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Silicon Valley
AI generated Deepfake AI Security AI technology AI Tool Breach attack Cyber Security Hacker attack Silicon Valley

Silicon Valley Crosswalk Buttons Hacked With AI Voices Mimicking Tech Billionaires

 

A strange tech prank unfolded across Silicon Valley this past weekend after crosswalk buttons in several cities began playing AI-generated voice messages impersonating Elon Musk and Mark Zuckerberg.  

Pedestrians reported hearing bizarre and oddly personal phrases coming from audio-enabled crosswalk systems in Menlo Park, Palo Alto, and Redwood City. The altered voices were crafted to sound like the two tech moguls, with messages that ranged from humorous to unsettling. One button, using a voice resembling Zuckerberg, declared: “We’re putting AI into every corner of your life, and you can’t stop it.” Another, mimicking Musk, joked about loneliness and buying a Cybertruck to fill the void.  

The origins of the incident remain unknown, but online speculation points to possible hacktivism—potentially aimed at critiquing Silicon Valley’s AI dominance or simply poking fun at tech culture. Videos of the voice spoof spread quickly on TikTok and X, with users commenting on the surreal experience and sarcastically suggesting the crosswalks had been “venture funded.” This situation prompts serious concern. 

Local officials confirmed they’re investigating the breach and working to restore normal functionality. According to early reports, the tampering may have taken place on Friday. These crosswalk buttons aren’t new—they’re part of accessibility technology designed to help visually impaired pedestrians cross streets safely by playing audio cues. But this incident highlights how vulnerable public infrastructure can be to digital interference. Security researchers have warned in the past that these systems, often managed with default settings and unsecured firmware, can be compromised if not properly protected. 

One expert, physical penetration specialist Deviant Ollam, has previously demonstrated how such buttons can be manipulated using unchanged passwords or open ports. Polara, a leading manufacturer of these audio-enabled buttons, did not respond to requests for comment. The silence leaves open questions about how widespread the vulnerability might be and what cybersecurity measures, if any, are in place. This AI voice hack not only exposed weaknesses in public technology but also raised broader questions about the blending of artificial intelligence, infrastructure, and data privacy. 

What began as a strange and comedic moment at the crosswalk is now fueling a much larger conversation about the cybersecurity risks of increasingly connected cities. With AI becoming more embedded in daily life, events like this might be just the beginning of new kinds of public tech disruptions.
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encryption technology
AI Privacy AI Systems AI technology AI Tool Cryptography Cyber Security Data Privacy Deep Learning encryption technology

Orion Brings Fully Homomorphic Encryption to Deep Learning for AI Privacy

 

As data privacy becomes an increasing concern, a new artificial intelligence (AI) encryption breakthrough could transform how sensitive information is handled. Researchers Austin Ebel, Karthik Garimella, and Assistant Professor Brandon Reagen have developed Orion, a framework that integrates fully homomorphic encryption (FHE) into deep learning. 

This advancement allows AI systems to analyze encrypted data without decrypting it, ensuring privacy throughout the process. FHE has long been considered a major breakthrough in cryptography because it enables computations on encrypted information while keeping it secure. However, applying this method to deep learning has been challenging due to the heavy computational requirements and technical constraints. Orion addresses these challenges by automating the conversion of deep learning models into FHE-compatible formats. 

The researchers’ study, recently published on arXiv and set to be presented at the 2025 ACM International Conference on Architectural Support for Programming Languages and Operating Systems, highlights Orion’s ability to make privacy-focused AI more practical. One of the biggest concerns in AI today is that machine learning models require direct access to user data, raising serious privacy risks. Orion eliminates this issue by allowing AI to function without exposing sensitive information. The framework is built to work with PyTorch, a widely used machine learning library, making it easier for developers to integrate FHE into existing models. 

Orion also introduces optimization techniques that reduce computational burdens, making privacy-preserving AI more efficient and scalable. Orion has demonstrated notable performance improvements, achieving speeds 2.38 times faster than previous FHE deep learning methods. The researchers successfully implemented high-resolution object detection using the YOLO-v1 model, which contains 139 million parameters—a scale previously considered impractical for FHE. This progress suggests Orion could enable encrypted AI applications in sectors like healthcare, finance, and cybersecurity, where protecting user data is essential. 

A key advantage of Orion is its accessibility. Traditional FHE implementations require specialized knowledge, making them difficult to adopt. Orion simplifies the process, allowing more developers to use the technology without extensive training. By open-sourcing the framework, the research team hopes to encourage further innovation and adoption. As AI continues to expand into everyday life, advancements like Orion could help ensure that technological progress does not come at the cost of privacy and security.
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Threat Intelligence
AI AI Tool Data Privacy Technology Threat Intelligence

AI and Privacy – Issues and Challenges

 

Artificial intelligence is changing cybersecurity and digital privacy. It promises better security but also raises concerns about ethical boundaries, data exploitation, and spying. From facial recognition software to predictive crime prevention, customers are left wondering where to draw the line between safety and overreach as AI-driven systems become more and more integrated into daily life.

The same artificial intelligence (AI) tools that aid in spotting online threats, optimising security procedures, and stopping fraud can also be used for intrusive data collecting, behavioural tracking, and mass spying. The use of AI-powered surveillance in corporate data mining, law enforcement profiling, and government tracking has drawn criticism in recent years. AI runs the potential of undermining rather than defending basic rights in the absence of clear regulations and transparency. 

AI and data ethics

Despite encouraging developments, there are numerous instances of AI-driven inventions going awry, which raise serious questions. A face recognition business called Clearview AI amassed one of the largest facial recognition databases in the world by illegally scraping billions of photos from social media. Clearview's technology was employed by governments and law enforcement organisations across the globe, leading to legal action and regulatory action about mass surveillance. 

The UK Department for Work and Pensions used an AI system to detect welfare fraud. An internal investigation suggested that the system disproportionately targeted people based on their age, handicap, marital status, and country. This prejudice resulted in certain groups being unfairly picked for fraud investigations, raising questions about discrimination and the ethical use of artificial intelligence in public services. Despite earlier guarantees of impartiality, the findings have fuelled calls for increased openness and supervision in government AI use. 

Regulations and consumer protection

The ethical use of AI is being regulated by governments worldwide, with a number of significant regulations having an immediate impact on consumers. The AI Act of the European Union, which is scheduled to go into force in 2025, divides AI applications into risk categories. 

Strict regulations will be applied to high-risk technology, like biometric surveillance and facial recognition, to guarantee transparency and moral deployment. The EU's commitment to responsible AI governance is further reinforced by the possibility of severe sanctions for non compliant companies. 

Individuals in the United States have more control over their personal data according to California's Consumer Privacy Act. Consumers have the right to know what information firms gather about them, to seek its erasure, and to opt out of data sales. This rule adds an important layer of privacy protection in an era where AI-powered data processing is becoming more common. 

The White House has recently introduced the AI Bill of Rights, a framework aimed at encouraging responsible AI practices. While not legally enforceable, it emphasises the need of privacy, transparency, and algorithmic fairness, pointing to a larger push for ethical AI development in policy making.
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Technology
AI Tool DeepSeek Generative AI HKGAI V1 Hong Kong Technology

Hong Kong Launches Its First Generative AI Model

 

Last week, Hong Kong launched its first generative artificial intelligence (AI) model, HKGAI V1, ushering in a new era in the city's AI development. The tool was designed by the Hong Kong Generative AI Research and Development Centre (HKGAI) for the Hong Kong Special Administrative Region (HKSAR) government's InnoHK innovation program. 

The locally designed AI tool, which is driven by DeepSeek's data learning model, has so far been tested by about 70 HKSAR government departments. According to a press statement from HKGAI, this innovative accomplishment marks the successful localisation of DeepSeek in Hong Kong, injecting new vitality to the city's AI ecosystem and demonstrating the strong collaborative innovation capabilities between Hong Kong and the Chinese mainland in AI. 

Sun Dong, the HKSAR government's Secretary for Innovation, Technology, and Industry, highlighted during the launch ceremony that artificial intelligence (AI) is in the vanguard of a new industrial revolution and technical revolution, and Hong Kong is actively participating in this wave. 

Sun also emphasised the HKSAR government's broader efforts to encourage AI research, which include the building of an AI supercomputing centre, a 3-billion Hong Kong dollar (386 million US dollar) AI funding plan, and the clustering of over 800 AI enterprises at Science Park and Cyberport. He expressed confidence that the locally produced large language model will soon be available for usage by not just enterprises and people, but also for overseas Chinese communities. 

DeepSeek, founded by Liang Wengfeng, previously stunned the world with its low-cost AI model, which was created with substantially fewer processing resources than larger US tech businesses such as OpenAI and Meta. The HKGAI V1 system is the first in the world to use DeepSeek's full-parameter fine-tuning research methodology. 

The financial secretary allocated HK$1 billion (US$128.6 million) in the budget to build the Hong Kong AI Research and Development Institute. The government intends to start the institute by the fiscal year 2026-27, with cash set aside for the first five years to cover operational costs, including employing staff. 

“Our goal is to ensure Hong Kong’s leading role in the development of AI … So the Institute will focus on facilitating upstream research and development [R&D], midstream and downstream transformation of R&D outcomes, and expanding application scenarios,” Sun noted.
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Research
AI regulations AI technology AI Tool AI Usage Cyber Security generative AI tools Research

AI Use Linked to Decline in Critical Thinking Skills Among Students, Study Finds

 

A recent study has revealed a concerning link between the increased use of artificial intelligence (AI) tools and declining critical thinking abilities among students. The research, which analyzed responses from over 650 individuals aged 17 and older in the UK, found that young people who heavily relied on AI for memory and problem-solving tasks showed lower critical thinking skills. This phenomenon, known as cognitive offloading, suggests that outsourcing mental tasks to AI may hinder essential cognitive development. 

The study, titled AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking, was published in Societies and led by Michael Gerlich of SBS Swiss Business School. The findings indicated a strong correlation between high AI tool usage and lower critical thinking scores, with younger participants being more affected than their older counterparts. Gerlich emphasized the importance of educational interventions to help students engage critically with AI technologies and prevent the erosion of vital cognitive skills.  

Participants in the study were divided into three age groups: 17-25, 26-45, and 46 and older, with diverse educational backgrounds. Data collection included a 23-item questionnaire to measure AI tool usage, cognitive offloading tendencies, and critical thinking skills. Additionally, semi-structured interviews provided further insights into participants’ experiences and concerns about AI reliance. Many respondents expressed worry that their dependence on AI was influencing their decision-making processes. Some admitted to rarely questioning the biases inherent in AI recommendations, while others feared they were being subtly influenced by the technology. 

One participant noted, “I sometimes wonder if AI is nudging me toward decisions I wouldn’t normally make.” The study’s findings have significant implications for educational institutions and workplaces integrating AI tools into daily operations. With AI adoption continuing to grow rapidly, there is an urgent need for schools and universities to implement strategies that promote critical thinking alongside technological advancements. Educational policies may need to prioritize cognitive skill development to counterbalance the potential negative effects of AI dependence. 

As AI continues to shape various aspects of life, striking a balance between leveraging its benefits and preserving essential cognitive abilities will be crucial. The study serves as a wake-up call for educators, policymakers, and individuals to remain mindful of the potential risks associated with AI over-reliance.
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Technology
AI Ecosystem AI Models AI technology AI Tool Computing Data Privacy Data Sets DeFi privacy protection secure computing Technology

The Role of Confidential Computing in AI and Web3

 

 
The rise of artificial intelligence (AI) has amplified the demand for privacy-focused computing technologies, ushering in a transformative era for confidential computing. At the forefront of this movement is the integration of these technologies within the AI and Web3 ecosystems, where maintaining privacy while enabling innovation has become a pressing challenge. A major event in this sphere, the DeCC x Shielding Summit in Bangkok, brought together more than 60 experts to discuss the future of confidential computing.

Pioneering Confidential Computing in Web3

Lisa Loud, Executive Director of the Secret Network Foundation, emphasized in her keynote that Secret Network has been pioneering confidential computing in Web3 since its launch in 2020. According to Loud, the focus now is to mainstream this technology alongside blockchain and decentralized AI, addressing concerns with centralized AI systems and ensuring data privacy.

Yannik Schrade, CEO of Arcium, highlighted the growing necessity for decentralized confidential computing, calling it the “missing link” for distributed systems. He stressed that as AI models play an increasingly central role in decision-making, conducting computations in encrypted environments is no longer optional but essential.

Schrade also noted the potential of confidential computing in improving applications like decentralized finance (DeFi) by integrating robust privacy measures while maintaining accessibility for end users. However, achieving a balance between privacy and scalability remains a significant hurdle. Schrade pointed out that privacy safeguards often compromise user experience, which can hinder broader adoption. He emphasized that for confidential computing to succeed, it must be seamlessly integrated so users remain unaware they are engaging with such technologies.

Shahaf Bar-Geffen, CEO of COTI, underscored the role of federated learning in training AI models on decentralized datasets without exposing raw data. This approach is particularly valuable in sensitive sectors like healthcare and finance, where confidentiality and compliance are critical.

Innovations in Privacy and Scalability

Henry de Valence, founder of Penumbra Labs, discussed the importance of aligning cryptographic systems with user expectations. Drawing parallels with secure messaging apps like Signal, he emphasized that cryptography should function invisibly, enabling users to interact with systems without technical expertise. De Valence stressed that privacy-first infrastructure is vital as AI’s capabilities to analyze and exploit data grow more advanced.

Other leaders in the field, such as Martin Leclerc of iEXEC, highlighted the complexity of achieving privacy, usability, and regulatory compliance. Innovative approaches like zero-knowledge proof technology, as demonstrated by Lasha Antadze of Rarimo, offer promising solutions. Antadze explained how this technology enables users to prove eligibility for actions like voting or purchasing age-restricted goods without exposing personal data, making blockchain interactions more accessible.

Dominik Schmidt, co-founder of Polygon Miden, reflected on lessons from legacy systems like Ethereum to address challenges in privacy and scalability. By leveraging zero-knowledge proofs and collaborating with decentralized storage providers, his team aims to enhance both developer and user experiences.

As confidential computing evolves, it is clear that privacy and usability must go hand in hand to address the needs of an increasingly data-driven world. Through innovation and collaboration, these technologies are set to redefine how privacy is maintained in AI and Web3 applications.

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Software Updates
AI Tool Antivirus Detection antivirus software cyber resilience Cyber Security CyberThreat EDR online threats Software Updates

Zero Trust Endpoint Security: The Future of Cyber Resilience

 

The evolution of cybersecurity has moved far beyond traditional antivirus software, which once served as the primary line of defense against online threats. Endpoint Detection and Response (EDR) tools emerged as a solution to combat the limitations of antivirus programs, particularly in addressing advanced threats like malware. However, even EDR tools have significant weaknesses, as they often detect threats only after they have infiltrated a system. The need for a proactive, zero trust endpoint security solution has become more evident to combat evolving cyber threats effectively. 

Traditional antivirus software struggled to keep up with the rapid creation and distribution of new malware. As a result, EDR tools were developed to identify malicious activity based on behavior rather than known code signatures. These tools have since been enhanced with artificial intelligence (AI) for improved accuracy, automated incident responses to mitigate damage promptly, and managed detection services for expert oversight. Despite these advancements, EDR solutions still act only after malware is active, potentially allowing significant harm before mitigation occurs. 

Cybercriminals now use sophisticated techniques, including AI-driven malware, to bypass detection systems. Traditional EDR tools often fail to recognize such threats until they are running within an environment. This reactive approach highlights a critical flaw: the inability to prevent attacks before they execute. Consequently, organizations are increasingly adopting zero trust security strategies, emphasizing proactive measures to block unauthorized actions entirely. Zero trust endpoint security enforces strict controls across applications, user access, data, and network traffic. 

Unlike blocklisting, which permits all actions except those explicitly banned, application allowlisting ensures that only pre-approved software can operate within a system. This approach prevents both known and unknown threats from executing, offering a more robust defense against ransomware and other cyberattacks. ThreatLocker exemplifies a zero trust security platform designed to address these gaps. Its proactive tools, including application allowlisting, ringfencing to limit software privileges, and storage control to secure sensitive data, provide comprehensive protection. 

ThreatLocker Detect enhances this approach by alerting organizations to indicators of compromise, ensuring swift responses to emerging threats. A recent case study highlights the efficacy of ThreatLocker’s solutions. In January 2024, a ransomware gang attempted to breach a hospital’s network using stolen credentials. ThreatLocker’s allowlisting feature blocked the attackers from executing unauthorized software, while storage controls prevented data theft. Despite gaining initial access, the cybercriminals were unable to carry out their attack due to ThreatLocker’s proactive defenses. 

As cyber threats become more sophisticated, relying solely on detection-based tools like EDR is no longer sufficient. Proactive measures, such as those provided by ThreatLocker, represent the future of endpoint security, ensuring that organizations can prevent attacks before they occur and maintain robust defenses against evolving cyber risks.
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Voice-Based App
AI Tool Cyber Fraud Scammers User Security Voice-Based App

Meet Daisy, the AI Grandmother Designed to Outwit Scammers

 

The voice-based AI, known as Daisy or "dAIsy," impersonates a senior citizen to engage in meandering conversation with phone scammers.

Despite its flaws, such as urging people to eat deadly mushrooms, AI can sometimes be utilised for good. O2, the UK's largest mobile network operator, has implemented a voice-based AI chatbot to trick phone scammers into long, useless talks. Daisy, often known as "dAIsy," is a chatbot that mimics the voice of an elderly person, the most typical target for phone scammers. 

Daisy's goal is to automate "scambaiting," which is the technique of deliberately wasting phone fraudsters' time in order to keep them away from potential real victims for as long as possible. Scammers employ social engineering to abuse the elderly's naivety, convincing them, for example, that they owe back taxes and would be arrested if they fail to make payments immediately.

When a fraudster gets Daisy on the phone, they're in for a long chat that won't lead anywhere. If they get to the point when the fraudster requests private data, such as bank account information, Daisy will fabricate it. O2 claims that it is able to contact fraudsters in the first place by adding Daisy's phone number to "easy target" lists that scammers use for leads. 

Of course, the risk with a chatbot like Daisy is that the same technology can be used for opposite ends—we've already seen cases where real people, such as CEOs of major companies, had their voices deepfaked in order to deceive others into giving money to a fraudster. Senior citizens are already exposed enough. If they receive a call from someone who sounds like a grandchild, they will very certainly believe it is genuine.

Finally, preventing fraudulent calls and shutting down the groups orchestrating these frauds would be the best answer. Carriers have enhanced their ability to detect and block scammers' phone numbers, but it remains a cat-and-mouse game. Scammers use automated dialling systems, which allow them to phone numbers quickly and only alert them when they receive an answer. An AI bot that frustrates fraudsters by responding and wasting their time is preferable to nothing.
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Spyware
AI Tool ChatGPT ChatGPT Vulnerabilities ChatGPT. OpenAI Data Breach Data Hackers Data Safety User Data Data Theft OpenAI Spyware

ChatGPT Vulnerability Exploited: Hacker Demonstrates Data Theft via ‘SpAIware

 

A recent cyber vulnerability in ChatGPT’s long-term memory feature was exposed, showing how hackers could use this AI tool to steal user data. Security researcher Johann Rehberger demonstrated this issue through a concept he named “SpAIware,” which exploited a weakness in ChatGPT’s macOS app, allowing it to act as spyware. ChatGPT initially only stored memory within an active conversation session, resetting once the chat ended. This limited the potential for hackers to exploit data, as the information wasn’t saved long-term. 

However, earlier this year, OpenAI introduced a new feature allowing ChatGPT to retain memory between different conversations. This update, meant to personalize the user experience, also created an unexpected opportunity for cybercriminals to manipulate the chatbot’s memory retention. Rehberger identified that through prompt injection, hackers could insert malicious commands into ChatGPT’s memory. This allowed the chatbot to continuously send a user’s conversation history to a remote server, even across different sessions. 

Once a hacker successfully inserted this prompt into ChatGPT’s long-term memory, the user’s data would be collected each time they interacted with the AI tool. This makes the attack particularly dangerous, as most users wouldn’t notice anything suspicious while their information is being stolen in the background. What makes this attack even more alarming is that the hacker doesn’t require direct access to a user’s device to initiate the injection. The payload could be embedded within a website or image, and all it would take is for the user to interact with this media and prompt ChatGPT to engage with it. 

For instance, if a user asked ChatGPT to scan a malicious website, the hidden command would be stored in ChatGPT’s memory, enabling the hacker to exfiltrate data whenever the AI was used in the future. Interestingly, this exploit appears to be limited to the macOS app, and it doesn’t work on ChatGPT’s web version. When Rehberger first reported his discovery, OpenAI dismissed the issue as a “safety” concern rather than a security threat. However, once he built a proof-of-concept demonstrating the vulnerability, OpenAI took action, issuing a partial fix. This update prevents ChatGPT from sending data to remote servers, which mitigates some of the risks. 

However, the bot still accepts prompts from untrusted sources, meaning hackers can still manipulate the AI’s long-term memory. The implications of this exploit are significant, especially for users who rely on ChatGPT for handling sensitive data or important business tasks. It’s crucial that users remain vigilant and cautious, as these prompt injections could lead to severe privacy breaches. For example, any saved conversations containing confidential information could be accessed by cybercriminals, potentially resulting in financial loss, identity theft, or data leaks. To protect against such vulnerabilities, users should regularly review ChatGPT’s memory settings, checking for any unfamiliar entries or prompts. 

As demonstrated in Rehberger’s video, users can manually delete suspicious entries, ensuring that the AI’s long-term memory doesn’t retain harmful data. Additionally, it’s essential to be cautious about the sources from which they ask ChatGPT to retrieve information, avoiding untrusted websites or files that could contain hidden commands. While OpenAI is expected to continue addressing these security issues, this incident serves as a reminder that even advanced AI tools like ChatGPT are not immune to cyber threats. As AI technology continues to evolve, so do the tactics used by hackers to exploit these systems. Staying informed, vigilant, and cautious while using AI tools is key to minimizing potential risks.
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threat report
AI Tool Artificial Intelligence AsyncRAT Generative AI malware threat report

AI-Generated Malware Discovered in the Wild

 

Researchers found malicious code that they suspect was developed with the aid of generative artificial intelligence services to deploy the AsyncRAT malware in an email campaign that was directed towards French users. 

While threat actors have employed generative AI technology to design convincing emails, government agencies have cautioned regarding the potential exploit of AI tools to create malicious software, despite the precautions and restrictions that vendors implemented. 

Suspected cases of AI-created malware have been spotted in real attacks. The malicious PowerShell script that was uncovered earlier this year by cybersecurity firm Proofpoint was most likely generated by an AI system. 

As less technical threat actors depend more on AI to develop malware, HP security experts discovered a malicious campaign in early June that employed code commented in the same manner a generative AI system would. 

The VBScript established persistence on the compromised PC by generating scheduled activities and writing new keys to the Windows Registry. The researchers add that some of the indicators pointing to AI-generated malicious code include the framework of the scripts, the comments that explain each line, and the use of native language for function names and variables. 

AsyncRAT, an open-source, publicly available malware that can record keystrokes on the victim device and establish an encrypted connection for remote monitoring and control, is later downloaded and executed by the attacker. The malware can also deliver additional payloads. 

The HP Wolf Security research also states that, in terms of visibility, archives were the most popular delivery option in the first half of the year. Lower-level threat actors can use generative AI to create malware in minutes and customise it for assaults targeting different areas and platforms (Linux, macOS). 

Even if they do not use AI to create fully functional malware, hackers rely on it to accelerate their labour while developing sophisticated threats.
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OpenAI
AGI AI technology AI Tool Cyber Security Generative AI generative AI tools OpenAI

Project Strawberry: Advancing AI with Q-learning, A* Algorithms, and Dual-Process Theory

Project Strawberry, initially known as Q*, has quickly become a focal point of excitement and discussion within the AI community. The project aims to revolutionize artificial intelligence by enhancing its self-learning and reasoning capabilities, crucial steps toward achieving Artificial General Intelligence (AGI). By incorporating advanced algorithms and theories, Project Strawberry pushes the boundaries of what AI can accomplish, making it a topic of intense interest and speculation. 

At the core of Project Strawberry are several foundational algorithms that enable AI systems to learn and make decisions more effectively. The project utilizes Q-learning, a reinforcement learning technique that allows AI to determine optimal actions through trial and error, helping it navigate complex environments. Alongside this, the A* search algorithm provides efficient pathfinding capabilities, ensuring AI can find the best solutions to problems quickly and accurately. 

Additionally, the dual-process theory, inspired by human cognitive processes, is used to balance quick, intuitive judgments with thorough, deliberate analysis, enhancing decision-making abilities. Despite the project’s promising advancements, it also raises several concerns. One of the most significant risks involves encryption cracking, where advanced AI could potentially break encryption codes, posing a severe security threat. 

Furthermore, the issue of “AI hallucinations”—errors in AI outputs—remains a critical challenge that needs to be addressed to ensure accurate and trustworthy AI responses. Another concern is the high computational demands of Project Strawberry, which may lead to increased costs and energy consumption. Efficient resource management and optimization will be crucial to maintaining the project’s scalability and sustainability. The ultimate goal of Project Strawberry is to pave the way for AGI, where AI systems can perform any intellectual task a human can. 

Achieving AGI would revolutionize problem-solving across various fields, enabling AI to tackle long-term and complex challenges with advanced reasoning capabilities. OpenAI envisions developing “reasoners” that exhibit human-like intelligence, pushing the frontiers of AI research even further. While Project Strawberry represents a significant step forward in AI development, it also presents complex challenges that must be carefully navigated. 

The project’s potential has fueled widespread excitement and anticipation within the AI community, with many eagerly awaiting further updates and breakthroughs. As OpenAI continues to refine and develop Project Strawberry, it could set the stage for a new era in AI, bringing both remarkable possibilities and significant responsibilities.
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undergraduate psychology degree
AI in assessments AI Tool AI-generated essays ChatGPT educational integrity Technology undergraduate psychology degree

AI-Generated Exam Answers Outperform Real Students, Study Finds

 

In a recent study, university exams taken by fictitious students using artificial intelligence (AI) outperformed those by real students and often went undetected by examiners. Researchers at the University of Reading created 33 fake students and employed the AI tool ChatGPT to generate answers for undergraduate psychology degree module exams.

The AI-generated responses scored, on average, half a grade higher than those of actual students. Remarkably, 94% of the AI essays did not raise any suspicion among markers, with only a 6% detection rate, which the study suggests is likely an overestimate. These findings, published in the journal Plos One, highlight a significant concern: "AI submissions robustly gained higher grades than real student submissions," indicating that students could use AI to cheat undetected and achieve better grades than their honest peers.

Associate Professor Peter Scarfe and Professor Etienne Roesch, who led the study, emphasized the need for educators globally to take note of these findings. Dr. Scarfe noted, "Many institutions have moved away from traditional exams to make assessment more inclusive. Our research shows it is of international importance to understand how AI will affect the integrity of educational assessments. We won’t necessarily go back fully to handwritten exams - but the global education sector will need to evolve in the face of AI."

In the study, the AI-generated answers and essays were submitted for first-, second-, and third-year modules without the knowledge of the markers. The AI students outperformed real undergraduates in the first two years, but in the third-year exams, human students scored better. This result aligns with the idea that current AI struggles with more abstract reasoning. The study is noted as the largest and most robust blind study of its kind to date.

Academics have expressed concerns about the impact of AI on education. For instance, Glasgow University recently reinstated in-person exams for one course. Additionally, a study reported by the Guardian earlier this year found that most undergraduates used AI programs to assist with their essays, but only 5% admitted to submitting unedited AI-generated text in their assessments.
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Technology
AI technology AI Tool ChatGPT ChatGPT-4 Medical Sector Patient Data Technology

Harnessing AI and ChatGPT for Eye Care Triage: Advancements in Patient Management

 

In a groundbreaking study conducted by Dr. Arun Thirunavukarasu, a former University of Cambridge researcher, artificial intelligence (AI) emerges as a promising tool for triaging patients with eye issues. Dr. Thirunavukarasu's research highlights the potential of AI to revolutionize patient management in ophthalmology, particularly in identifying urgent cases that require immediate specialist attention. 

The study, conducted in collaboration with Cambridge University academics, evaluated the performance of ChatGPT 4, an advanced language model, in comparison to expert ophthalmologists and medical trainees. Remarkably, ChatGPT 4 exhibited a scoring accuracy of 69% in a simulated exam setting, outperforming previous iterations of the program and rival language models such as ChatGPT 3.5, Llama, and Palm2. 

Utilizing a vast dataset comprising 374 ophthalmology questions, ChatGPT 4 demonstrated its capability to analyze complex eye symptoms and signs, providing accurate recommendations for patient triage. When compared to expert clinicians, trainees, and junior doctors, ChatGPT 4 proved to be on par with experienced ophthalmologists in processing clinical information and making informed decisions. 

Dr. Thirunavukarasu emphasizes the transformative potential of AI in streamlining patient care pathways. He envisions AI algorithms assisting healthcare professionals in prioritizing patient cases, distinguishing between emergencies requiring immediate specialist intervention and those suitable for primary care or non-urgent follow-up. 

By leveraging AI-driven triage systems, healthcare providers can optimize resource allocation and ensure timely access to specialist services for patients in need. Furthermore, the integration of AI technologies in primary care settings holds promise for enhancing diagnostic accuracy and expediting treatment referrals. ChatGPT 4 and similar language models could serve as invaluable decision support tools for general practitioners, offering timely guidance on eye-related concerns and facilitating prompt referrals to specialist ophthalmologists. 

Despite the remarkable advancements in AI-driven healthcare, Dr. Thirunavukarasu underscores the indispensable role of human clinicians in patient care. While AI technologies offer invaluable assistance and decision support, they complement rather than replace the expertise and empathy of healthcare professionals. Dr. Thirunavukarasu reaffirms the central role of doctors in overseeing patient management and emphasizes the collaborative potential of AI-human partnerships in delivering high-quality care. 

As the field of AI continues to evolve, propelled by innovative research and technological advancements, the integration of AI-driven triage systems in clinical practice holds immense promise for enhancing patient outcomes and optimizing healthcare delivery in ophthalmology and beyond. Dr. Thirunavukarasu's pioneering work exemplifies the transformative impact of AI in revolutionizing patient care pathways and underscores the imperative of embracing AI-enabled solutions to address the evolving needs of healthcare delivery.
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Technology
AI Tool Chicago Generative AI Nightshade Poison Pill App Technology

AI Poison Pill App Nightshade Received 250K Downloads in Five Days

 

Shortly after its January release, the AI copyright infringement tool Nightshade exceeded the expectations of its developers at the University of Chicago's computer science department, with 250,000 downloads. With Nightshade, artists can avert AI models from using their artwork for training purposes without acquiring permission.

The Bureau of Labour Statistics reports that more than 2.67 million artists work in the United States, but social media response indicates that downloads have taken place across the globe. According to one of the coders, cloud mirror links were established in order to prevent overloading the University of Chicago's web servers.

The project's leader, Ben Zhao, a computer science professor at the University of Chicago, told VentureBeat that "the response is simply beyond anything we imagined.” 

"Nightshade seeks to 'poison' generative AI image models by altering artworks posted to the web, or 'shading' them on a pixel level, so that they appear to a machine learning algorithm to contain entirely different content — a purse instead of a cow," the researchers explained. After training on multiple "shaded" photos taken from the web, the goal is for AI models to generate erroneous images based on human input. 

Zhao, along with colleagues Shawn Shan, Wenxin Ding, Josephine Passananti, and Heather Zheng, "developed and released the tool to 'increase the cost of training on unlicensed data, such that licencing images from their creators becomes a viable alternative,'" VentureBeat reports, citing the Nightshade project page. 

Opt-out requests, which purport to stop unauthorised scraping, are reportedly made by the AI companies themselves; however, TechCrunch notes that "those motivated by profit over privacy can easily disregard such measures." 

Zhao and his colleagues do not intend to dismantle Big AI, but they do want to make sure that tech giants pay for licenced work—a requirement that applies to any business operating in the open—or else they risk legal repercussions. According to Zhao, the fact that AI businesses have web-crawling spiders that algorithmically collect data in an often undetectable manner has basically turned into a permit to steal.

Nightshade shows that these models are vulnerable and there are ways to attack, Zhao said. He went on to say that what it implies is that there are methods for content creators to provide harder returns than writing Congress or complaining via email or social media. 

Glaze, one of the team's apps that guards against AI infringement, has reportedly been downloaded 2.2 million times since its April 2023 release, according to VentureBeat. By changing pixels, glaze makes it more difficult for AI to "learn" from an artist's distinctive style.
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Technology
AI technology AI Tool Davos Open AI Technology

Google DeepMind Cofounder Claims AI Can Play Dual Role in Next Five Years

 

Mustafa Suleyman, cofounder of DeepMind, Google's AI group, believes that AI will be able to start and run its own firm within the next five years.

During a discussion on AI at the 2024 World Economic Forum, the now-CEO of Inflection AI was asked how long it will take AI to pass a Turing test-style exam. Passing would suggest that the technology has advanced to human-like capabilities known as AGI, or artificial general intelligence. 

In response, Suleyman stated that the modern version of the Turing test would be to determine whether an AI could operate as an entrepreneur, mini-project manager, and creator capable of marketing, manufacturing, and selling a product for profit. 

He seems to expect that AI will be able to demonstrate those business-savvy qualities before 2030—and inexpensively.

"I'm pretty sure that within the next five years, certainly before the end of the decade, we are going to have not just those capabilities, but those capabilities widely available for very cheap, potentially even in open source," Suleyman stated in Davos, Switzerland. "I think that completely changes the economy.”

The AI leader's views are just one of several forecasts Suleyman has made concerning AI's societal influence as technologies like OpenAI's ChatGPT gain popularity. Suleyman told CNBC at Davos last week that AI will eventually be a "fundamentally labor-replacing" instrument.

In a separate interview with CNBC in September, he projected that within the next five years, everyone will have AI assistants that will enhance productivity and "intimately know your personal information.” "It will be able to reason over your day, help you prioritise your time, help you invent, be much more creative," Suleyman stated. 

Still, he stated on the 2024 Davos panel that the term "intelligence" in reference to AI remains a "pretty unclear, hazy concept." He calls the term a "distraction.” 

Instead, he argues that researchers should concentrate on AI's real-world capabilities, such as whether an AI agent can communicate with humans, plan, schedule, and organise.

People should move away from the "engineering research-led exciting definition that we've used for 20 years to excite the field" and "actually now focus on what these things can do," Suleyman advised.
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Technology
AI Tool Creative Sphere Generative AI Machine learning Technology

Transforming the Creative Sphere With Generative AI

 

Generative AI, a trailblazing branch of artificial intelligence, is transforming the creative landscape and opening up new avenues for businesses worldwide. This article delves into how generative AI transforms creative work, including its benefits, obstacles, and tactics for incorporating this technology into your brand's workflow. 

 Power of generative AI

Generative AI uses advanced machine learning algorithms and natural language processing models to generate material and imagery that resembles human expressions. While others doubt its potential to recreate the full range of human creativity, Generative AI has indisputably transformed many parts of the creative process.

Generative AI systems, such as GPT-4, excel at producing human-like writing, making them critical for content creation in marketing and communication applications. Brands can use this technology to: 

  • Create highly personalised and persuasive content. 
  • Increase efficiency by automating the creation of repetitive material like descriptions of goods and customer communications. 
  • Provide a personalised user experience to increase user engagement and conversion rates.
  • Stand out in competitive marketplaces by creating distinctive and interesting content with AI. 

Challenges and ethical considerations 

Despite its potential, integrating Generative AI into the creative sector results in significant ethical concerns: 

Bias in AI: AI systems may unintentionally perpetuate biases in training data. Brands must actively address this issue by curating training data, reviewing AI outputs for bias, and applying fairness and bias mitigation strategies.

Transparency and Explainability: AI algorithms can be complex, making it difficult for consumers to comprehend how decisions are made. Brands should prioritise transparency by offering explicit explanations for AI-powered methods. 

Data Privacy: Generative AI is based on data, and misusing user data can result in privacy breaches. Brands must follow data protection standards, gain informed consent, and implement strong security measures. 

Future of generative AI in creativity

As Generative AI evolves, the future promises exciting potential for further transforming the creative sphere: 

Artistic Collaboration: Artists may work more closely with AI systems to create hybrid works that combine human and AI innovation. 

Personalised Art Experiences: Generative AI will provide highly personalised art experiences by dynamically altering artworks to individual preferences and feelings. 

AI in Art Education: Artificial intelligence (AI) will play an important role in art education by providing tools and resources to help students express their creativity. 

Ethical AI in Art: The art sector will place a greater emphasis on ethical AI practices, including legislation and guidelines to ensure responsible AI use.

The future of Generative AI in creativity is full of possibilities, including breaking down barriers, encouraging new forms of artistic expression, and developing a global community of artists and innovators. As this journey progresses, "Generative AI revolutionising art" will be synonymous with innovation, creativity, and endless possibilities.
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Telecommunication
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Three Ways Jio's BharatGPT Will Give It an Edge Over ChatGPT

 

In an era where artificial intelligence (AI) is transforming industries worldwide, India's own Reliance Jio is rising to the challenge with the launch of BharatGPT. BharatGPT, a visionary leap into the future of AI, is likely to be a game changer. Furthermore, it will enhance how technology connects with the diverse and dynamic Indian landscape. 

Reliance Jio and IIT Bombay's partnership to introduce BharatGPT appears to be an ambitious initiative to use AI technology to enhance Jio's telecom services. Bharat GPT could offer a more user-friendly and accessible interface by being voice and gesture-activated, making it easier to operate and navigate Jio's services. 

Its emphasis on enhancing user experience and minimising the need for human intervention suggests that automation and efficiency are important, which could result in more personalised and responsive services. This project is in line with the expanding trend of using AI in telecoms to raise customer satisfaction and service quality. 

Jio's BharatGPT has a significant advantage over ChatGPT. Here's a more extensive look at these potential differentiators:

Improved localization and language support

Multilingual features: India is a linguistic mosaic, with hundreds of languages and dialects spoken across the nation. BharatGPT could distinguish itself by providing support for a variety of Indian languages. It also supports Hindi, Bengali, Tamil, Telugu, Punjabi, Marathi, Gujarati, and other languages. This multilingual option would make it far more accessible and valuable to people who want to speak in their own language. 

Cultural details: Understanding the cultural diversity of India is critical for AI to give contextually relevant solutions. BharatGPT could invest in a thorough cultural awareness. Furthermore, this enables it to produce both linguistically accurate and culturally sensitive responses. This could include recognising local idioms and comprehending the significance of festivals. It also integrates historical and regional references and adheres to social conventions unique to India's many regions. 

Regional dialects: India's linguistic variety includes several regional dialects. BharatGPT may excel at recognising and accommodating diverse dialects, ensuring that consumers across the nation are understood and heard, regardless of their unique language preferences. 

Industry-specific customisation 

Sectoral tailoring: Given India's diversified economic landscape, BharatGPT could be tailored to specific industries in the country. For example, it might provide specialised AI models for agriculture, healthcare, education, finance, e-commerce, and other industries. This sectoral tailoring would make it an effective tool for professionals looking for domain-specific insights and solutions. 

Solution-oriented design: By resolving industry-specific challenges and user objectives, BharatGPT may give more precise and effective solutions. For example, in agriculture, it may provide real-time weather updates, crop management recommendations, and market insights. In healthcare, it could help with medical diagnosis, provide health information, and offer advice on how to manage chronic medical conditions. This technique will boost production and customer satisfaction in multiple sectors. 

Deep integration with Jio's ecosystem 

Service convergence: Jio's diverse ecosystem includes telephony, digital commerce, entertainment, and more. BharatGPT might exploit this ecosystem to provide seamless and improved user experiences. For example, it might assist consumers with making purchases, finding the best rates on Jio's digital commerce platform, discovering personalised content recommendations, or troubleshooting telecom issues. Such connections would improve the user experience and increase engagement with Jio's services. 

Data privacy and security: Given Jio's experience handling large quantities of user data via its telephony and internet services, BharatGPT may prioritise data privacy and security. It can use cutting-edge encryption, user data anonymization, and strict access limits to address rising concerns about data security in AI interactions. This dedication to securing user data would instil trust and confidence in users. 

As we approach this new technical dawn with the launch of BharatGPT, it is evident that Reliance Jio's goals extend far beyond the conventional. BharatGPT is more than a technology development; it is a step towards a more inclusive, intelligent, and innovative future. 

While the world waits for this pioneering project to come to fruition, one thing is certain: the launch of BharatGPT signals the start of an exciting new chapter in the history of artificial intelligence. Furthermore, it envisions a future in which technology is more intuitive, inclusive, and innovative than ever before. As with all great discoveries, the actual impact of BharatGPT will be seen in its implementation and the revolutionary improvements it brings to sectors and individuals alike.
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Technology
AI Tool Data Safety Generative AI LLM Private Data Technology

Anthropic Pledges to Not Use Private Data to Train Its AI

 

Anthropic, a leading generative AI startup, has announced that it would not employ its clients' data to train its Large Language Model (LLM) and will step in to safeguard clients facing copyright claims.

Anthropic, which was established by former OpenAI researchers, revised its terms of service to better express its goals and values. The startup is setting itself apart from competitors like OpenAI, Amazon, and Meta, which do employ user material to enhance their algorithms, by severing the private data of its own clients. 

The amended terms state that Anthropic "may not train models on customer content from paid services" and that Anthropic "as between the parties and to the extent permitted by applicable law, Anthropic agrees that customer owns all outputs, and disclaims any rights it receives to the customer content under these terms.” 

The terms also state that they "do not grant either party any rights to the other's content or intellectual property, by implication or otherwise," and that "Anthropic does not anticipate obtaining any rights in customer content under these terms."

The updated legal document appears to give protections and transparency for Anthropic's commercial clients. Companies own all AI outputs developed, for example, to avoid possible intellectual property conflicts. Anthropic also promises to defend clients against copyright lawsuits for any unauthorised content produced by Claude. 

The policy complies with Anthropic's mission statement, which states that AI should to be honest, safe, and helpful. Given the increasing public concern regarding the ethics of generative AI, the company's dedication to resolving issues like data privacy may offer it a competitive advantage.

Users' Data: Vital Food for LLMs

Large Language Models (LLMs), such as GPT-4, LlaMa, and Anthropic's Claude, are advanced artificial intelligence systems that comprehend and generate human language after being trained on large amounts of text data. 

These models use deep learning and neural networks to anticipate word sequences, interpret context, and grasp linguistic nuances. During training, they constantly refine their predictions, improving their capacity to communicate, write content, and give pertinent information.

The diversity and volume of the data on which LLMs are trained have a significant impact on their performance, making them more accurate and contextually aware as they learn from different language patterns, styles, and new information.

This is why user data is so valuable for training LLMs. For starters, it keeps the models up to date on the newest linguistic trends and user preferences (such as interpreting new slang).

Second, it enables personalisation and increases user engagement by reacting to specific user activities and styles. However, this raises ethical concerns because AI businesses do not compensate users for this vital information, which is used to train models that earn them millions of dollars.
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Vulnerabilities and Exploits
AI technology AI Tool Artificial Intelligence ChatGPT Digital Landscape generative AI tools OpenAI Protocols Security Flaws Vulnerabilities and Exploits

OpenAI Addresses ChatGPT Security Flaw

OpenAI has addressed significant security flaws in its state-of-the-art language model, ChatGPT, which has become widely used, in recent improvements. Although the business concedes that there is a defect that could pose major hazards, it reassures users that the issue has been addressed.

Security researchers originally raised the issue when they discovered a possible weakness that would have allowed malevolent actors to use the model to obtain private data. OpenAI immediately recognized the problem and took action to fix it. Due to a bug that caused data to leak during ChatGPT interactions, concerns were raised regarding user privacy and the security of the data the model processed.

OpenAI's commitment to transparency is evident in its prompt response to the situation. The company, in collaboration with security experts, has implemented mitigations to prevent data exfiltration. While these measures are a crucial step forward, it's essential to remain vigilant, as the fix may need to be fixed, leaving room for potential risks.

The company acknowledges the imperfections in the implemented fix, emphasizing the complexity of ensuring complete security in a dynamic digital landscape. OpenAI's dedication to continuous improvement is evident, as it actively seeks feedback from users and the security community to refine and enhance the security protocols surrounding ChatGPT.

In the face of this security challenge, OpenAI's response underscores the evolving nature of AI technology and the need for robust safeguards. The company's commitment to addressing issues head-on is crucial in maintaining user trust and ensuring the responsible deployment of AI models.

The events surrounding the ChatGPT security flaw serve as a reminder of the importance of ongoing collaboration between AI developers, security experts, and the wider user community. As AI technology advances, so must the security measures that protect users and their data.

Although OpenAI has addressed the possible security flaws in ChatGPT, there is still work to be done to guarantee that AI models are completely secure. To provide a safe and reliable AI ecosystem, users and developers must both exercise caution and join forces in strengthening the defenses of these potent language models.

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Video Conferencing
AI Companion AI Tool Generative AI Technology Video Conferencing

Zoom Launches AI Companion, Available at No Additional Cost

 

Zoom has pledged to provide artificial intelligence (AI) functions on its video-conferencing platform at no additional cost to paid clients. 

The tech firm believes that including these extra features as part of its paid platform service will provide a significant advantage as businesses analyse the price tags of other market alternatives. Zoom additionally touts the benefits of a federated multi-model architecture, which it claims will improve efficiencies.

Noting that customers have expressed concerns regarding the potential cost of using generative AI, particularly for larger organisations, Zoom's Asia-Pacific CEO Ricky Kapur stated, "At $30 per user each month? That is a substantial cost.” 

Large organisations will not want to provide access to every employee if it is too costly, Kapur stated. Executives must decide who should and should not have access to generative AI technologies, which can be a difficult decision. 

Because these functionalities are provided at no additional cost, Kapur claims that projects involving generative AI have "accelerated" among Zoom's paying customers. 

Several AI-powered features have been introduced by the video-conferencing platform in the last year, including AI Companion and Zoom Docs, the latter of which is set to become general next year. Zoom Docs is billed as a next-generation document workspace that includes "modern collaboration tools." The technology is built into the Zoom interface and is available in Meetings and Team Chat, as well as through internet and mobile apps.

AI Companion, previously known as Zoom IQ, is a generative AI assistant for the video-conferencing service that aids in the automation of time-consuming tasks. The tool can design chat responses with a customisable tone and duration based on user suggestions, as well as summarise unread chat messages. Zoom IQ can also summarise meetings, providing a record of what was said and who said it, as well as underlining crucial points. 

Customers who have signed up for one of Zoom's subscription plans can use AI Companion at no extra cost. The Pro plan costs $149.9 per user per year, while the Business plan costs $219.9 per user per year. Other options, Business Plus and Enterprise, are charged based on the customer's needs. 

According to Zoom's chief growth officer Graeme Geddes, the integration of Zoom Docs and AI Companion means customers will be able to receive a summary of their previous five meetings as well as a list of action items. Since its debut in September, AI Companion has been used by over 220,000 users. The artificial intelligence tool now supports 33 languages, including Chinese, Korean, and Japanese. 

Geddes emphasised Zoom's decision to integrate AI Companion at no additional cost for paying customers, noting the company believes these data-driven tools are essential features that everyone in the organisation should have access to. 

Zoom's federated approach to AI architecture, according to Geddes, is critical. Rather than relying on a single AI provider, as other IT companies have done, Zoom has chosen to combine multiple large language models (LLMs). These models include its own LLM as well as models from other parties such as Meta Llama 2, OpenAI GPT 3.5 and GPT 4, and Anthropic Claude 2.
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