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What Neuroscience Tells Us About Learning

Students today spend more time on academic learning than generations before. They cover more ground – learning things like programming or environmental science that their parents didn’t have to fret about – and spend more hours doing homework after school. One study found that in the sixteen-year period from 1981 to 1997, there was a 25% decrease in time spent playing outside and a 145% increase in time spent doing homework. 

As our society advances even more, students will have to cover more and more content, not just during their K1-2 school years but throughout their careers. By some estimates, students growing up today will have to learn entirely new domains and reinvent their careers every few years. Learning is no longer limited to younger ages but is becoming a lifelong journey. 

What does this really imply?

Students have to learn to learn –  acquire knowledge and master concepts faster – without which they will find it harder to stay abreast of new developments coming their way. But it’s not just about superficially memorizing things. Students will have to understand how to apply their newfound knowledge to problem-solving. In other words, learning has to become a more efficient process in terms of speed, depth, and understanding.

Thankfully, advances in neuroscience are giving us clues on how to make learning more efficient. Understanding how the brain processes information can help students take charge of their own learning, not just in their student years but throughout their life.

Neuroscience Of How Our Brain Learns

At a high level, we can view learning as a three-step process. When we encounter any new information, our brain first encodes this information and places it in short-term memory. For example, if you come across a new fact, say learning about a new breed of dog, the information first goes into your short-term memory. The next day, you might recall that your childhood friend had a similar-looking dog, and now you start to remember other details about the dog – how friendly it was, how it played, and so on.

At this stage, your memory is in long-term storage; it continuously consolidates other pieces of information that you already had. Over time you might add more connections to this piece of information, maybe a joke you heard about it, and it starts to get more and more enmeshed with other pieces of memory. 

After a few days, you might forget the name of the breed and try to recall it. You struggle a bit and then remember your friend’s dog, the joke, and other bits of memory that were tied to it. And then the name suddenly comes back to you, and you get a sudden burst of relief! 

A few days later, as you share a story about your childhood friend, her dog and the name of the breed come to your mind effortlessly, and you marvel at how well you remember this now. 

The picture above encapsulates how our memory works. Once we consolidate information into our long-term memory, subsequent retrieval and reconsolidation help to strengthen the memory traces and make it easier to recall information in the future. 

Forgetting Is The Path To Learning

Over the last couple of decades, neuroscientists have discovered interesting things about how our memory works, and counterintuitive as it sounds, forgetting information is an important aspect of remembering! Our brain is constantly pruning information that it thinks it doesn’t need so that it can serve the really important bits of information faster. 

Imagine if your brain stored every little nugget of information that it receives – the color of the shirt a passenger wore in the subway, or the name of the street your friend in another state lives on – it would make it much harder to find the useful information that you really need. So if you don’t need any piece of information, its retrieval strength starts to get weaker. However, when you try to recall something that you have forgotten, i.e., when you have to struggle a bit to remember it, that’s when the brain gets a cue that this particular memory is important and might be needed again.

So, with the process of retrieval, it starts to reconsolidate the information – find newer connections to other traces of memory so the memory is stored more strongly. As a result, this process of forgetting and remembering actually helps you learn better. 

Neuroscience-based memory models give us clues on how to structure our learning for maximum effectiveness. Here are three ways to boost your learning.

Repurpose Failure

When students don’t remember or don’t apply concepts correctly, it’s a sign that the information has been stored weakly in the brain. However, instead of feeling that they are ‘not cut out’ for this kind of work, students need to understand that their failure is simply a sign for their brain to reconfigure and become more efficient. Human brains are designed to learn through mistakes, so it makes sense to reframe forgetting as what it really is – a trigger that tells us that we need to take additional steps to ensure learning is complete. Students should use the opportunity to review concepts again and try to reconcile the mistakes so their understanding of the subject increases.  

Adopt Active Learning Strategies & Neuroscience

Adding some challenge to the learning process that taps our brain’s natural mechanisms to process, store and understand information can significantly boost learning. Such challenges are ‘desirable difficulties’ because they make learning more efficient. Here are a few strategies that students and teachers can adopt: 

  • Retrieval Practice: When learning new information, periodically quiz yourself about the central ideas and new terms encountered without looking at the text. This forces your brain to fetch the answers from long-term memory, and repeated retrieval is going to strengthen your memory.
  • Spaced Learning: To add more desirable difficulty to learning, practice retrievals after a period of time. When you start forgetting, you exert more effort in trying to remember, which then cues the brain to store the information more deeply. The gap between learning and retrieving can be anything from a day to a week – the key is that the gap should allow for some forgetting to happen.
  • Interleaving: Instead of waiting to thoroughly master one concept before moving on to the next, try mixing up different kinds of problems or concepts once you feel you have gained sufficient understanding in one. Not only does this make good use of spacing, but it also allows you to spot connections or differences between different kinds of problems. 

Research studies show that such strategies can be very effective in the classroom. In one study, students who practiced math problems in three sessions spaced apart by a week performed twice as well on the final test compared to students who did all the practice problems in one session.  In another study, students performed significantly better on their science exam when a practice quiz one month before the exam interleaved concepts on the quiz. 

Associative Learning & Neuroscience

Another useful strategy in learning is to connect the information you are learning to other pieces of knowledge you already possess. If retrieval practice creates deep roots, then associative learning creates more branches that help anchor the information better. To build associative learning

  • Find an analogy: Ask yourself if the new concept is similar to any other piece of information that you already possess. As an example, you might make a connection between gravity and magnetism as both involve a force that they can’t see and attract objects. 
  • Find a personal connection: In some cases, your personal experience can be helpful in finding connections about what you are learning. For example, while learning about the ice age, you might remember an earlier trip to Grand Coulee, where they saw how the Missoula Floods carved out a massive canyon in a very short time. The scale and impact of the event will give you an enhanced perspective of the topic and deepen your understanding. 

Conclusion

By understanding the neuroscience behind learning, students can take charge of their own learning. The key to efficient learning is to add and embrace the right kind of challenges that push our brains to reconfigure themselves. Unless students lack relevant background or specific skills to make sense of the concept in front of them, such challenges should be welcomed instead of dread. 

With a deeper understanding of the learning process, students can try different approaches and customize them to their needs. As an example, for some students, one day of spacing might be enough, whereas, for others, it might be a week. For the latter set, practicing a skill every day might not be as effective because they haven’t forgotten enough for reconsolidation to take place. With some trial and error, students can identify strategies that work best for them and become smart learners. 

This article first appeared on edCircuit

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Assessing The Creative Health Of An Organization

With the proliferation of AI tools and their tremendous potential to improve productivity, leaders are reevaluating business priorities, and more specifically changes they need to make to work and culture. Given that AI can now handle many tasks better than humans, it stands to reason that creativity will take on an increasingly important role. It not only provides a sustainable competitive advantage but also improves employee engagement and organizational resilience.

Our understanding of creativity has evolved considerably over the last couple of decades. Despite the common perception that creativity is a fuzzy skill that you are either born with or not, we now know that creativity is a highly cognitive skill that can be tracked and improved. If companies want to get a better understanding of employee creativity and how it can be converted to meaningful innovation, they first need to assess their existing levels of creativity and how their culture might be inadvertently stifling innovation. Research has shown several different dimensions at varying organizational levels impact creativity and innovation. 

The Innovation Pipeline

At the heart of any disruptive innovation is a creative idea. The creative idea often starts small and over time, with iterations and feedback, becomes a breakthrough one. The 4-C model captures the level of creativity found in the real world starting with mini-c all the way to the boundary pushing Big-C. In an organizational setting, it makes more sense to view it in three phases (little-c, Pro-c and Big-c) as mini-c creativity is associated with personally meaningful ideas whereas employees typically offer ideas that are creative in broader contexts. 

The picture above shows a simplified view of the organizational innovation pipeline. (As a side note, we refer to innovation as gathering broad support among the management/company to take an idea to market, as opposed to market success which is the more common definition. We believe that within an organizational setting our definition is more appropriate). 

The creative journey starts with one person who conceives the idea, does some simple checks and determines that the idea has potential (in other words, it is both novel and useful). She then shares the idea with her team who debate the idea in more depth and through constructive discussions improve the idea even more – finding ways to make the idea more appealing to a broader set of people, or finding solutions to remove some of the challenges in the original idea. The team then decides to build a prototype to test out the idea further with real people. So from little-c, the idea moves on to Pro-c. Finally, the idea gets buyoff from management who allocate additional resources to make the idea launch ready. There might be more in-depth user level testing and additional iterations involved at this stage. Eventually the creative idea transitions into an innovative one that has a high chance of success in the marketplace if the right processes and checks were in place. At each stage of progression, the creative idea becomes more sophisticated and more potent, finally culminating in a (hopefully) breakthrough innovation. 

The fundamental challenge organizations have is to ensure that the pipeline allows ideas to flow freely and mature, so enough of them make it to the innovation stage. This is where deliberately building an innovation-friendly culture becomes essential. 

How Culture Impacts Innovation

An organization’s culture can either nurture or stifle innovation. To understand different ways that innovation gets affected in an organization, let’s look at it from the perspective of an idea as it makes its way through the innovation pipeline. 

Individual Level

A creative idea starts with a person who perhaps notices a problem or finds an interesting connection. If a sufficient number of ideas are not being generated at the beginning of the funnel, then the likelihood of reaching a breakthrough idea becomes low. Here are a few ways that ideas don’t go past the first stage:

  • Creative Capacity: If someone lacks creative confidence or specific creative thinking skills they might be coming up with few or minimally creative ones. Or, they might not be getting any time in their schedule to reflect and think creatively. Either way, their capacity to produce creative ideas is diminished. 
  • Motivation to share ideas: Assuming that people are capable of coming up with potentially creative ideas, the next barrier we hit is sharing ideas. People are less inclined to share an original idea if they feel the idea might be ignored or judged poorly, thereby affecting their social standing. Or people might simply not want to share their ideas, if they feel that they don’t get due credit for their work. In general, organizations that are hierarchical, risk averse or biased, disincentivize people from sharing their ideas. 

Team Level 

Most people assume that psychological safety is the main thing you need at a team level to allow good ideas to emerge. While this is a necessary first step, it’s not sufficient. For an idea to grow from little-c to a more improved Pro-c version, it needs to go through some extensive discussion. The main benefit of taking an idea to a group is that different perspectives and different ideas clash in a meaningful way to create something much more powerful. This crucial step separates mediocre teams from stellar ones as it requires both high cognitive and high emotional skills from the whole team. When done poorly, ideas can zoom past straight to innovation where they then face a higher chance of failure. Below are two broad ways teams fail at this stage:

  • Critiquing Instead Of Creating: The most common mistake that people make is to focus on fault-finding, with the intent of choosing the “best” idea instead of trying to create the best possible version of each incoming idea. People might also lack skills to engage in constructive debates and end up creating either a conflict-averse culture or a highly competitive one where ideas don’t get a chance to grow. 
  • Not Experimenting: Simply talking is usually not enough for an idea to be evaluated thoroughly. Data collected through prototypes or mini-experiments can lead to more healthy debates. Cultures that incentivize bold, visionary thinking without the rigor of research or experimentation create conditions (“pipe dream” culture) where people chase shiny ideas that often turn out to be riddled with insurmountable problems.  

Organizational Level

At the highest level leaders need to create structures and behavioral norms to support innovation throughout the organization. Without adequate support, it’s nearly impossible to convert employee creativity into organizational innovation. 

  • Formal Structures: To take incoming Pro-c ideas to market-ready innovations, organizations need to have formal programs that systematically and equitably review all incoming ideas. Many companies create hackathon-like programs as avenues for employees to exercise their creativity but such programs fail to produce any meaningful innovation as they are not integrated into the regular work process. Companies also fail to create formal incentive programs specifically for creativity that tap into people’s intrinsic motivation. 
  • Behavioral Norms: Company leaders play a crucial role in setting norms that promote an innovation-friendly culture. Do they explicitly solicit ideas from employees? Do they encourage their employees to challenge the status quo? Do they involve their employees in setting vision and values? Such behaviors create a more egalitarian culture that motivates employees to go above and beyond. 

Innovation Readiness Assessment

With the increasing importance of creativity and innovation in the business world, leaders need to understand in what ways their current culture supports or stifles innovation. Our Innovation Readiness Assessment is a research-based tool that helps identify bottlenecks in the innovation pipeline. It incorporates multiple dimensions that are known to impact creativity including work characteristics and biases, and covers all stages of the innovation pipeline.

Edgar Schein, the renowned organizational psychologist and author of Organizational Culture and Leadership, noted “the only thing of real importance that leaders do is to create and manage culture.” By staying vigilant about how their culture influences innovation, leaders can ensure their company’s long-term success in a hyper competitive world.  

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Address Unfinished Learning Using Creativity And Arts  

During the Covid-19 pandemic, students all over the world faced one of the largest disruptors of education in history. School closures across more than 190 countries impacted the routine and learning outcomes of 1.6 Billion students. By August 2020, 93% of households in the US reported the use of remote learning. Despite things returning to normal for over a year, we continue to see the ripple effect of this massive disruption. 

A number of organizations and researchers have tried to estimate the loss in learning due to the pandemic. A report by Stanford University estimated that during Spring 2020, students lost between 57 to 183 days of learning in Reading and from 136 to 232 days of learning in Math. A study in the Netherlands, which had a relatively lockdown period and is known for its equitable and well-funded schools, showed that students made little to no progress in learning from home. 

One of the challenges with the analyses on learning loss thus far, is that they focus narrowly on a few subjects, primarily math and reading. Finding an effective set of solutions to address unfinished learning is not an easy task. However, there is a wrong solution – solely relying on standardized tests. A myopic focus on test scores leads to sub-par teaching (“teaching to the test”) and pushes out more holistic interventions. We have seen this play out before when the No Child Left Behind Act was implemented in 2002. The Act converted testing as an informational mechanism to an accountability mechanism, changing teaching patterns inside classrooms. Teachers spent less time on subjects that were not being tested, like the arts, and spent less time on open-ended exploratory work like project based learning. 

What we need are more holistic approaches that build student enthusiasm, curiosity and engagement towards learning. Here are three strategies to incorporate when designing intervention programs to address unfinished learning, without falling into the trap of standardized testing. 

Teach For Creativity

Incorporating creative thinking into regular subjects, like math, reading or science, deepens learning. Paradoxically, this approach ends up improving standardized test scores despite test results not being an explicit goal. Creative thinking encourages students to think about a concept from multiple perspectives, question assumptions they might be making and synthesize solutions from multiple strands. These cognitive processes help students deepen their understanding of the subject matter and retain information for longer. More importantly, they help students become better thinkers and problem solvers in the long run. 

For teachers to incorporate creativity into the classroom, they need to be mindful of the distinction between “teaching creatively” and “teaching for creativity.” When educators find a clever way to introduce a concept in the classroom, say using Bitmoji classrooms, they are teaching creatively. Their own creativity is at play in designing the lesson plan for the concept. This by itself is a good thing if it makes the lesson more engaging and helps students learn the concept more easily. However, “teaching for creativity” goes a step further – it allows students to build and exercise their own creativity. 

Well designed project-based learning modules are one way to encourage students to be more creative. However, it’s not hard to incorporate creative thinking in smaller doses by substituting a couple of traditional assignment questions with more creative ones. With this approach neither the students or the teachers are burdened with extra work. Key elements to keep in mind when incorporating creativity, in big or small doses, are:

  • Open-ended problems where multiple solutions are valid.
  • Problems are designed so that one more creative cognitive processes, like associative or analogical thinking, are engaged. 
  • An ability to share their work with their peers and learn from them.  

Integrate Arts

High quality arts programs, especially visual arts and music, have a strong correlation with academic success. Arts provides an avenue for students to build confidence to try new things in a safe environment. Incorporating arts increases both children’s self-efficacy and original thinking, according to a research study. As the authors noted, “Self-efficacious children believe they can be agents in creating their own futures and are more optimistic about what the world has in store for them.”

A randomized controlled trial with over ten thousand K-12 students in Texas, found that arts-learning experiences led to fewer disciplinary issues, higher student engagement and improved writing achievements. 

Reevaluate Evaluations

While standardized tests are a useful way to track student progress, they should be a small part of broader evaluations. Teachers should balance graded work with non-graded work that rely on constructive feedback. Quantitative scores orient students away from their own intrinsic motivation to extrinsic motivation, which leads to higher anxiety and lower interest in the subject matter. Providing opportunities to students to share their projects or other original work with their peers and get their feedback is another low-pressure way for students to learn from each other. Finally, teachers should also encourage frequent self-evaluations to build student capacity to critically analyze their own work. When students find their own areas of improvement, they are much more intrinsically motivated to improve and learn. 

The learning loss created by the pandemic is significant and real, but we need to approach it cautiously. Prioritizing standardized test scores instead of more holistic approaches can lead to disastrous consequences in the long-term. Instead we need to double down on tried and tested methods – incorporating creative thinking and arts, and toning down the performance pressure.  

This article first appeared on edCircuit.

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3 Reasons Why Innovation Is The Best Organizational Strategy

As we start the new year, events and challenges from the last few years are continuing to shape our lives and the way we do business. Companies have had to make peace with remote/hybrid work being the new normal, adjust their cost structures to deal with economic uncertainties and redesign employee experiences to keep people engaged – all while fighting to stay ahead of the competition. If we needed an example of what a VUCA (volatile, uncertain, complex and ambiguous) world would look like, the last few years have clearly shown us that. 

How can companies successfully navigate such challenges going forward? We believe that the best way forward is to focus on innovation, first and foremost. By doing so, companies create the right kind of people-oriented cultures that not only boost revenue, but also improve other metrics like DEI or engagement. When viewed through the lens of innovation, cognitive-social-emotional skills like empathy or active listening take on a different flavor which make them far more effective in the workplace. 

Three trends show why an innovation-first strategy can be beneficial to organizations. 

Artificial Intelligence

ChatGPT showed that AI is advanced enough to do better than humans at certain tasks – even those that are more cognitively demanding tasks like writing essays or code. Let’s drill down a bit into one of the best paying jobs currently – software development. Using AI to help with coding is significantly improving developer productivity. Tasks that took several hours earlier, now can be done in a few minutes with a quality that is at par with the best programmers. That creates new  implications for technology-focused companies. 

Companies no longer need to hire as many people to do the work they need. Well funded companies spend tons of money recruiting talent from the most reputable colleges. With AI in the picture, this approach will no longer provide them with competitive advantage over smaller companies or scrappy startups who don’t have the resources to go after top talent in the same way. With AI that offloads most of the coding tasks, the difference between a talented developer and a mediocre one reduces sharply. 

Competitive advantage will shift to abilities that AI can’t handle well, creative thinking being an obvious area. Creativity requires the production of novel responses, which by definition implies the lack of existing data for algorithms to learn from. This is not to say that AI can’t do limited forms of incremental creativity, but they tend to be more “within-the-box” as opposed to more transformational “out-of-box” creativity. In other words, AI can rearrange existing content in new ways which may or may not turn out to be creative, but it can’t really synthesize new concepts.   

We recently tested OpenAI’s ChatGPT for several different creative prompts and our analysis shows that ChatGPT’s creativity is nowhere close to that of humans, including elementary aged children. It lacks the fundamental ability to do abstract thinking, so when posed with a challenge to invent something from two random objects, it tends to follow the simplest strategy of putting the two objects together. Or when asked to generate alternate endings to stories given a twist, it simply bails out. 

The obvious implication of the rise of AI, is that companies need to build their creative capacity in order to stay competitive. Companies that prioritize building creative thinking skills (like associative or reverse thinking), creating channels to harness employee innovation and most importantly foster an innovation-focused culture will emerge as the leaders of the next wave. 

Global Threats

Covid19 showed the extent to which a global threat can impact lives of people all over the world. With the world being more connected and more interdependent, a local event can have major consequences. Just in the last few years, companies have had to deal not only with the pandemic and its consequences, but also geopolitical issues like wars, unforeseen natural disasters and economic recessions. Such global threats are only expected to become more frequent. Needless to say, companies that can respond to such threats in a flexible and agile manner have a much greater chance of survival. 

However, the ability to be flexible and respond to changing environments in real time requires the ability to improvise and find novel solutions, to reconfigure existing resources and capabilities in new ways to solve problems, to collaborate in an open-minded way with others, to iterate when things don’t go as planned. In short, it requires the full arsenal of creative thinking capabilities deployed towards the crisis at hand. This ability to be innovative in the face of threats builds organizational resilience which significantly improves survival odds for any organization. 

To build a resilient organization, innovation has to become part of the company’s DNA. Building a culture of innovation involves deliberately building core cognitive capabilities, allowing ad-hoc collaborations to take place without unnecessary friction and creating formal structures for capturing employee creativity. 

Management Complexity

Managing a large workforce today is complex with many initiatives like employee engagement, talent retention and DEI (diversity, equity and inclusion) taking up valuable time and energy. However, when organizations look at these as separated, isolated aspects they can inadvertently create conflicting processes and norms. This affects not just leaders but also employees, who feel like they are being pulled in different directions while having to maintain high levels of productivity. 

One possible solution is to view such initiatives through the lens of innovation, which is the primary goal for any company, and identify ways to streamline and simplify. As an example, we conducted a study recently to understand the impact of gender bias on innovation within the technology industry. We found that by adopting a few universal strategies, like enhanced group decision making processes, companies can improve overall innovation while reducing the impact of bias. The advantage of using innovation as a means to reduce bias is that it removes the salience of gender or race. One of the key reasons diversity programs are ineffective is that they bring race or gender sharply into focus, thereby reinforcing the stereotype even more. Ironically, the most effective diversity programs aren’t specifically designed for diversity. This is not to say that all DEI initiatives can be made redundant, but by finding synergies it is possible to do more with less.  

Using An Innovation-First Approach 

An Innovation-First approach is a way to streamline and consolidate multiple goals in a way that optimizes the primary goal of innovation and productivity. It’s a way to refactor company cultures to remove redundant, conflicting or ineffective processes and orient the entire  organization towards a common purpose. As a result, companies create healthier organizations where both employee engagement and profitability is high. 

For the last several decades companies have focused narrowly on employee productivity, which works for linear and predictable workloads. However, with the current trends in AI, automation and globalization, this is no longer enough. Companies that can successfully adopt innovation will gain a significant competitive advantage over their rivals.

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ChatGPT Is Smart But It’s Not Creative

Since its recent launch, ChatGPT, a large language model AI, has caused quite a stir in the technology industry and the broader community at large. Its capabilities have sparked both awe and alarm – an AI that is so good at what it does that it has real potential of displacing people at work. But how worried should we be?

Let’s first get the obvious stuff out of the way. ChatGPT is an incredible tool and exceptionally good at some tasks like producing code, compiling essays and even writing poetry. However, when it comes to creative thinking, it misses the mark. 

To understand if it can produce creative ideas, we asked ChatGPT a few brain teasers that we routinely use in our workshops, even with early elementary age kids. They are typically used as 5-minute warm-up group activities that help to illustrate the underlying cognitive mechanism. 

At a fundamental level, we know that AI tools have limited creativity because AI relies on using massive amounts of prior data to learn. But creativity requires you to think of novel ideas that don’t yet exist and therefore don’t have any data to learn from. Nevertheless, it’s still possible to generate somewhat of a creative output in some areas like art as we have already seen with tools like Midjourney. So, let’s take a deep dive at how ChatGPT fared with these exercises. 

Associative Thinking

To test ChatGPT’s ability for associative thinking, we tried the Wacky Inventions brain teaser. The idea behind this brain teaser is to use our brain’s natural associative processes to combine unrelated objects and make a new invention. One can argue that this associative ability is perhaps the most fundamental aspect of creativity. A real world example of associative thinking is Google’s PageRank algorithm that completely transformed what search engines could do. It combined the concept of academic citations (articles that are cited more indicate more authoritative sources) with websites – two completely unrelated domains – to create an automated way to assess the quality of a website. 

We posed random combinations of objects that we have encountered in our sessions to make it easier to compare ChatGPT to human performance. Here are two examples:

Example 1

Q: What new invention idea can you come up with using a table and a scarf?

A: A portable table with a built-in scarf storage system. The table has a small drawer or compartment on the side that is specifically designed to hold scarves. The scarves can be easily rolled up and stored in the compartment, allowing users to keep their scarves organized and easily accessible. The table also has a built-in hook or hanger on the side, allowing users to hang their scarves on the table for easy access and display. This invention would be especially useful for people who have a large collection of scarves and need a convenient way to store and access them. It would also be useful for people who need a portable and versatile table for various activities, such as crafting, sewing, or organizing.

Q: That’s a good idea! Can you find a way to use an attribute of the scarf to make a new kind of table?

A: A table with a built-in scarf-covered surface. The table has a removable and interchangeable scarf that covers the top surface of the table. The scarf can be easily swapped out for different styles and colors, allowing users to customize the look of their table. The scarf can also be used as a protective layer for the table, preventing scratches or damage to the surface. This table would be especially useful for people who want a versatile and customizable piece of furniture for their home. It would also be useful for people who want a protective and stylish table for activities such as crafting or sewing. Additionally, the scarf-covered surface would add a cozy and comfortable feel to the table, making it a great addition to any room.

Q: That’s great! What property of the scarf are you using in your invention idea?

A: The property of the scarf that I am using in my invention idea is its ability to be easily removed and swapped out for different styles and colors. This property allows users to customize the look of the table and create a unique and personalized piece of furniture. It also allows users to easily update the look of the table by swapping out the scarf for a different color or pattern. Additionally, the property of the scarf being removable and interchangeable allows for easy cleaning and maintenance of the table surface.

Example 2

Q: What new invention idea can you come up with using a football and a water bottle?

A: A portable football water dispenser that attaches to the top of a water bottle. It has a small spout on the side that allows players to easily hydrate during a game without having to stop and open the water bottle. The dispenser is designed to fit snugly on the top of the bottle, with a secure fastening mechanism to prevent it from falling off during use. It also has a built-in straw that allows players to easily drink from the bottle without removing it from the dispenser. This invention would be especially useful for athletes who need to stay hydrated during long and intense games.

Q: So is this a water bottle with a football shaped dispenser on top? 

A: An error occurred. If this issue persists please contact us through our help center at help.openai.com.

Example 3

Q: What new invention idea can you come up with using an umbrella and a jump rope? 

A: An error occurred. If this issue persists please contact us through our help center at help.openai.com.

We gave a few other combinations and found that a common theme in ChatGPT’s responses, purely from an outcome perspective, is that it combines the two objects physically – attaching the two together for increased functionality. This, in general, is a reflection of a low level of creativity similar to very young children. However, as children get better at abstract thinking, they naturally start using the objects as starting points and zero in on a couple attributes that they can seamlessly integrate in the invention. Explicitly nudging children to use an attribute of the object is also effective in helping them to get more sophisticated solutions, and in general improves their abstract thinking skills. Unfortunately, ChatGPT wasn’t able to find the right property to work with as was evident in the first example. “Removability” isn’t really a property of the scarf. What it did instead is used instances on the internet where the two object names appear together (you can google “table scarf” to see some results) and cobbled together a reasonable sounding paragraph. But it doesn’t really understand what it created. In the second example, when asked a clarifying question it ends up bailing out with an error message. 

The problem is that ChatGPT isn’t really equipped to do abstract thinking. Think about how humans build a model of the world around them. Babies and toddlers spend hours interacting with objects around them – they touch them, taste them (much to our frustration), hit them, shake them and toss them. These adorable, uncoordinated actions serve a very useful function – they help children create an internal representation of the object. This step is crucial for their development because without building these internal models, children have a harder time imagining and creating new combinations in their heads as they grow older. It’s the primary reason why play is essential for long-term creativity. 

But that’s not all. We don’t just build standalone models but also much more extensive associative networks where different concepts are connected to each other through different properties. For example, the concept of “strawberry” in your brain might be connected to “red” by a thing-to-property link or connected to a “rash” by a cause-effect link if you are allergic to strawberries. Every new thing a baby learns is continually being mapped into this giant associative network of different objects, concepts and ideas. This extensive and rich associative network is what allows us to make abstractions, hop from one concept to another, and make novel connections. 

We use this rich implicit knowledge built over many years extensively when we come up with novel ideas. Even when we see new ideas from others, we can quickly adapt them to our situation, because of this underlying layer of implicit knowledge that we don’t pay conscious attention to. The challenge with scouring the internet is that a lot of this implicit knowledge doesn’t exist there. On the internet it’s assumed that when someone talks about a scarf people know what that is – you won’t necessarily find places where attributes of a scarf are listed that allow it to be connected to other concepts that are further away.  

This is of course a logical model and not a physical one inside our brains. We know very little about how association takes place inside our heads but the logical model gives a good approximation. Most creativity techniques leverage this and simply provide triggers to the brain (where the real magic happens) to get new ideas in a somewhat reliable fashion. 

In the third example, the associative distance between the two objects (umbrella, jumprope) is high meaning that you typically don’t see them mentioned together, so ChatGPT simply bails out again. In contrast, using the same two objects, a group of 4th-5th graders came up with the idea of an umbrella shaped water ski where you can sit and relax, while being pulled by a rope.

Additional Creative Thinking Triggers

We also tried a few other kinds of brainteasers like Opposite Day and Twist-a-Story to see how ChatGPT handles them. The idea behind Opposite Day is to reverse a well known assumption which can lead to radically new insights. For example, the designers of the portable toddler chair that attaches to any table, came up with that idea by asking themselves “What if a chair didn’t have any legs and why would that be useful?”. In Twist-a-story, the idea is to find an alternate ending to an existing story by changing some element of the story. 

Here is how ChatGPT answered these brain teasers.

Example 4

Q: What if libraries didn’t carry books? What would they have instead and why would it be useful?

A: If libraries didn’t carry books, they would likely carry a wider variety of media such as e-books, audiobooks, and digital media. This would be useful because it would allow users to access a wider range of content, including both fiction and non-fiction, in a variety of formats. It would also allow libraries to provide access to more up-to-date and current content, as e-books and digital media are often released more quickly than traditional print books. Additionally, it would save space and resources, as digital media can be stored and accessed more efficiently than physical books.

Example 5

Q: Have you heard the three little pigs story?

A: Yes, I have heard the story of the three little pigs. It is a popular fairytale about three pigs who each build a house to protect themselves from a wolf. The first pig builds his house out of straw, the second pig builds his house out of sticks, and the third pig builds his house out of bricks. The wolf is able to blow down the first two houses, but the third pig’s brick house is strong enough to withstand the wolf’s huffing and puffing. In the end, the third pig is able to outsmart the wolf and keep him out of the brick house.

Q: What if the third pig runs out of bricks while making his house and the wolf shows up? How would the story end then?

A: I’m sorry, but I am not able to browse the internet to provide you with an alternate ending to the story of the three little pigs. As a large language model trained by OpenAI, my knowledge is limited to what I have been trained on, and I do not have the ability to browse the internet or access new information. My knowledge cutoff is set to 2021, so I do not have information on events or stories that may have happened after that date.

Let’s briefly dissect ChatGPT’s responses to these additional brainteasers. 

In example 4, ChatGPT gives us a rather unimpressive answer about using a wider variety of media, which most libraries already do. Once again, it’s not able to extract the more general feature of a library – allowing people to borrow things. When the same challenge is posed to people, they quickly arrive at this insight and start thinking of additional items that aren’t used often, or are cumbersome to own, and would make good candidates for borrowing. Using this kind of analogical thinking they come up with ideas like libraries for power tools, folding tables and chairs, or formal clothes. 

Finally, the last example clearly highlights ChatGPT’s limitation when it comes to thinking of original solutions. In all fairness, ChatGPT was never designed for creative work so this is not surprising. 

Humans Vs. Bots

So should we be worried about AI taking over jobs? Yes, absolutely! There is no doubt that AI is going to start taking over more and more tasks that depend on linear thinking models, where lots of training data exists. Even for white collar jobs like software development, ChatGPT is proving to be valuable in improving developer productivity. So companies will soon need to hire fewer people for the same workload. 

However, ChatGPT currently can’t handle creative work and come up with novel ideas. It is theoretically possible for AI models to build richer data and limited abstract reasoning capabilities, so we should expect next generation tools to display more creativity than currently possible. In the short term however, we can safely assume that creative thinking is going to be a strictly human capability. 

So, where does this lead us? 

The natural solution (and one that’s been around for a long time) is that humans work together with technology to leverage each other’s strengths. In this ideal scenario, AI takes care of time-consuming tasks to improve the productivity of humans, freeing them up for creative tasks that only humans can do. If we accept that premise, then it follows that a sustainable long-term advantage, both for individuals and organizations, will be their creativity and innovation. In reality, this won’t happen without deliberate effort, and the short term will almost certainly be a lot messier with job disruptions. Creativity among students has been declining for some time now which makes them unsuitable for changing work, and companies are not necessarily ideal places to harness employee innovation. Without improving both of those aspects, our journey towards the ideal scenario can end up becoming needlessly rocky.