Introduction
The game of chess is the most widely-studied domain in the history of artificial intelligence. Since the dawn of artificial intelligence, chess has been a battleground where human intellect and machine learning collide. From the iconic Deep Blue to the modern-day neural networks, the story of chess tournaments between humans and AI is a testament to the progress of technology and the enduring capabilities of the human mind.
In this blog post, we will analyze AI based decision making process using chess example throughout the history in order to offer a comprehensive framework and basis for understanding the broader implications for business and the economy.
*images are created by AI. See the note below
Human vs AI battles in chess
In a 1997 showdown billed as the final battle for supremacy between natural and artificial intelligence, IBM supercomputer Deep Blue defeated Garry Kasparov. Deep Blue evaluated two hundred million positions per second. That is a tiny fraction of possible chess positions—the number of possible game sequences is
more than atoms in the observable universe—but plenty enough to beat the best human.
Although he lost, Kasparov believed in the potential of human-computer collaboration. He proposed that the best chess might not come from humans or computers alone but from a combination of both.
This idea evolved into the concept of "Advanced Chess," where human players use AI assistance to make better moves. Kasparov's vision materialized with the development of Hydra, an advanced chess engine designed to work with human intuition and strategic thinking. Hydra was built to leverage immense computational power and advanced algorithms, enabling it to analyze positions and generate suggestions that human players could use to refine their strategies.
Kasparov's idea was fundamentally based on Moravec's Paradox, which posits that machines and humans often possess opposing strengths and weaknesses.
Moravec's Paradox, named after AI researcher Hans Moravec, highlights an intriguing phenomenon in the development of artificial intelligence: tasks that humans find difficult are often easier for computers to perform, while tasks that are easy for humans are incredibly challenging for AI. This paradox underscores the complexity of replicating human cognitive functions and intuitive abilities with machines.
For instance, computers excel at tasks requiring brute-force calculations and extensive data processing, such as playing chess or solving mathematical problems. These tasks involve well-defined rules and can be broken down into a series of logical steps that AI can execute rapidly. However, tasks that humans perform effortlessly, like recognizing faces, interpreting emotions, or navigating through a crowded room, require a level of perception, sensory integration, and adaptability that is extremely difficult for AI to replicate.
In the context of chess, Moravec's Paradox explains why AI engines can analyze millions of possible moves per second but struggle with intuitive, context-based decision-making that human grandmasters excel at. While AI can suggest optimal moves based on calculations, it lacks the nuanced understanding and strategic foresight that come naturally to experienced human players.
The supremacy of human intuition and strategic thinking compared to AI was demonstrated when Kasparov and Hydra lost the match against two chess amateurs using standard computers. The main reasons for this outcome include the effective human-AI collaboration, the flexibility and adaptability of human players, and the efficient use of technology by the amateurs.
"Kasparov concluded that the humans on the winning team were the best at “coaching” multiple computers on what to examine, and then synthesizing that information for an overall strategy. Human/Computer combo teams—known as “centaurs”—were playing the highest level of chess ever seen. If Deep Blue’s victory over Kasparov signaled the transfer of chess power from humans to computers, the victory of centaurs over Hydra symbolized something more interesting still: humans empowered to do what they do best without the prerequisite of years of specialized pattern recognition."
When playing in combination with computers, it is similar to an executive with a team of mega-grandmaster tactical advisers, deciding whose advice to probe more deeply respectively which option to very quickly direct the computers to examine more depth. By outsourcing tactics, the part of human expertise that is most easily replaced, humans rather focus on strategies, the part which is very hard to successfully do with the help of AI.
The latest advancement in this field was made by the AlphaZero chess program (owned by an AI arm of Google’s parent company). It uses deep neural networks and reinforcement learning to teach itself the game from scratch, rather than relying on pre-programmed knowledge and brute-force calculations. However, the program is still operating in a constrained, rule-bound world.
The more a task shifts to an open world of big-picture strategy, the more humans have to add.
Human vs AI use in the business landscape
The history of chess tournaments between humans and AI, from Deep Blue to AlphaZero, highlights the evolving capabilities of artificial intelligence and the enduring strengths of human cognition. AI has made remarkable strides, with programs like AlphaZero using deep neural networks and reinforcement learning to surpass traditional chess engines. However, human intuition, creativity, and strategic thinking remain critical, showcasing abilities that AI cannot fully replicate.
The collaboration between human players and AI, as demonstrated by the success of amateurs defeating Hydra with standard computers, exemplifies the potential for synergistic partnerships. This blend of human insight and computational power extends beyond chess, offering valuable lessons for business and the economy.
Ultimately, Humans vs AI and the Implications for Business and the Economy can be observed through the interplay between human and AI capabilities in chess because it provides a powerful metaphor for the broader implications of AI in our society. As we continue to explore and develop these technologies, the collaboration between humans and AI will be key to unlocking new possibilities and achieving greater success across diverse fields.
The interplay between human intelligence and artificial intelligence (AI) in chess offers valuable insights into how these two can collaborate across various fields to achieve optimal outcomes. Just as in chess, where AI excels in deep data analysis while human players bring strategic thinking and intuition, similar synergies can be leveraged in business, education, finance, and more.
In business and management AI can process large datasets, identify patterns, and generate actionable insights from market trends, customer behaviours, and operational metrics. Business leaders and managers can use these insights to inform strategic decisions, considering broader business contexts, ethical considerations and long-term goals that AI might not fully grasp.
For example, a retail company might use AI to analyze customer purchase data and predict trends. On the other hand, human managers then decide on product launches, marketing strategies, and inventory management based on these predictions, coupled with their market experience and creative vision.
In education AI can analyze student performance data to tailor educational content and recommend personalized learning paths. Educators use AI insights to identify areas where students need additional support and provide customized guidance, mentorship, and encouragement. For example, an online learning platform uses AI to adapt lessons to individual student needs. Teachers monitor AI-generated reports to offer targeted interventions and foster a supportive learning environment.
In finances and investments, AI algorithms can monitor market conditions, detect fraud, and predict market movements by analyzing vast amounts of financial and other data in real-time. Financial advisors and fund managers then evaluate these recommendations and make portfolio adjustments, considering risk tolerance and long-term investment strategies.
Conclusion:
The interplay between human intelligence and artificial intelligence (AI) in chess has provided profound insights into how these two can collaborate effectively across various fields to achieve optimal outcomes. Overall, the combination of AI's computational power and human strategic thinking leads to superior outcomes in various fields. This collaboration leverages AI for tasks requiring precision and data analysis while relying on human expertise for nuanced, creative, and ethical decision-making. By embracing this synergistic approach, organizations and professionals can navigate complex environments more effectively, fostering innovation and achieving greater success across diverse domains.
So if we are to combine AI with human intervention focused on strategic guidance consequently we are going to need people who understand not only how AI works and how it can be combined with various business fields, but also how different business fields are working together in ever changing business landscape. So called, T-shaped professionals who combine deep expertise in specific areas with broad knowledge across disciplines are well-positioned to harness AI effectively. This is something we will be focusing at in our next article.
Sources:
"Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins" by Garry Kasparov - Kasparov’s reflections on his matches with Deep Blue and his thoughts on AI and human collaboration.
"Thinking, Fast and Slow" by Daniel Kahneman - Insights into human decision-making processes and cognitive biases.
Moravec, H. (1988). "Mind Children: The Future of Robot and Human Intelligence" - Discussions on Moravec's Paradox and the capabilities of AI versus human intelligence.
"The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" by Pedro Domingos - Examination of machine learning and AI advancements.
Research papers and articles on AlphaZero by DeepMind - Including the landmark paper "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm."
ChessBase and other chess analysis platforms - Providing data and insights into various AI chess engines like Hydra, Fritz, and Stockfish.
"T-shaped Skills" concept popularized by IDEO and Tim Brown - Articles and discussions on the value of T-shaped professionals in modern business environments.
Range: Why Generalists Triumph in a Specialized World by Epstein, David J, Penguin Publishing Group. Kindle Edition.
*Images were created by AI tool ArtFlow. Lack of contextual generation shown especially with body parts (such as hands) which were not included in the original photos.
Comments