What is inductive reasoning (a definition), video: deduction vs. induction (deductive/inductive reasoning).
Examples of inductive reasoning.
Inductive reasoning in qualitative research, inductive reasoning in science.
Inductive reasoning examples in literature.
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“The grand aim of all science is to cover the greatest number of empirical facts by logical deduction from the smallest number of hypotheses or axioms.”
― Albert Einstein
Deductive logic is referred to as top-down logic, drawing conclusions through the elimination or examination of the disaggregated elements of a situation. Think about the simple example of the profit of a company, which equals revenue minus costs. Let’s say a company’s profit is declining, yet its revenues are increasing. By deduction, their costs must be increasing faster than their revenues, hence shrinking their profits, even though revenues are increasing.
The process of deductive logic is the typical problem solving process for management consulting projects . Once a team creates a hypothesis tree, then the team typically focuses on discovering and analyzing facts to prove or disprove the hypotheses of the tree. And, through proving or disproving hypotheses, the team creates conclusions and recommendations. Deductive logic is used when there is a discrete set of hypotheses or options , such as when trying to find the root cause of a process issue or trying to optimize a discrete system.
On the other hand, inductive logic is the inverse of deductive logic, taking observations or facts and creating hypotheses or theories from them. Inductive logic is known as bottom-up logic, which starts with selective observations and facts that lead to generalizing and inducing potential hypotheses or theories.
Imagine there is a barrel of 100 apples and 5 apples are picked from the barrel, and they were all rotten. Using inductive logic, the fact that the first 5 apples are rotten can be generalized into a hypothesis that all the apples are rotten. The key with inductive logic is it doesn’t determine factual conclusions, only hypotheses. If all 100 apples were examined then this would be deductive logic. And, if all 100 were rotten, then it could be concluded as fact that all the apples in the barrel are indeed rotten. Though, just picking 5 that are rotten can only create a hypothesis that all 100 are rotten. Inductive logic should be used when there is an open-ended set of options or potential hypotheses, such as trying to figure out the best marketing campaign to drive sales, or potential innovations for a product, where there might be selective facts and observations that point to potential good solutions, but only after being tested can be truly confirmed as fact.
People using inductive logic to derive conclusions is a large and somewhat invisible issue in strategic thinking and problem solving. I often run across situations where someone observes something and then makes a conclusion about the root cause of a discrete problem.
Let’s go through a simple example to understand this issue better. Let’s say a company has a quality issue where customers are receiving a broken product. And, a product manager states, “The issue must be the shipping department. I’ve seen people in the warehouse drop products and then package them up and ship them.” The product manager uses inductive logic to try and conclude that the quality issue is because of the shipping department mishandling the product. Yet, this inductive logic only creates a hypothesis that the shipping department is to blame.
Deductive logic, not inductive logic, must be used to factually determine the root cause of the quality issue. With deductive logic, we first need to create MECE ( Mutually Exclusive , Collectively Exhaustive) hypotheses of what is driving the quality issue. By creating a hypothesis tree , our quality issue can be from four main hypotheses, which are poor design, the wrong materials, bad manufacturing, or mishandling of the product by the shipping department.
Then the path of deductive logic would lead one to prove or disprove the main hypotheses. To prove or disprove whether it was mishandled by the shipping department an audit could be conducted, which could include inspecting the product before shipping and inspecting the shipping & handling processes . Let’s say the shipping & handling audit showed no issues but did find that 40% of the product had a faulty part, let’s call this part B. Then, we could conduct an audit of the manufacturing and assembly processes. Let’s say during assembly process A, part B broke 40% of the time, even though the manufacturer was consistently following the assembly process instructions. Then, a supplier audit on part B could be conducted to ensure part B is authentic, high quality, and designed to specifications. Let’s say the supplier audit came back with no issues. And, then the product design could be evaluated, and let’s say it was found part B wasn’t properly designed for the assembly process and broke 40% of the time in assembly. By deductive logic, we can conclude that the quality issue was due to the poor design of Part B. Above is a visual representation of the example.
Both inductive and deductive logic are fundamental in problem solving. Though, inductive logic is often used when deductive logic is appropriate. This is a subtle issue that most people don’t ever think about, but the consequences are often significant since false conclusions often come from inductive logic. One of the main reasons companies use top strategy consulting firms is because of their strong deductive problem solving methodologies. Deductive problem solving is comprehensive and derives factual conclusions. Most people or teams tasked with solving a problem don’t start with a problem statement , then build a hypothesis tree, and then spend weeks or months proving and disproving the different branches of the hypothesis tree, but top strategy consulting firms do. If somebody wants to figure out the true root causes of a problem, they will use deductive problem solving.
Inductive logic is also critical to strategy when it comes to connecting the dots in creating great options and solutions to a problem. Inductive logic is necessary when the context of a situation is understood, and creative and innovative options and solutions are needed. Elegant inductive logic was the driver for the simplicity of the iPhone, many of the innovations in the Tesla, and the most creative solutions to challenging situations.
One of the core strengths of strategic leaders is the high-quality logic they apply to problems and situations. Regarding inductive and deductive logic, most of the time people use inductive logic. They take a few thoughts or facts and create hypotheses. Typically, what most people need to build up is their deductive logic. That is why we focus on it so much in this problem solving module.
Exercise 1 – Build Your Logic Awareness
Can you tell when people or even yourself are using deductive vs. inductive logic? Can you determine which logic is needed in which situation? If not, in meetings, when people are recommending a course of action, or are breaking down an argument, see if you can determine if they are using inductive or deductive logic, or no logic at all (gut feelings and emotion). And, then figure out your logic and when best to use deductive vs. inductive arguments.
Exercise 2 – Use Deduction, When you should Use Deduction
When you have a significant problem or opportunity you need to solve or build a strategy for, start with a deductive problem solving process. Use the tools in this module, by defining the problem statement, disaggregating the problem, building a hypothesis tree, prove or disprove hypotheses through facts and analysis . And, then switch to inductive logic when creating creative potential solutions and synthesizing those solutions.
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Most everyone who thinks about how to solve problems in a formal way has run across the concepts of deductive and inductive reasoning. Both deduction and induction help us navigate real-world problems, such as who committed a crime, the most likely cause of an accident, or how many planets might contain life in the Milky Way galaxy.
But while they’re both practical tools for practical problems, but they approach problem-solving in opposite ways.
Both deduction and induction are a type of inference, which means reaching a conclusion based on evidence and reasoning.
Deduction moves from idea to observation, while induction moves from observation to idea.
Deduction is idea-first, followed by observations and a conclusion. Induction is observation first, followed by an idea that could explain what’s been seen.
The other big difference is that deduction’s conclusions are bulletproof assuming you don’t make a mistake along the way. The conclusion is always true as long as the premises are true. With induction you don’t get absolute certainty; the quality of the idea or model or theory depends on the quality of the observations and analysis.
All men are mortal. Harold is a man. Therefore, Harold is mortal.
This third sentence is absolutely true because the first two sentences are true.
I have a bag of many coins, and I’ve pulled 10 at random and they’ve all been pennies, therefore this is probably a bag full of pennies.
This gives some measure of support for the argument that the bag only has pennies in it, but it’s not complete support like we see with deduction.
Deduction has theories that predict an outcome, which are tested by experiments. Induction makes observations that lead to generalizations for how that thing works.
If the premises are true in deduction, the conclusion is definitely true. If the premises are true in induction, the conclusion is only probably true—depending on how good the evidence is.
There’s another type of reasoning called Abductive Reasoning, where you take a set of observations and simply take the most likely explanation given the evidence you have.
Deduction is hard to use in everyday life because it requires a sequential set of facts that are known to be true. Induction is used all the time in everyday life because most of the world is based on partial knowledge, probabilities, and the usefulness of a theory as opposed to its absolute validity.
Deduction is more precise and quantitative, while induction is more general and qualitative.
If A = B and B = C, then A = C.
Since all squares are rectangles, and all rectangles have four sides, so all squares have four sides.
All cats have a keen sense of smell. Fluffy is a cat, so Fluffy has a keen sense of smell.
Every time you eat peanuts, your throat swells up and you can’t breathe. This is a symptom of people who are allergic to peanuts. So, you are allergic to peanuts.
Ray is a football player. All football players weigh more than 170 pounds. Ray weighs more than 170 pounds.
All cars in this town drive on the right side of the street. Therefore, all cars in all towns drive on the right side of the street.
We can see here that deduction is a nice-to-have. It’s clean. But life is seldom clean enough to be able to apply it perfectly.
Most real problems and questions deal more in the realm of induction, where you might have some observations—and those observations might be able to take you to some sort of generalization or theory—but you can’t necessarily say for sure that you’re right. It’s about working as best you can within a world where knowledge is usually incomplete.
Deduction gets you to a perfect conclusion—but only if all your premises are 100% correct.
Deduction moves from theory to experiment to validation, where induction moves from observation to generalization to theory.
Deduction is harder to use outside of lab/science settings because it’s often hard to find a set of fully agreed-upon facts to structure the argument.
Induction is used constantly because it’s a great tool for everyday problems that deal with partial information about our world, and coming up with usable conclusions that may not be right in all cases.
Be willing to use both types of reasoning to solve problems, and know that they can often be used together cyclically as a pair, e.g., use induction to come up with a theory, and then use deduction to determine if it’s actually true.
The main thing to avoid with these two is arguing with the force of deduction (guaranteed to be true) while actually using induction (probability based on strength of evidence).
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Uncover the secrets behind inductive reasoning and how it differs from deductive thinking.
Inductive reasoning is a type of logical thinking that involves making generalizations based on specific observations or evidence.
Unlike deductive reasoning, which starts with a general premise and applies it to specific cases to reach a conclusion, inductive reasoning works in the opposite direction.
Inductive reasoning begins with specific observations or examples and uses them to infer a general pattern or principle.
For example, if you observe that all the crows you have seen are black, you might make the inductive inference that all crows are black.
However, it's important to note that inductive reasoning does not guarantee absolute certainty or truthfulness of the conclusions.
The strength of an inductive argument lies in the degree of support provided by the specific observations or evidence.
Exploring the basics of inductive reasoning can help improve your critical thinking skills and enhance your problem-solving abilities.
Inductive reasoning is a fundamental part of our everyday lives, often used to make predictions, form hypotheses, and draw conclusions.
Here are some examples of inductive reasoning in action:
- You notice that every time you eat a certain food, you experience an allergic reaction. Based on this observation, you might infer that you have a food allergy.
- You observe that whenever it rains, the streets become wet. From this, you might conclude that rain causes wet streets.
- You notice that every time you press a button on your TV remote, the channel changes. You might then infer that the button is responsible for changing the channel.
These examples highlight how inductive reasoning allows us to make educated guesses and draw plausible conclusions based on specific observations.
By recognizing and understanding inductive reasoning in everyday life, you can become a more effective problem solver and decision maker.
Inductive reasoning plays a crucial role in problem-solving by providing a framework for generating hypotheses, exploring patterns, and making informed decisions.
When faced with a complex problem or puzzle, inductive reasoning allows you to analyze specific instances, identify commonalities, and develop a general understanding or solution.
By using inductive reasoning, you can:
- Identify patterns and trends that can guide problem-solving strategies.
- Generate hypotheses or possible explanations based on observed data.
- Make predictions about future outcomes or events based on past observations.
- Test and refine hypotheses through further observation and analysis.
Inductive reasoning helps you think creatively, consider multiple perspectives, and approach problems from different angles.
By recognizing the importance of inductive reasoning in problem-solving, you can enhance your ability to tackle complex challenges and find innovative solutions.
While inductive reasoning is a valuable thinking tool, it is not without its challenges and limitations.
One of the main challenges is the potential for biased or insufficient observations.
If your observations are limited or biased, the inductive conclusions you draw may not accurately represent the broader reality.
Additionally, inductive reasoning relies on probabilities and generalizations, rather than absolute certainty.
The conclusions reached through inductive reasoning are always subject to revision or refinement as new evidence emerges.
Furthermore, inductive reasoning can be affected by cognitive biases, personal beliefs, and cultural influences.
It's important to be aware of these challenges and limitations when using inductive reasoning, and to supplement it with other forms of thinking, such as deductive reasoning and critical analysis.
Inductive reasoning is a skill that can be improved with practice and conscious effort.
Here are some tips to enhance your inductive reasoning skills:
- Observe and gather data: Pay attention to the details of specific situations, events, or phenomena. Collect data and information that can serve as evidence for your reasoning.
- Look for patterns and connections: Analyze the collected data to identify patterns, trends, or relationships. Look for similarities or commonalities that can help you make inductive inferences.
- Consider alternative explanations: Challenge your initial inductive inferences by considering alternative explanations or hypotheses. This helps you avoid jumping to conclusions based on limited evidence.
- Test and refine your conclusions: Subject your inductive conclusions to further testing and analysis. Seek additional evidence or observations that can support or challenge your reasoning.
- Seek feedback and multiple perspectives: Share your inductive reasoning with others and seek feedback. Consider different viewpoints and perspectives to refine your reasoning.
By following these tips, you can enhance your ability to think inductively, make informed judgments, and arrive at well-supported conclusions.
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On whether generative ai and large language models are better at inductive reasoning or deductive reasoning and what this foretells about the future of ai.
Inductive reasoning and deductive reasoning go to battle but might need to be married together for ... [+] the sake of reaching true AI or AGI (artificial general intelligence).
In today’s column, I continue my ongoing analysis of the latest advances and breakthroughs in AI, see my extensive posted coverage at the link here , and focus in this discussion on the challenges associated with various forms of reasoning that are mathematically and computationally undertaken via modern-day generative AI and large language models (LLM). Specifically, I will do a deep dive into inductive reasoning and deductive reasoning.
Here’s the deal.
One of the biggest open questions that AI researchers and AI developers are struggling with is whether we can get AI to perform reasoning of the nature and caliber that humans seem to do.
This might at an initial cursory glance appear to be a simple question with a simple answer. But the problems are many and the question at hand is extraordinarily hard to answer. One difficulty is that we cannot say for sure the precise way that people reason. By this, I mean to say that we are only guessing when we contend that people reason in one fashion or another. The actual biochemical and wetware facets of the brain and mind are still a mystery as to how we attain cognition and higher levels of mental thinking and reasoning.
Some argue that we don’t need to physically reverse engineer the brain to proceed ahead with devising AI reasoning strategies and approaches. The viewpoint is that it would certainly be a nice insight to know what the human mind really does, that’s for sure. Nonetheless, we can strive forward to develop AI that has the appearance of human reasoning even if the means of the AI implementation is potentially utterly afield of how the mind works.
Think of it this way.
We might be satisfied if we can get AI to mimic human reasoning from an outward perspective, even if the way in which the AI computationally works is not what happens inside the heads of humans. The belief or assertion would be that you don’t have to distinctly copy the internals if the seen-to-be external performance matches or possibly exceeds what’s happening inside a human brain. I liken this to an extreme posture by noting that if you could assemble a bunch of Lego bricks and get them to seemingly perform reasoning, well, you might take that to the bank as a useful contraption, despite that it isn’t working identically as human minds are.
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That being said, if you have in fact managed to assemble Lego bricks into a human-like reasoning capacity, please let me know. Right away. A Nobel Prize is undoubtedly and indubitably soon to be on your doorstep.
The Fascinating Nature Of Human Reasoning
Please know that the word “reasoning” carries a lot of baggage.
Some would argue that we shouldn’t be using the watchword when referring to AI. The concern is that since reasoning is perceived as a human quality, talking about AI reasoning is tantamount to anthropomorphizing AI. To cope with this expressed qualm, I will try to be cautious in how I make use of the word. Just wanted to make sure you knew that some experts have acute heartburn about waving around the word “reasoning”. Let’s try to be mindful and respectful of how the word is to be used.
Disclaimer noted.
Probably the most famous primary forms of human reasoning consist of inductive reasoning and deductive reasoning.
I’m sure you’ve been indoctrinated in the basics of those two major means of reasoning. Whether the brain functions by using those reasoning methods is unresolved. It could be that we are merely rationalizing decision-making by conjuring up a logical basis for reasoning, trying to make pretty the reality of whatever truly occurs inside our heads.
Because inductive reasoning and deductive reasoning are major keystones for human reasoning, AI researchers have opted to pursue those reasoning methods to see how AI can benefit from what we seem to know about human reasoning. Yes, indeed, lots of AI research has been devoted to exploring how to craft AI that performs inductive reasoning and performs deductive reasoning.
Some results have come up with AI that is reasonably good at inductive reasoning but falters when doing deductive reasoning. Likewise, the other direction is the case too, namely that you might come up with AI that is pretty good at deductive reasoning but thin on inductive reasoning. Trying to achieve both on an equal and equally heightened basis is tricky and still being figured out.
You might be wondering what the deal is with generative AI and large language models (LLM) in terms of how those specific types of AI technology fare on inductive and deductive reasoning. I’m glad that you asked.
That’s the focus of today’s discussion.
Before we make the plunge into the meaty topic, let’s ensure we are all on the same page about inductive and deductive reasoning. Perhaps it has been a while since you had to readily know the differences between the two forms of reasoning. No worries, I’ll bring you quickly up-to-speed at a lightning pace.
An easy way to compare the two is by characterizing inductive reasoning as being a bottoms-up approach while deductive reasoning is considered a tops-down approach to reasoning.
With inductive reasoning, you observe particular facts or facets and then from that bottoms-up viewpoint try to arrive at a reasoned and reasonable generalization. Your generalization might be right. Wonderful. On the other hand, your generalization might be wrong. My point is that inductive reasoning, and also deductive reasoning, are not surefire guaranteed to be right. They are sensible approaches and improve your odds of being right, assuming you do the necessary reasoning with sufficient proficiency and alertness.
Deductive reasoning generally consists of starting with a generalization or theory and then proceeding to ascertain if observed facts or facets support the overarching belief. That is a proverbial top-down approach.
We normally expect scientists and researchers to especially utilize deductive reasoning. They come up with a theory of something and then gather evidence to gauge the validity of the theory. If they are doing this in a far and-square manner, they might find themselves having to adjust the theory based on the reality of what they discover.
Okay, we’ve covered the basics of inductive and deductive reasoning in a nutshell. I am betting you might like to see an example to help shake off any cobwebs on these matters.
Happy to oblige.
Illustrative Example Of Inductive And Deductive Reasoning
I appreciate your slogging along with me on this quick rendition of inductive and deductive reasoning. Hang in there, the setup will be worth it. Time to mull over a short example showcasing inductive reasoning versus deductive reasoning.
When my kids were young, I used to share with them the following example of inductive reasoning and deductive reasoning. Maybe you’ll find it useful. Or at least it might be useful for you to at some point share with any youngsters that you happen to know. Warning to the wise, do not share this with a fifth grader since they will likely feel insulted and angrily retort that you must believe them to be a first grader (yikes!).
Okay, here we go.
Imagine that you are standing outside and there are puffy clouds here and there. Let’s assume that on some days the clouds are there and on other days they are not. Indeed, on any given day, the clouds can readily come and go.
What is the relationship between the presence of clouds and the outdoor temperature?
That seems to be an interesting and useful inquiry. A child might be stumped, though I kind of doubt they would. If they’ve been outside with any regularity, and if clouds come and go with any regularity, the chances are they have already come up with a belief on this topic. Maybe no one explicitly asked them about it. Thus, this question might require a moment or two for a youngster to collect their thoughts.
Envision that we opt to ask a youngster to say aloud their reasoning as they figure out an answer to the posed question.
One angle would be to employ inductive reasoning to solve the problem.
It might go like this when using inductive reasoning to answer the question about clouds and outdoor temperature:
Seems sensible and orderly.
The act consisted of a bottoms-up method. There were prior and current observations that the child identified and used when processing the perplexing matter. Based on those observations, a seemingly logical conclusion can be reached. In this instance, since the clouds often were accompanied by a drop in temperature, you might suggest that when it gets cloudy the temperate will tend to drop.
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Another angle would be to employ deductive reasoning.
Here we go with answering the same question but using deductive reasoning this time:
The youngster began by formulating a theory or premise.
How did they come up with it?
We cannot say for sure. They may have already formed the theory based on a similar inductive reasoning process as I just gave. There is a chance too that they might not be able to articulate why they believe in the theory. It just came to them.
Again, this is the mystery of how the brain and mind function. From the outside of a person’s brain, we do not have the means to reach into their head and watch what logically happens during their thinking endeavors (we can use sensors to detect heat, chemical reactions, and other wiring-like actions, but that is not yet translatable into full-on articulation of thinking processes at a logical higher-level per se). We must take their word for whatever they proclaim has occurred inside their noggin. Even they cannot say for sure what occurred inside their head. They must guess too.
It could be that the actual internal process is nothing like the logical reasoning we think it is. People are taught that they must come up with justifications and explanations for their behavior. The explanation or justification can be something they believe happened in their heads, though maybe it is just an after-the-fact concoction based on societal and cultural demands that they provide cogent explanations.
As an aside, you might find of interest that via the use of BMI (brain-machine interfaces), researchers in neuroscience, cognitive science, AI, and other disciplines are hoping to one day figure out the inner sanctum and break the secret code of what occurs when we think and reason. See my coverage on BMI and akin advances at the link here .
One other aspect to mention about the above example of deductive reasoning about the cloud and temperature is that besides a theory or premise, the typical steps entail an effort to apply the theory to specific settings. In this instance, the child was able to reaffirm the premise due to the observation that today was cloudy and that it seemed that the temperature had dropped.
Another worthy point to bring up is that I said earlier that either or both of those reasoning methods might not necessarily produce the right conclusion. The act of having and using a bona fide method does not guarantee a correct response.
Does the presence of clouds always mean that temperatures will drop?
Exceptions could exist.
Plus, clouds alone do not impact temperature and other factors need to be incorporated.
Generative AI And The Two Major Reasoning Approaches
You are now versed in or at least refreshed about inductive and deductive reasoning. Good for you. The world is a better place accordingly.
I want to now bring up the topic of generative AI and large language models. Doing so will allow us to examine the role of inductive reasoning and deductive reasoning when it comes to the latest in generative AI and LLMs.
I’m sure you’ve heard of generative AI, the darling of the tech field these days.
Perhaps you’ve used a generative AI app, such as the popular ones of ChatGPT, GPT-4o, Gemini, Bard, Claude, etc. The crux is that generative AI can take input from your text-entered prompts and produce or generate a response that seems quite fluent. This is a vast overturning of the old-time natural language processing (NLP) that used to be stilted and awkward to use, which has been shifted into a new version of NLP fluency of an at times startling or amazing caliber.
The customary means of achieving modern generative AI involves using a large language model or LLM as the key underpinning.
In brief, a computer-based model of human language is established that in the large has a large-scale data structure and does massive-scale pattern-matching via a large volume of data used for initial data training. The data is typically found by extensively scanning the Internet for lots and lots of essays, blogs, poems, narratives, and the like. The mathematical and computational pattern-matching homes in on how humans write, and then henceforth generates responses to posed questions by leveraging those identified patterns. It is said to be mimicking the writing of humans.
I think that is sufficient for the moment as a quickie backgrounder. Take a look at my extensive coverage of the technical underpinnings of generative AI and LLMs at the link here and the link here , just to name a few.
When using generative AI, you can tell the AI via a prompt to make use of deductive reasoning. The generative AI will appear to do so. Similarly, you can enter a prompt telling the AI to use inductive reasoning. The generative AI will appear to do so.
I am about to say something that might be surprising, so I am forewarning you and want you to mentally prepare yourself.
Have you braced yourself for what I am about to say?
When you enter a prompt telling generative AI to proceed with inductive or deductive reasoning, and then you eyewitness what appears to be such reasoning as displayed via the presented answer, there is once again a fundamental question afoot regarding the matter of what you see versus what actually happened internally.
I’ve discussed this previously in the use case of explainable AI, known as XAI, see my analysis at the link here . In brief, just because the AI tells you that it did this or that step, there is not necessarily an ironclad basis to assume that the AI solved the problem in that particular manner.
The explanation is not necessarily the actual work effort. An explanation can be an after-the-fact rationalization or made-up fiction, which is done to satisfy your request to have the AI show you the work that it did. This can be the case too when requesting to see a problem solved via inductive or deductive reasoning. The generative AI might proceed to solve the problem using something else entirely, but since you requested inductive or deductive reasoning, the displayed answer will be crafted to look as if that’s how things occurred.
Be mindful of this.
What you see could be afar of what is happening internally.
For now, let’s put that qualm aside and pretend that what we see is roughly the same as what happened to solve a given problem.
How Will Generative AI Fare On The Two Major Forms Of Reasoning
I have a thought-provoking question for you:
Take a few reflective seconds to ponder the conundrum.
Tick tock, tick tock.
The usual answer is that generative AI and LLMs are better at inductive reasoning, the bottoms-up form of reasoning.
Recall that generative AI and LLMs are devised by doing tons of data training. You can categorize data as being at the bottom side of things. Lots of “observations” are being examined. The AI is pattern-matching from the ground level up. This is similar to inductive reasoning as a process.
I trust that you can see that the inherent use of data, the data structures used, and the algorithms employed for making generative AI apps are largely reflective of leaning into an inductive reasoning milieu. Generative AI is therefore more readily suitable to employ inductive reasoning for answering questions if that’s what you ask the AI to do.
This does not somehow preclude generative AI from also or instead performing deductive reasoning. The upshot is that generative AI is likely better at inductive reasoning and that it might take some added effort or contortions to do deductive reasoning.
Let’s review a recent AI research study that empirically assessed the inductive reasoning versus deductive reasoning capabilities of generative AI.
New Research Opens Eyes On AI Reasoning
In a newly released research paper entitled “Inductive Or Deductive? Rethinking The Fundamental Reasoning Abilities Of LLMs” by Kewei Cheng, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Binxuan Huang, Ruirui Li, Shiyang Li, Zheng Li, Yifan Gao, Xian Li, Bing Yin, Yizhou Sun, arXiv , August 7, 2024, these salient points were made (excerpts):
As stated in those points, the reasoning capabilities of generative AI and LLMs are an ongoing subject of debate and present interesting challenges. The researchers opted to explore whether inductive reasoning or deductive reasoning is the greater challenge for such AI.
They refer to the notion of whether generative AI and LLMs are symbolic reasoners.
Allow me a moment to unpack that point.
The AI field has tended to broadly divide the major approaches of devising AI into two camps, the symbolic camp and the sub-symbolic camp. Today, the sub-symbolic camp is the prevailing winner (at this time). The symbolic camp is considered somewhat old-fashioned and no longer in vogue (at this time).
For those of you familiar with the history of AI, there was a period when the symbolic approach was considered top of the heap. This was the era of expert systems (ES), rules-based systems (RBS), and often known as knowledge-based management systems (KBMS). The underlying concept was that human knowledge and human reasoning could be explicitly articulated into a set of symbolic rules. Those rules would then be encompassed into an AI program and presumably be able to perform reasoning akin to how humans do so (well, at least to the means of how we rationalize human reasoning). Some characterized this as the If-Then era, consisting of AI that contained thousands upon thousands of if-something then-something action statements.
Eventually, the rules-based systems tended to go out of favor. If you’d like to know more about the details of how those systems worked and why they were not ultimately able to fulfill the quest for top-notch AI, see my analysis at the link here .
The present era of sub-symbolics went a different route. Generative AI and LLMs are prime examples of the sub-symbolic approach. In the sub-symbolic realm, you use algorithms to do pattern matching on data. Turns out that if you use well-devised algorithms and lots of data, the result is AI that can seem to do amazing things such as having the appearance of fluent interactivity. At the core of sub-symbolics is the use of artificial neural networks (ANNs), see my in-depth explanation at the link here .
You will momentarily see that an unresolved question is whether the sub-symbolic approach can end up performing symbolic-style reasoning. There are research efforts underway of trying to logically interpret what happens inside the mathematical and computational inner workings of ANNs, see my discussion at the link here .
Getting back to the inductive versus deductive reasoning topic, let’s consider the empirical study and the means they took to examine these matters:
Their experiment consisted of coming up with tasks for generative AI to solve, along with prompting generative AI to do the solution process by each of the two respective reasoning processes. After doing so, the solutions provided by AI could be compared to ascertain whether inductive reasoning (as performed by the AI) or deductive reasoning (as performed by the AI) did a better job of solving the presented problems.
Tasks Uniformity And Reasoning Disentanglement
The research proceeded to define a series of tasks that could be given to various generative AI apps to attempt to solve.
Notice that a uniform set of tasks was put together. This is a good move in such experiments since you want to be able to compare apples to apples. In other words, purposely aim to use inductive reasoning on a set of tasks and use deductive reasoning on the same set of tasks. Other studies will at times use a set of tasks for analyzing inductive reasoning and a different set of tasks to analyze deductive reasoning. The issue is that you end up comparing apples versus oranges and can have muddled results.
Are you wondering what kinds of tasks were used?
Here are the types of tasks they opted to apply:
Something else that they did was try to keep inductive reasoning and deductive reasoning from relying on each other.
Unfortunately, both approaches can potentially slop over into aiding the other one.
Remember for example when I mentioned that a youngster using deductive reasoning about the relationship between clouds and temperatures might have formulated a hypothesis or premise by first using inductive reasoning? If so, it is difficult to say which reasoning approach was doing the hard work in solving the problem since both approaches were potentially being undertaken at the same time.
The researchers devised a special method to see if they could avoid a problematic intertwining:
Kudos to them for recognizing the need to try and make that separation on a distinctive basis.
Hopefully, other researchers will take up the mantle and further pursue this avenue.
The Results And What To Make Of It
I’m sure that you are eagerly awaiting the results of what they found.
Drum roll, please.
Highlights of their key outcomes include:
Let’s examine those results.
First, they reaffirmed what we would have anticipated, namely that the generative AI apps used in this experiment were generally better at employing inductive reasoning rather than deductive reasoning. I mentioned earlier that the core design and structure of generative AI and LLMs lean into inductive reasoning capabilities. Thus, this result makes intuitive sense.
For those of you who might say ho-hum to the act of reaffirming an already expected result, I’d like to emphasize that doing experiments to confirm or disconfirm hunches is a very worthwhile endeavor. You do not know for sure that a hunch is on target. By doing experiments, your willingness to believe in a hunch can be bolstered, or possibly overturned if the experiments garner surprising results.
Not every experiment has to reveal startlingly new discoveries (few do).
Second, a related and indeed interesting twist is that the inductive reasoning performance appeared to differ somewhat based on which of the generative AI apps was being used. The gist is that depending upon how the generative AI was devised by an AI maker, such as the nature of the underlying foundation model, the capacity to undertake inductive reasoning varied.
The notable point about this is that we need to be cautious in painting with a broad brush all generative AI apps and LLMs in terms of how well they might do on inductive reasoning. Subtleties in the algorithms, data structures, ANN, and data training could impact the inductive reasoning proclivities.
This is a handy reminder that not all generative AI apps and LLMs are the same.
Third, the researchers acknowledge a heady topic that I keep pounding away at in my analyses of generative AI and LLMs. It is this. The prompts that you compose and use with AI are a huge determinant of the results you will get out of the AI. For my comprehensive coverage of over fifty types of prompt engineering techniques and tips, see the link here .
In this particular experiment, the researchers used a straight-ahead prompt that was not seeking to exploit any prompt engineering wizardry. That’s fine as a starting point. It would be immensely interesting to see the experimental results if various prompting strategies were used.
One such prompting strategy would be the use of chain-of-thought (COT). In the COT approach, you explicitly instruct AI to provide a step-by-step indication of what is taking place. I’ve covered extensively the COT since it is a popular tactic and can boost your generative AI results, see my coverage at the link here , along with a similar approach known as skeleton-of-thought (SOT) at the link here.
If we opted to use COT for this experiment, what might arise?
I speculate that we might enhance inductive reasoning by having directly given a prompt that tends to seemingly spur inductive reasoning to take place. It is almost similar to my assertion that sometimes you can improve generative AI results by essentially greasing the skids, see the link here . Perhaps the inductive reasoning might be more pronounced by a double-barrel dose of guiding the AI correspondingly to that mode of operation.
Prompts do matter.
I’ll conclude this discussion with something that I hope will stir your interest.
Where is the future of AI?
Should we keep on deepening the use of sub-symbolics via ever-expanding the use of generative AI and LLMs? That would seem to be the existing course of action. Toss more computational resources at the prevailing sub-symbolic infrastructure. If you use more computing power and more data, perhaps we will attain heightened levels of generative AI, maybe verging on AGI (artificial general intelligence).
Not everyone accepts that crucial premise.
An alternative viewpoint is that we will soon reach a ceiling. No matter how much computing you manage to corral, the incremental progress is going to diminish and diminish. A limit will be reached. We won’t be at AGI. We will be better than today’s generative AI, but only marginally so. And continued forceful efforts will gain barely any additional ground. We will be potentially wasting highly expensive and prized computing on a losing battle of advancing AI.
I’ve discussed this premise at length, see the link here .
Let’s tie that thorny topic to the matter of inductive reasoning versus deductive reasoning.
If you accept the notion that inductive reasoning is more akin to sub-symbolic, and deductive reasoning is more akin to symbolic, one quietly rising belief is that we need to marry together the sub-symbolic and the symbolic. Doing so might be the juice that gets us past the presumed upcoming threshold or barrier. To break the sound barrier, as it were, we might need to focus on neuro-symbolic AI.
Neuro-symbolic AI is a combination of sub-symbolic and symbolic approaches. The goal is to harness both to their maximum potential. A major challenge involves how to best connect them into one cohesive mechanization. You don’t want them to be infighting. You don’t want them working as opposites and worsening your results instead of bettering the results. See my discussion at the link here .
I’d ask you to grab yourself a glass of fine wine, sit down in a place of solitude, and give these pressing AI questions some heartfelt thoughts:
That should keep your mind going for a while.
If you can find a fifth grader who can definitively answer those vexing and course-changing questions, make sure to have them write down their answers. It would be history in the making. You would have an AI prodigy in your midst.
Meanwhile, let’s all keep our noses to the grind and see what progress we can make on these mind-bending considerations. Join me in doing so, thanks.
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1.1 Solving Problems by Inductive Reasoning. The Moscow papyrus, which dates back to about 1850 B.C., provides an example of inductive reasoning by the early Egyptian mathematicians. Problem 14 in the document reads: You are given a truncated. The development of mathematics can be traced to the Egyptian and Babylonian cul-tures (3000 B.C.-.
Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: https://www.khanacademy.org/math/algebra-home/alg-series-and-ind...
Examples: Inductive reasoning. Nala is an orange cat and she purrs loudly. Baby Jack said his first word at the age of 12 months. Every orange cat I've met purrs loudly. All observed babies say their first word at the age of 12 months. All orange cats purr loudly. All babies say their first word at the age of 12 months.
That's what inductive reasoning is all about. You're not always going to be 100%, or you definitely won't be 100% sure that you're right, that the nth number will be n squared minus 1. But based on the pattern you've seen so far, it's a completely reasonable thing to-- I guess you could say-- to induce. Learn for free about math, art, computer ...
Solving Problems by Inductive Reasoning. Identify the reasoning process, inductive or deductive. I got up at nine o'clock for the past week. I will get up at nine o'clock tomorrow. James Cameron's last three movies were successful. His next movie will be successful. Jim has 20 pencils. He gives half of them to Dan.
In this video you will learn to define the terms and concepts problem solving and employ inductive and deductive reasoning in problem solving. References: Au...
This is an example of inductive reasoning because the premises are specific instances, while the conclusion is general. b) The premise is: Every day for the past year, a plane flies over my house at 2 p.m The conclusion is: A plane will fly over my house every day at 2 p.m.
Problem Solving. We want to divide a circle into regions by selecting points on its circumference and drawing line segments from each point to each other point. The figure (on the next slide) shows the greatest number of regions that we get if we have one point (no line segment is possible for this case), two, three, and four points.
3 years ago. It is inductive because it is based upon observing the pattern in the given numbers. Conclusions based on observations are inductive. Sal to specific observations and used them to draw a general conclusions. Deductive reasoning is when you start with a general rule (s) and you draw a specific conclusion.
Inductive reasoning is when you start with true statements about specific things and then make a more general conclusion. For example: "All lifeforms that we know of depend on water to exist. Therefore, any new lifeform we discover will probably also depend on water." A conclusion drawn from inductive reasoning always has the possibility of ...
Problem 5 Prove that 3 n > n 2 for n = 1, n = 2 and use the mathematical induction to prove that 3 n > n 2 for n a positive integer greater than 2. Solution to Problem 5: Statement P (n) is defined by 3 n > n 2 STEP 1: We first show that p (1) is true. Let n = 1 and calculate 3 1 and 1 2 and compare them 3 1 = 3 1 2 = 1 3 is greater than 1 and ...
Inductive reasoning is a type of reasoning that involves drawing general conclusions from specific observations. It's often called "bottom-up" reasoning because it starts with specific details and builds up to broader conclusions (The Decision Lab, n.d.). ... Problem-solving: In many problem-solving scenarios, especially those with incomplete ...
The Role of Inductive Reasoning in Problem Solving and Mathematics Gauss turned a potentially onerous computational task into an interesting and relatively speedy process of discovery by using inductive reasoning. Inductive reasoning can be useful in many problem-solving situations and is used commonly by practitioners of mathematics (Polya, 1954).
Reasoning is an important aspect of solving mathematical problems. Essentially, reasoning is the process of combining logic and evidence to draw conclusions. Mathematicians reason by applying ...
Inductive reasoning is any of various methods of reasoning in which broad generalizations or principles are derived from a body of observations. [1] [2] This article is concerned with the inductive reasoning other than deductive reasoning (such as mathematical induction), where the conclusion of a deductive argument is certain given the premises are correct; in contrast, the truth of the ...
The role of inductive reasoning in problem solving and mathematics. Gauss turned a potentially onerous computational task into an interesting and relatively speedy process of discovery by using inductive reasoning. Inductive reasoning can be useful in many problem-solving situations and is used commonly by practitioners of mathematics (Polya ...
Regarding inductive and deductive logic, most of the time people use inductive logic. They take a few thoughts or facts and create hypotheses. Typically, what most people need to build up is their deductive logic. That is why we focus on it so much in this problem solving module. Exercise 1 - Build Your Logic Awareness.
One of the major keys to understand inductive reasoning is to know its boundaries. In this case, we start with the basic house shape and keep adding additions to it, so the formula only works for n=1. After this point, Sal found a way to make sense of the case where n=0, so the single "house wall" toothpick becomes the base case.
Most everyone who thinks about how to solve problems in a formal way has run across the concepts of deductive and inductive reasoning. Both deduction and induction help us navigate real-world problems, such as who committed a crime, the most likely cause of an accident, or how many planets might contain life in the Milky Way galaxy.
Inductive reasoning plays a crucial role in problem-solving by providing a framework for generating hypotheses, exploring patterns, and making informed decisions. When faced with a complex problem or puzzle, inductive reasoning allows you to analyze specific instances, identify commonalities, and develop a general understanding or solution.
Inductive reasoning is a logical process that involves using specific experiences, observations or facts to evaluate a situation. This is an essential tool in statistics, research, probability and day-to-day decision-making. ... decision-making and problem-solving. For this reason, it may be helpful to focus on this skill throughout the job ...
This paper focuses on three different types of reasoning: domain-specific problem solving, complex (general) problem solving, and inductive reasoning. The objective of the study is to examine the differences in the developmental levels of inductive reasoning, domain-specific problem solving, and complex problem solving between three age groups ...
Abstract. Researchers have examined inductive reasoning to identify different cognitive processes when participants deal with inductive problems. This article presents a prescriptive theory of inductive reasoning that identifies cognitive processing using a procedural strategy for making comparisons. It is hypothesized that training in the use ...
One angle would be to employ inductive reasoning to solve the problem. It might go like this when using inductive reasoning to answer the question about clouds and outdoor temperature: