Q* and its impact on the pharmaceutical industry

Introduction

Over the last couple of days buzzwords like Q*, AGI, Super AGI etc. dominated AI-news and kicked off the next media hype 5 minutes after the OpenAI chaos-days. As a computer-scientist and a leader of a large and talented software development team, I always try to look beyond the hype. What is the potential of a new technology; how can it be used to empower the company I work for; How can it be used to maybe reduce the constant effort we have in the pharmaceutical industry to uphold GxP regulations and requirements, without reducing our quality but still be compliant? In today's article I would like to demystify Q*, explain why it's not AGI and why it will still impact especially our industry in the upcoming years.

Before we start our journey understanding Q, I would like to get a few things out of the way and that is: Why is Q* not an AGI. For that we need to understand what AGI actually is, or what we understand when talking about it.

Definition of AGI (Artificial General Intelligence):

1. Learning Ability: The capability to learn from experience, data, or instruction.
2. Understanding and Reasoning: The ability to comprehend complex concepts and engage in logical reasoning.
3. Adaptability: The capacity to adapt to new, unforeseen challenges across various domains.
4. Problem-Solving: Competence in solving a wide range of problems, not limited to specific domains.
5. Autonomy: The ability to operate without human intervention, making independent decisions based on its learning and understanding.
6. Human-Like Cognition: Exhibiting cognitive abilities that are similar to human intelligence, encompassing creativity, generalization, and abstraction.
7. Versatility in Tasks: The capability to perform a variety of tasks that are traditionally done by humans, including those requiring understanding of context and nuance.
8. Emotional and Social Intelligence: Understanding and interpreting human emotions and social cues, necessary for interaction and collaboration.

Capabilities of Q* (as reported):

1. Problem-Solving in Mathematics: Q* is designed to solve mathematical problems it has not encountered before.
2. Developing Mathematical Understanding: It currently operates at the level of a grade-school student in terms of mathematical problem-solving.
3. Potential for Rapid Improvement: There is an expectation that Q*'s mathematical ability will improve quickly.
4. Step Towards AGI: Some researchers view Q* as a significant step towards achieving AGI, indicating a level of adaptability and learning in unfamiliar situations.
5. Focus on Specific Domain: Currently, Q*'s capabilities seem to be focused mainly on mathematical problems, without evidence of broader cognitive abilities across diverse domains.

While Q* shows promise in certain aspects like problem-solving and potentially learning and adapting within its domain, it does not exhibit the full range of capabilities associated with AGI, such as broad adaptability, understanding, and reasoning across diverse domains, and human-like cognition.

The Foundations of Q*: A Deep Dive into Its Core

Introducing Reinforcement Learning: Central to Q* is Q-learning, a form of reinforcement learning. Reinforcement Learning (RL) is a critical area of machine learning where an agent learns to make decisions by performing actions in an environment and receiving feedback in the form of rewards or penalties. This feedback helps the agent learn which actions are most beneficial over time. The distinctive feature of RL is its focus on learning through interaction, rather than from a predefined dataset.

How Reinforcement Learning Works:

• Agent and Environment Interaction: In RL, an agent interacts with its environment in discrete steps. At each step, the agent selects an action, which alters the state of the environment.
• Rewards and Penalties: Following each action, the agent receives a reward (or penalty). This reward is a signal that helps the agent learn the value of its actions over time.
• Goal: The ultimate goal of an RL agent is to maximize the cumulative reward it receives over time. This is achieved by learning a policy – a strategy that specifies the best action to take in each state.

Reinforcement Learning's Connection to Q-Learning:

• A Specialized Approach: As mentioned, Q-learning is a specific type of RL. It is a model-free algorithm, meaning it can learn optimal actions without needing a model of the environment.
• Learning Policy without a Model: Q-learning enables the agent to learn the best actions to take in various states (policy) without requiring a model of the environment. This is especially useful in complex or unpredictable environments where modeling the entire environment is impractical.
• Focus on Long-Term Rewards: One key aspect of Q-learning is its focus on long-term rewards. This means it evaluates the potential future rewards of actions, not just the immediate benefits.

Q-Learning as an Evolution of Reinforcement Learning:

• Enhanced Decision Making: By updating the Q-table and focusing on long-term outcomes, Q-learning allows for more sophisticated and forward-thinking decision-making compared to basic RL methods.
• Application in Complex Scenarios: The ability of Q-learning to handle complex, dynamic environments makes it a powerful tool in situations where the outcomes of actions are not immediately apparent, such as in strategic game playing or, in the context of Q*, advanced mathematical problem-solving.

The Q-Learning Algorithm:

• Initialization: The Q-table is initially filled with arbitrary values.
• Exploration and Exploitation: The agent explores the environment, randomly choosing actions and observing the rewards. Over time, it starts to 'exploit' its learned values, choosing actions that maximize the predicted reward.
• Updating the Q-Table: After each action, the Q-table is updated using the formula: Q(state,action)=Q(state,action)+α?(reward+γ?max(Q(nextstate,allactions))?Q(state,action))

Q's Enhanced Learning Mechanism: Q* builds upon traditional Q-learning by incorporating advanced techniques like deep learning and neural networks. This enables Q* to handle more complex, higher-dimensional environments, making it suitable for applications like mathematical problem-solving.

Q and Determinism: Redefining Predictability in AI and what does it mean for Pharma?

The Essence of Determinism in AI:

In the context of artificial intelligence, determinism refers to the ability of an AI system to consistently produce the same output or result from a given input or set of conditions. This predictability is crucial in applications where consistency and reliability are of the essence like in the pharmaceutical industry.

Challenges in Traditional AI: Many AI models, especially those based on probabilistic algorithms or with a high degree of variability in learning, struggle with determinism. They might produce different outcomes under the same conditions due to inherent uncertainties in their learning processes often refered to as hallucinations in the context of chatGPT.

Q's Contribution to Enhanced Determinism:

• Predictable Outcomes Through Q-Learning: Q*, with its foundation in Q-learning, addresses the challenge of determinism by learning a policy that guides it to take the best action in each state. This approach reduces variability in responses, leading to more predictable and consistent outcomes.
• Stability of the Q-Table: The Q-table in Q-learning, which Q* utilizes, is a deterministic tool by nature. Once fully learned, it provides a stable and consistent reference for decision-making, further enhancing the determinism of the system.

Impact of Determinism in Q:

• Reliability in Complex Environments: In complex environments, particularly those requiring mathematical precision or logical consistency, Q*'s deterministic nature ensures that it can produce reliable and repeatable results, essential for tasks like data analysis, pattern recognition, and problem-solving.
• Building Trust in AI Systems: The increased determinism in Q* can significantly enhance the trustworthiness of AI systems in critical applications. In sectors like healthcare, finance, or pharmaceuticals, where decision consistency can have significant implications, Q*'s predictability is a valuable asset.

Q's Deterministic Approach: A Step Towards More Reliable AI:

• Setting a New Standard: Q* sets a new standard in AI determinism, providing a framework for developing AI systems that can offer reliable and consistent performance, even in scenarios with complex variables and requirements.
• Future Implications: The deterministic nature of Q* opens the door for its application in a broader range of fields, where predictability and reliability are as important as the intelligence of the system itself. It paves the way for more advanced AI applications that can function with a high degree of certainty and trust.

Q's Enhanced Determinism and Its Impact on the Pharmaceutical Industry a possible outlook and conclusion

While Q* doesn't bring back Turing-Like determinism that is the foundation of so many GxP regulations for computerized systems it will have a huge impact on the way and especially the speed how fast we can introduce generative AI in the pharmaceutical sector.

Currently everything we do in regard to genAI integration in the pharmaceutical sector involves HITL, human in the loop, sometimes to the point that the benefit of LLM generated context will not be visible, will not even reduce time and effort because you can't trust a system in the pharma context that hallucinates, no matter how much you reduce the temperature depending on the task. Imagine a large recipe with a few hundred or even a thousand generated steps and you manually have to check each value, the benefit wouldn't be there. A while ago I intensively analyzed the GuardRails project that defines measurable guards and thus enforcing expected outcomes, while this one component, relying on a computerized system to not hallucinate is necessary, even is imperative for our industry.

I see in Q-Stars expected evolution not a fear of AGI and media hype. What I see is the potential to release the full impact of genAI for our industry, enabling us to create the next foundation of computerized systems that will most likely be very different, but will increase productivity exponentially.

Until next time