Road Map for Our Journey to Safe AI
In preparing the technical code examples for this book I evaluated several dimensions for implementing safe and private systems. Roughly, these dimensions are:
- Using closed models like OpenAI ChatGPT on public cloud servers.
- Using open models such as those distributed and maintained by Hugging Face on public cloud servers.
- Using open models by Hugging Face on private cloud servers, such as those provided by Lambda Labs.
- Using open models by Hugging Face on your own PC or your own cloud server.
This is assuming that you are using LLMs to build your AI systems. If you are using other techniques like constraint satisfaction (e.g., MiniZinc) of conventional machine learning models (e.g., using Scikit-learn) then you can run on local hardware.
Your Requirements for Privacy and AI Safety vs. Making Maximum Business Use of LLMs
The rapid advancement of large language models (LLMs) in artificial intelligence (AI) has led to an array of opportunities for both the business world and academia. Their potential to revolutionize industries and research fields is remarkable, given their abilities in language translation, creative writing, and information extraction, among other tasks. However, it’s crucial to note that alongside these benefits, there are substantial challenges tied to privacy and AI safety that need to be addressed. Balancing these concerns with the desire to maximize the use of LLMs is not only a technical challenge but also an ethical one.
Privacy is one of the top concerns when dealing with LLMs. These models are typically trained on vast amounts of data, often containing personal or sensitive information. While they do not specifically remember this data, there’s a slight risk of inadvertent disclosure of private information through their responses. Both students and knowledge workers (e.g., software developers, business analysts, etc.), particularly those involved in research and development, need to be aware of these risks, and business leaders should ensure that privacy policies are in place when implementing LLMs. Privacy-enhancing technologies like differential privacy can be instrumental in mitigating these risks.
AI safety is another significant consideration. A poorly designed or misused LLM can lead to harmful outcomes, from the propagation of misinformation to the reinforcement of biased views. Both students and business leaders need to understand these risks and be committed to reducing them. This might involve careful testing and auditing of models, investing in research to understand and counteract biases, and establishing clear usage guidelines.
Despite these challenges, it is important to recognize the potential benefits of LLMs. They can assist businesses in providing better customer service, streamline workflows, and enable the development of innovative products and services. In academia, they can accelerate research, provide tutoring, and serve as invaluable tools for learning and discovery. Balancing privacy and safety concerns with these benefits is key. It involves a commitment to ethical practices, ongoing learning, and a willingness to engage with the complex issues that LLMs present. With careful management, the power of LLMs can be harnessed responsibly, for the benefit of all.
A Prompt Template For Evaluating Safety vs. Opportunity Tradeoffs
Here is a sample template:
1 You are an analyst evaluating AI safety vs. opportunity tradeoffs at your company.
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3 You take into account:
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5 1. safety of customer data
6 2. opporational costs
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8 You want to (choose 1 of the following):
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10 1. use commercial LLM APIs from vendors like OpenAI and Hugging Face.
11 2. self-host open models on either your own servers of leased GPU servers.
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13 Your choice:
When I add Your choice: self-host open models on leased GPU server to the template and run with ChatGPT, output looks like:
1 Choosing to self-host open models on leased GPU servers has its own set of pros and cons when considering AI safety and operational costs.
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3 Safety of Customer Data:
4 When you self-host models, you have complete control over your data. You can implement the security measures that best fit your company's and customers' needs. This can enhance the safety of your customer data as it never has to leave your control. However, it's crucial to keep in mind that this also means the responsibility of maintaining data security falls solely on your organization. You'll need to ensure that your security measures are robust and up-to-date.
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6 Operational Costs:
7 Leasing GPU servers can have significant operational costs, particularly if you have heavy computational needs. The cost of the lease, combined with maintenance, security, and potentially hiring staff with the necessary expertise, can add up. On the flip side, you could potentially save on costs that would be spent on commercial API calls if your usage is high enough.
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9 In contrast, using commercial LLM APIs from vendors like OpenAI and Hugging Face means you would not have to worry about server maintenance and security, as these responsibilities are taken care of by the service provider. However, these services have costs that scale with usage, which can become substantial for large-scale applications.
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11 In summary, self-hosting open models on leased GPU servers can offer you greater control and potentially save on costs if your usage is high. However, it also brings additional responsibilities and potential costs related to server management and data security. It's crucial to thoroughly evaluate your organization's specific needs, resources, and risk tolerance before making a decision.
In writing an analyzing business and technology plans it is effective to start with a team meeting and write down requirements and objectives, possible risks, technology suggestions, and then convert the discussion to a long prompt. The original team meeting notes and the LLM output can be used to write a road map report.