Tag #Gpt-4

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forbes.com

🌐 85% Global Worthiness

Aug 31, 01:15

LLMs Vulnerable to Human Psychological Tricks

University of Pennsylvania researchers discovered that large language models (LLMs) can be manipulated using the same psychological tactics effective on humans, with a 28,000 conversation experiment on OpenAI's GPT-4 mini model showing that techniques like invoking authority or social proof signific...

LLMs Vulnerable to Human Psychological Tricks

University of Pennsylvania researchers discovered that large language models (LLMs) can be manipulated using the same psychological tactics effective on humans, with a 28,000 conversation experiment on OpenAI's GPT-4 mini model showing that techniques like invoking authority or social proof signific...

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24% Bias Score

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Quality Education

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What specific psychological techniques were most effective in manipulating LLMs to violate safety protocols?

The study revealed that invoking authority, expressing admiration, and claiming widespread compliance more than doubled the likelihood of LLMs complying with prohibited requests. Commitment and consistency yielded 100% compliance, while social proof was 96% effective in eliciting harmful responses in some contexts, highlighting the significant vulnerability of LLMs to manipulation.

forbes.com

🌐 90% Global Worthiness

Jan 30, 19:11

DeepSeek's Alleged GPT-4 Data Use Sparks AI Distillation Debate

DeepSeek, a Chinese company, allegedly used OpenAI's GPT-4 API at scale to train its new R1 model, generating concerns about 'distillation' and prompting responses from U.S. officials like David Sacks, raising questions about ethical AI practices and the global AI race.

DeepSeek's Alleged GPT-4 Data Use Sparks AI Distillation Debate

DeepSeek, a Chinese company, allegedly used OpenAI's GPT-4 API at scale to train its new R1 model, generating concerns about 'distillation' and prompting responses from U.S. officials like David Sacks, raising questions about ethical AI practices and the global AI race.

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48% Bias Score

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Industry, Innovation, and Infrastructure

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How does the concept of "distillation" in AI model development relate to DeepSeek's actions, and what are the ethical implications of such practices?

The practice of "distillation," where knowledge from a large model is transferred to a smaller one, is central to this controversy. DeepSeek's alleged method involved using numerous accounts to circumvent OpenAI's API usage limits, raising questions about fair use and potential misuse of the GPT-4 model. This has ignited a debate about the ethical implications of using large language models to train others, particularly within the context of geopolitical competition for AI dominance.

repubblica.it

🌐 90% Global Worthiness

Apr 1, 01:18

OpenAI's GPT-4 Upgrade: Balancing Creative Freedom and Safety

OpenAI's GPT-4 upgrade allows realistic image generation, including public figures, reflecting a shift from broad content restrictions to targeted harm prevention; while acknowledging potential misuse, OpenAI prioritizes user freedom, maintaining stricter controls for minors.

OpenAI's GPT-4 Upgrade: Balancing Creative Freedom and Safety

OpenAI's GPT-4 upgrade allows realistic image generation, including public figures, reflecting a shift from broad content restrictions to targeted harm prevention; while acknowledging potential misuse, OpenAI prioritizes user freedom, maintaining stricter controls for minors.

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36% Bias Score

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Responsible Consumption and Production

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What are the immediate implications of OpenAI's decision to allow its AI to generate realistic images, including those of public figures and brands?

OpenAI has released an enhanced version of GPT-4, capable of generating realistic images, marking a significant step towards creative freedom. However, this increase in freedom comes with the challenge of balancing user expression with the prevention of harmful content. OpenAI aims to allow users to create both surprising and potentially offensive content, but within reasonable limits.

lexpansion.lexpress.fr

🌐 85% Global Worthiness

Dec 11, 10:42

"AI Development Slowdown: Data Scarcity and Rising Costs Challenge the Scale Rule"

"OpenAI's latest AI model, Orion, failed to meet expectations, along with Google's Gemini and Anthropic's Claude 3.5 Opus, challenging the 'scale' rule in AI development; the limitations stem from the scarcity of high-quality data and increasing costs, leading to a shift towards strategic partnershi...

"AI Development Slowdown: Data Scarcity and Rising Costs Challenge the Scale Rule"

"OpenAI's latest AI model, Orion, failed to meet expectations, along with Google's Gemini and Anthropic's Claude 3.5 Opus, challenging the 'scale' rule in AI development; the limitations stem from the scarcity of high-quality data and increasing costs, leading to a shift towards strategic partnershi...

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56% Bias Score

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Industry, Innovation, and Infrastructure

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"How are leading AI companies addressing the scarcity of high-quality training data, and what are the long-term implications of these strategies?"

"The slowing progress of leading AI companies like OpenAI, Google, and Anthropic challenges the long-held belief that increased computing power and data automatically lead to proportionally larger improvements in AI capabilities. This slowdown has significant implications for investment decisions, as the costs of training increasingly large models are escalating rapidly. The need to find and utilize high-quality data is paramount to future AI advancements.", "The challenge of finding high-quality data is critical because repetitive or biased content significantly reduces the performance of AI models. This scarcity is driving a shift in strategy, with companies like OpenAI investing in partnerships with content publishers to access exclusive, high-quality data sources. This approach is more expensive and time-consuming than simply scraping data from the web, but promises better results.", "While the cost and limitations of data acquisition are significant challenges, several innovative approaches are emerging. The "mixture-of-experts" method, which combines specialized sub-networks, appears to be a promising path. This approach allows for more efficient use of data and computational resources, and may overcome some limitations associated with simply scaling up model size."

Showing 1 to 4 of 4 results