is the driving force behind problem solving in science

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The Science of Problem-Solving

It turns out practices that might seem a little odd—like talking to yourself—can be pretty effective

The Science of Problem-Solving

For Gurpreet Dhaliwal, just about every decision is a potential opportunity for effective problem solving. What route should he take into the office? Should Dhaliwal write his research paper today or next week? "We all do problem solving all day long," Dhaliwal told me.

An emergency medicine physician, Dhaliwal is one of the leaders in a field known as clinical reasoning , a type of applied problem solving. In recent years, Dhaliwal has mapped out a better way to solve thorny issues, and he believes that his problem solving approach can be applied to just about any field from knitting to chemistry.

For most of us, problem solving is one of those everyday activities that we do without much thought. But it turns out that many common approaches like brainstorming don’t have much research behind them. In contrast, practices that might seem a little odd—like talking to yourself —can be pretty effective.

I came across the new research on problem solving as part of my reporting on a book on the science of learning, and it was mathematician George Polya who first established the field, detailing a four-step approach to cracking enduring riddles.

is the driving force behind problem solving in science

For Polya, the first phase of problem solving is “understanding.” In this phase, people should look to find the core idea behind a problem. “You have to understand the problem,” Polya argued. “What is the unknown? What are the data?”

The second phase is “devising a plan,” in which people map out how they’d address the problem. “Find the connection between the data and the unknown,” Polya counseled. 

The third phase of problem solving is “carrying out the plan.” This is a matter of doing—and vetting: “Can you prove that it is correct?”

The final phase for Polya is “looking back.” Or learning from the solution: People should "consolidate their knowledge.”

While Dhaliwal broadly follows this four-step method, he stresses that procedures are not enough. While a focused method is helpful, thorny issues don’t always fit nicely into categories.

This idea is clear in medicine. After all, symptoms rarely match up perfectly with an illness. Dizziness can be the signal of something serious—or a symptom of a lack of sleep. “What is tricky is to figure out what’s signal and what’s noise,” Dhaliwal told me.

In this regard, Dhaliwal argues that what’s at the heart of effective problem solving is making a robust connection between the problem and the solution. "Problem solving is part craft and part science, " Dhaliwal says, a type of "matching exercise. "

To get a sense of Dhaliwal’s approach, I once watched him solve a perplexing case. It was at a medical conference, and Dhaliwal stood at a dais as a fellow doctor explained the case: Basically, a man came into ER one day—let’s call him Andreas—and he spat up blood, could not breath very well, and had a slight fever.

At the start of the process, Dhaliwal recommends developing a one-sentence description of the problem. "It’s like a good Google search,” he said. “You want a concise summary,” and in this case, it was: Sixty-eight-year-old man with hemoptysis, or coughing up blood.

Dhaliwal also makes a few early generalizations, and he thought that Andreas might have a lung infection or an autoimmune problem. There wasn’t enough data to offer any sort of reliable conclusion, though, and really Dhaliwal was just gathering information.

Then came an x-ray, an HIV test, and as each bit of evidence rolled in, Dhaliwal detailed various scenarios, assembling the data in different ways. “To diagnosis, sometimes we are trying to lump, and sometimes trying to split,” he said.

Dhaliwal’s eyes flashed, for instance, when it became apparent that Andreas had worked in a fertilizer factory. It meant that Andreas was exposed to noxious chemicals, and for a while, it seemed like a toxic substance was at the root of Andreas’s illness.

Dhaliwal had a few strong pieces of evidence that supported the theory including some odd-looking red blood cells. But Dhaliwal wasn't comfortable with the level of proof. “I'm like an attorney presenting in a court of law,” Dhaliwal told me. “I want evidence.”

As the case progressed, Dhaliwal came across a new detail, and there was a growth in the heart. This shifted the diagnosis, knocking out the toxic chemical angle because it doesn't spark tumors.

Eventually, Dhaliwal uncovered a robust pattern, diagnosing Andreas with a cardiac angiosarcoma, or heart cancer. The pattern best explained the problem. “Diagnosing often comes down the ability to pull things together,” he said.

Dhaliwal doesn’t always get the right answer. But at the same time, it was clear that a more focused approach to problem solving can make a clear difference. If we’re more aware of how we approach an issue, we are better able to resolve the issue.

This idea explains why people who talk to themselves are more effective at problem solving. Self-queries—like is there enough evidence? —help us think through an issue.

As for Dhaliwal, he had yet another problem to solve after his diagnosis of Andreas: Should he take an Uber to the airport? Or should he grab a cab? After a little thought, Dhaliwal decided on an Uber. It was likely to be cheaper and equally comfortable. In other words, it was the solution that best matched the problem.

The views expressed are those of the author(s) and are not necessarily those of Scientific American.

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An unresolved issue in innovation studies is to what extent and how innovation is affected by changes in the economic environment of firms. This study elaborates on a theoretical framework that unites theories of innovation as creative response and the economics of complexity. In the empirical section, results from a new micro-based database on Swedish product innovations, 1970–2007, are introduced. Applying the theoretical framework, both quantitative evidence and collected innovation biographies inform of the historical impulses that have shaped innovation activity in the Swedish economy in two broad surges during the 1970s and 1990s. The study shows that, rather than being the result of continuous efforts, most innovations were developed as a response to discrete events, history-specific problems and new technological opportunities. It is also suggested that patterns of creative response are industry-specific and associated with the radicalness and complexity of innovation processes.

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The science of motivation

Kou Murayama

Motivation is important in almost every aspect of human behavior. When you make a decision, your choice is certainly influenced by your motivational state. When you study mathematics, your motivation to study mathematics clearly affects the way you learn it. Despite its obvious importance, empirical research on motivation has been segregated in different areas for long years, making it difficult to establish an integrative view on motivation. For example, I studied a number of motivation theories proposed in educational psychology (as my PhD is in educational psychology) but these theories are not connected with the motivational theories studied in social psychology or organizational psychology. Furthermore, the way motivation is defined and theorized is fundamentally different in cognitive/affective neuroscience (Murayama, in press). In other fields such as cognitive psychology, motivation has been normally treated as a nuisance factor that needs to be controlled (see Simon, 1994).

The times have changed, however. In recent years, researchers have recognized the importance of more unified and cross-disciplinary approach to study motivation (Braver et al., 2014). This multidisciplinary, multimethod pursuit, called Motivation Science, is now an emerging field (Kruglanski, Chemikova & Kopez, 2015). Our Motivation Science lab takes an integrative approach, drawing from multiple disciplines (e.g., cognitive, social and educational psychology, cognitive/social neuroscience) and multiple approaches (e.g., behavioral experiments, longitudinal data analysis, neuroimaging, meta-analysis, statistical simulation/computational modeling, network analysis ). We explore a number of overlapping basic and applied research questions with the ultimate goal of providing an integrated view on human motivation.

Motivation and learning

If you are motivated, you learn better and remember more of what you learned. This sounds like an obvious fact, but our lab showed that the reality is more nuanced. The critical fact is that not all motivations are created equal.

In the literature of achievement goals, for example, people study primarily for two different goals — to master materials and develop their competence, which are called mastery goals, and to perform well in comparison to others, which are called performance goals (Dweck, 1986; Nicholls, 1984). Mastery goals and performance goals represent the same overall quantity of motivation, but they are qualitatively distinct types of motivation. We conducted a series of behavioral experiments to examine how these two different types of motivation influence learning (Murayama & Elliot, 2011).

In the study, participants were engaged in a problem-solving task and received a surprise memory test related to the task. Critically, participants performed the problem-solving task with different goals. Participants in the mastery goal condition were told that the goal was to develop their cognitive ability through the task, whereas those in the performance goal condition were told that their goal was to demonstrate their ability relative to other participants. The participants in the performance goal condition showed better memory performance in an immediate memory test, but when the memory was assessed one week later, participants in the mastery goal condition showed better memory performance. These results indicate that performance goals help short-term learning, whereas mastery goals facilitate long-term learning.

That was a laboratory study where the learning situation was somewhat artificial. To further test whether mastery orientation facilitates long-term learning, we turned to an existing longitudinal survey dataset. In this study, we used longitudinal survey data on more than 3,000 schoolchildren from German schools (Murayama, Pekrun, Lichtenfeld & vom Hofe, 2013). Using latent growth curve modeling, we showed that items which focus on the performance aspect of learning (“In math I work hard, because I want to get good grades”) in Grade 7 predicted the immediate math achievement score whereas items focusing on the mastery aspect of learning (“I invest a lot of effort in math, because I am interested in the subject”) in Grade 7 predicted the growth in math achievement scores over three years. These results mirror our findings from the lab, providing convergent evidence that mastery-based motivation supports long-term learning whereas performance-based motivation only helps short-term learning.

With some additional neuroimaging and behavioral experiments, we are now examining the underlying mechanisms of this time dependent effect of motivation (Ikeda, Castel, & Murayama, 2015; Murayama et al., 2015).

Reward and motivation

Do rewards enhance learning outcomes? This is a question that has long sparked controversy in education literature. According to recent findings in cognitive neuroscience, the answer seems to be yes. Indeed, there have been a number of studies, including ours (Murayama & Kitagami, 2014), that have shown that rewards (e.g., money) enhance learning due to the modulation of hippocampal function by the reward network in the brain (Adcock, Thangavel, Whitfield-Gabrielli, Knutson & Gabrieli, 2006). On this basis, some argue for the value of reward in education (Howard-Jones & Jay, 2016).

But research in social psychology has also found that extrinsic rewards can sometimes undermine intrinsic motivation when people are engaged in an interesting task. This phenomenon, called the undermining effect or overjustification effect (Deci, Koestner & Ryan, 1999; Lepper, Greene & Nisbett, 1973), suggests that extrinsic rewards are not always beneficial for learning.

To demonstrate this possibility, we replicated the undermining effect using a neuroimaging method (Murayama, Matsumoto, Izuma & Matsumoto, 2010). Participants were randomly assigned to a reward group or a control group and engaged in a game task while being scanned inside an fMRI machine. Participants in the reward group were instructed that they would receive performance-based monetary rewards whereas participants in the control condition did not receive such instructions (i.e., they played the game just for fun). After the scanning session, we found that participants in the reward group showed less voluntary engagement in the task than those in the control group, indicating that their intrinsic motivation for the task was undermined by the introduction of extrinsic rewards. A follow-up brain imaging session showed that the undermining effect was reflected in the decreased activation in the striatum, part of the reward network in the brain.

The undermining effect suggests that rewards may not benefit learning on tasks that people would perform without extrinsic incentives (i.e., interesting tasks). To directly test this possibility, we examined learning performance on interesting and boring trivia questions when participants were rewarded (Murayama & Kuhbandner, 2011). The results showed that working on a trivia question task for a reward enhanced memory performance (in comparison to a non-reward condition) after a delay, but this was the case only for boring trivia questions. This outcome indicates an important limit of the facilitation of learning by extrinsic rewards — they may be effective only when the task does not have intrinsic value. As we showed elsewhere, intrinsically interesting tasks are memorable by themselves (Fastrich, Kerr, Castell & Murayama, in press; McGillivray, Murayama & Castel, 2015), and rewarding intrinsically interesting learning materials may be a waste of money (i.e., no benefit of rewards) or even detrimental to later engagement or performance.

In sum, this line of findings showed a nuanced picture of how rewards facilitate learning. Surely rewards are effective in motivating people and enhancing learning, and this is supported by a neural link between the motivation (reward) and memory systems in the brain. But there are certain conditions, such as when a task is intrinsically interesting, where rewards may undermine motivation and thus bring no benefits for learning.

Competition and motivation

In our society, it is common for authority figures to introduce competition as a means to increase people’s motivation and performance. But does this assumption that competition is an effective way to increase people’s motivation and performance have an empirical basis? A large empirical literature has addressed the effects of competition on performance, but these studies have been conducted rather separately and no integrated theoretical perspective has been offered.

To address this issue, we conducted a meta-analysis to quantitatively synthesize the previous studies on the effects of competition (Murayama & Elliot, 2012). When we computed the average effect of competition on performance, with 174 studies (more than 30,000 participants) including both experimental and survey studies, we found a very small average effect (r = 0.03, 95% CI = [-.00, .06]). We tried to identify potential moderating factors, but none emerged. However, we observed considerable variability in effect sizes across studies.

One straightforward interpretation is that competition has virtually no effects on task performance. But this does not fit with our phenomenological experience of competition. When we are placed in competitive situations, we can clearly feel that our motivation is altered. Therefore, we proposed an alternative motivational model that could explain the puzzlingly weak competition-performance link.

According to our model, when we face competition, we adopt two different types of motivational goals: performance-approach goals and performance-avoidance goals (Elliot & Harackiewicz, 1996).  Performance-approach goals are goals that focus on positive outcomes of the competition (“My goal is to outperform others”) whereas performance-avoidance goals focus on negative outcomes (“My goal is not to do worse than others”). Importantly, previous research has shown that performance-approach goals positively predict task performance whereas performance-avoidance goals negatively predict performance (Elliot & Church, 1997).

We posited that competition triggers both performance-approach and performance-avoidance goals, and that these co-activated goals cancel each other out (because they have opposing effects), producing an ostensiblye weak effect. We tested this “opposing processes model of competition and performance” with an additional meta-analysis, longitudinal surveys, and a behavioral experiment, providing strong support for the model. These results indicate that competition engages multi-faceted motivational processes, which explains why the introduction of competition does not consistently bring motivational benefits (see also Murayama & Elliot, 2009).

Curiosity, metamotivation and motivation contagion

We are currently working on several different projects on motivation, with the core aim of unraveling the nature and function of intrinsic rewards in human behavior. Although extrinsic incentives undoubtedly play an important role in shaping our behavior, humans are endowed with the remarkable capacity to engage in a task without such incentives, by self-generating intrinsic rewards. Forms of motivation triggered by intrinsic rewards are often referred to as interest, curiosity or intrinsic motivation. But the psychological and neural mechanisms underlying the generation of intrinsic rewards are largely unclear (Braver et al., 2014).

For example, we are currently examining the neural correlates when curiosity leads us to make a seemingly irrational decision. There are a number of anecdotal stories where curiosity pushes people to expose themselves knowingly to bad consequences, such as Pandora’s box, Eve and the forbidden tree, and Orpheus, but this seductive rewarding power of curiosity has been underexamined in the literature (for exceptions, see Hsee and Ruan, 2015; Oosterwijk, 2017). In our ongoing project, we present participants with magic tricks (to induce curiosity) and ask them whether they are willing to take a risk of receiving electric shock to know the secret behind the magic tricks. The preliminary findings from our neuroimaging analysis indicated that the striatum is associated with people’s decision to take such a risk to satisfy their curiosity, suggesting that internal “rewards” play a critical role for curiosity to guide our decision making.

Although intrinsic rewards and extrinsic rewards play a similar role in some situations, some aspects of intrinsic rewards are unique. One such aspect is metamotivation. Metamotivational belief refers to our beliefs and understanding of how motivation works (Miele & Scholer, 2018; Murayama, 2014; Scholer, Miele, Murayama & Fujita, in press). Like recent findings on metacognition (Kornell & Bjork, 2008; Murayama, Blake, Kerr & Castel, 2016), our studies indicate that people are often inaccurate in their beliefs about the motivating property of intrinsic rewards. Specifically, when we asked participants to work on a boring task and to make a prediction about how interesting the task would be, their prediction was inaccurate. Their predicted task engagement was less than their actual task engagement, indicating that people tend to underestimate their power to generate intrinsic rewards when faced with boring tasks (Murayama, Kuratomi, Johnsen, Kitagami & Hatano., under review). This inaccuracy of our metamotivational belief could partly explain why authority figures are often so reliant on extrinsic rewards to motivate other people (Murayama et al., 2016).

There may be multiple ways that we generate intrinsic rewards. One may be through observational effects (Bandura, 1977). Imagine that you have a friend who likes mathematics. Even if you initially did not like mathematics, observing your friend enjoying mathematics repeatedly may create a fictive internal reward, making you feel as if you also like mathematics. We call this motivation contagion (Burgess, Riddell, Fancourt & Murayama, under review), and we are working on several different behavioral and neuroimaging studies to explore this idea using a variety of network analysis methodologies. Through behavioral experiments, diary methods and computational modeling, our lab also explores other channels through which humans generate intrinsic rewards (e.g., intrinsic rewards produced by challenging situation).

In sum, motivation matters. But at the same time, we need a comprehensive picture of how different types of motivation fit and function together to produce behavior. Our Motivation Science Lab is working to achieve this integrated understanding of human motivation.


The work described here was funded by the Marie Curie Career Integration Grant (PCIG14-GA-2013-630680), JSPS KAKENHI (15H05401 and 16H06406), a grant from the American Psychological Foundation (F.J. McGuigan Early Career Investigator Prize), Leverhulme Trust Project Grant (RPG-2016-146), and Leverhulme Research Leadership Award (RL-2016-030). I thank my collaborators on these projects, including Andrew Elliot, Reinhard Pekrun, Alan Castel and Kenji Matsumoto.

Adcock, R.A., Thangavel, A., Whitfield-Gabrieli, S., Knutson, B., & Gabrieli, J.D.E. (2006). Reward-motivated learning: Mesolimbic activation precedes memory formation. Neuron, 50 (3), 507-517.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Educational Psychology Review, 84 , 191-215.

Braver, T.S., Krug, M.K., Chiew, K.S., Kool, W., Clement, N.J., Adcock, A., Barch, D.M., Botvinick, M.M., Carver, C.S., Cols, R., Custers, R., Dickinson, A.R., Dweck, C.S., Fishbach, A., Gollwitzer, P.M., Hess, T.M., Isaacowitz, D.M., Mather, M., Murayama, K., Pessoa, L., Samanez-Larkin, G.R., & Somerville, L.H. (2014). Mechanisms of motivation-cognition interaction: Challenges and opportunities. Cognitive, Affective, & Behavioral Neuroscience, 14 , 443-472.

Burgess, L., Riddell, P., Fancourt, A., & Murayama, K. (under review). The influence of social contagion within education: A review.

Deci, E.L., Koestner, R., & Ryan, R.M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125 , 627-668.

Dweck, C.S. (1986). Motivational process affects learning. American Psychologist, 41 , 1010-1018.

Elliot, A.J., & Church, M.A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72 , 218-232.

Elliot, A.J., & Harackiewicz, J.M. (1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 70 , 461-475.

Fastrich, G.M., Kerr, T., Castel, A.D., & Murayama, K. (in press). The role of interest in memory for trivia questions: An investigation with a large-scale database. Motivation Science .  

Howard-Jones, P. & Jay, T. (2016). Reward, learning and games. Current Opinion in Behavioral Sciences, 10 , 65-72.

Hsee, C.K., & Ruan, B. (2016). The pandora effect: The power and peril of curiosity. Psychological Science .

Ikeda, K., Castel, A.D., & Murayama, K. (2015). Mastery-approach goals eliminate retrieval-induced forgetting: The role of achievement goals in memory inhibition. Personality and Social Psychology Bulletin, 41 , 687-695.

Kornell, N., & Bjork, R.A. (2008). Learning concepts and categories: Is spacing the “enemy of induction”? Psychological Science, 19 (6), 585-592.

Kruglanski, A., Chernikova, M., & Kopetz, C. (2015). Motivation science. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences. New York: Wiley.

Lepper, R.M., Greene. D., Nisbett. E.R., (1973). Undermining children's Intrinsic interest with extrinsic reward: A test of the "overjustification" hypothesis. Journal of Personality and Social Psychology, 28 , 129-137.

McGillivray, S., Murayama, K., & Castel, A. D. (2015). Thirst for knowledge: The effects of curiosity and interest on memory in younger and older adults. Psychology and Aging, 30 (4), 835-841.

Miele, D.B., & Scholer, A.A. (2018). The role of metamotivational monitoring in motivation regulation. Educational Psychologist, 53 (1), 1-21.

Murayama, K. (2014). Knowing your motivation: Metamotivation. Annual Review of Japanese Child Psychology (Special Issue on Motivation and Psychology) , 112–116 (in Japanese).

Murayama, K. (in press). Neuroscientific and psychological approaches to incentives: Commonality and multi-faceted views. In A. Renninger & S. Hidi (Eds.), Cambridge handbook on motivation and learning. Cambridge, U.K.: Cambridge University Press.

Murayama, K., Kitagami, S., Tanaka, A., & Raw, J. A. (2016). People's naiveté about how extrinsic rewards influence intrinsic motivation. Motivation Science, 2 , 138-142.

Murayama, K., Pekrun, R., Lichtenfeld, S., & vom Hofe, R. (2013). Predicting long-term growth in students' mathematics achievement: The unique contributions of motivation and cognitive strategies. Child Development, 84 (4), 1475-1490.

Murayama, K., & Elliot, A.J. (2009). The joint influence of personal achievement goals and classroom goal structures on achievement-relevant outcomes. Journal of Educational Psychology, 101 (2), 432-447.

Murayama, K., & Elliot, A.J. (2011). Achievement motivation and memory: Achievement goals differentially influence immediate and delayed remember–know recognition memory. Personality and Social Psychology Bulletin, 37 (10), 1339-1348.

Murayama, K., & Elliot, A.J. (2012). The competition–performance relation: A meta-analytic review and test of the opposing processes model of competition and performance. Psychological Bulletin, 138 (6), 1035-1070.

Murayama, K., Blake, A., Kerr, T., & Castel, A. D (2016). When enough is not enough: Information overload and metacognitive decisions to stop studying information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42 (6), 914-924.

Murayama, K. & Kitagami, S. (2014). Consolidation power of extrinsic rewards: Reward cues enhance long-term memory for irrelevant past events. Journal of Experimental Psychology: General, 143 , 15-20.

Murayama, K., & Kuhbandner, C. (2011). Money enhances memory consolidation — but only for boring material. Cognition, 119 (1), 120-124.

Murayama, K., Kuratomi, K., Johnsen, L., Kitagami, S., & Hatano, A. (under review). Metacognitive inaccuracy of predicting one’s intrinsic motivation.

Murayama, K., Matsumoto, M., Izuma, K., & Matsumoto, K. (2010). Neural basis of the undermining effect of monetary reward on intrinsic motivation. PNAS Proceedings of the National Academy of Sciences of the United States of America, 107 (49), 20911-20916.

Murayama, K., Matsumoto, M., Izuma, K., Sugiura, A., Ryan, R.M., Deci, E.L., & Matsumoto, K. (2015). How self-determined choice facilitates performance: A key role of the ventromedial prefrontal cortex. Cerebral Cortex, 25 (5), 1241-1251.

Nicholls, J. G. (1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91 , 328-346.

Oosterwijk S (2017) Choosing the negative: A behavioral demonstration of morbid curiosity. PLoS ONE 12 (7): e0178399.

Scholer, A.A., Miele, D.B., Murayama, K., & Fujita, K. (in press). New directions in self-regulation: The role of metamotivational beliefs. Current Directions in Psychological Science.

Simon, H.A. (1994). The bottleneck of attention: Connecting thought with motivation. In W. D. Spaulding (Ed.), Nebraska symposium on motivation, Vol. 41. Integrative views of motivation, cognition, and emotion . (pp. 1-21): Lincoln, Nebraska, U.S.: University of Nebraska Press.

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is the driving force behind problem solving in science

is the driving force behind problem solving in science

The engines behind research and development are creativity and innovation. C re at i v i ty is typically defined as the ability to generate ideas. Creativity is actually a subset of innovation and refers primarily to the process of idea generation. I n n o v at i o ns are defined more narrowly as the ideas, the products, the services, and processes that (a) are perceived as being new and different and (b) have been designed, built, and commercialized. Innovation thus includes both creative idea generation and the actual implementation of the idea. 1 An invention is an innovation that is not ready for prime time. Inventions are ideas that have been built or conceptualized, but not widely used and available and usually not commercialized.

Creativity is the force behind innovation and invention. Creativity has been studied for many years and a variety of models and insights have been developed in order to understand and facilitate the creative process. Figure 6.1 illustrates an updated five-phase model of the creative process that incorporates problem solving, leaning-about, and the learning-by-doing concepts. 2 Here are the details of the model:

T ri g g e r . This is the problem or opportunity that initiates the creative process. The trigger could occur at home, work, play, or while traveling.

L e a r n - a bo ut a c ti v i ty . This involves searching for information and synthesizing that information. It also involves struggling to understand the information and the creation of new knowledge by analyzing the problem or opportunity. The learning-about activities include reading books and magazines; one-on-one dialog with colleagues and knowledgeable individuals; looking at competitor offerings; interaction with suppliers, customers, universities and research institutes; and attending courses, trade shows, symposia, and conferences.

In c u ba t e . I n c u b at i o n gives the mind time to work on the problem in the background. This not only involves contemplation, but also involves engaging in one-to-one dialog with family, friends, and colleagues on the problem or opportunity.

Lea rn - by - d o in g . This involves designing and constructing a solution to the problem or opportunity. It also involves designing and building a prototype, modeling with diagrams, drawing pictures, developing flowcharts, drawing digital or CAD diagrams in 2D or 3D CAD, conducting simulation, identifying system specifications, developing system mock-ups, developing business plans, and even the use of narratives. Designing and constructing might include very rough diagrams or developing mock-up pictures of the product or service by using sketching, drawing software, photo software, or presentation software. If the product is software, then a mock-up screen can be designed by using a word processor, presentation software, or mock-up software. If the idea behind the product or service involves a complex process or business process, then flow diagrams can be constructed or a business process diagram can be developed with presentation software or specialized flowchart and business process diagramming software.

D e v el o p m e nt o f kn o w - h o w . This is the expertise, skill, and knowledge that can be used to produce a product or service. 3 It is the outcome of the creative process that can be used to provide insight and to build and construct products, services, and business processes. It is the applied and practical knowledge that can be used to make the product or service. In start-ups and small organizations, this knowledge is in the minds of the owner, management and staff, and developers. The knowledge may be codified in lists or in what we refer to as Knowledge Books. These Knowledge Books can be maintained on tablets and spiral notebooks and in computer files. They can contain the following information:

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Importance of Science Education in Schools

Published On:    September 08, 2017

Updated On:    

Importance of science education in schools

Why is science education important in our schools? We are surrounded by technology and the products of science every day. Public policy decisions that affect every aspect of our lives are based in scientific evidence. And, of course, the immensely complex natural world that surrounds us illustrates infinite scientific concepts. As children grow up in an increasingly technologically and scientifically advanced world, they need to be scientifically literate to succeed.

Ideally, teaching the scientific method to students is teaching them how to think, learn, solve problems and make informed decisions. These skills are integral to every aspect of a student’s education and life, from school to career. With a graduate degree in science education such as the online Master of Education in Curriculum and Instruction in Science Education from the University of Texas at Arlington, teachers can use what they learn about science instruction techniques and curriculum design to advance science education and student learning as a whole.

How Is Science Involved in Students’ Everyday Lives?

Science is everywhere. A student rides to school on a bus, and in that instance alone, there are many examples of technology based on the scientific method. The school bus is a product of many areas of science and technology, including mechanical engineering and innovation. The systems of roads, lights, sidewalks and other infrastructure are carefully designed by civil engineers and planners. The smartphone in the student’s hand is a miracle of modern computer engineering.

Outside the window, trees turn sunlight into stored energy and create the oxygen we need to survive. Whether “natural” or human-derived, every aspect of a student’s life is filled with science — from their own internal biology to the flat-screen TV in the living room.

Scientific Inquiry and Scientific Method

Perhaps even more important than specific examples of science in our lives are the ways we use scientific thought, method and inquiry to come to our decisions. This is not necessarily a conscious thing. The human need to solve problems can arise from curiosity or from necessity. The process of inquiry is how we find answers and substantiate those answers.

In the fields of hard science, the process of inquiry is more direct and finite: Take a question; use evidence to form an explanation; connect that explanation to existing knowledge; and communicate that evidence-based explanation. Experimentation based on the scientific method follows a similar course: Combine a scientific question with research to construct a hypothesis; conduct experiments to test that hypothesis; evaluate the results to draw conclusions; and communicate those conclusions.

Critical Thinking

Although inquiry and the scientific method are integral to science education and practice, every decision we make is based on these processes. Natural human curiosity and necessity lead to asking questions (What is the problem?), constructing a hypothesis (How do I solve it?), testing it with evidence and evaluating the result (Did the solution work?), and making future decisions based on that result.

This is problem-solving: using critical thinking and evidence to create solutions and make decisions. Problem-solving and critical thinking are two of the most important skills students learn in school. They are essential to making good decisions that lead to achievement and success during and after school.

Yet, although they are nearly synonymous, scientific inquiry in schools is not always explicitly tied to problem-solving and critical thinking. The process students learn when creating, executing, evaluating and communicating the results of an experiment can be applied to any challenge they face in school, from proving a point in a persuasive essay to developing a photo in the darkroom. In this way, science is one of the most important subjects students study, because it gives them the critical thinking skills they need in every subject.

The Importance of Science in Early Education

Governmental guidelines and tests often focus on middle and high school-level STEM (science, technology, engineering and math) education. Yet, many educators believe science education should begin much earlier . Not only does science education teach young learners problem-solving skills that will help them throughout their schooling, it also engages them in science from the start.

Kids usually form a basic opinion about the sciences shortly after beginning school. If this is a negative opinion, it can be hard to engage those students in science as they grow older. Engaging young students with exciting material and experiences motivates them to learn and pursue the sciences throughout school.

Science is one of the most important subjects in school due to its relevance to students' lives and the universally applicable problem-solving and critical thinking skills it uses and develops. These are lifelong skills that allow students to generate ideas, weigh decisions intelligently and even understand the evidence behind public policymaking. Teaching technological literacy, critical thinking and problem-solving through science education gives students the skills and knowledge they need to succeed in school and beyond.

Learn more about the  UTA online M.Ed. in Curriculum and Instruction – Science Education program .

National Science Teaching Association: Early Childhood Science Education

Untamed Science: What Is the Scientific Method?

U.S. Department of Education: Science, Technology, Engineering, and Math, Including Computer Science: Background

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Global Warming Solutions: Stop Deforestation

Published Jul 18, 2014

Brazil rainforest

Tropical Deforestation and Global Warming

Tropical deforestation accounts for about  10 percent of the world’s heat-trapping emissions  — equivalent to the annual tailpipe emissions of 600 million average U.S. cars.

Reducing tropical deforestation can significantly lower global warming emissions and—together with efforts to  reduce emissions from fossil fuels —plays an integral role in a comprehensive long-term solution to global warming.

To accomplish this, we need to understand the driving forces behind deforestation today and the  many reasons  why reducing deforestation must be a priority.

is the driving force behind problem solving in science

What's Driving Deforestation?

What's driving deforestation.

It turns out that the bulk of tropical deforestation is currently driven by just four commodities:  beef cattle ,  soybeans ,  palm oil , and  wood products . Other commodities and activities play a relatively minor role.

Amazon River in Brazil

Deforestation Success Stories

Despite rapid expansion of the drivers of deforestation, there have been notable successes in channeling their growth in ways that no longer cause deforestation. Businesses can move to become deforestation-free, and consumers can make sure businesses know this is a priority. This strategy has produced encouraging progress on deforestation-free palm oil.

Strong policies can also play an important role. REDD+, which offers rewards to developing countries for reducing their deforestation rates, is one of the best, most affordable strategies for reducing tropical deforestation. On the demand side, the U.S. has used the Lacey Act to close the market for illegally sourced wood. However, these policies require effective implementation and enforcement in order to work.

Finally, voluntary agreements between businesses, policymakers and non-governmental organizations (NGOs), such as the Soy Moratorium in Brazil, have proved to be a promising approach.

is the driving force behind problem solving in science

Halfway There?

The way we use our planet's forested ecosystems and agricultural land can have a big impact on climate change. Currently, inefficiencies in food and farming systems threaten tropical forests by increasing the demand for the drivers of deforestation. To help stop deforestation—and to reduce the heat-trapping emissions that cause global warming—we need to make smart decisions that shift consumption and land use patterns in less wasteful directions.

Biofuels can also contribute to deforestation. When land used for food or feed production is turned over to growing biofuel crops, agriculture has to expand elsewhere. The resulting emissions from clearing new land can outweigh any emissions savings from the use of biofuels. Effective biofuel policies must fully address this issue.

Related resources

Got Science? The Podcast - Jon Trapp

Trial By Fire

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Truck Pollution in the United States

Got Science? The Podcast - Adam Kennedy

Saving Endangered Species: Turtle by Turtle

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The Case Against ExxonMobil, Chevron, and other Fossil Fuel Companies

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The Physics of Driving

The Physics of Driving: Natural Forces, Friction, Traction and Balance

Everything in the known universe is subject to natural forces like inertia, gravity, friction and energy. Your car is no exception, in fact, it relies on the laws of physics to operate. Without natural forces, your car would be unable to start, move, stop or change direction. The way in which your vehicle interacts with these forces is somewhat determined by its design. As the laws of physics are constant and unchangeable, car manufacturers can use them to create stable, safer and more efficient vehicles.

Gravitational force

Newton’s laws of motion, energy and potential energy, centrifugal and centripetal forces, friction and your tires, friction and braking, what affects traction, how to correct traction loss, vehicle balance, maintaining vehicle balance, vehicle balance in complex environments, the physics of collisions, working with natural forces.

Even more so than design, the way your car behaves in response to the invisible, natural forces acting upon it is determined by the way you drive it . As part of your driver’s training, you must learn how different forces and natural laws affect your car, in order to maintain control and respond appropriately in emergency situations .

This may seem like an enormous undertaking but remember that you are already a skilled driver of another, quite different vehicle which is also at the mercy of natural forces: your body. Just like your car, your body is powered by energy, resisted by friction, subject to inertia and pulled toward the center of the earth by gravity. Despite constant interference from these forces, you can walk, run, jump, crouch and engage in countless other physical activities. Eventually, you will have a similar level of intuitive control over your car.

Natural forces are always working on your vehicle, while it is stationary and in motion. In an ideal driving situation on a flat, straight, level and well-surfaced roadway, you may not pause to consider the effect these forces have. In more complex environments , you will need an awareness and an understanding of natural forces in order to make safe driving decisions and avoid collisions . If you are driving on a hill, through a curve , executing a turn , or negotiating bad weather , your knowledge of natural laws could easily mean the difference between an uneventful trip or a catastrophic crash.

If you are feeling a little intimidated by the prospect of studying the laws of physics as part of your driver’s ed course – don’t panic. Learning how to control your car does not require knowledge of complex mathematical equations or abstract theories. All you need is a general understanding of the natural forces working on your car and how you can work with them to maintain control and stay safe on the roads. Let’s begin with a quick run-down of the topics covered in this section.

Our exploration of the science of driving begins with a look at gravity . This force exists to some degree between any two objects in the universe, though we generally only notice the gravitational pull of the Earth. Gravity attracts objects; the force with which it pulls them together is determined by their mass and the distance between them. Larger objects (like the Earth) have a stronger gravitational pull. We will delve into the theory of gravity, its history and effects in greater detail in the next module of this section. For now, all you need to know is that gravity pulls your vehicle toward the center of the earth and will affect it in different ways, depending on its weight distribution, the gradient of the road you are driving on and various other natural forces.

The downward pull of gravity will affect your vehicle’s speed when you are driving or parking on a slope . While driving uphill , your car will be pulled backward by gravity and effectively slowed. On a downhill slope, it will be pulled forward, resulting in an increase in speed. This section will teach you how to adjust your driving behavior to compensate for the effects of gravity in hill driving situations.

Before we can go any further in our quest to understand how natural forces affect the movement of a vehicle, we must first look at the universal rules which govern motion. This module covers Newton’s three laws of motion, which together, can predict and explain the way any object moves (or doesn’t move) in response to outside forces.

Newton’s first law concerns inertia . Inertia is the reason that changing a moving object’s speed or direction always takes more effort than keeping them constant. In driving, inertia makes maneuvering a vehicle around a bend or bringing it to a stop physically challenging – particularly if you do not have power steering or brakes!

His second law relates to the force which a moving object exerts. Ever wondered why a car colliding with a wall will do more damage at 30mph than at 15mph? Our exploration of force will show you how it is calculated and therefore, why some objects exert greater force than others. Newton’s third law deals with action and reaction. This rule explains why a car colliding with a wall not only damages the wall but sustains damage itself. The information covered in this module will help you to understand the concepts discussed across the rest of the section; make sure you take the time to read it!

The amount of energy an object has determines its ability to do work. Objects with more energy can work harder - or for longer - than objects with less energy. While energy comes in many forms, our focus during this course will be movement energy or kinetic energy. Kinetic energy is produced by an object in motion. Stationary objects still have energy, though it is known as potential energy. An object is more likely to begin moving, the more potential energy it has. When it comes to driving, the amount of kinetic energy your car has will affect how easy it is to slow down or stop; the greater the energy, the harder it will be.

Speed has a significant effect on energy, and consequently, how long it will take your vehicle to come to a complete stop in an emergency. Learn how energy affects braking distance and the force of impact in collisions, in this essential module.

Centrifugal and centripetal forces work in opposition to each other and affect objects traveling on a curved path. Unless you spend all your time driving on straight, high-speed expressways , these forces will regularly play a part in the way your vehicle behaves on the road. To understand centrifugal and centripetal forces , it helps to imagine any curve you are driving on as part of a complete circle. The centripetal force pulls your vehicle towards the center of this circle, while centrifugal force pulls it directly away from the center. While driving on a curved road your car will always be acted on by both these forces. You must learn to find a balance between them to avoid understeer (not turning enough) and oversteer (turning too much), while driving along a curved pathway. The skills and techniques required to achieve this are explained in full here.

Next up, we look at the resisting forces which influence the internal moving components of your car and the way its tires grip the surface of the road. Central to this discussion is friction . Friction describes the resistance between any two contacting surfaces as they slide against each other. Low friction results in ease of movement, while high friction makes movement difficult. The friction which occurs between car tires and the road is known as traction.

The friction between your vehicle’s tires and the road must be high for the vehicle to move and for you to steer effectively. Without friction, the tires would slide across the surface of the asphalt rather than gripping and rolling over it. Car tires and road surfaces are designed to maximize friction but there is also a lot you can do while driving to improve grip on the road or regain friction if your wheels have begun to skid. Learn about friction, why it is so important and what you can do to maintain it, in this part of the section.

We also deal with a concept known as rolling resistance here. Rolling resistance refers to how much your tires are compressed as they roll over the surface of the road and is largely determined by tire pressure. This is an important issue to get to grips with, as excessive rolling resistance can result in overheating and eventually, tire failure .

In addition to the connection between the tires and the road, friction plays a part in various other essential vehicle systems. Perhaps most important among them is the brakes! When you depress the brake pedal, pads are pressed against the turning wheels to create friction , which will slow down and eventually stop the vehicle. Friction will ultimately lead to wear, which is why it is so important to observe proper braking techniques . All brake systems will wear out eventually, but it will happen far sooner than it should if you ride the brakes while driving.

Braking correctly is essential for your safety as well as the health of your vehicle. To slow down or stop, the brakes must absorb the vehicle’s kinetic energy. There may not be enough time for this to occur if you slam your foot down on the brakes suddenly. When the vehicle’s movement energy has not been fully absorbed, the wheels will lock, and you will continue to move forward. Unless you drive an ABS-equipped vehicle, gradual braking is key.

At this point in the section, you will have learned about friction and the vital role it plays in allowing your tires to grip the surface of the road. This resistance between tire and tarmac is known as traction . Creating good traction is a primary goal behind vehicle, tire and roadway designs. However, these are not the only factors that influence your grip on the road. Others include:

In this module, you will learn how to adjust your driving behavior to suit the roadway environment and maintain traction.

Despite your best efforts, you may one day find yourself in a frightening situation where your tires lose traction and begin to skid across the surface of the road. The key to regaining control when this happens is to remain calm and apply the traction correction techniques discussed in this module.

When traction loss occurs in your vehicle’s front wheels, steering may become totally ineffective. On a corner or bend, this will result in a phenomenon known as understeer. The opposite effect, known as oversteer, occurs when traction is lost in the rear wheels while driving through a curve in the road. Both situations can result in serious crashes if not handled correctly. Oversteer can cause your rear wheels to spin out and leave the road or enter a lane of opposing traffic. When understeer occurs, it can cause drivers to plow directly off the side of the road. If you ever find yourself dealing with either of these situations, recalling the information covered in this module could save your life.

The next three modules of this section deal with the distribution of weight across your vehicle’s four wheels and why this should matter to you, as a driver. Ideal vehicle balance would be an equal amount of weight pushing each tire against the surface of the road. Whenever weight is shifted toward the front, back, left or right of the vehicle, the tires bearing less weight will suffer from reduced traction. The condition of your vehicle, any maneuvers you perform, adjustments in speed and gravity will all determine how weight is distributed across the car’s wheels. We explore these ideas fully in this first vehicle balance module. Here is a quick summary to get you started:

As you become more comfortable behind the wheel, you will learn to sense shifts in your vehicle’s balance as you speed up, slow down or perform maneuvers. Ultimately, your goal will be to maximize traction by adjusting your driving behavior to keep the vehicle as balanced as possible.

Our next module covers the four types of weight imbalance that can occur in a moving vehicle. These are:

Often, you will encounter driving environments that create pitch and roll weight shifts simultaneously. Maintaining control of your vehicle will require predicting and compensating for these balance shifts with appropriately timed speed adjustments. You must also ensure your tires are in good condition and can grip the surface of the road, as the effects of vehicle imbalance are harder to manage when traction is already poor.

Changes in your vehicle’s balance are easy to predict and manage when you are driving in ideal conditions on a straight, level road. In challenging driving environments, maintaining vehicle balance is a slightly more complex task. As we have already discussed, a vehicle’s center of gravity (and therefore, its weight distribution) are influenced by the gradient and shape of a road, the road’s surface, weather conditions and other environmental factors.

This module will walk you through common driving scenarios, teaching you how to compensate for shifts in weight and optimize traction . The secret to executing weight adjustments successfully is giving yourself enough time to plan and perform them. As a defensive driver , you must constantly scan the roadway ahead to look for changes in gradient, road surface and curves in the road that will affect your vehicle’s balance.

Our science module rounds off with a look at how natural forces interact and determine severity in traffic collisions. Here’s what you need to know:

When a driver understands how energy and force influence the severity of traffic collisions , they can take steps to minimize severity when faced with an imminent crash. Learn how to protect yourself in an emergency and get familiar with your vehicle’s in-built safety features here.

The rules governing movement in our universe cannot be changed, bent or broken. Gravity, resistance, inertia, energy, force and other concepts discussed in this module are constant and as such, they are predictable . By understanding how your car moves and the forces which act upon it, you can work with natural forces to get the best performance from your vehicle. Disregard these laws and controlling your vehicle will always be an uphill struggle – sometimes quite literally! On that note, let’s get started with our first important concept: gravity.

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Effect of Gravity on Driving

The Force of Gravity

Though we rarely stop to consider its effects, gravity is an ever-present force which acts on you, everything you can see in the room around you and of course, your vehicle. The force of gravity pulling your vehicle toward the center of the Earth will influence your speed when traveling on a hill. It will also affect the way weight is distributed across your vehicle’s four tires.

The Laws of Motion and Inertia

Inertia & The Laws of Motion

Using the three laws of motion, we can accurately predict how an object will move under different circumstances. Getting your head around the idea of inertia is necessary to understand vehicle-occupant safety.

The Physics of Braking

Potential and Kinetic Energy

Energy is the capacity to do work. The more energy something has, the longer or harder it can work. When slowing down or stopping, your vehicle’s brakes must overcome its kinetic energy. If your vehicle collides with an object, the force of the impact will be equal to its kinetic energy, divided by your stopping distance.

Road Rage & Aggressive Driving

Aggressive driving is a behavior that can result from unchecked emotions or excessive stress while behind the wheel. Any purposeful, dangerous action fueled by frustration, impatience, anger or stress qualifies as aggressive driving.

The Causes of Aggressive Driving & Road Rage

To tackle the threat posed by aggressive driving we must understand its causes. The increasing number of vehicles our roads, day-to-day stresses, tiredness and the growing prevalence of disrespectful behavior have all created an environment in which aggressive driving is rife.

Common Road Rage Behaviors

While scanning the roadway for potential threats, you must watch out for any motorists displaying aggressive behavior. If you encounter a driver experiencing road rage, it is likely that they are the most dangerous hazard in the roadway environment. You must pay attention and act defensively, to protect yourself against the threat such drivers pose.

How to Avoid Provoking Road Rage

As part of your road safety training, you must learn how to avoid becoming the target of another driver’s road rage. Every day, you will be sharing the road with stressed out and frustrated drivers, many of which are capable of succumbing to road rage if things don’t go their way.

Dealing with Aggression on the Road

You must know how to deal with aggression on the road just in case you ever find yourself in hot water. It is important not to take an aggressive driver’s actions personally, even if you are the target. When a driver’s aggression develops into rage, there is very little you can do or say to calm them down.

Controlling Your Emotions

Holding a driver’s license is both a privilege and a responsibility. It is not enough simply to say that you will not become an aggressive driver, you must continually monitor yourself and be proactive about avoiding aggressive behaviors behind the wheel. Most new motorists believe that they will not act aggressively on the road and yet many drivers end up doing it anyway.

is the driving force behind problem solving in science

Accessibility links

Forces, acceleration and Newton's laws - AQA

Falling objects eventually reach terminal velocity – where their resultant force is zero. Stopping distances depend on speed, mass, road surface and reaction time.

Newton's Second Law

Force, mass and acceleration.

Newton's Second Law of motion can be described by this equation:

resultant force = mass × acceleration

\[F = m~a\]

This is when:

The equation shows that the acceleration of an object is:

In other words, the acceleration of an object increases if the resultant force on it increases, and decreases if the mass of the object increases.

Inertial mass - Higher

The ratio of force over acceleration is called inertial mass . Inertial mass is a measure of how difficult it is to change the velocity of an object.

Calculate the force needed to accelerate a 22 kg cheetah at 15 m/s².

\[F = 22 \times 15\]

\[F = 330~N\]

Calculate the force needed to accelerate a 15 kg gazelle at 10 m/s².

\[F = 15 \times 10\]

\[F = 150~N\]


It is important to be able to estimate speeds, accelerations and forces involved in road vehicles. The symbol ~ is used to indicate that a value or answer is an approximate one. The table gives some examples.

Estimate the force needed to accelerate a family car to its top speed on a single carriageway.

Using values of ~1,600 kg and ~3 m/s 2 , and F = m a :

1,600 × 3 = ~4,800 N

Estimate the force needed to accelerate a lorry to its top speed on a single carriageway.

Using values of ~36,000 kg and ~0.4 m/s 2 , and F = m a :

Force ( F ) is ~14,400 N

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While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004  paper  (PDF, 106 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, " Computing Machinery and Intelligence " (PDF, 89.8 KB) (link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?"  From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish,  Artificial Intelligence: A Modern Approach  (link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Ideal approach:

Alan Turing’s definition would have fallen under the category of “systems that act like humans.”

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in  Gartner’s hype cycle  (link resides outside IBM), product innovations like, self-driving cars and personal assistants, follow “a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovation’s relevance and role in a market or domain.” As Lex Fridman notes  here  (01:08:05) (link resides outside IBM) in his MIT lecture in 2019, we are at the peak of inflated expectations, approaching the trough of disillusionment.  

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation around  AI ethics , read more  here .

Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in  2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. “Deep” in deep learning refers to a neural network comprised of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. This is generally represented using the following diagram:

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

There are numerous, real-world applications of AI systems today. Below are some of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

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is the driving force behind problem solving in science

Chapter 17 Sections

The nature of problems

Clarifying the problem, deciding to solve the problem, analyzing the problem.

We've all had our share of problems - more than enough, if you come right down to it. So it's easy to think that this section, on defining and analyzing the problem, is unnecessary. "I know what the problem is," you think. "I just don't know what to do about it."

Not so fast! A poorly defined problem - or a problem whose nuances you don't completely understand - is much more difficult to solve than a problem you have clearly defined and analyzed. The way a problem is worded and understood has a huge impact on the number, quality, and type of proposed solutions.

In this section, we'll begin with the basics, focusing primarily on four things. First, we'll consider the nature of problems in general, and then, more specifically, on clarifying and defining the problem you are working on. Then, we'll talk about whether or not you really want to solve the problem, or whether you are better off leaving it alone. Finally, we'll talk about how to do an in-depth analysis of the problem.

So, what is a problem? It can be a lot of things. We know in our gut when there is a problem, whether or not we can easily put it into words. Maybe you feel uncomfortable in a given place, but you're not sure why. A problem might be just the feeling that something is wrong and should be corrected. You might feel some sense of distress, or of injustice.

Stated most simply, a problem is the difference between what is , and what might or should be . "No child should go to bed hungry, but one-quarter of all children do in this country," is a clear, potent problem statement. Another example might be, "Communication in our office is not very clear." In this instance, the explanation of "what might or should be" is simply alluded to.

As these problems illustrate, some problems are more serious than others; the problem of child hunger is a much more severe problem than the fact that the new youth center has no exercise equipment, although both are problems that can and should be addressed. Generally, problems that affect groups of people - children, teenage mothers, the mentally ill, the poor - can at least be addressed and in many cases lessened using the process outlined in this Chapter.

Although your organization may have chosen to tackle a seemingly insurmountable problem, the process you will use to solve it is not complex. It does, however, take time, both to formulate and to fully analyze the problem. Most people underestimate the work they need to do here and the time they'll need to spend. But this is the legwork, the foundation on which you'll lay effective solutions. This isn't the time to take shortcuts.

Three basic concepts make up the core of this chapter: clarifying, deciding, and analyzing. Let's look at each in turn.

If you are having a problem-solving meeting, then you already understand that something isn't quite right - or maybe it's bigger than that; you understand that something is very, very wrong. This is your beginning, and of course, it makes most sense to...

Compare this problem statement on child hunger to the one given in "The nature of problems" above. How might solutions for the two problems be different?

When you are gathering information, you will probably hear all four types of information, and all can be important. Speculation and opinion can be especially important in gauging public opinion. If public opinion on your issue is based on faulty assumptions, part of your solution strategy will probably include some sort of informational campaign.

For example, perhaps your coalition is campaigning against the death penalty, and you find that most people incorrectly believe that the death penalty deters violent crime. As part of your campaign, therefore, you will probably want to make it clear to the public that it simply isn't true.

Where and how do you find this information? It depends on what you want to know. You can review surveys, interviews, the library and the internet.

You can define the problem in several ways; The facilitator can write a problem statement on the board, and everyone can give feedback on it, until the statement has developed into something everyone is pleased with, or you can accept someone else's definition of the problem, or use it as a starting point, modifying it to fit your needs.

After you have defined the problem, ask if everyone understands the terminology being used. Define the key terms of your problem statement, even if you think everyone understands them.

The Hispanic Health Coalition, has come up with the problem statement "Teen pregnancy is a problem in our community." That seems pretty clear, doesn't it? But let's examine the word "community" for a moment. You may have one person who defines community as "the city you live in," a second who defines it as, "this neighborhood" and a third who considers "our community" to mean Hispanics.

At this point, you have already spent a fair amount of time on the problem at hand, and naturally, you want to see it taken care of. Before you go any further, however, it's important to look critically at the problem and decide if you really want to focus your efforts on it. You might decide that right now isn't the best time to try to fix it. Maybe your coalition has been weakened by bad press, and chance of success right now is slim. Or perhaps solving the problem right now would force you to neglect another important agency goal. Or perhaps this problem would be more appropriately handled by another existing agency or organization.

You and your group need to make a conscious choice that you really do want to attack the problem. Many different factors should be a part of your decision. These include:

Importance . In judging the importance of the issue, keep in mind the f easibility . Even if you have decided that the problem really is important, and worth solving, will you be able to solve it, or at least significantly improve the situation? The bottom line: Decide if the good you can do will be worth the effort it takes. Are you the best people to solve the problem? Is someone else better suited to the task?

For example, perhaps your organization is interested in youth issues, and you have recently come to understand that teens aren't participating in community events mostly because they don't know about them. A monthly newsletter, given out at the high schools, could take care of this fairly easily. Unfortunately, you don't have much publishing equipment. You do have an old computer and a desktop printer, and you could type something up, but it's really not your forte. A better solution might be to work to find writing, design and/or printing professionals who would donate their time and/or equipment to create a newsletter that is more exciting, and that students would be more likely to want to read.

Negative impacts . If you do succeed in bringing about the solution you are working on, what are the possible consequences? If you succeed in having safety measures implemented at a local factory, how much will it cost? Where will the factory get that money? Will they cut salaries, or lay off some of their workers?

Even if there are some unwanted results, you may well decide that the benefits outweigh the negatives. As when you're taking medication, you'll put up with the side effects to cure the disease. But be sure you go into the process with your eyes open to the real costs of solving the problem at hand.

Choosing among problems

You might have many obstacles you'd like to see removed. In fact, it's probably a pretty rare community group that doesn't have a laundry list of problems they would like to resolve, given enough time and resources. So how do you decide which to start with?

A simple suggestion might be to list all of the problems you are facing, and whether or not they meet the criteria listed above (importance, feasibility, et cetera). It's hard to assign numerical values for something like this, because for each situation, one of the criteria may strongly outweigh the others. However, just having all of the information in front of the group can help the actual decision making a much easier task.

Now that the group has defined the problem and agreed that they want to work towards a solution, it's time to thoroughly analyze the problem. You started to do this when you gathered information to define the problem, but now, it's time to pay more attention to details and make sure everyone fully understands the problem.

Answer all of the question words.

The facilitator can take group members through a process of understanding every aspect of the problem by answering the "question words" - what, why, who, when, and how much. This process might include the following types of questions:

What is the problem? You already have your problem statement, so this part is more or less done. But it's important to review your work at this point.

Why does the problem exist? There should be agreement among meeting participants as to why the problem exists to begin with. If there isn't, consider trying one of the following techniques.

"Children often fall asleep in class," But why? "Because they have no energy." But why? "Because they don't eat breakfast." But why?

Continue down the line until participants can comfortably agree on the root cause of the problem . Agreement is essential here; if people don't even agree about the source of the problem, an effective solution may well be out of reach.

Clearly, these two exercises are meant for different times. The "but why" technique is most effective when the facilitator (or the group as a whole) decides that the problem hasn't been looked at deeply enough and that the group's understanding is somewhat superficial. The force field analysis, on the other hand, can be used when people are worried that important elements of the problem haven't been noticed -- that you're not looking at the whole picture.

Who is causing the problem, and who is affected by it? A simple brainstorming session is an excellent way to determine this.

When did the problem first occur, or when did it become significant? Is this a new problem or an old one? Knowing this can give you added understanding of why the problem is occurring now. Also, the longer a problem has existed, the more entrenched it has become, and the more difficult it will be to solve. People often get used to things the way they are and resist change, even when it's a change for the better.

How much , or to what extent, is this problem occurring? How many people are affected by the problem? How significant is it? Here, you should revisit the questions on importance you looked at when you were defining the problem. This serves as a brief refresher and gives you a complete analysis from which you can work.

If time permits, you might want to summarize your analysis on a single sheet of paper for participants before moving on to generating solutions, the next step in the process. That way, members will have something to refer back to during later stages in the work.

Also, after you have finished this analysis, the facilitator should ask for agreement from the group. Have people's perceptions of the problem changed significantly? At this point, check back and make sure that everyone still wants to work together to solve the problem.

The first step in any effective problem-solving process may be the most important. Take your time to develop a critical definition, and let this definition, and the analysis that follows, guide you through the process. You're now ready to go on to generating and choosing solutions, which are the next steps in the problem-solving process, and the focus of the following section.

Print Resources

Avery, M., Auvine, B., Streibel, B., & Weiss, L. (1981). A handbook for consensus decision making: Building united judgement . Cambridge, MA: Center for Conflict Resolution.

Dale, D., & Mitiguy, N. Planning, for a change: A citizen's guide to creative planning and program development .

Dashiell, K. (1990). Managing meetings for collaboration and consensus . Honolulu, HI: Neighborhood Justice Center of Honolulu, Inc.

Interaction Associates (1987). Facilitator institute . San Francisco, CA: Author.

Lawson, L., Donant, F., & Lawson, J. (1982). Lead on! The complete handbook for group leaders . San Luis Obispo, CA: Impact Publishers.

Meacham, W. (1980). Human development training manual . Austin, TX: Human Development Training.

Morrison, E. (1994). Leadership skills: Developing volunteers for organizational success . Tucson, AZ: Fisher Books.  

driving force

The impetus, power, or energy behind something in motion, as in He was clearly the driving force in the new administration . This term transfers the force that sets in motion an engine or vehicle to other enterprises. Ralph Waldo Emerson was among the first to use it figuratively ( English Traits , 1856): “The ability of its journals is the driving force.”

Words nearby driving force

Words related to driving force, how to use driving force in a sentence.

Yet for a vivid decade or so, sleaze was, somewhat paradoxically, a force for literacy and empowerment.

Shortly after dawn, there was another outbreak of deadly force.

And Air Force assessors are the first to say such imaging never tells the whole story.

Detectives with a fugitive task force caught up with Polanco and a friend on a Bronx street in the early afternoon.

Father José Julián was shot and wounded driving in a car through the sierra of Ajuchitán.

The Goliath wouldn't answer; the Dublin said the force was coming off, and we could not get into touch with the soldiers at all.

For this use of the voice in the special service of will-power, or propelling force, it is necessary first to test its freedom.

But you are mistaken in thinking the force west consists of the entire Merrill Horse.

She and her younger sister, Janet, had quarreled a good deal through force of unfortunate habit.

In the time of destruction they shall pour out their force: and they shall appease the wrath of him that made them.

10 Driving Force Examples in Real Life

Driving force is the force that is responsible to put an object into motion. In the absence of the driving force, an object is not able to change its shape, state, size, or position. The driving force is also known as the external force . Whenever there exists a driving force in action, an opposing force, known as a retarding force, is also present. The main task of a retarding force is to oppose the motion of the object or slow down the speed with which it is moving. If the magnitude of the driving force is greater than the retarding force, the object speeds up. If the value of the driving force is less than the retarding force, the object slows down. If both the forces are equal in magnitude, then there exists no change in the state, shape, or position of the object. This is because both the forces acting from opposite directions with equal magnitude establish a condition of equilibrium and forbid any motion.

Index of Article (Click to Jump)

1. Using Sound to Break a Glass

When a glass is subjected to high-frequency sound waves, it tends to develop certain cracks. On increasing the frequency of the sound wave, it might shatter into pieces. The sound energy causes a certain amount of deformation and damage to the structure of the glass. Therefore, here, sound acts as a driving force.

2. Pendulum Action

A pendulum is put into motion with the help of a mechanical force. In the equilibrium condition, the pendulum hangs to the hook and does not move. The tension force possessed by the string, with which the pendulum is tied, and the mechanical force that supplies energy to the hanging mechanism of the pendulum, are the driving forces responsible to keep a pendulum in motion.

3. Free-falling Objects

An object freely falling towards the ground is pulled towards the center of the earth’s core due to the gravitational force in action. Here, gravity is accountable to put the objects into motion. Therefore, in the case of a freely falling object, the force of gravitation acts as a driving force.

4. Spinning a Top

To spin a top, it has to be supplied with a significant amount of rotational force. The rotating force sets the top to move in a circular direction about its axis. Without the application of the rotating force, the top cannot be put into motion. Therefore, the rotating force here acts as the driving force. A force of friction acts in the direction opposite to the driving force that tends to slow down the speed of the spinning top.

5. Driving a Car

To provide acceleration to a car, the engine of the car is required to be ignited. The engine of the car then is able to develop the necessary driving force to put the vehicle into motion. When the ignition system or the driving force is cut off, the vehicle comes to rest.

6. Merry-Go-Round

The swings attached to the rotating roof of a merry-go-round ride tend to move towards the outer side. The force that compels the swings to leave their state of equilibrium and get dislocated from their original position is the centrifugal force. The centrifugal force is a pull force applied to every rotating object in nature. This force, here, is regarded as a driving force.

7. Winding a Toy

There are certain toys in the market, which are operated with the help of the mechanical force stored with the help of winding a key. The key is rotated multiple times in a particular direction. This helps to store a considerable amount of energy in it. When the key is released, the toy exhibits motion. Hence, this stored mechanical force, here, acts as a driving force to the toy.

8. Dragging a Strolly

When a strolly is dragged, it is set to motion. The motion takes place as long as the pull force is applied to the suitcase. This means that the pull force supplied by the user is responsible to drive the suitcase. Hence, this force required to pull or drag the suitcase is known as a driving force.

9. Twisting a Screw

To fix or embed a screw into a rigid wall or a wooden plank, the tip of the screw is placed on the surface, and the top of the screw is made to rotate in a particular direction. This helps the thread cuttings on the screw to form grooving into the structure as per requirement. The force that gets developed due to the turning of the screw is called the twisting force. The twist or the torsion force sets the screw into motion. Hence, it is known as the driving force.

10. Nail Attracted towards Magnet

When an iron nail is placed in close proximity to a magnet, it experiences a strong force of attraction. This force of attraction pulls the nail towards the magnetic body. The force is said to be a non-contact force because the motion of the nail occurs indirectly without any physical contact of both bodies. The magnetic force, here, is responsible to put the nail into motion and is regarded as the driving force.

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