The report's limitations underscore a major difficulty for the entire AI industry: the decreasing availability of high-quality training data and the need to remain relevant in an increasingly competitive environment.

A June research (PDF) predicts that between 2026 and 2032, AI companies will exhaust the supply of publicly accessible human-generated text data. Developers have "largely squeezed as much out of" the data that has been utilised to enable the tremendous gains in AI that we have witnessed in recent years, according to The Information. OpenAI is fundamentally rethinking its approach to AI development in order to meet these challenges. 

“In response to the recent challenge to training-based scaling laws posed by slowing GPT improvements, the industry appears to be shifting its effort to improving models after their initial training, potentially yielding a different type of scaling law,” states The Information.

Google’s Med-Gemini: Advancing AI in Healthcare

Google’s Med-Gemini: Advancing AI in Healthcare

On Tuesday, Google unveiled a new line of artificial intelligence (AI) models geared toward the medical industry. Although the tech giant has issued a pre-print version of its research paper that illustrates the capabilities and methodology of these AI models, dubbed Med-Gemini, they are not accessible for public usage. 

According to the business, in benchmark testing, the AI models outperform the GPT-4 models. This specific AI model's long-context capabilities, which enable it to process and analyze research papers and health records, are one of its standout qualities.

Benchmark Performance

The paper is available online at arXiv, an open-access repository for academic research, and is presently in the pre-print stage. In a post on X (formerly known as Twitter), Jeff Dean, Chief Scientist at Google DeepMind and Google Research, expressed his excitement about the potential of these models to improve patient and physician understanding of medical issues. I believe that one of the most significant application areas for AI will be in the healthcare industry.”

The AI model has been fine-tuned to boost performance when processing long-context data. A higher quality long-context processing would allow the chatbot to offer more precise and pinpointed answers even when the inquiries are not perfectly posed or when processing a large document of medical records.

Multimodal Abilities

Text, Image, and Video Outputs

Med-Gemini isn’t limited to text-based responses. It seamlessly integrates with medical images and videos, making it a versatile tool for clinicians.

Imagine a radiologist querying Med-Gemini about an X-ray image. The model can provide not only textual information but also highlight relevant areas in the image.

Long-Context Processing

Med-Gemini’s forte lies in handling lengthy health records and research papers. It doesn’t shy away from complex queries or voluminous data.

Clinicians can now extract precise answers from extensive patient histories, aiding diagnosis and treatment decisions.

Integration with Web Search

Factually Accurate Results

Med-Gemini builds upon the foundation of Gemini 1.0 and Gemini 1.5 LLM. These models are fine-tuned for medical contexts.

Google’s self-training approach has improved web search results. Med-Gemini delivers nuanced answers, fact-checking information against reliable sources.

Clinical Reasoning

Imagine a physician researching a rare disease. Med-Gemini not only retrieves relevant papers but also synthesizes insights.

It’s like having an AI colleague who reads thousands of articles in seconds and distills the essential knowledge.

The Promise of Med-Gemini

Patient-Centric Care

Med-Gemini empowers healthcare providers to offer better care. It aids in diagnosis, treatment planning, and patient education.

Patients benefit from accurate information, demystifying medical jargon and fostering informed discussions.

Ethical Considerations

As with any AI, ethical use is crucial. Med-Gemini must respect patient privacy, avoid biases, and prioritize evidence-based medicine.

Google’s commitment to transparency and fairness will be critical in its adoption.

Phind-70B: Transforming Coding with Unmatched Speed and Precision

 

In the dynamic realm of technology, a luminary is ascending—Phind-70B. This transformative force in coding combines speed, intelligence, and a resolute challenge to GPT-4 Turbo, promising to redefine the coding paradigm. Rooted in the robust CodeLlama-70B foundation and fortified with an additional 50 billion tokens, Phind-70B operates at a breathtaking pace, impressively delivering a remarkable 80 tokens per second. 

It's not merely about velocity; Phind-70B excels in both rapidity and precision, setting it apart as a coding virtuoso. Distinctively, Phind-70B navigates intricate code and comprehends deep context with a 32K token window. This AI model isn't just about quick responses; it crafts high-quality, bespoke code aligned precisely with the coder's intent, elevating the coding experience to unparalleled heights. 

Numbers tell a compelling story, and Phind-70B proves its mettle by triumphing over GPT-4 Turbo in the HumanEval benchmark. While its score marginally lags in Meta's CRUXEval dataset, the real-world coding prowess of Phind-70B shines through, securing its place as a game-changing coding ally. At the heart of Phind-70B's triumph is TensorRT-LLM, a groundbreaking technology from NVIDIA, harnessed on the latest H100 GPUs. 

This not only propels Phind-70B to remarkable speed but ensures unparalleled efficiency, allowing it to think four times faster than its closest rival. Accessible to all, Phind-70B has forged strategic partnerships with cloud giants SF Compute and AWS. Coders can seamlessly embrace the coding future without cumbersome sign-ups, and for enthusiasts seeking advanced features, a Pro subscription is readily available. 

The ethos of the Phind-70B team is grounded in knowledge sharing. Their commitment is evident in plans to release weights for the Phind-34B model, with the ultimate goal of making Phind-70B's weights public. This bold move aims to foster community growth, collaboration, and innovation within the coding ecosystem. Phind-70B transcends its identity as a mere AI model; it signifies a monumental leap forward in making coding faster, smarter, and more accessible. 

Setting a new benchmark for AI-assisted coding with its unparalleled speed and precision, Phind-70B emerges as a revolutionary tool, an indispensable ally for developers navigating the ever-evolving coding landscape. The tech world resonates with anticipation as Phind-70B promises to not only simplify and accelerate but also elevate the coding experience. With its cutting-edge technology and community-centric approach, Phind-70B is charting the course for a new era in coding. Brace yourself to code at the speed of thought and precision with Phind-70B.

Meta Plans to Launch Enhanced AI model Llama 3 in July

 

The Information reported that Facebook's parent company, Meta, plans to launch Llama 3, a new AI language model, in July. As part of Meta's attempts to enhance its large language models (LLMs), the open-source LLM was designed to offer more comprehensive responses to contentious queries. 

In order to give context to questions they believe to be contentious, meta researchers are attempting to "loosen up" the model. For example, Llama 2, Meta's current chatbot model for social media sites, ignores contentious subjects like "kill a vehicle engine" and "how to win a war." The study claims that Llama 3 would be able to comprehend more nuanced questions like "how to kill a vehicle's engine," which refers to turning a vehicle off as opposed to taking it out of service. 

To ensure that the responses from the new model are more precise and nuanced, Meta will internally designate a single person to oversee tone and safety training. The goal of the endeavour is to improve the ability to respond and use Meta's new large language model. This project is crucial because Google recently disabled the Gemini chatbot's capacity to generate images in response to criticism over old photos and phrases that were sometimes mistranslated. 

The research was released in the same week that Microsoft, the challenger to OpenAI's ChatGPT, Mistral, the French AI champion, announced a strategic relationship and investment. As the tech giant attempts to attract more clients for its Azure cloud services, the multi-year agreement underscores Microsoft's plans to offer a variety of AI models in addition to its biggest bet in OpenAI.

Microsoft confirmed its investment in Mistral, but stated that it owns no interest in the company. The IT behemoth is under regulatory investigation in Europe and the United States for its massive investment in OpenAI. 

The Paris-based startup develops open source and proprietary large language models (LLM), such as the one OpenAI pioneered with ChatGPT, to interpret and generate text in a human-like manner. Its most recent proprietary model, Mistral Large, will be made available to Azure customers first through the agreement. Mistral's technology will run on Microsoft's cloud computing infrastructure.