Nikolas Weber
⚠️ OpenAI Reforça Segurança e Concede Poder de Veto ao Conselho
🧠 2023: O Ano da IA Generativa e o Que Esperar em 2024
✨ OpenAI Lança Guia Prático de Engenharia de Prompts
OpenAI is expanding its internal security processes to combat the threat of harmful AI by creating a new "security advisory group" and granting veto power to the board.
Security Advisory Group: This group will oversee the technical teams and make recommendations to leadership on security issues.
Board Veto Power: OpenAI's board has been granted veto power over decisions related to AI models deemed high-risk.
Preparedness Framework Update: OpenAI has updated its preparedness framework to identify, analyze, and decide on "catastrophic" risks inherent in the models they are developing.
Risk Categories: Models are assessed in four risk categories: cybersecurity, persuasion (disinformation), model autonomy, and CBRN (chemical, biological, radiological, and nuclear) threats.
These changes at OpenAI reflect an ongoing effort to ensure safety and responsibility in AI development, especially at a time when technology is advancing rapidly.
2023 was marked as the year of Generative AI, with significant advances impacting work, study, and even religion. Here are some highlights and expectations for 2024.
Widespread Adoption: Data shows that four out of five teenagers in the UK are using generative AI tools. About two-thirds of Australian employees report using generative AI at work.
Transformation of Work and Study: Generative AI tools have changed the way we work and study, assisting in everything from coding to creating images, videos, and audio.
Innovation in Various Sectors: Adobe has started integrating generative AI into widely used applications such as Illustrator and Photoshop, democratizing access to the technology. Large companies, such as Coca-Cola and Levi's, have adopted AI to create advertising and virtual models, demonstrating the versatility and commercial potential of AI.
Predictions for 2024
Integration and Expanded Functionality: Companies are expected to continue to integrate AI into their existing services, combining functions to make AI more intuitive, accessible, and useful.
Improvement in Quality and Realism: With continuous technological advancement, AI generations will become more realistic and of higher quality, overcoming previous challenges such as rendering of human hands and limbs.
Calls for Transparency and Media Standards: As AI-generated content becomes more common, the need for media standards denoting the use of AI will grow, improving public trust.
Expansion of Sovereign AI Capacity: Visionary leaders are likely to encourage the development of sovereign AI capabilities through funding research and development, training programs, and other investments.
The year 2023 was a turning point for Generative AI, demonstrating its transformative potential. In 2024, we can expect incremental advancements that will continue to shape technology, the economy, and society in innovative and unpredictable ways.
OpenAI has released a practical guide to help users get better results from large language models, such as GPT-4. This guide offers strategies and tactics that can be combined for greater effectiveness, encouraging experimentation to find the most efficient methods.
Main Points of the Guide
Clear Instructions: To get more accurate answers, it is crucial to provide clear and detailed instructions.
Use of Delimiters: Delimiters help indicate the different parts of the input clearly.
Specification of Steps: Detailing the steps necessary to complete a task can improve the accuracy of the responses.
Providing Examples: Including examples can help the model better understand the desired style or format.
Specification of Desired Length: Asking the model to produce responses of a specific length can help achieve the desired result.
Use of Reference Texts: Providing reference texts can help the model respond with fewer fabrications.
Division of Complex Tasks: Breaking down complex tasks into simpler sub-tasks can reduce error rates.
Time to 'Think': Asking the model to work on a solution before reaching a conclusion can lead to more accurate responses.
Use of External Tools: Complementing the model's weaknesses with external tools can improve effectiveness.
Systematic Testing: Systematically evaluating changes in the model's performance is crucial for ensuring improvements.
This guide is a valuable tool for anyone looking to maximize the potential of OpenAI's language models, whether for work, study, or entertainment.
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