Will Anthropic Open-Source Its Models Like OpenAI?2024 OpenAI made waves in the AI community when they open sourced GPT-3, their powerful natural language model. This allows anyone to access and build upon GPT-3 for their own projects and applications. With other companies like Anthropic also developing advanced AI models for language, an important question arises – will Anthropic follow OpenAI’s lead and open source their models as well?
An Introduction to Anthropic and Their AI Models
Founded in 2021, Anthropic is a startup focused on developing safe and beneficial artificial intelligence. They are specifically working on natural language models that can communicate clearly, avoid falsehoods, and maintain positive intentions.
Some of Anthropic’s key models include:
Claude
A conversational assistant trained to be helpful, harmless, and honest using a technique called Constitutional AI. Claude can understand instructions, answer questions accurately, perform analysis, write content, generate code, and more.
Philosopher AI
A language model designed to avoid false claims, admit the limits of its knowledge, and reject dangerous, unethical, or inappropriate requests. It serves as an AI assistant able to clearly explain its reasoning.
These models demonstrate Anthropic’s commitment to safety and ethics in AI development. The company takes explainability and alignment seriously so models like Claude and Philosopher AI can be reliably helpful for human users.
Will Anthropic Take an Open Source Approach?
OpenAI provides free access to some models like GPT-3 and Codex via API keys. Full open sourcing means the code, model architecture, weights, and training procedures are all publicly available. This transparency gives users insight into how models work and allows the community to build upon the technology.
So far Anthropic has not open sourced Claude, Philosopher AI, or their other models. There are several reasons this closed development approach makes sense:
1. Prevent Misuse
Full open sourcing comes with risks, as bad actors could grab copies of a model to develop unethical, dangerous applications. By maintaining control over their models, Anthropic can better prevent misuse. Limited API access allows the benefits of public testing while protecting against harms.
2. Maintain Business Incentives
Anthropic develops AI for commercial purposes as well as research. Releasing models with an open source license may undermine their business model and ability to sustain innovation. API services allow monetization that supports future progress.
3. Prioritize Safety
Developing safe, reliable AI is extremely complex, requiring intense research and testing. By avoiding full open sourcing for now, Anthropic can move carefully while better understanding model behaviors and failure modes before releasing code.
However, Anthropic does aim to allow responsible third party testing and auditing to validate model safety, ethics, and performance. So they may pursue strategies like:
- Releasing sandboxed environments for testing models
- Creating verification suites to analyze model behaviors
- Enabling third-party audits through regulatory collaboration
Do Considerations Around Openness Shift Over Time?
For the short and medium term, maintaining control and selectivity around their models makes strategic sense for Anthropic. However, perspectives on openness at AI research organizations can shift over longer periods.
Once models become very advanced and pervasive in society, researchers balance risks versus benefits differently. They tend to prioritize transparency, accountability, and enabling distributed oversight through open access.
OpenAI’s Evolving Approach
OpenAI itself did not immediately open source their models when the lab was founded. They carefully developed GPT-2 and its successors in a closed environment for over two years before selectively opening access.
Once models like GPT-3 demonstrated strong performance and safety, OpenAI deemed it responsible to gradually enable third party testing via API keys and sandboxed environments. They gather feedback to improve safety as functionality expands.
This step-by-step opening process continues today. As models grow more central to business and culture, oversight and governance adaptation becomes critical.
The Arc of AI Responsibility
Major AI labs like Anthropic and OpenAI tend to follow an “arc of responsibility” for emerging technologies:
- Closed Development – Early R&D happens behind closed doors as core capabilities are established and risks assessed.
- Measured Openness – As models demonstrate reliability/safety, limited third party testing is enabled, balancing transparency with precaution.
- Accountability at Scale – If development continues successfully, researchers take steps to support distributed auditing, oversight and governance as benefits/risks grow.
Anthropic’s Constitutional AI approach suggests they are already deeply focused on the responsible development path. While full open sourcing seems unlikely near term, pressure for transparency and accountability systems will increase over time.
What Technical Barriers Exist to Open Sourcing Large Models?
Beyond ethical considerations around openness, there are also major technical barriers to simply publishing the full code and weights behind advanced AI models:
1. Compute Requirements
Training models like GPT-3 or Claude takes massive computing power most researchers lack access to. For example, GPT-3 cost an estimated $12 million to develop, largely driven by compute. Replicating results requires data center-level resources.
2. Data Dependencies
The training data used to develop proprietary models is also crucial to performance. Without the exact dataset, reproducing a model like Philosopher AI would be extremely difficult even with sufficient compute power.
3. Architecture Complexity
Finally, state-of-the-art models have complex neural network architectures combining many techniques. Simply sharing code may enable only surface level reproducibility without architectural details.
As models grow in size and complexity, these barriers will only increase. Even open sourcing requires major investments in documentation, access tools, and data/compute support.
Government sponsorships like DARPA’s GAIA program which supports third party auditing, may help address these technical hurdles around transparency. But for now they pose real limits even for organizations that aim to open source.
Could Federated Learning Help Enable Responsible Openness?
A technique called federated learning offers an interesting path to balance model access with ethical precautions around advanced AI.
With federated learning, a central model can be improved by training updates from users’ local data without requiring that sensitive information to be sent to a central server. This keeps the data decentralized for privacy while enabling many parties to collaborate on a shared model.
If Anthropic embraced federated learning, outside users could potentially contribute safety improvements to Claude without Anthropic losing control of core model access. Researchers have already begun experimenting with federated learning for medicine and edge computing, proving validity at smaller scales.
The biggest challenge with expanding federated learning is coordinating a very large group of actors. Great governance and cryptographic coordination would be essential to ensuring updates contribute to safety and fairness rather than degrading the core model. But techniques like secure aggregation and differential privacy offer promising solutions.
For beneficial general intelligence that necessarily intersects with personal user data, federated techniques may ultimately be critical to enable continuous coordination between organizations and distributed contributors over time while retaining individual privacy.
Long Term Movement Towards Cooperative AI Ecosystems
The current period of rapid AI progress has a competitive, proprietary feel as companies like Anthropic, OpenAI, Google and others race to reach key capability milestones surrounding beneficial AGI. Each organization must balance advancing capabilities with self-imposed safety practices.
But as language models and robotics systems take on more pivotal economic and social roles, researchers argue AI development should shift to a coordinated ecosystem across institutional, governmental and industrial parties rather than isolated efforts.
These ecosystems require open interfaces, transparency, monitoring, and collaboration far beyond what most leading labs embrace today. We must acknowledge no single entity can understand societal risks, set standards, and create governance for emerging technologies alone.
OpenAI CEO Sam Altman wrote in 2022 that we need “adversarial collaboration” where competitive priorities do not prevent coordination focused on collective risk. The dangers of unsecured AI capabilities necessitate pooling insights around safety despite commercial incentives.
This collaborative philosophy aligns with Constitutional AI principles of “multilateral accountability” already developed by Anthropic researchers. As models become more capable and intrinsic to global systems, Anthropic will likely face growing pressure to interface with oversight networks for AI. Which could warrant measured open access practices, however gradual.
The competitive stage seems necessary to prove baseline technical capabilities, but leading researchers anticipate an inflection point where groups are compelled to intensively coordinate around AI development, even if underlying technologies remain largely proprietary.
Here are some key takeaways and a conclusion for the FAQ on whether Anthropic will open source its models:
Key Takeaways:
- Anthropic has not announced any plans to open source its core AI models like Claude or Constitutional AI. Their techniques for model training and alignment remain proprietary to control safety and prevent misuse.
- As a company focused on safe AI, Anthropic closely guards details of its models and training procedures. Releasing models publicly could allow testing that diminishes safety standards.
- Anthropic has released some datasets and plans to publish AI safety frameworks for others to build on responsibly. But core model architectures are still proprietary.
- Responsible disclosure practices regarding beneficial AI may adapt in the future as capabilities improve. But currently, Anthropic prioritizes model safety over transparency or public release.
Conclusion:
Anthropic is dedicated to advancing trustworthy AI focused on safety and ethics rather than on full transparency. As model capabilities continue to progress, responsible open source practices may evolve across the AI field. However, Anthropic currently has no plans to fully release proprietary models or core training techniques that could diminish safety if widely replicated without proper alignment. Their priority is delivering useful AI assistants that are helpful, harmless, and honest.
