TL;DR:
- AI’s progress raises concerns about control and regulation.
- Existing AI tools have limitations in understanding causation and uncertainty.
- They incentivize data accumulation, posing risks to privacy and ethics.
- AI systems should focus on valuable information rather than big data.
- Robots on the Moon exemplify efficient learning through Bayesian optimization.
- Decision-makers can apply similar principles to explore complex landscapes.
- AI aids in identifying valuable information and storing it effectively.
- Bayesian decision-theoretic frameworks enable optimal interventions.
- Human values are incorporated through co-design and co-implementation.
- Market implications include the need for AI regulations and the selection of appropriate tools.
Main AI News:
How Should an AI Explore the Moon? Rapid advancements in artificial intelligence (AI) have ignited debates, with prominent figures in the field urging a research hiatus, expressing concerns about AI-driven human extinction, and advocating for government regulation. While the worry centers around the potential loss of control over increasingly powerful AI, have we overlooked a more fundamental issue?
The ultimate goal of AI systems is to assist humans in making better, more precise decisions. Paradoxically, even today’s most impressive and adaptable AI tools, such as the expansive language models powering ChatGPT, can have unintended consequences.
Why? These systems possess two critical vulnerabilities. Firstly, they fail to aid decision-makers in comprehending causation or uncertainty. Secondly, they incentivize the accumulation of vast amounts of data, potentially fostering a lackadaisical attitude toward privacy, legal and ethical matters, and associated risks.
Cause, effect, and certainty ChatGPT and other “foundation models” utilize deep learning techniques to sift through colossal datasets, identifying connections between data points like language patterns or associations between images and descriptions. Consequently, these models excel at interpolation—predicting or filling in missing information between known values.
However, interpolation is distinct from creation. It does not engender knowledge or the insights essential for decision-makers grappling with complex environments.
Moreover, these approaches necessitate enormous volumes of data, inadvertently encouraging organizations to amass gargantuan repositories or comb through existing datasets collected for unrelated purposes. Handling “big data” entails significant risks concerning security, privacy, legality, and ethics.
While predictions based on “what the data suggests will happen” can be remarkably valuable in low-stakes scenarios, higher stakes call for additional inquiries.
Firstly, we must probe how the world operates: “What drives this outcome?” Secondly, we need to evaluate our comprehension of the world: “How confident are we in this understanding?“
Transitioning from big data to valuable information Interestingly, AI systems aimed at inferring causal relationships do not rely on big data; instead, they prioritize useful information. The usefulness of the information depends on the specific question, the decisions at hand, and the value attributed to the consequences of those decisions.
To paraphrase the American statistician and writer Nate Silver, the amount of truth remains relatively constant regardless of the data volume we accumulate.
So, what is the solution? The process commences by developing AI techniques that genuinely unveil what we don’t know, as opposed to producing variations of existing knowledge.
Why? Because this approach helps us identify and obtain the minimum amount of valuable information, enabling the disentanglement of causes and effects.
A lunar expedition by a robot AI system geared towards knowledge-building already exists.
Consider a straightforward example: a robot dispatched to the Moon to answer the question, “What does the Moon’s surface look like?“
The robot’s designers may equip it with an initial “belief” about what it expects to find, accompanied by an indication of the confidence it should place in that belief. The level of confidence is as crucial as the belief itself since it gauges the extent of what the robot doesn’t know.
Once the robot lands, it faces a decision: which direction should it pursue?
Given that the robot’s objective is to swiftly learn about the Moon’s surface, it should proceed in the direction that maximizes its learning. This can be measured by determining which new knowledge will minimize the robot’s uncertainty about the lunar landscape or boost its confidence in its existing knowledge.
Consequently, the robot traverses to its new location, employing its sensors to record observations, subsequently updating its belief and associated confidence. Through this iterative process, the robot learns about the Moon’s surface in the most efficient manner possible.
Mapping uncharted terrain While a government or industry decision-maker faces greater complexity than the lunar robot, the underlying principle remains the same. Their roles involve exploring and mapping unfamiliar social or economic landscapes.
Suppose our objective is to formulate policies that ensure every child thrives academically and completes high school. To achieve this, we require a conceptual map outlining the actions, timing, and conditions that foster the desired outcomes.
Using the robot’s guiding principles, we formulate an initial question: “Which interventions will have the greatest positive impact on children?”
Next, we construct an initial conceptual map leveraging existing knowledge while gauging our confidence in that knowledge.
Subsequently, we develop a model that incorporates diverse sources of information—although not from robotic sensors but from communities, lived experiences, and any pertinent insights gleaned from recorded data.
Building on this foundation and based on a thorough analysis that considers community and stakeholder preferences, we make informed decisions regarding the implementation of specific actions and under what conditions.
Finally, we engage in discussions, learn from the outcomes, update our beliefs, and repeat the process as new information emerges.
Adaptive learning This approach embodies a “learning as we go” methodology. As new information surfaces, we select new actions that maximize predetermined criteria.
AI can prove immensely valuable in identifying the most valuable information using algorithms that quantify our knowledge gaps. Automated systems can also gather and store this information at a pace and in locations that might challenge human capabilities.
AI systems of this nature adhere to a Bayesian decision-theoretic framework. They construct models that are explainable, transparent, and founded on explicit assumptions. These models maintain mathematical rigor and offer guarantees.
They are designed to estimate causal pathways, enabling optimal interventions at the appropriate times. Additionally, they incorporate human values through co-design and co-implementation processes involving the impacted communities.
Certainly, we must reform our laws and establish new regulations to govern the use of potentially dangerous AI systems. However, it is equally crucial to choose the appropriate tool for the task at hand.
Conclusion:
The rapid advancement of AI presents both challenges and opportunities. To maximize AI’s potential in the market, it is crucial to address concerns regarding control and regulation. Decision-makers should prioritize understanding causation and uncertainty while also being mindful of privacy and ethical considerations. By focusing on valuable information rather than simply amassing big data, AI systems can drive efficient learning and decision-making, similar to the way robots explore the Moon. The application of Bayesian decision-theoretic frameworks and the incorporation of human values through co-design and co-implementation processes offer promising avenues for the market. As the market evolves, careful selection of AI tools and adherence to regulations will be essential for success.
