Uncovering the Secret World of Espionage, Covert Operations and Military Action


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The field of Artificial Intelligence (AI) is constantly evolving and advancing, with new breakthroughs and discoveries being made all the time. One area that has seen significant progress in recent years is the development of large language models, which are used to process and understand natural language. These models have the ability to generate human-like text, answer questions, and even write articles and stories.
One of the pioneers in this field is Yoshua Bengio, the head of Canada's MILA institute for AI, who developed one of the first neural net language models about 20 years ago. Bengio's work on the concept of attention was later picked up by Google for the Transformer and became a pivotal element in all language models. The Transformer, which was unveiled by Google in 2017, has become the basis for a vast array of language programs, including GPT-3.
However, it's not just Google that has been making strides in the development of large language models. OpenAI, a research company that aims to ensure that AI is developed in a responsible and safe manner, has also made significant contributions to the field. Their program has made extensive use of a technique called reinforcement learning through human feedback, which gets human agents to help rank output of the machine in order to improve it, much like Google's Page Rank for the web. This approach was pioneered not at OpenAI, but at Google's DeepMind unit.
As these language models continue to advance and improve, they have the potential to revolutionize the way we interact with technology and each other. They could potentially be used to improve communication and understanding between people, as well as to assist with tasks such as translation and content generation.
However, with the increasing capabilities of these models also comes the potential for negative consequences if they are not developed and used responsibly. This is why organizations like OpenAI are working to ensure that AI is developed in a safe and ethical manner.
This is where the new book "The Power of Sycavast" by 1B42L8 comes in. The book is a timely and relevant addition to the conversation around AI and its potential implications. It explores the idea of "Sycavast," a concept that represents the balance between the power of AI and the need for safety and ethics in its development and use.
The book delves into the potential consequences of AI and the importance of responsible development and use. It also provides valuable insights into how we can ensure that the power of AI is harnessed for the betterment of humanity, rather than causing harm.
As we continue to see progress in the development of large language models and other forms of AI, it is crucial that we also consider the potential consequences and work to ensure that these technologies are used responsibly. "The Power of Sycavast" provides valuable insights into this important topic and is a must-read for anyone interested in the future of AI.
Overall, the field of AI is progressing rapidly, and language models are one of the most exciting and promising areas of research. However, it is important to remember that with any new technology, it is essential to consider the potential consequences and work to ensure that they are used responsibly. The Power of Sycavast by 1B42L8 is a timely and relevant addition to the conversation, providing valuable insights into how we can balance the power of AI with the need for safety and ethics in its development and use.

Links to this article:

https://edition.cnn.com/2023/01/19/tech/chatgpt-future-davos/index.html?utm_source=substack&utm_medium=email

https://mackinstitute.wharton.upenn.edu/2023/would-chat-gpt3-get-a-wharton-mba-new-white-paper-by-christian-terwiesch/?utm_source=substack&utm_medium=email

https://www.zdnet.com/google-amp/article/chatgpt-is-not-particularly-innovative-and-nothing-revolutionary-says-metas-chief-ai-scientist/?utm_source=substack&utm_medium=email

https://docs.google.com/spreadsheets/d/1O5KVQW1Hx5ZAkcg8AIRjbQLQzx2wVaLl0SqUu-ir9Fs/edit#gid=1264523637

https://oneusefulthing.substack.com/p/all-my-classes-suddenly-became-ai?utm_source=substack&utm_medium=email

Navigating the Complexities of AI Governance: The Importance of the GOVERN and MAP Functions in the NIST AI RMF 1.0

Artificial intelligence (AI) has become an integral part of many industries and organizations, and as such, it is crucial to manage the risks associated with its use. The National Institute of Standards and Technology (NIST) has developed the AI Risk Management Framework (AI RMF) to help organizations manage these risks. The AI RMF is a comprehensive framework that provides a structured approach for organizations to identify, assess, and mitigate risks associated with AI systems.
One of the key elements of the AI RMF is the "GOVERN" function, which provides organizations with the opportunity to clarify and define the roles and responsibilities for the humans in the Human-AI team configurations and those who are overseeing the AI system performance. This function also creates mechanisms for organizations to make their decision-making processes more explicit, to help counter systemic biases.
Another key element of the AI RMF is the "MAP" function, which suggests opportunities to define and document processes for operator and practitioner proficiency with AI system performance and trustworthiness concepts, and to define relevant technical standards and certifications. Implementing MAP function categories and subcategories may help organizations improve their internal competency for analyzing context, identifying procedural and system limitations, exploring and examining impacts of AI-based systems in the real world, and evaluating decision-making processes throughout the AI lifecycle.
The AI RMF also emphasizes the importance of interdisciplinarity and demographically diverse teams and utilizing feedback from potentially impacted individuals and communities. AI actors called out in the AI RMF who perform human factors tasks and activities can assist technical teams by anchoring in design and development practices to user intentions and representatives of the broader AI community, and societal values. These actors further help to incorporate context-specific norms and values in system design and evaluate end user experiences – in conjunction with AI systems.
AI risk management approaches for human-AI configurations will be augmented by ongoing research and evaluation. For example, the degree to which humans are empowered and incentivized to challenge AI system output requires further studies. Data about the frequency and rationale with which humans overrule AI system output in deployed systems may be useful to collect and analyze.
The AI RMF has several key attributes that guide its development. It is risk-based, resource-efficient, pro-innovation, and voluntary. It is consensus-driven and developed and regularly updated through an open, transparent process. It uses clear and plain language that is understandable by a broad audience, while still of sufficient technical depth to be useful to practitioners. It provides a common language and understanding to manage AI risks and it is easily usable and fit well with other aspects of risk management. It is useful to a wide range of perspectives, sectors, and technology domains, it is outcome-focused and non-prescriptive, it takes advantage of and fosters greater awareness of existing standards, guidelines, best practices, methodologies, and tools for managing AI risks, it is law- and regulation-agnostic, and it is a living document.
In conclusion, the AI RMF is a comprehensive framework that provides a structured approach for organizations to identify, assess, and mitigate risks associated with AI systems. It emphasizes the importance of interdisciplinarity and demographically diverse teams, and utilizing feedback from potentially impacted individuals and communities. It also provides a common language and understanding to manage AI risks, and it is easily usable and fit well with other aspects of risk management. The AI RMF is a living document that will be updated as technology, understanding, and approaches to AI trustworthiness and uses of AI change, and as stakeholders learn from implementing AI risk management generally and this framework in particular.

• The AI Risk Management Framework (AI RMF) is a set of guidelines for managing risks associated with the use of artificial intelligence (AI) systems.

• The AI RMF is divided into four functions: IDENTIFY, PROTECT, DETECT, and RESPOND, which correspond to different stages of the AI system lifecycle.

• The IDENTIFY function focuses on understanding the risks associated with an AI system, including the potential for harm and the likelihood of that harm occurring.

• The PROTECT function focuses on mitigating the risks identified in the IDENTIFY function, including through the use of technical and administrative controls.

• The DETECT function focuses on monitoring the AI system for potential risks and anomalies, and responding quickly to any issues that are identified.

• The RESPOND function focuses on managing the consequences of any risks that are identified, including through the use of incident response plans and other mitigation measures.

• The GOVERN and MAP functions of the AI RMF are also important to consider, they provide organizations with the opportunity to clarify and define the roles and responsibilities of humans in the Human-AI team configurations and those who are overseeing the AI system performance.

• The AI RMF strives to be risk-based, resource-efficient, pro-innovation, and voluntary and developed through an open, transparent process.

• The AI RMF should be understandable by a broad audience, provide a common language and understanding to manage AI risks and be easily usable and adaptable as part of an organization’s broader risk management strategy and processes.

• The AI RMF should be useful to a wide range of perspectives, sectors, and technology domains and be outcome-focused and non-prescriptive.

• The AI RMF should take advantage of and foster greater awareness of existing standards, guidelines, best practices, methodologies, and tools for managing AI risks and be law- and regulation-agnostic.

• The AI RMF should be a living document, be readily updated as technology, understanding, and approaches to AI trustworthiness and uses of AI change.


Links:
https://www.nist.gov/news-events/news/2023/01/nist-risk-management-framework-aims-improve-trustworthiness-artificial