AI-Hard Problems

In complexity theory, an NP-hard problem is one that is at least as difficult as the hardest problems in NP (nondeterministic polynomial time). While NP-hard problems do not need to be in NP themselves, any problem in NP can be reduced to an NP-hard problem in polynomial time. This essentially means NP-hard problems are at least as challenging as the most complex problems within NP, and efficiently solving them would imply that all NP problems could also be solved efficiently.

An AI-hard problem refers to a challenge within artificial intelligence that demands significant advancements in AI techniques and computational resources to resolve. Historically, tasks like image recognition and natural language processing were considered AI-hard due to the complex pattern recognition and contextual understanding required. These challenges have largely been overcome with the development of transformer and diffusers architectures, which have significantly improved AI capabilities in these areas.

However, present-day AI-hard problems persist. One major challenge is achieving general artificial intelligence (AGI), specifically when AGI is defined as creating machines capable of performing any intellectual task a human can. At Robometrics® Machines, our focus has been to build AGI from a perspective of replicating some aspects of human feeling and consciousness into machines, which is a different approach in defining and building AGI.

Another example includes developing AI systems that are robust, interpretable, and unbiased, ensuring they can make fair decisions in critical areas such as healthcare, finance, and law  (AI2050)  (SpringerLink).

Additional AI-hard problems today include:

1. Causal Reasoning: Developing AI that can understand and reason about cause and effect relationships.

2. Multi-Agent Systems: Enabling multiple AI systems to interact and collaborate effectively.

3. Human-Machine Cooperation: Ensuring smooth and effective collaboration between humans and increasingly capable AI systems.

4. Bias and Fairness: Addressing and mitigating biases in AI outputs to ensure fair and equitable decision-making.

5. Robustness and Security: Enhancing the reliability and security of AI systems to prevent adversarial attacks and ensure safe deployment in real-world scenarios.

These challenges highlight the ongoing complexity and evolving nature of AI research and development.

Further read

• LLM, Us and Neuroplasticity

• "Are you sure?": Unveiling the Quest for Human-Like Artificial Intelligence

• Designers, creativity and AI

• A LLM as a reflection of our inner most desires

• Should AGI be a she, he, or a it? 

• Teaching LLMs common sense thinking

• The Ethical Nexus in an Era of Surrogate Machines

• Applying Asimov's Laws to Robot Taxis Confronted by Threats

• The Moat Matrix: LLMs Shape, but Don't Define the Moat

• From Infinite Improbability to Generative AI: Navigating Imagination in Fiction and Technology

• Human vs. AI in Reinforcement Learning through Human Feedback

• LinkedIn Profiles in the Age of Generative AI

• Generative AI for Law: The Agile Legal Business Model for Law Firms

• Generative AI for Law: From Harvard Law School to the Modern JD

• Unjust Law is Itself a Species of Violence: Oversight vs. Regulating AI

• Generative AI for Law: Technological Competence  of a Judge & Prosecutor

• AI to Make us Super-Scientist, not Super-humans

• Law is Not Logic: The Exponential Dilemma in Generative AI Governance

• Generative AI & Law: I Am an American Day in Central Park, 1944

• Pushpaka Vimana, Conceptualizing Vedic Technology & Diwali

• Generative AI & Law: Title 35 in 2024++ with Non-human Inventors

• Generative AI & Law: Similarity Between AI and Mice as a Means to Invent

• Generative AI & Law: LLMs are not Stochastic Parrots

• The Age of AI and the Charm of Tradition

• Diversity, equity, and inclusion (DEI) with Generative AI

• Need for Machine Consciousness & The Chinese Room Argument

• Generative AI & Law: The Evolving Role of Judges in the Federal Judiciary in the Age of AI

• Eating Shellfish to Artificial General Intelligence (AGI)

• Embedding Cultural Value of a Society into Large Language Models (LLMs)

• Lessons in Leadership: The Fall of the Roman Republic and the Rise of Julius Caesar

• Selling Dreams vs. Racing to Win

• LLMs as a Tool to Preserve a Society in History

• Beginning of The End for AI; Beginning for Generative AI

• Decentralized Truth & What is Means for AI Regulations

• Anthropomorphizing AGI

• The First Amendment & The Freedom to Think For Ourselves

• AI as a Common Carrier - A Utility

• Generative Native World: Deliberating Blame

• Generative Native World: The Right to be Let Alone

• One Day Can Last For Ever: Work & Life 

• Justice Sotomayor on Consequence of a Procedure or Substance

• The Moral LLM

• From France to the EU: A Test-and-Expand Approach to EU AI Regulation

• Beyond Human: Envisioning Unique Forms of Consciousness in AI

• Silent Enim Leges Inter Arma

• Protoconsciousness in AGI: Pathways to Artificial Consciousness

• Building Moral Machines

• AI Regulation: Reflections from Roe v. Wade (1973)

• Internet Scare (1996) v. the Generative AI Scare

• NYSERNet to Empire AI? Not Quite

• Artificial Consciousness as a Way to Mitigate AI Existential Risk

• Imagining AGI as a Digital Homo sapien

• Autonomous AI Agents as Economic Actors

• Human Memory & LLM Efficiency: Optimized Learning through Temporal Memory

• Building Greatness in AGI