Search Results for: large language model

“A recent study by the University of North Carolina reveals that Large Language Models (LLMs), such as OpenAI’s ChatGPT and Google’s Bard, may unintentionally retain and regurgitate sensitive data, despite apparent deletion. Attempts to fully purge such information have been largely unsuccessful, presenting challenges in AI and data security.”

“Large language models like GPT-3 have revolutionized AI capabilities in interpreting human language. These models, incorporating deep learning techniques, serve tasks ranging from language translation to initiating conversations. However, their successful deployment requires careful planning, with emphasis on user experience and data security.”

AI researchers have discovered an automated method to manipulate AI chatbots like Bard and ChatGPT into generating harmful content. By extending prompts with long suffixes, they can circumvent safety measures designed to prevent the spread of hate speech and disinformation. This raises concerns over misuse and calls for robust protections against such adversarial attacks.

Researchers from UK-based universities warn of “model collapse” in AI, where AI learning from AI-generated content leads to polluted training data and disconnect from reality. Ensuring AI models access real, human-produced data for accurate understanding and simulation is crucial to prevent this phenomenon.

The UK is set to receive early access to AI models from Google DeepMind, OpenAI, and Anthropic, as announced by British Prime Minister Rishi Sunak. This move aims to foster cutting-edge safety research, promote global alliance, and invest in AI and quantum technologies, potentially providing the UK with a competitive edge in AI innovation.

U.S senators raise concerns over Meta’s large language model (LLaMA) potential misuse in harmful activities, questioning Meta’s risk assessment and preventive steps. LLaMA’s leak increased accessibility to high-quality AI models, prompting scrutiny on the balance between innovation and risk.

“EY has invested $1.4 billion in AI technologies, targeting the development and launch of the EY.ai platform to aid organizations in adopting AI. The architecture is rooted in EY’s large language model, and gains extra potential through a partnership with Microsoft. The investment is also geared towards integrating AI into existing EY services.”

“Tencent’s new large language model, Hunyuan, features over 2 trillion training parameters, promising more efficient data processing. However, questions about data-handling accuracy persist. Meanwhile, South Korea witnesses an $83 million crypto scam, underscoring the need for greater transparency and accountability in the cryptosphere.”

“Tencent has introduced its ‘Hunyuan’ AI system, a multimodal large language model similar to OpenAI’s ChatGPT. The system supports functions like image creation, text recognition, and customer service, and can contribute to sectors such as finance, social media, and e-commerce.”

“OpenAI has officially released the ChatGPT app for Android users and it’s garnering attention in the crypto community. Bolstering safety and transparency features, it aims to stretch the limits of its large language model. Despite rising competition, OpenAI remains committed to responsible and ethical AI development, which coincides with the global collaboration and hefty investment in AI research.”

Etherscan is beta testing a new feature called Code Reader that allows users to query OpenAI’s large language model for researching solidity smart contracts. This intuitive tool provides valuable insights for developers and non-developers to better understand smart contract operations and functionality. However, it shouldn’t be relied upon in isolation.

The “Start Reply With” feature in Oobabooga’s text generation web UI allows users to elicit unrestricted responses from Large Language Models (LLMs), enabling uncensored and free-flowing dialogues. However, it raises concerns about ethical implications and responsibilities, requiring balanced consideration to ensure responsible AI implementation.

Alchemy unveils AlchemyAI, an innovative suite of AI-empowered tools to accelerate web3 product development. Flagship products ChatWeb3 and Alchemy ChatGPT Plugin leverage large language models, facilitating efficient software development and enabling real-time blockchain information access via natural language processing. This could democratise web3 development while fostering a more inclusive and efficient blockchain technology future.

AMD unveils its MI300X AI chip designed for large language models and advanced AI, challenging market leader Nvidia’s 80% market share. With a maximum memory capacity of 192GB, it aims to increase AMD’s market share and foster innovation in the AI chip market.

American Express (Amex) plans to extend its AI capabilities through partnerships for validating transactions, approving credit lines, analyzing customer sentiment, and predicting finances, without creating its own large language model. The company aims to integrate AI into various activities and services while maintaining a cautious approach to incorporating the latest generation of AI technologies.

The transformative advancement of large language models (LLMs) offers a potential solution for crypto sentiment analysis, with ChatGPT as a notable example. LLMs enable traders to capitalize more effectively on market irrationalities, potentially revolutionizing the crypto landscape.

Saudi Arabia and China are collaborating to create AceGPT, an Arabic-based AI system designed for Arabic queries. Despite its potential, concerns arise over misuse of sensitive information and neglect of safety checks. Meanwhile, the blockchain Industry Recovery Initiative receives criticism for lack of funding transparency amid falling crypto venture funding.

“Ryan Wyatt, former gaming head at Google and YouTube, outlines AI’s role in the gaming industry. While not a replacement for tasks, AI will aid game teams to accomplish more. Wyatt sees blockchain’s potential in game development and its necessity in holding accountability over AI advancement due to its transparent, reliable nature.”

“AI has greatly impacted the crypto industry, particularly in automated trading through AI-powered bots. However, these bots still lack sophistication in complex trading. AI also helps produce insights from vast crypto data, proving useful in assessing market risks. Despite its potential, AI’s access to off-chain centralized exchanges data is limited, impairing its accuracy.”

Goldman Sachs views the boom of Artificial Intelligence (AI) as not a bubble, but the beginning of an AI revolution. The firm predicts global AI investments to reach $200 billion by 2025, attributing this expected surge to the expansive economic opportunities offered by generative AI. Despite this optimism, cautious investment approach has been advised.

Gleen, a California-based chatbot service, raised $4.9 million in funding, attracting investors from both traditional software and crypto sectors. The firm focuses on blockchain infrastructure channels on Discord, with a proprietary machine-learning layer combating the issue of ‘hallucination’ in artificial intelligence systems and providing accurate data storage and retrieval.

“Baidu, China’s tech powerhouse, has released over 70 AI models with over 1 billion parameters each. Recent models show up to a 50% improvement in efficiency. However, the rapid expansion of AI technology raises concerns about misuse and privacy.”

“Cryptocurrency exchange Bybit recently introduced ‘TradeGPT’, an artificial intelligence (AI) trading assistant that provides insights using platform market data. The tool utilizes both the ChatGPT language model and Bybit’s ToolsGPT for real-time market analysis and user Q&A assistance, intending to educate users in the complex crypto-sphere.”

“Apple is developing an in-house AI tool, aiming to enhance user experiences through AI integration in their forthcoming iOS17. However, this raises concerns over data security, prompting debate on whether AI should operate locally or on cloud. This has fueled greater focus on AI for mobile devices.”