What is Anthropic AI?

by Stephen M. Walker II, Co-Founder / CEO

What is Anthropic AI?

Anthropic is an American artificial intelligence (AI) startup company based in San Francisco, California. It was founded by former members of OpenAI in 2021. The company is a public-benefit corporation, meaning it aims to make a profit while also working on something that will have a positive impact on humanity.

Anthropic focuses on developing general AI systems and large language models. It describes itself as an AI "safety and research" business, aiming to build reliable, interpretable, and steerable AI systems.

One of Anthropic's notable products is Claude, a sophisticated chatbot underpinned by a large language model. Claude is designed to be fast, capable, and truly conversational.

Anthropic has also developed a unique approach to AI safety and alignment called Constitutional AI (CAI). CAI is a method for aligning general-purpose language models to abide by high-level normative principles and values. This is achieved by creating a custom set of principles, or a "constitution", that guides the model's outputs. The constitution lays out norms, ethics, and intended behaviors that are encoded into the model through the CAI training process.

In an effort to democratize AI alignment, Anthropic has explored the use of public input in shaping the constitution for their AI models. They partnered with the Collective Intelligence Project to run a public input process, asking a representative sample of U.S. adults to help pick rules for their AI. This approach aims to ensure that AI systems reflect broad societal priorities, not just those of the model developers.

What is Anthropics Responsible Scaling Policy (RSP)?

Anthropic has developed a Responsible Scaling Policy (RSP) as a commitment to manage the risks associated with increasingly capable AI models. The RSP introduces a framework called AI Safety Levels (ASL), which is inspired by the US government's biosafety level (BSL) standards for handling dangerous biological materials.

The ASL framework is designed to require safety, security, and operational standards that are appropriate to a model's potential for catastrophic risk. Higher ASL levels demand more stringent demonstrations of safety. The policy aims to balance the economic and social value of AI with the need to mitigate severe risks, particularly catastrophic risks that could arise from deliberate misuse by malicious actors or unintended destructive behaviors by the models themselves.

Anthropic's RSP has been formally approved by its board, and any changes to the policy must also be approved by the board. The policy includes a commitment to pause the scaling or delay the deployment of AI models that reach new ASL thresholds until the necessary safety measures are in place. This is intended to incentivize progress in safety measures by temporarily halting the training of more powerful models if AI scaling outpaces safety advancements.

The RSP is not meant to alter the current uses of Anthropic's AI models, such as Claude, or disrupt their availability. Instead, it is compared to pre-market testing and safety feature design in industries like automotive and aviation, where the goal is to demonstrate the safety of a product before its release, ultimately benefiting customers.

Anthropic's approach to responsible scaling is also seen as a prototype for future regulation, not as a substitute for it. The policy is a proactive step in AI regulation, which is an area where governments are still struggling to keep pace with the rapid advancements in AI technology.

The RSP also includes provisions for anonymous feedback and a reporting chain, ensuring that there is a mechanism for accountability and continuous improvement. Anthropic hopes that the adoption of such standards across frontier labs might create a 'race to the top' in AI safety practices.

What is the effective altruism movement?

The Effective Altruism movement is a philosophical and social movement that focuses on using evidence and reason to determine the most effective ways to benefit others and to take action accordingly. It encompasses a range of cause priorities, including global health and development, social inequality, animal welfare, and long-term risks to humanity.

Anthropic, on the other hand, is an AI safety and research company that, while not identifying as an Effective Altruist company, has leadership and ethos that are closely connected to the Effective Altruism movement. Founded by Dario Amodei and Daniela Amodei in May 2021, Anthropic has raised significant funding from individuals and entities associated with Effective Altruism, including Sam Bankman-Fried. The company develops general AI systems and large language models and operates as a public-benefit corporation.

Many individuals involved with Anthropic, including its leadership, have shown interest in Effective Altruism-related causes. The company's approach to AI development is influenced by longtermism, a philosophy associated with Effective Altruism that values the lives of future generations as much as those of the present. Despite this connection, Anthropic does not officially label itself as an Effective Altruist company, but the movement's principles are evident in its operations and decision-making processes.

How does Anthropic ensure that its AI systems are reliable and interpretable?

Anthropic is committed to the reliability and interpretability of its AI systems, achieved through rigorous research and the application of advanced safety techniques. The company's work in understanding AI learning processes and developing scalable oversight is pivotal to creating transparent AI systems.

A significant breakthrough in interpretability is Anthropic's use of sparse autoencoders for 'Monosemantic Feature Extraction,' which simplifies complex neural networks into understandable components. These components, or monosemantic features, are tied to specific inputs, enhancing clarity in the AI's decision-making process.

The company's Mechanistic Interpretability research dissects deep learning models to comprehend their internal processes, fostering trust and facilitating the identification and correction of biases.

To address catastrophic risks, Anthropic has instituted a Responsible Scaling Policy (RSP) and an AI Safety Levels (ASL) framework, inspired by biosafety standards. The ASL demands rigorous safety demonstrations, with higher levels requiring stricter protocols. The RSP ensures safety advancements keep pace with AI development, with provisions to pause model training if necessary.

Evaluations of AI systems are conducted to refine safety and reliability. Anthropic's methodologies allow for comparative assessments, contributing to the development of safer AI.

The RSP emphasizes transparency and accountability, mandating board approval for policy changes after consulting with the Long Term Benefit Trust, which includes experts in AI safety and public policy. This policy is a forward-thinking approach to AI regulation, encouraging a safety-first culture across the AI industry.

What are the safety levels defined in Anthropic's responsible scaling policy (RSP)?

Anthropic's Responsible Scaling Policy (RSP) introduces a framework called AI Safety Levels (ASL), which is inspired by the US government's biosafety level (BSL) standards for handling dangerous biological materials. The ASL framework is designed to require safety, security, and operational standards that are appropriate to a model's potential for catastrophic risk. Higher ASL levels demand more stringent demonstrations of safety. Here is a brief overview of the ASLs as defined in the RSP:

  • ASL-1: Refers to systems which pose no meaningful catastrophic risk. Examples include a 2018 large language model (LLM) or an AI system that only plays chess.

  • ASL-2: Represents the current safety and security standards at Anthropic. It overlaps significantly with ASL-1 but likely involves qualitative escalations in catastrophic misuse potential and autonomy.

  • ASL-3: Includes stricter standards that will require intense research and engineering effort, such as unusually strong security requirements. There is a commitment not to deploy ASL-3 models if they do not meet these stringent safety measures.

The specific measures for each ASL level are detailed in the main document of the RSP, and the policy is designed to evolve over time as new information becomes available and as AI capabilities advance.

What is Anthropic's approach to building reliable and interpretable AI systems?

Anthropic's approach to building reliable and interpretable AI systems is multifaceted, involving both theoretical research and practical applications. They focus on creating AI systems that are not only advanced but also transparent and understandable to users. Here are the key components of their approach:

Mechanistic Interpretability

Anthropic invests in mechanistic interpretability, which involves dissecting and understanding the internal workings of AI systems, particularly deep learning models. This research aims to make the behavior of AI systems more predictable and understandable, which is crucial for trust and safety.

Monosemantic Feature Extraction

The company has made strides in breaking down complex neural networks into interpretable components through sparse autoencoders. These components, or monosemantic features, are responsive to specific inputs, allowing for a clearer understanding of the model's decision-making processes.

Safety Techniques and Policies

Anthropic develops and applies a variety of safety techniques to ensure the reliability of their AI systems. They have introduced detection models to flag potentially harmful content and safety filters on prompts to block responses when harmful content is detected. Additionally, they have a Responsible Scaling Policy and AI Safety Levels to address catastrophic risks.

Red Teaming for AI Safety

"Red teaming" or adversarial testing is used to measure and increase the safety and security of systems. Anthropic employs this technique to understand potential risks and develop scalable solutions to mitigate them.

Public Input for AI Alignment

Anthropic has explored using public input to shape the "constitution" for their AI models, ensuring that the AI systems reflect broad societal priorities and values.

Research and Development

The company conducts frontier research to improve the understanding of how AI systems learn and to develop techniques for scalable oversight and review. They also focus on training AI systems to follow safe processes instead of pursuing outcomes.

Deployment and Evaluation

Anthropic develops large-scale AI systems to study their safety properties and uses these insights to create safer, steerable, and more reliable models. They also evaluate their AI systems to understand and improve safety.

Through these efforts, Anthropic aims to build AI systems that are not only cutting-edge but also safe, transparent, and aligned with human values, ensuring that they can be relied upon by users and society at large.

What is the difference between Anthropic and other AI startups?

Anthropic is an AI safety and research company based in San Francisco, founded by former members of OpenAI, Dario and Daniela Amodei. The company's primary focus is on creating reliable, beneficial, interpretable, and steerable AI systems. This focus on AI safety and alignment sets Anthropic apart from many other AI startups, which often prioritize technological advancement over governance.

Anthropic's main product is Claude, a generative AI assistant. Claude differentiates itself from competing models like OpenAI's ChatGPT by focusing on responsible AI and ethical considerations. The company's commitment to responsible AI is also reflected in its status as a public-benefit corporation, indicating a commitment to having a positive impact on society.

In terms of funding and partnerships, Anthropic has attracted significant investments from tech giants like Amazon and Google. Amazon's investment aligns closely with the Microsoft and OpenAI partnership model, and Anthropic has pledged to use Amazon Web Services (AWS) as its primary cloud provider. Google has also committed to investing up to $2 billion in Anthropic.

Despite being a relatively new player in the AI industry, Anthropic has quickly gained recognition and is considered a rising star in the AI race. However, it's worth noting that Anthropic's approach is more conservative compared to other tech teams in the same field known for their aggressive approaches.

In comparison to OpenAI, Anthropic does not specifically target the achievements of Artificial General Intelligence (AGI), which is a primary focus area of OpenAI. Instead, Anthropic aims to create dependable and trustworthy AI systems. This difference in focus, along with differences in their structures and products, sets Anthropic apart from OpenAI.

The key differences between Anthropic and other AI startups lie in Anthropic's focus on AI safety and alignment, its significant investments and partnerships, and its main product, Claude, which emphasizes responsible and ethical AI.

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