Let's coin a term: Artificial Wisdom (AW)

Artifical Wisdom

Artificial Intelligence

In general terms, "intelligence" refers to the ability to make decisions based on information. Today, at the SAS Explore 2023 Conference (on its closing day), I had the pleasure of meeting with Mary Osborne and Kirk Swilley, both from the SAS Institute. During our conversation, we decided to introduce a new term: Artificial Wisdom, abbreviated as AW. We defined Artificial Wisdom as the process of utilizing computer-based intelligence and decision-making systems to make informed choices not only based on available information but also in situations where information is lacking.  

We decided to coin a term:  Artificial Wisdom or AW-- which we defined as the process of using computer-based intelligence and decision-making systems to make decisions based on a combination of information but also where there is a lack of information.

• Intelligent decisions are made by utilizing available information.
• Wise decisions, on the other hand, involve considering the information at hand while acknowledging the existence of gaps in knowledge that may never be filled. Despite these gaps, the goal is to make the most prudent decision possible.

It's on the horizon—let's dive in!  Let's start by asking ChatGPT about this idea and just see where that takes us...

Me:  Hey ChatGPT:  What are your thoughts on this idea of "Artificial Wisdom" ?

ChatGPT:  Artificial Wisdom, as a concept, has intriguing potential. It signifies a move beyond traditional Artificial Intelligence (AI), which primarily focuses on processing data and making decisions based solely on available information. Instead, Artificial Wisdom encompasses a more holistic approach to decision-making by acknowledging the limitations of data and addressing situations where information is scarce or incomplete.

Here are some thoughts on the idea of Artificial Wisdom:

1. Holistic Decision-Making: Artificial Wisdom introduces a dimension of wisdom into AI systems. While AI excels at data-driven decision-making, it often lacks the ability to consider the broader context, ethical implications, or uncertainties. Artificial Wisdom aims to bridge this gap by encouraging decision-making that takes into account both known information and the recognition of unknowns.
2. Real-World Applications: The concept of Artificial Wisdom has the potential to be valuable in various fields. For instance, in healthcare, it could help doctors make treatment decisions when dealing with rare diseases or complex patient histories where complete information is not always available.
3. Ethical Considerations: Implementing Artificial Wisdom raises ethical questions about how AI systems should handle decisions when faced with incomplete information. Striking the right balance between action and caution in such situations is crucial.
4. Technical Challenges: Developing AI systems that exhibit Artificial Wisdom requires advancements in machine learning, natural language processing, and reasoning capabilities. It also necessitates robust methodologies for dealing with uncertainty.
5. Human Collaboration: Artificial Wisdom doesn't replace human judgment but enhances it. In scenarios where critical decisions are made, involving human experts to complement AI's wisdom is vital.
6. Transparency and Accountability: As with any AI system, ensuring transparency and accountability in the decision-making process is essential. Users and stakeholders should have insight into how and why decisions are made, especially when dealing with uncertain or incomplete data.

In summary, the concept of Artificial Wisdom is an exciting development in the AI field, emphasizing the need for AI systems to go beyond mere data analysis and incorporate a degree of wisdom and judgment into their decision-making processes. Its successful implementation can lead to more responsible and robust AI applications, with potential benefits across various industries. However, it also presents challenges that need careful consideration, particularly regarding ethics, technical feasibility, and human collaboration.

Me:  What are some next steps to take in the development of AW?

ChatGPT:  The development of Artificial Wisdom (AW) is an evolving and complex endeavor that involves multiple facets, including technical, ethical, and practical considerations. Here are some essential next steps to take in the development of AW:  

1. Research and Development:  Invest in extensive research to advance machine learning techniques, natural language processing, and reasoning capabilities. AW systems must be able to process and interpret incomplete or uncertain data effectively.
2. Data Acquisition and Management:  Develop methods for handling incomplete, uncertain, or conflicting data. This may involve integrating multiple sources of information, including structured and unstructured data.
3. Ethical Frameworks:  Establish ethical guidelines and principles for AW development and deployment. Address issues related to transparency, fairness, privacy, and bias to ensure responsible AI decision-making.
4. Human-AI Collaboration:  Promote collaboration between AI systems and human experts. AW should augment human decision-making, not replace it. Develop interfaces and processes that facilitate this collaboration effectively.
5. Interdisciplinary Collaboration:  Encourage collaboration between AI researchers, domain experts, ethicists, and policymakers. Developing AW requires expertise from various fields to address complex challenges adequately.
6. Testing and Validation:  Implement rigorous testing and validation procedures for AW systems. Evaluate their performance in real-world scenarios, focusing on both accuracy and ethical considerations.
7. User Education:  Educate users and stakeholders about the capabilities and limitations of AW systems. Ensure that they understand how to work with these systems and interpret their recommendations.
8. Regulation and Governance:  Engage with policymakers and regulatory bodies to establish guidelines and regulations for AW development and deployment. Ensure compliance with legal and ethical standards.
9. Use Case Exploration:  Identify specific use cases where AW can provide significant value. Industries such as healthcare, finance, and law enforcement may benefit from AW's ability to make informed decisions with incomplete data.
10. Continuous Improvement:  AW systems should be designed with the flexibility to adapt and improve over time. Implement mechanisms for ongoing learning and refinement of decision-making processes.
11. Security Considerations:  Address cybersecurity concerns related to AW systems. Safeguard data, algorithms, and decision-making processes against potential threats and vulnerabilities.
12. Public Engagement:  Engage with the public and seek input on the development and deployment of AW systems. Foster transparency and trust through open dialogue.
13. Standardization:  Work towards standardizing AW development practices, ensuring interoperability and consistency in how AW systems are designed and evaluated.
14. Case Studies and Best Practices:  Share successful case studies and best practices in AW development to help guide others in the field.
15. Collaboration Platforms:  Create collaborative platforms and communities where researchers, developers, and practitioners can share knowledge and work together to advance AW.

Developing Artificial Wisdom is a dynamic and ongoing process. Collaboration, ethical considerations, and a commitment to responsible AI development are key to realizing its full potential while mitigating risks and challenges.

Me:  What do you think might be some of the first fields or areas to benefit from success in AW?

ChatGPT:  Artificial Wisdom (AW) has the potential to benefit a wide range of fields and industries by enhancing decision-making processes in situations where information is incomplete or uncertain. Here are some of the first fields or areas that could benefit from success in AW:

1. Healthcare: Medical diagnoses often involve complex patient histories and incomplete information. AW could assist healthcare professionals in making more accurate and timely decisions, especially in rare disease cases or when dealing with diverse patient data sources.
2. Finance and Investment: AW could provide valuable insights into financial markets, helping investors make more informed decisions by considering not only available data but also economic uncertainties and market volatility.
3. Law Enforcement and Security: In situations where law enforcement agencies need to make critical decisions rapidly, AW could help in analyzing limited or conflicting information to prioritize actions or allocate resources effectively.
4. Environmental Science: Environment and natural disaster modeling often involve dealing with incomplete data. AW could improve climate forecasting and disaster response by factoring in data uncertainties.
5. Supply Chain Management: Supply chain disruptions, such as those caused by global events, can be challenging to navigate. AW could optimize logistics and decision-making by considering various sources of information, including real-time data and unexpected disruptions.
6. Legal Services: Lawyers often have to make strategic decisions based on incomplete legal information or evolving case law. AW could assist legal professionals in researching and formulating arguments more effectively.
7. Research and Development: In scientific research, especially in fields like genomics and drug discovery, AW could help scientists make breakthroughs by integrating data from various experiments and studies.
8. Humanitarian Aid: In disaster relief efforts, AW could aid organizations in assessing the severity of situations, determining where resources are most needed, and adapting strategies as new information becomes available.
9. Agriculture: Farmers could benefit from AW in optimizing crop management and pest control strategies by accounting for variables like weather, soil quality, and market demand, which are subject to fluctuations.
10. Energy Sector: The energy industry could use AW for decision-making in energy generation and distribution, considering factors such as weather patterns, demand fluctuations, and resource availability.
11. National Security: Governments could employ AW in intelligence analysis, helping to connect disparate pieces of information and assess potential threats more comprehensively.
12. Education: AW could enhance personalized learning experiences by assessing individual student strengths and weaknesses, adapting educational content, and recommending appropriate interventions.

These fields are just the starting point, and the applications of AW can extend to virtually any domain where decision-making involves uncertainty or incomplete information. Success in AW could revolutionize how professionals in these fields approach complex problems, ultimately leading to more efficient, effective, and responsible decision-making processes.