When tackling tricky challenges that require complex reasoning – like solving a math puzzle or writing a coherent story – how we structure our thought process greatly impacts the outcome. Typically, there are two frameworks people use: 

• Chain of Thought (CoT): Linear, step-by-step thinking;
• Tree of Thought (ToT): Branching, exploring many sub-ideas.  

Intuitively, mapping out all facets of an issue enables deeper analysis than a single train of logic. An intriguing AI technique called Tree of Thoughts formally integrates this concept into advanced systems known as large language models. 

Inside the AI: Tree of Thoughts 

In a paper from Princeton and Google AI researchers, a framework dubbed "Tree of Thoughts" (ToT) enhances deliberate planning and problem solving within language models – AI systems trained on vast texts that can generate writing or answer questions when prompted. 

Specifically, ToT formulates thinking as navigating a tree, where each branch represents exploring another consideration or intermediate step toward the final solution. For example, the system logically breaks down factors like space, allergies, and care needs to recommend the best family pet, gradually elaborating the options. This branching structure resembles visual concept maps that aid human creativity and comprehension.

Crucially, ToT incorporates two integral facets of higher-level cognition that sets it apart from standard AI:

• Evaluating ideas: The system assesses each branch of reasoning via common sense and looks a few steps ahead at possibilities.
• Deciding and backtracking: It continually judges the most promising path to continue thinking through, backtracking as needed.  

This deliberate planning technique enabled significant advances in challenging puzzles requiring creative mathematical equations or coherent story writing that stump today's best AIs.

Chain vs. Tree: A Superior Way to Reason 

Compared to a chain of thought's linear, one-track reasoning, experiments reveal ToT's branching approach to thinking:

• Better handles complexity as ideas divide into sub-topics
• Allows more comprehensive exploration of alternatives  
• Keeps sight of the central issue as all branches connect to the main trunk

Yet the chain of thought's simplicity has merits, too, in clearly conveying ideas step-by-step.

In essence, ToT combines people's innate tree-like conceptualization with AI's scaling computational power for more brilliant exploration. Its versatility also allows customizing across different tasks and systems.

So, while both frameworks have roles depending on needs and individual thinking style preferences, ToT's deliberate branching is uniquely suited to steering AI's problem-solving today. 

As AI becomes more autonomous in real-world decision-making, ensuring deliberate, structured thinking will only grow in importance – making the tree of thought an increasingly essential capability that today's promising explorations point toward.

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This video starts by revisiting the 'Tree of Thoughts' prompting technique, demonstrating its effectiveness in guiding Language Models to solve complex problems. Then, it introduces LangChain, a tool that simplifies prompt creation, allowing for easier and more efficient problem-solving. 

Imagine an AI system that writes moving poetry one minute and fails to determine if five is odd or even the next. Or one that fluently discusses literature but needs help to infer essential cause-and-effect relationships. This strange paradox reveals the hidden struggles of artificial intelligence.  A groundbreaking new study exposes the yawning gap between machine and human reasoning. Through insightful experiments, AI experts demonstrated that today's supposedly "intelligent" systems lack the innate flexible thinking skills that even young minds possess. Please read on to find out why, despite the hype, today's AI still cannot match human mental dexterity.

The Trouble with Reasoning

The researchers designed a clever experiment to test the reasoning capacity of AI systems systematically. They created a dataset of 100,000 simulated biographies representing fictional people. This was a significant "homework" assignment: consume these biographies and learn as much as possible about the individuals described. The biographies contained diverse facts about each person, including birthdates, family background, education, career highlights, and hobbies. But unlike endless streams of internet text, these structured biographies allowed controlled testing of an AI's ability to comprehend and manipulate knowledge.

When trained on the dataset, today's state-of-the-art AI systems excel at absorbing the biographical information. Given a name, they could fluently generate lengthy profiles summarizing the significant facts. But could they do more than memorize and regurgitate information? To find out, the researchers quizzed the AI systems with questions designed to assess genuine reasoning skills. The results exposed glaring holes in the systems' abilities. First, while the AI could easily retrieve straightforward facts, it needed help classifying information it had memorized. For example, when given a date, today's best AI systems faltered at determining whether it fell in an odd or even month. This is despite having memorized dates perfectly and possessing full "knowledge" of the calendar. Such a basic inference requires scarcely more than memorization yet lies beyond these systems' limited reasoning capacity.

Another remarkable finding emerged when the researchers tested the AI's skills at comparing facts. When given two people's birthdates, the systems floundered at deducing which person was older. This essential skill underpins more complex temporal reasoning yet stumped the AI. The tests revealed the systems' inability to integrate facts into a flexible mental framework to support analysis from different perspectives.

But perhaps the most shocking results came from what experts called "inverse search" questions. Suppose you asked a human, "Who has a March birthday?" We effortlessly sift the possibilities in our minds, scanning memories until we lock in the right person. But despite having thoroughly memorized 100,000 profiles, today's AI systems ultimately failed this test. When queried in this inverse fashion, they could not isolate the relevant knowledge from their vast stores. This starkly spotlighted the rigid nature of these systems' internal representations.

The Chain of Thought Approach

Fortunately, in their experiments, the researchers discovered a technique that significantly boosted performance on the reasoning tests. They found that providing an explicit, step-by-step chain of logic enabled AI systems to answer correctly. This "Chain of Thought" approach guides reasoning through intermediate inferences, similar to showing mathematical work.  For example:

Step 1) Alice's birthday is September 17th.

Step 2) September is the 9th month.

Step 3) 9 is an odd number.

Conclusion: Alice has an odd birth month.

Laying out the reasoning chain in structured English enabled AI systems to connect the dots and derive the correct conclusions. But notably, their reasoning proficiency collapsed with little hand-holding, even on simple inference tasks. 

This starkly exposed the lack of an internal reasoning framework that fluidly combines facts. The researchers concluded today's AI lacks the bidirectional knowledge connections that support analysis from diverse vantage points. This innate human capacity develops rapidly during childhood but remains conspicuously absent in AI.

The Path Ahead

Together, these limitations cast severe doubt on AI's ability to achieve human levels of intelligence. Today's dominant approaches rely heavily on pattern recognition and predictive statistics. Enormous datasets train systems to recognize correlations and make reasonable predictions within narrow domains. But general human reasoning requires much more. Truly reproducing the multifaceted, flexible thinking of the human mind demands a paradigm shift. The blunt force of brute computation must give way to architectures that can construct rich mental models encoding concepts, semantic relationships, and chains of causality. 

Fortunately, promising new directions are emerging, often at the intersection of previously disparate fields. For example, cognitive architectures incorporate theoretical insights from psychology and neuroscience to instill more human-like faculties. New connectionist architectures mimic the brain's graphical networks of conceptual relationships. Evolutionary algorithms allow the gradually accumulating reasoning skills over many generations of training. Multidisciplinary infusion will likely prove essential to imparting AI with nuanced judgment.

For now, matching the context-dependent, imaginative reasoning humans acquire in childhood remains largely out of reach. But the quest to build genuinely thoughtful machines continues. With continued progress, today's fragile pattern recognizers may evolve into flexible, human-like minds. The path ahead will demand patience and creative persistence. But possibilities abound for imparting AI with open-ended reason in the years ahead. Though today's hype far outpaces reality, the march toward artificial general intelligence continues. With rigorous science and human inspiration, these narrow systems may develop keen perspective, judgment, and creativity, surpassing their designers'. But replicating human thought remains an epic challenge. For now, even simple reasoning tasks ensnare our "intelligent" machines, but the future holds promise.