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AI accelerates problem-solving in complex scenarios

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While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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How to choose your ai problem-solving tool in machine learning.

For a computer to perform a task, it must have a set of instructions to follow - which we provide. But machine learning - a subset of artificial intelligence (AI) - is quite different. It involves training computers to learn to do things. This approach can range from simple to very complex, based on the issue we want computers to address, and involves the use of various AI problem-solving tools.

Why is problem-solving important in artificial intelligence?

The ultimate aim of artificial intelligence is to create systems that can solve real-world problems. It does this by employing efficient and logical algorithms, utilizing polynomial and differential equations, and executing them using modeling paradigms. Such problem-solving techniques improve the performance of machine learning models so that they can ultimately be used in real-world applications.

AI systems themselves must overcome several barriers. Some of the major types of obstacles to problem-solving include unnecessary constraints and irrelevant information. A single problem may have unique or various solutions which are achieved by different heuristics.

This article will explore some of the things to consider when choosing an AI problem-solving tool as well as the various types of in-demand tools currently available.

How to choose the right artificial intelligence problem-solving tool

Real-world problems are often complex and involve having to deal with massive amounts of data. A single machine learning tool cannot fix all problems but a group of them can provide prospective solutions.

Before selecting a tool, consider a few things:

• Analyze the problem.
• Prioritize what you want from the tool.
• Be clear with your expectations.
• Compare different tools.
• Consider a tool that provides updated service with every change.
• Assess your model’s metadata such as experiment metrics, data versions, training parameters, etc.

In-demand artificial intelligence tools

While there are many AI problem-solving tools, the ones listed below are among the most sought-after.

TensorFlow is a free and open-source library developed by Google for machine learning and artificial intelligence applications. It takes input data in the form of tensors which are multi-dimensional arrays of higher dimensions. These multi-dimensional arrays are great at handling large amounts of data.

One of the reasons for the popularity of TensorFlow is that developers can easily build and deploy applications. TensorFlow works on the basis of data flow graphs, and can easily be executed in a distributed manner across a cluster of computers while using GPUs.

The following are the machine learning algorithms supported by TensorFlow:

• Linear regression: tf.estimator.LinearRegressor
• Classification: tf.estimator.LinearClassifier
• Boosted tree classification: tf.estimator.BoostedTreesClassifier
• Deep learning wipe and deep: tf.estimator.DNNLinearCombinedClassifier
• Boosted tree regression: tf.estimator.BoostedTreesRegressor
• Deep learning classification: tf.estimator.DNNClassifier

TensorFlow is best suited for applications such as classification, perception, understanding, discovering, prediction and creation.

Keras is a powerful open-source high-level neural network library. It uses Theano, TensorFlow, or CNTK at the back-end which acts as a high-level API wrapper for the low-level API. It supports both convolutional and recurrent neural networks as well as a combination of both.

Keras is easy to understand and supports multiple backends. A huge amount of data can be easily processed. The speed of training models is also higher as it can be run on multiple GPU instances at the same time. Keras can be one of the best tools for building neural network models in a user-friendly way.

Scikit-learn

Scikit -learn is a robust open-source tool for machine learning and statistical modeling. It was built on top of NumPy, SciPy, and matplotlib. It can be used to implement a wide range of algorithms including support vector machines, random forests, gradient boosting, k-means, etc.

Scikit-learn can be used for:

Supervised models, such as classification, regression, clustering

• Ensemble methods
• Feature extraction
• Feature selection
• Preprocessing
• Cross-validation
• Model selection
• Dimensionality reduction.

PyTorch is an open-source machine learning library that was developed by using Torch - a library for Python programming. PyTorch can be used to build complex neural networks easily. It has support for GPU and CPU, and supports cloud platforms.

ML and AI developers will find PyTorch easy to learn and build models with.

The features provided are:

• Autograd module
• Optim module

PyTorch is one of the emerging trends in the machine learning field and is being increasingly applied in industries. It can extensively be used for computer vision, deep learning, natural language processing, and reinforcement learning applications.

XGBoost stands for Extreme Gradient Boost. It is an open-source machine learning algorithm that is mainly used for implementing gradient boosting decision trees. Decision trees can be considered the best algorithm for structured/semi-structured data.

XGBoost greatly improves the speed and performance of ML models. It supports tree learning algorithm and linear model learning, making it suitable for parallel computation on a single machine. Hence, it is 10 times faster than all other algorithms. It also offers a good number of advanced features, one being scikit-learn regularization.

XGBoost can be used to solve problems in

• Classification
• User-defined prediction challenges

Catalyst is a machine learning framework built on top of PyTorch and is designed specifically for deep learning problems. It simplifies researchers’ tasks through features such as code reusability and reproducibility as well as by supporting faster experimentation. Catalyst enables developers to solve complex problems with few lines of code. It also offers a range of deep learning models like one-cycle training, range optimizer, etc.

Caffe2 is a lightweight, open-source machine learning tool and an updated version of Caffe. It provides n number of machine learning libraries through which complex models can easily be built and run. It supports mobile deployment and, hence, offers higher optimization for developers. It is used in computer vision , speech recognition, translation, chatbots, IoT , and medical applications.

OpenNN is an open-source machine learning tool for neural networks, and is the most successful method of ML to implement neural networks. OpenNN is used to solve many real-world applications in the fields of marketing, health, and more. It consists of many sophisticated algorithms that help to provide solutions for artificial intelligence problems.

OpenNN is best suited for solving issues involving:

• Forecasting
• Association

Apache Spark MLlib

Apache Spark MLlib is an open-source distributed machine learning framework built on top of Apache Spark core. Since it works on in-memory computation, it is nine times faster when compared to other disk-based implementations. It has a good number of ML libraries too which makes training of machine learning models easier. It also provides algorithms such as:

• Decision trees
• Collaborative filters
• Pipeline APIs of higher levels

Other machine learning tools

There are many other machine learning tools that help build and deploy models efficiently such as:

• Theano, which can be used for limited GPU resources with high speed
• ML.NET, for .NET developers
• LightGBM, for working with large datasets
• Weka tool, which provides machine learning algorithms for data mining
• Accord.NET, which helps in image and audio processing.

As discussed, always perform a complete analysis of your requirements as well as AI problem-solving tools before choosing one. Sometimes, a well-known tool may not necessarily be the right one for your project.

Considering the sheer number of ML tools available today, selecting the best is no easy task. Each has its advantages but may not be capable of addressing all your requirements. A combination of them can sometimes be the best way to get sound results.

1. What are the main problems that AI can solve?

AI can solve many real-world problems including enabling personalized shopping, fraud detection, virtual assistance, voice assistance, spam filtering, facial recognition, and recommendation systems. It can also be applied to common game problems such as water jug, travelling salesman, magic squares, Tower of Hanoi, sudoku, N Queen, chess, crypt-arithmetic, logical puzzles, etc.

2. What are problem-solving techniques in AI?

Problems in artificial intelligence can be solved by using techniques such as searching algorithms, genetic algorithms, evolutionary computations, knowledge representations, etc.

3. What is the role of AI in real-world problem solving?

One of the biggest benefits of AI is its ability to solve many real-world problems. AI problem-solving techniques can be applied in the fields of marketing, banking, gaming, healthcare, finance, virtual assistance, agriculture, space exploration, and autonomous vehicles, to name a few.

4. What problems can AI not solve?

AI is not suitable for creating, conceptualizing, or planning strategically. It can’t deal with unstructured and unknown spaces, especially ones it hasn’t experienced before. It can’t interact or feel compassion and empathy. Without training data, it can’t do anything meaningful.

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When Should You Use AI to Solve Problems?

Not every challenge requires an algorithmic approach.

AI is increasingly informing business decisions but can be misused if executives stick with old decision-making styles. A key to effective collaboration is to recognize which parts of a problem to hand off to the AI and which the managerial mind will be better at solving. While AI is superior at data-intensive prediction problems, humans are uniquely suited to the creative thought experiments that underpin the best decisions.

Business leaders often pride themselves on their intuitive decision-making. They didn’t get to be division heads and CEOs by robotically following some leadership checklist. Of course, intuition and instinct can be important leadership tools, but not if they’re indiscriminately applied.

• Bob Suh is the founder and CEO of OnCorps, which provides AI-based decision guidance systems to the financial services industry. The firm works with leading scientists at Yale, Oxford, and Harvard to test decision making and behavioral algorithms. Previously, he was the chief technology strategist at Accenture.

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How leaders are using ai as a problem-solving tool.

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Leaders face more complex decisions than ever before. For example, many must deliver new and better services for their communities while meeting sustainability and equity goals. At the same time, many need to find ways to operate and manage their budgets more efficiently. So how can these leaders make complex decisions and get them right in an increasingly tricky business landscape? The answer lies in harnessing technological tools like Artificial Intelligence (AI).

CHONGQING, CHINA - AUGUST 22: A visitor interacts with a NewGo AI robot during the Smart China Expo ... [+] 2022 on August 22, 2022 in Chongqing, China. The expo, held annually in Chongqing since 2018, is a platform to promote global exchanges of smart technologies and international cooperation in the smart industry. (Photo by Chen Chao/China News Service via Getty Images)

What is AI?

AI can help leaders in several different ways. It can be used to process and make decisions on large amounts of data more quickly and accurately. AI can also help identify patterns and trends that would otherwise be undetectable. This information can then be used to inform strategic decision-making, which is why AI is becoming an increasingly important tool for businesses and governments. A recent study by PwC found that 52% of companies accelerated their AI adoption plans in the last year. In addition, 86% of companies believe that AI will become a mainstream technology at their company imminently. As AI becomes more central in the business world, leaders need to understand how this technology works and how they can best integrate it into their operations.

At its simplest, AI is a computer system that can learn and work independently without human intervention. This ability makes AI a powerful tool. With AI, businesses and public agencies can automate tasks, get insights from data, and make decisions with little or no human input. Consequently, AI can be a valuable problem-solving tool for leaders across the private and public sectors, primarily through three methods.

1) Automation

One of AI’s most beneficial ways to help leaders is by automating tasks. This can free up time to focus on other essential things. For example, AI can help a city save valuable human resources by automating parking enforcement. In addition, this will help improve the accuracy of detecting violations and prevent costly mistakes. Automation can also help with things like appointment scheduling and fraud detection.

2) Insights from data

Another way AI can help leaders solve problems is by providing insights from data. With AI, businesses can gather large amounts of data and then use that data to make better decisions. For example, suppose a company is trying to decide which products to sell. In that case, AI can be used to gather data about customer buying habits and then use that data to make recommendations about which products to market.

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3) Simulations

Finally, AI can help leaders solve problems by allowing them to create simulations. With AI, organizations can test out different decision scenarios and see what the potential outcomes could be. This can help leaders make better decisions by examining the consequences of their choices. For example, a city might use AI to simulate different traffic patterns to see how a new road layout would impact congestion.

Choosing the Right Tools

Artificial intelligence and machine learning technologies can revolutionize how governments and businesses solve real-world problems,” said Chris Carson, CEO of Hayden AI, a global leader in intelligent enforcement technologies powered by artificial intelligence. His company addresses a problem once thought unsolvable in the transit world: managing illegal parking in bus lanes in a cost effective, scalable way.

Illegal parking in bus lanes is a major problem for cities and their transit agencies. Cars and trucks illegally parked in bus lanes force buses to merge into general traffic lanes, significantly slowing down transit service and making riders’ trips longer. That’s where a company like Hayden AI comes in. “Hayden AI uses artificial intelligence and machine learning algorithms to detect and process illegal parking in bus lanes in real-time so that cities can take proactive measures to address the problem ,” Carson observes.

Illegal parking in bus lanes is a huge problem for transit agencies. Hayden AI works with transit ... [+] agencies to fix this problem by installing its AI-powered camera systems on buses to conduct automated enforcement of parking violations in bus lanes

In this case, an AI-powered camera system is installed on each bus. The camera system uses computer vision to “watch” the street for illegal parking in the bus lane. When it detects a traffic violation, it sends the data back to the parking authority. This allows the parking authority to take action, such as sending a ticket to the offending vehicle’s owner.

The effectiveness of AI is entirely dependent on how you use it. As former Accenture chief technology strategist Bob Suh notes in the Harvard Business Review, problem-solving is best when combined with AI and human ingenuity. “In other words, it’s not about the technology itself; it’s about how you use the technology that matters. AI is not a panacea for all ills. Still, when incorporated into a company’s problem-solving repertoire, it can be an enormously powerful tool,” concludes Terence Mauri, founder of Hack Future Lab, a global think tank.

Split the Responsibility

Huda Khan, an academic researcher from the University of Aberdeen, believes that AI is critical for international companies’ success, especially in the era of disruption. Khan is calling international marketing academics’ research attention towards exploring such transformative approaches in terms of how these inform competitive business practices, as are international marketing academics Michael Christofi from the Cyprus University of Technology; Richard Lee from the University of South Australia; Viswanathan Kumar from St. John University; and Kelly Hewett from the University of Tennessee. “AI is very good at automating repetitive tasks, such as customer service or data entry. But it’s not so good at creative tasks, such as developing new products,” Khan says. “So, businesses need to think about what tasks they want to automate and what tasks they want to keep for humans.”

Khan believes that businesses need to split the responsibility between AI and humans. For example, Hayden AI’s system is highly accurate and only sends evidence packages of potential violations for human review. Once the data is sent, human analysis is still needed to make the final decision. But with much less work to do, government agencies can devote their employees to tasks that can’t be automated.

Backed up by efficient, effective data analysis, human problem-solving can be more innovative than ever. Like all business transitions, developing the best system for combining human and AI work might take some experimentation, but it can significantly impact future success. For example, if a company is trying to improve its customer service, it can use AI startup Satisfi’s natural language processing technology . This technology can understand a customer’s question and find the best answer from a company’s knowledge base. Likewise, if a company tries to increase sales, it can use AI startup Persado’s marketing language generation technology . This technology can be used to create more effective marketing campaigns by understanding what motivates customers and then generating language that is more likely to persuade them to make a purchase.

Look at the Big Picture

A technological solution can frequently improve performance in multiple areas simultaneously. For instance, Hayden AI’s automated enforcement system doesn’t just help speed up transit by keeping bus lanes clear for buses; it also increases data security by limiting how much data is kept for parking enforcement, which allows a city to increase the efficiency of its transportation while also protecting civil liberties.

This is the case with many technological solutions. For example, an e-commerce business might adopt a better data architecture to power a personalized recommendation option and benefit from improved SEO. As a leader, you can use your big-picture view of your company to identify critical secondary benefits of technologies. Once you have the technologies in use, you can also fine-tune your system to target your most important priorities at once.

In summary, AI technology is constantly evolving, becoming more accessible and affordable for businesses of all sizes. By harnessing the power of AI, leaders can make better decisions, improve efficiency, and drive innovation. However, it’s important to remember that AI is not a silver bullet. Therefore, organizations must use AI and humans to get the best results.

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Can AI help you solve problems?

May 21, 2023 AI technology has revolutionized the way organizations do business; now, with proper guardrails in place, generative AI promises to not only unlock novel use cases for businesses but also speed up, scale, or otherwise improve existing ones. “Companies across sectors, from pharmaceuticals to banking to retail, are already standing up a range of use cases to capture value creation potential,” write Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya  in a new article . Generative AI is nascent, but as it develops and becomes increasingly, and more seamlessly, incorporated into business, its problem-solving potential will intensify. Check out these insights to understand how both AI and generative AI can help your organization solve complex problems, transform operations, improve products, and realize new revenue streams.

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Principles of Creative Problem Solving in AI Systems

Ana-Maria Oltețeanu: Cognition and Creative Machine: Cognitive AI for Creative Problem Solving. Freie Universität Berlin, Berlin, Germany, Springer, Cham, 2020 (Online ISBN: 978–3-030–30322-8), 282 pages, price: €117.69 (eBook), DOI: https://doi.org/10.1007/978–3-030–30322-8

• Book Review
• Published: 24 August 2021
• Volume 31 , pages 555–557, ( 2022 )

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• Zhihui Chen 1 &
• Ruixing Ye 1  

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The utilization of Artificial Intelligence (AI) is springing up through all spheres of human activities due to the current global pandemic (COVID-19), which has limited human interactions in our societies and the corporate world. Undoubtedly, AI has innovatively transformed our ways of living and understanding how mechanical systems work on problem solving as or even beyond human beings. The core issues of this book include the following issues: (1) understanding the working mechanism of the human mind on problem solving, and (2) exploring what it means to be computationally creative and how it can be evaluated. By having an overview of the development of AI and Cognitive Science and rebranding the strands of creativity and problem solving, Dr. Ana-Maria Oltețeanu attempts to build cognitive systems, which propose a type of knowledge organization and a small set of processes aimed at solving a diverse number of creative problems. Furthermore, with the help of the defined framework, the relevant computational system is implemented and evaluated by investigating the classical and insight problem solving performance.

Part I of this book includes the previous four chapters, which introduces a series of theories such as creativity (p.11), insight (p.16), and visuospatial intelligence (p.20) to illustrate the necessary process and structure of creative problem solving. The author concludes from the relevant literature that the interplay between knowledge representations and organization processes would play an important role in searching for solutions. For better illustration and understanding, a selection of computational creativity systems is presented, such as AM, HR, Aaron, the Painting fool, Poetry systems, and BACON (p.34–37). Subsequently, from a methodological perspective, Dr. Oltețeanu introduces two different creativity evaluations for human beings and computational machines respectively. On the one hand, when measuring creativity of human, the thinking characteristics of the participants such as divergent thinking (the ability to diverge from subjectively familiar uses and think of other uses) and creative thinking are the primary objective for measurement in some of the most important empirical models. On the other hand, when assessing the creativity in the computational systems, various models of evaluating the behaviors or programs of creative systems are proposed mainly in terms of typicality, quality, and novelty.

In the second part, which comprises chapter 5 th to 8 th , the author develops a cognitive framework to explore how a diverse set of creative problem solving tasks can be solved computationally using a unified set of principles. To facilitate the understanding of insight and creative problem solving, Dr. Oltețeanu puts forward a metaphor, in which representations are seen as cogs in a creative machine and problem solving processes are regarded as clockwork, to view the relationship between creative processes and knowledge (p.69). Building on this idea, a theoretical framework (named as CreaCogs) is proposed based on encoding knowledge, which permits processes of fast and informed search and construction, for creative problem solving. These processes take place conceptually at three levels involving Feature Spaces, Concepts, and Problem Templates (p.91–94). Firstly, whenever an object encoded symbolically is observed, its sensors will be enrolled in the sub-symbolical level of feature maps and spaces. Then, in the following level, various known concepts are grounded in a distributed manner in organized feature spaces, and their names are encoded in a different name tag mapped for functionally constituting another feature. Lastly in the highest level, problem templates are structured representations, which are encoded over multiple concepts, their relations, and the affordance they provide. On the basis of the steps above, an integration of a wide set of principles in the framework would be accessible.

Part III, which forms chapter 9 th to 12 th , mainly focuses on applying the CreaCogs in a set of practical cognitive system cases, and developing a set of tools through which the performance of such systems could be evaluated. It is worth noticing that several evaluation tests of creativity are introduced to illustrate about how to apply implementation of the framework built above. In the preamble of this part, the CreaCogs mechanism of Remote Associates creativity Test (RAT) and Alternative Uses Test (AUT) are explored to develop the corresponding computational systems to solve these test tasks. Based on the practice of implementation and investigation, Dr. Oltețeanu analyzes how to evaluate the performance of the artificial cognitive prototype systems by solving different creativity tasks via inference mechanism or matching algorithm from CreaCogs. The book ends with an overview of the journey of exploring the creative problem solving and an outlook of the relevant experimental work.

Overall, the author provides a revolutionary academic framework to understand the theoretical and empirical cognitive processes involved in creative problem solving by computational systems. Various evaluation of creativity tests and tasks are drawn to illustrate how the cognitive framework works to find solutions of classical or even insight problems, which are stressed in the 2012 paper by Batchelder and Alexander (Insight problem solving: A critical examination of the possibility of formal theory, in The Journal of Problem Solving ), as the alternative productive representations are necessary to overcome the failures of discovering solutions. Besides, it is deep insight when the author describes the cognitive models of creativity through using a variety of schematic diagrams and pictures in this book. That is rather helpful to illustrate how insight and creative problem solving can be viewed as processes of memory management, with both associationist and gestaltic (template pattern-filling) underpinnings, and with processes of recasting and restructuring using from the memory and the environment. From the theoretical matters to the variate practical domains, Dr. Oltețeanu constructs the cognitive systems on the basis of the CreaCogs and develops a set of tools through which the performance of such systems can be evaluated similarly to that of human participants. In short, the theoretical framework and empirical computational exploration contribute to creating the imagination of the efficacy of AI in the area of creative problem solving.

However, the critical issue of the possibility of developing self-adaptive learning by the creative systems has not been further discussed yet. To quote the annotation in the fields of behavioral psychology and cognitive psychology, self-adaptive learning in AI refers to human’s self-adapted learning methods and the habitual condition information processing systems, which forms a method that AI can solve theories and problems independently through discovering and summarizing in operations. Due to emphasizing to develop a framework for analyzing the creative problem solving, the author focuses on introducing the value, mechanism, application, and evaluation of the computational system based on the CreaCogs that is why the issue of self-adaptive learning has rarely been taken into account for now. In summary, this book enhances our understanding of the principles of problem solving in the epoch of AI and deserves to be widely read in this age of intelligent machines. The CreaCogs cognitive framework proposed here could be served as an applicable guide for graduate students and researchers in the sphere of Cognitive Science, AI, and Education.

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Chen, Z., Ye, R. Principles of Creative Problem Solving in AI Systems. Sci & Educ 31 , 555–557 (2022). https://doi.org/10.1007/s11191-021-00270-7

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DOI : https://doi.org/10.1007/s11191-021-00270-7

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The Intersection of Math and AI: A New Era in Problem-Solving

By Whitney Clavin, California Institute of Technology (Caltech) December 11, 2023

The Mathematics and Machine Learning 2023 conference at Caltech highlights the growing integration of machine learning in mathematics, offering new solutions to complex problems and advancing algorithm development.

Conference is exploring burgeoning connections between the two fields.

Traditionally, mathematicians jot down their formulas using paper and pencil, seeking out what they call pure and elegant solutions. In the 1970s, they hesitantly began turning to computers to assist with some of their problems. Decades later, computers are often used to crack the hardest math puzzles. Now, in a similar vein, some mathematicians are turning to machine learning tools to aid in their numerical pursuits.

Embracing Machine Learning in Mathematics

“Mathematicians are beginning to embrace machine learning,” says Sergei Gukov, the John D. MacArthur Professor of Theoretical Physics and Mathematics at Caltech, who put together the Mathematics and Machine Learning 2023 conference, which is taking place at Caltech December 10–13.

“There are some mathematicians who may still be skeptical about using the tools,” Gukov says. “The tools are mischievous and not as pure as using paper and pencil, but they work.”

Machine Learning: A New Era in Mathematical Problem Solving

Machine learning is a subfield of AI, or artificial intelligence, in which a computer program is trained on large datasets and learns to find new patterns and make predictions. The conference, the first put on by the new Richard N. Merkin Center for Pure and Applied Mathematics, will help bridge the gap between developers of machine learning tools (the data scientists) and the mathematicians. The goal is to discuss ways in which the two fields can complement each other.

Mathematics and Machine Learning: A Two-Way Street

“It’s a two-way street,” says Gukov, who is the director of the new Merkin Center, which was established by Caltech Trustee Richard Merkin.

“Mathematicians can help come up with clever new algorithms for machine learning tools like the ones used in generative AI programs like ChatGPT, while machine learning can help us crack difficult math problems.”

Yi Ni, a professor of mathematics at Caltech, plans to attend the conference, though he says he does not use machine learning in his own research, which involves the field of topology and, specifically, the study of mathematical knots in lower dimensions. “Some mathematicians are more familiar with these advanced tools than others,” Ni says. “You need to know somebody who is an expert in machine learning and willing to help. Ultimately, I think AI for math will become a subfield of math.”

The Riemann Hypothesis and Machine Learning

One tough problem that may unravel with the help of machine learning, according to Gukov, is known as the Riemann hypothesis. Named after the 19th-century mathematician Bernhard Riemann, this problem is one of seven Millennium Problems selected by the Clay Mathematics Institute; a $1 million prize will be awarded for the solution to each problem.

The Riemann hypothesis centers around a formula known as the Riemann zeta function, which packages information about prime numbers. If proved true, the hypothesis would provide a new understanding of how prime numbers are distributed. Machine learning tools could help crack the problem by providing a new way to run through more possible iterations of the problem.

Mathematicians and Machine Learning: A Synergistic Relationship

“Machine learning tools are very good at recognizing patterns and analyzing very complex problems,” Gukov says.

Ni agrees that machine learning can serve as a helpful assistant. “Machine learning solutions may not be as beautiful, but they can find new connections,” he says. “But you still need a mathematician to turn the questions into something computers can solve.”

Knot Theory and Machine Learning

Gukov has used machine learning himself to untangle problems in knot theory. Knot theory is the study of abstract knots, which are similar to the knots you might find on a shoestring, but the ends of the strings are closed into loops. These mathematical knots can be entwined in various ways, and mathematicians like Gukov want to understand their structures and how they relate to each other. The work has relationships to other fields of mathematics such as representation theory and quantum algebra, and even quantum physics.

In particular, Gukov and his colleagues are working to solve what is called the smooth Poincaré conjecture in four dimensions. The original Poincaré conjecture, which is also a Millennium Problem, was proposed by mathematician Henri Poincaré early in the 20th century. It was ultimately solved from 2002 to 2003 by Grigori Perelman (who famously turned down his prize of $1 million). The problem involves comparing spheres to certain types of manifolds that look like spheres; manifolds are shapes that are projections of higher-dimensional objects onto lower dimensions. Gukov says the problem is like asking, “Are objects that look like spheres really spheres?”

The four-dimensional smooth Poincaré conjecture holds that, in four dimensions, all manifolds that look like spheres are indeed actually spheres. In an attempt to solve this conjecture, Gukov and his team develop a machine learning approach to evaluate so-called ribbon knots.

“Our brain cannot handle four dimensions, so we package shapes into knots,” Gukov says. “A ribbon is where the string in a knot pierces through a different part of the string in three dimensions but doesn’t pierce through anything in four dimensions. Machine learning lets us analyze the ‘ribboness’ of knots, a yes-or-no property of knots that has applications to the smooth Poincaré conjecture.”

“This is where machine learning comes to the rescue,” writes Gukov and his team in a preprint paper titled “ Searching for Ribbons with Machine Learning .” “It has the ability to quickly search through many potential solutions and, more importantly, to improve the search based on the successful ‘games’ it plays. We use the word ‘games’ since the same types of algorithms and architectures can be employed to play complex board games, such as Go or chess, where the goals and winning strategies are similar to those in math problems.”

The Interplay of Mathematics and Machine Learning Algorithms

On the flip side, math can help in developing machine learning algorithms, Gukov explains. A mathematical mindset, he says, can bring fresh ideas to the development of the algorithms behind AI tools. He cites Peter Shor as an example of a mathematician who brought insight to computer science problems. Shor, who graduated from Caltech with a bachelor’s degree in mathematics in 1981, famously came up with what is known as Shor’s algorithm, a set of rules that could allow quantum computers of the future to factor integers faster than typical computers, thereby breaking digital encryption codes.

Today’s machine learning algorithms are trained on large sets of data. They churn through mountains of data on language, images, and more to recognize patterns and come up with new connections. However, data scientists don’t always know how the programs reach their conclusions. The inner workings are hidden in a so-called “black box.” A mathematical approach to developing the algorithms would reveal what’s happening “under the hood,” as Gukov says, leading to a deeper understanding of how the algorithms work and thus can be improved.

“Math,” says Gukov, “is fertile ground for new ideas.”

The conference will take place at the Merkin Center on the eighth floor of Caltech Hall.

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The 10 Best Problem-Solving Software to Use in 2024

Engineering Team

May 13, 2024

Do you want a solution to help your teams work well together, reduce friction, and speed up productivity?

The best problem-solving software has all the answers for you. Problem-solving software helps find bottlenecks, simplify workflows, and automate tasks to improve efficiency. The result? Communication is easy, and your team enjoys a collaborative work environment.

Problem-solving software gives you the right visualization tools and techniques to better articulate your ideas and concepts.

That’s not all; it also automates repetitive tasks while your team focuses on brainstorming and ideating. 

In this article, we’ll cover the best problem-solving software and highlight its various features, limitations, customer ratings, and pricing details to help you make an informed decision. 

What Should You Look For In Problem-Solving Software? 

1. clickup , 2. omnex systems , 5. meistertask, 6. teamwork, 10. airtable .

Businesses encounter many challenges, from operational inefficiencies and customer complaints to financial discrepancies. 

As your team slowly navigates through these issues, having problem-solving software with the right features will reduce the hassle. Before investing in one, consider some of these following factors:

• User-friendly interface: The software should have an intuitive and easy-to-use interface to minimize the learning curve for users
• Versatility: Look for software that addresses various problem types and complexities. It should be adaptable to different industries and scenarios
• Mind maps and Visualization features: Get yourself problem-solving software solutions that offers mind maps and other visual tricks. It must be a digital canvas for your team to brainstorm ideas, connect the dots, and execute strategies
• AI assistant: If your team is stuck with repetitive mundane tasks, then it’s time you let AI take over. With the right problem-solving tool comes in-built AI that handles  everyday tasks, leaving your team to focus on the important stuff
• Automation capabilities:  Look for problem-solving process that’s all about automation. This way, you ensure efficiency and effectiveness without the grunt work
• Goal tracking: Your efforts improve when you optimize your tracking process. You need goal monitoring and tracking features to ensure you are on track 
• Cost-effectiveness: Look for the features that various plans offer and compare them to choose an option that provides maximum features while the benefits justify the cost 

The 10 Best Problem Solving Software In 2024

While you have many options, select the one with the right features that suit your needs . 

Check out our list of the ten best problem-solving tools to ensure you have the features to solve complex issues effectively: 

Henry Ford once said that success takes care of itself if everyone moves forward together. ClickUp problem-solving software helps you succeed by ensuring all your team members are always on the same page. 

With its live collaboration, you can see if your teammates are looking at or editing documents. Also, edit documents together in real-time. Moreover, any changes on any device are updated instantly, so nobody falls behind. 

The whiteboard feature is super helpful in getting your team together for brainstorming and ideating. As problem-solving involves generating and evaluating multiple ideas, the whiteboard helps write, modify, and build ideas together. 

Now that you have brainstormed on core problems, you must establish a clear visual reference point for ongoing analysis. That’s where the ClickUp mind maps feature stands out. Create a hierarchical structure, with the main problem at the center and subtopics branching out.

Since these maps have interconnections, it is easy to visualize connections between different elements. This feature effectively identifies possible cause-and-effect relationships in a problem.

ClickUp best features 

• Documentation: Address and solve problems by storing and accessing project-related documents in ClickUp Docs
• Mind maps : Identify critical connections, uncover insights, and implement creative approaches by visually mapping relationships between concepts and information with ClickUp Mind Maps
• Task prioritization: Make problem-solving easier for your software developers—sort tasks by urgency. This helps your team focus on the most crucial aspects, making problem resolution more efficient 
• Virtual whiteboards: Enhance collaborative problem-solving and critical thinking through ClickUp Whiteboards . Brainstorm, visualize ideas, and collectively work towards solutions in an interactive setting 
• Goal monitoring: Set and monitor business metrics to address challenges, track progress, and ensure the software development team remains aligned with objectives 
• Custom access rights: Customizing access rights ensures that the right individuals have the necessary permissions to contribute to problem resolution 
• ClickUp AI: Use ClickUp AI to automate repetitive tasks, analyze data for insights, and enhance productivity in tackling complex problems 

ClickUp limitations 

• Learning curve is involved in fully grasping all features and capabilities

ClickUp pricing

• Free Forever Plan 
• Unlimited Plan: $7 per month per user
• Business Plan : $12 per month per user
• Business Plus Plan : $19 per month per user
• Enterprise Plan : custom pricing 
• ClickUp AI: $5 per Workspace on all paid plans

ClickUp ratings and reviews

• G2: 4.7/5 (2,000+ reviews)
• Capterra: 4.7/5 (2,000+ reviews)

Omnex’s problem-solving software has many helpful features to track, manage, and solve problems quickly. It’s a one-stop shop for dealing with internal and external issues. 

The platform is also customer-centric, which responds to customers in their preferred formats. This ensures a tailored and user-friendly experience, further enhancing problem resolution through seamless interaction with stakeholders. 

Omnex best features 

• Define timelines and metrics for problem resolution 
• Leverage several problem-solving tools, such as 5Why, Is/Is Not, etc
• Respond to customers in various formats, including 8D, 7D, and PRR

Omnex limitations

• Initiating projects involves many steps
• Temporary delays may occur

Omnex pricing 

• Omnex has custom pricing plans 

Hive is another excellent platform to instruct your teams better while solving complex challenges and enhancing their problem-solving skills. It’s highly interactive and lets all your team members view what’s happening and express their opinions simultaneously. 

Collaborative work management helps you solve issues effectively. Hive is your virtual file cabinet where sharing documents with different teams and collaboratively working becomes more accessible. 

Hive best features

• User-friendly interface ensures seamless navigation
• Gantt view helps in mapping out project timelines
• Project hierarchies allow for easy task execution
• Kanban view allows you to understand progress better

Hive limitations 

• Being a relatively new tool, it needs frequent updates and additional features
• There are occasional bugs that slow down processes
• Locating notes from tasks and meetings is time-consuming
• Auto-generated reports are not always accurate
• Apart from ticketing, the platform needs some intuitive features

Hive pricing 

• Teams: $12 per month per user
• Enterprise: custom plans

Hive customer ratings

• G2: 4.6/5 (480+ reviews)
• Capterra: 4.5/5 (190+ reviews)

Asana is a popular problem-solving tool that speeds up decision-making . It improves project management , and its many integrations are useful. The well-organized project documents make it easy to find what you need quickly.

It’s excellent for managing many small projects and suitable for teams without complex workflows or collaboration features.

Asana best features 

• The rules and workflow feature helps automate repeating activities
• Customizable workflows help teams adapt the tool to their unique needs
• For easy understanding, organize tasks as a list, calendar, timeline, Gantt chart, or Kanban board
• Integrate with popular tools and apps such as Google Drive, Dropbox, Slack, Zoom, Microsoft, etc. 

Asana limitations

• Inefficient for handling larger projects with sub-projects and multiple workstreams
• Limited capability to measure project deviations from original plans
• Lack of comprehensive workflows and customizable animations, a feature some competitors offer
• Pricing is less favorable for smaller teams; advanced features like custom fields, portfolios, and timeline views are only available in premium plans

Asana pricing 

• Personal (free)
• Starter: $10.99 per month per user
• Advanced: $24.99 per month per user 

Asana customer ratings 

• G2: 4.3/5 (9,520+ reviews)
• Capterra: 4.5/5 (12,290+ reviews) 

Mesitertask is one of those problem-solving tools that offers strong kanban boards. These boards visualize the workflow and make it easier to identify bottlenecks and trace issues back to their source. Such visualizing features are similar to the ones found in the best root cause analysis tools . 

A customizable drag-and-drop feature further allows users to rearrange and prioritize tasks easily. Therefore, your team members will easily play around the field and segregate tasks effectively. 

Meistertask best features 

• Gain a visual representation of task timelines with a timeline view
• Streamline processes with automated workflows
• Easily categorize and prioritize tasks within sections 
• Monitor and analyze time spent on tasks for valuable insights

Meistertask limitations 

• Unnecessary negative space impacts task visibility
• Limited report and analytics features, not accessible offline
• Confusing registration process

Meistertask pricing

• Basic (free)
• Pro: $6.50 per month per user 
• Business: $12 per month per user 
• Enterprise: custom pricing 

Meister task ratings and reviews 

• G2: 4.6/5 (170+ reviews)
• Capterra: 4.7/5 (1130+ reviews) 

Teamwork is another viable problem-solving software dealing with operational challenges. It provides a clear overview of task assignments, project profitability, and other essential details. 

When combined with effective brainstorming techniques , such a clear division of work will help you solve complex issues faster. 

Teamwork features 

• Get four distinct project views, including List, Table, Boards, and Gantt
• Efficient task management simplifies the process of creating and assigning tasks to users, enhancing team collaboration  
• The time tracking feature helps determine billable hours, aiding in project budgeting and resource allocation
• Standard communication features, such as commenting and mentioning coworkers, are seamlessly integrated, promoting practical collaboration 

Teamwork limitations 

• You need to subscribe to premium plans to unlock advanced features
• The user interface is intricate and poses a challenge for some users
• Certain features, like the reminder function, do not operate on mobile apps
• Continuous email notifications have the potential to disrupt focus, as not all updates or status changes are crucial

Teamwork Pricing 

• Free Forever
• Starter: $5.99 per month per user 
• Deliver: $9.99 per month per user
• Grow: $19.99 per month per user 
• Scale: custom pricing 

Teamwork Customer Ratings 

• G2: 4.4/5 (1,070+ reviews)
• Capterra: 4.5/5 (830+ reviews)

Trello is another good option if you are searching for efficient problem-solving software. With powerful task management tools, it ensures you handle your issues efficiently. 

However, Trello’s communication and collaboration tools are not up to the mark compared to other problem-solving tools. Also, it relies heavily on integrations to do the heavy lifting.

Trello Features 

• Streamline your workflow effortlessly by arranging tasks with a simple drag-and-drop interface
• The project map feature gives a complete overview to help you visualize tasks, dependencies, and progress at a glance
• Focus on what matters the most and prioritize tasks effectively with its intuitive tools 
• Stay on top of your responsibilities with dynamic to-do lists

Trello Limitations 

• The free version imposes limitations on file attachments, a lack of advanced integrations, and automation
• Manually arranging Trello cards one by one is a time-consuming task
• There is a lack of functionality for creating a comprehensive dashboard or Gantt chart to provide a clear overview
• The absence of restrictions on card movement poses a security risk, with anyone accessing and potentially disrupting the board
• Trello becomes less practical when the board becomes densely populated with cards

Trello pricing 

• Standard: $5 per month per user 
• Premium: $10 per month per user 
• Enterprise: $17.50 per month per user 

Trello customer ratings 

• G2: 4.4/5 (13,000+ reviews)
• Capterra: 4.5/5 (23,000+ reviews)

Wrike is one of the preferred project management collaboration tools that help businesses of all sizes. With preconfigured templates for tasks, workflows, and communication, it takes the burden off your shoulders. 

It also has a user-friendly dashboard with enterprise-grade tools to manage recurring and one-time projects. 

Wrike best features 

• Planning tools to outline tasks, set deadlines, and allocate resources
• A clear visual overview helps in identifying potential challenges
• Detailed reports to analyze project performance
• Helps efficiently address issues by prioritizing tasks

Wrike limitations 

• There are no options to view projects on the Kanban board (only tasks)
• Basic project management features are missing, such as time breaks for a task
• Pricing remains on the higher end

Wrike pricing 

• Professional variant: $9.80 per month per user 
• Business variant: $24.80 per month per user 

Wrike customer ratings 

• G2: 4.2/5 (3500+ reviews) 
• Capterra: 4.3/5 (2540+ reviews) 

Monday is a cloud-based open platform, allowing businesses to collaborate better on projects. Explore many pre-built templates or create one from scratch depending on what you need. 

Monday best features

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10 Best Revolutionary Math AI Tools for Students in 2024

By: Author Alex Lim

Posted on Last updated: May 18, 2024

Home > 10 Best Revolutionary Math AI Tools for Students in 2024

In the rapidly evolving world of educational technology, math AI tools have emerged as indispensable resources for students striving to overcome the challenges of mathematics. These tools not only offer solutions but also enhance understanding through detailed explanations and step-by-step guidance.

Among these innovations, Mathful stands out as the leading platform, thanks to its comprehensive features, accuracy, and user-friendly interface. This listicle explores the top 10 math AI tools that are transforming the way students learn mathematics, with a special focus on their unique features, including photo math solvers, calculators with steps, and overall effectiveness in solving a wide range of math problems.

Table of Contents

Mathful: A Comprehensive Math AI Solver and Calculator with Steps

The pinnacle of math problem solving, key features, why mathful tops the list, numworks: interactive math learning, photomath: visual math assistance, symbolab: advanced math solver, wolfram alpha: comprehensive computational tool, desmos: interactive graphing calculator, mathway: versatile problem solver, khan academy: comprehensive learning resource, cymath: math problem solver with steps, microsoft math solver: ai-powered learning tool.

Mathful distinguishes itself as the premier math AI platform, offering unparalleled precision and a vast array of features designed to cater to every student’s needs. Its standout offerings include a highly accurate math AI solver, an innovative photo math solver and a step-by-step math calculator that together provide a holistic learning experience.

• 98% Accuracy: Mathful’s algorithms ensure solutions are both precise and reliable, making it a trustworthy companion for students.
• Photo Math Solver: This cutting-edge feature allows students to upload images of math problems, receiving instant, detailed solutions.
• Calculator with Steps: Unlike traditional calculators, Mathful explains each step of the solution process, fostering a deeper understanding of mathematical concepts.
• 24/7 Solutions: Mathful’s round-the-clock availability means students can find help whenever they need it, regardless of time constraints.
• Comprehensive Coverage: From basic arithmetic to advanced calculus, Mathful supports a wide range of math topics, making it suitable for students at all levels.

Mathful is not just a tool; it’s a comprehensive educational platform that empowers students to master mathematics with confidence. Its emphasis on accuracy, detailed explanations, and user safety makes it the go-to resource for students worldwide.

NumWorks offers a graphing calculator designed to make complex mathematics more approachable for students. It’s known for its user-friendly interface and the ability to handle a variety of math functions, including calculus and statistics.

Photomath is popular among students for its ability to solve math problems using the camera on a smartphone. It provides step-by-step instructions, making it a helpful tool for visual learners.

Symbolab excels in solving advanced mathematics, including calculus, trigonometry, and algebra. It’s particularly appreciated for its detailed step-by-step solutions, which aid in understanding complex problems.

Wolfram Alpha is more than just a math solver; it’s a computational knowledge engine capable of answering questions across a wide range of subjects. Its math capabilities are extensive, offering solutions and explanations for higher-level math problems.

Desmos provides an advanced graphing calculator that allows students to visualize and interact with math concepts. It’s particularly useful for understanding functions and graph-related problems.

Looking for information on variable acceleration but don’t know where to find it? We’ve got the help you need right here .

Mathway is known for its versatility, offering solutions to problems across various math topics, including basic math, algebra, and calculus. It’s a handy tool for quick answers and understanding the steps involved in solving problems.

Khan Academy doesn’t just solve math problems; it offers a full-fledged learning experience with video lessons, practice problems, and personalized learning dashboards across a wide range of subjects, including mathematics.

Cymath offers a simple interface for solving math problems, providing step-by-step solutions that help students understand the methodology behind the answers. It covers topics from algebra and calculus.

Microsoft Math Solver utilizes advanced AI to provide solutions to various math problems, including arithmetic, algebra, and calculus. It offers step-by-step explanations, interactive graphs, and even video tutorials for a comprehensive learning experience.

In the realm of educational technology, these top 10 math AI tools offer a range of solutions tailored to different learning needs and preferences. Mathful, with its comprehensive features, accuracy, and ease of use, stands out as the premier choice for students seeking to enhance their math skills. Whether it’s through solving complex equations, visualizing problems, or interactive learning, each platform has something unique to offer, contributing to a richer, more accessible mathematical education for students everywhere.

Meet our funky new math teacher - an AI-powered whiz kid! From algebra to geometry, there's no problem too big for our digital Einstein. So buckle up and get ready to enjoy a wild math ride with the coolest teacher in town!

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Learn more about math ai:, what is genius mode.

It is an enhanced version of AI Chat that provides more knowledge, fewer errors, improved reasoning skills, better verbal fluidity, and an overall superior performance. Due to the larger AI model, Genius Mode is only available via subscription to DeepAI Pro. However, the added benefits often make it a worthwhile investment.

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10 Best Math AI Solvers to Solve Math Problems Online

Tackling challenging math problems can be a time-consuming endeavor. Math AI solvers make learning math simple. These AI-powered tools use machine learning and advanced algorithms to rapidly analyze math problems at all grade levels. Whether you’re a high school student or at the college level, an AI math problem solver can help save you time, ensure accuracy, and boost your school success.

With 24/7 convenience, they can be used anytime to get instant help with confusing math problems. When curating our list of top 10 best math AI solvers, we looked at several important factors, such as pricing, features, strengths, and weaknesses. Let’s explore these tools and learn in which areas they excel:

Mathful – Best Overall Math AI Solver to Solve Math Problems

Mathful is a valuable AI math solver often used by students to get help with complicated math problems. It delivers step-by-step guidance that breaks down the problem to make it easier to digest. The tool also provides the final answer to allow students to verify their solutions and gain confidence in their math skills.

Mathful is available 24/7, making it a convenient tool for students to get instant help with their math homework. Mathful acts as the ultimate solution for boosting academic success at all math levels. 

• Helps students gain a deeper understanding of math concepts with step-by-step guidance.
• Serves as a verification tool where students can check their work without costing a dime. 
• Enables students to complete their math homework with just one click. 
• Cannot be used to supplement a real classroom education. 

Solve Math Problems Instantly with Mathful’s AI Math Solver >>>

AI Math – Best AI Math Solver for All Grade Levels

AI Math is an innovative math AI solver designed to solve a wide range of mathematical problems, ranging from basic math concepts to more challenging equations. 

The way this unique tool works is by analyzing the math problem, finding potential solutions, validating those solutions, and returning the best possible results for the given problem.

While some math AI tools are only capable of solving basic math problems, AI Math is more diverse. Use it to solve math problems at all grade levels, from elementary to university and beyond. AI Math is trained on an extensive database of mathematical information and can guarantee 98% accuracy.

• Generates solutions to math problems with a high rate of accuracy.
• Designed to provide almost instant results for efficiency. 
• Covers all types of math and homework at all grade levels. 
• Requires a subscription after the initial trial period.

HIX Tutor – Best AI Math Solver for High Accuracy

HIX Tutor provides students with the solutions they need to understand and solve their math problems. 

With this powerful math solver AI tool, users can type in a math question or upload a document or image of the question. Using this input, Math AI Solver uses machine learning technology to perform dynamic calculations and give users the correct answer. 

HIX Tutor can provide solutions for math homework problems at all levels with a 98% accuracy rate. However, the tool goes beyond mere problem-solving by offering comprehensive calculations on how to solve the problem from start to finish. 

• Provides step-by-step solutions to math problems.
• Is less expensive than hiring a math tutor.
• Can type in math questions or upload documents or images of the problem.
• May have difficulty recognizing documents with messy handwriting.

HomeworkAI – Best All-in-One Solution for Homework Help

HomeworkAI is the ultimate homework assistant that can be used to get answers to any homework question, from math and science to history and geography. Its math AI solver acts as your personal AI tutor by providing comprehensive solutions to math problems that help students better understand the material.

This unique AI tool is available 24/7, making it a convenient and accessible resource for students who need immediate help with their homework. As the tool is free to try, students can get started right away by asking a math question that they’re struggling with. HomeworkAI can also help students prepare for important tests or exams by providing step-by-step instructions on how to work through certain math questions.

• Covers all school subjects, including math.
• Provides step-by-step explanations for each math problem.
• Supports multiple math problem input methods. 
• Some math solutions may be difficult to understand. 

Question AI – Best Homework AI Tool for Greater Academic Success

Question AI is a leading homework helper that uses powerful AI technology to instantly help with any school subject. Question AI is often used for any math problems, covering algebra, geometry, calculus, arithmetic, and trigonometry. Students often waste hours struggling with challenging homework questions they simply don’t understand. Question AI simplifies the homework process by breaking down the question to make it easier to learn. The math AI solver also promises a 99% accurate answer to the math question, allowing students to verify their own work.

Question AI serves as an innovative all-in-one product for AI homework help. It is a highly effective tool for helping students reach greater academic achievement.

• Covers all homework subjects, including all branches of mathematics. 
• Boosts student grades.
• Supports many different languages.
• Accuracy may be limited beyond core math subjects.
• The mobile app is not yet available. 
• Costs money after the free trial. 

QuickMath – Best Math AI Solver for High School and College Students

QuickMath is designed to automatically answer common math problems in algebra, calculus, and equations. The tool offers several options for solving math problems, including solving an equation, inequality, or a system. It can also simplify an expression, factor an expression, or expand a product or power. The diverse AI math solver can also graph, find the greatest common factor, and the least common measure. Advanced math solvers are also available for more complex math problems.

QuickMath is geared toward high school and college-level students. The tool’s features and capabilities are always being improved to ensure that users have access to the most comprehensive AI math tools available. 

• Offers a wide range of standard and advanced math solvers.
• Available to download on the App Store and Google Play.
• Quick-solve tutorials are available to help users learn math concepts. 
• Math problems must be typed in and cannot be uploaded. 
• Not suitable for grade levels lower than high school.
• Some users may receive Timeout messages due to 15-second CPU time rules.

Smodin – Best AI Math Solver for Acing Math Exams

Smodin Math AI Homework Solver offers a fast and effective way to prepare for difficult math exams. The versatile homework helper uses machine learning and advanced algorithms to help students learn core math concepts and boost their grades in school. Simply input a question or assignment and Smodin instantly generates step-by-step solutions.  The math solver AI tool consists of a simple interface that allows students to enter a question to solve. At the click of a button, the tool starts working to analyze the problem and present the best answer based on the equation. 

While Smodin Math AI Homework Solver does lack advanced features and capabilities, it can be a useful tool for students who want to study before a big math test.

• Provides in-depth answers to math questions to help students ace math exams.
• Users are given 3 free credits a day to ask math questions. 
• The clean interface makes it easy to enter math questions to solve. 
• Offers no advanced math features or capabilities.
• Users must pay for a subscription after reaching the daily credit limit. 

StudyMonkey – Best AI Math Problem Solver for Personalized Learning

StudyMonkey is a diverse homework helper for students that uses complex machine learning capabilities to provide step-by-step guidance on many school subjects, including math.  The innovative math AI solver can be used by learners of all backgrounds and ages to improve their understanding of math concepts and enhance their academic success. With round-the-clock availability, this unique tool can save students significant time when completing homework assignments.

• Students can personalize their output based on the subject and grade level. 
• There is a free plan for users that only requires occasional homework help.
• Users can see a history of past questions and answers. 
• Users must purchase a paid plan to ask more than 3 questions daily.
• Answers to more complex math questions may not be as accurate. 
• Must upgrade to the $8 plan to use the Advanced AI Model. 

Interactive Mathematics – Best Math AI Tool for Solving Word Problems

Interactive Mathematics claims to be more accurate than ChatGPT, faster than a math tutor, and more powerful than a math calculator. The advanced AI math problem solver has been trained in a wide array of math subjects, such as basic algebra, advanced algebra, trigonometry, geometry, calculus, advanced calculus, physics, and more. It is also highly effective at solving math word problems with step-by-step solutions. 

With Interactive Mathematics, users can type in their math problems or copy and paste a photo. The tool then analyzes the problem and delivers near-instant results. 

• Efficient at solving math word problems.
• Optimized for mobile phones and tablet use.
• A subscription comes with additional discounts and bonuses. 
• Users can only ask 3 questions before having to upgrade to the paid version. 
• No app is yet available. 
• Pushes tutoring services when the tool is unable to give an accurate answer. 

SnapXam – Best AI Math Solver for On-the-Go

SnapXam is an AI-powered math tutor that can help students better understand different types of math, from calculus to trigonometry.  After entering the math problem, the tool quickly presents the final answer, as well as a step-by-step solution that shows how to work out the problem.

The unique AI math problem solver also provides access to informative YouTube videos that students can watch to learn more about different concepts in math. 

Upgrading to a Premium account can give students access to even more features and perks, including no ads, support for more than 100 math topics, and premium access to the iOS and Android apps.

• Students can get AI math help with only access to a smartphone. 
• Offers multiple solving methods.
• Covers more than 100 different math topics. 
• An ad-free environment is only available with the premium version.
• Geared toward older students. 
• Accuracy issues may occur with advanced math problems.

Completing math assignments can be a long and tedious process. Having access to the best math AI tools can help eliminate the stress and frustration that come with not understanding math problems. For convenience, efficiency, and unmatched accuracy, try some of the top math AI tools like Mathful for free today!

This is a guest post, created for informational purposes only, and should not be considered as professional advice. Readers are encouraged to conduct their own research and consult with relevant experts before making any financial or investment decisions.

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Word Problem Solver is a specialized GPT designed to assist users in tackling mathematical word problems with ease. Leveraging the robust capabilities of ChatGPT, this tool offers step-by-step explanations to guide users through the problem-solving process.

Users engage with the Word Problem Solver through prompts that frame their mathematical queries, such as ‘Help me solve this math problem’ or ‘Can you explain the steps to solve this problem?’. By providing clear and concise prompts, users can effectively communicate their mathematical challenges to the GPT.

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Computer Science > Artificial Intelligence

Title: fusion intelligence: confluence of natural and artificial intelligence for enhanced problem-solving efficiency.

Abstract: This paper introduces Fusion Intelligence (FI), a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms such as bees and ants are integrated with the computational power of Artificial Intelligence (AI). This interdisciplinary field seeks to create systems that are not only smart but also adaptive and responsive in ways that mimic the nature. As FI evolves, it holds the promise of revolutionizing the way we approach complex problems, leveraging the best of both biological and digital worlds to create solutions that are more effective, sustainable, and harmonious with the environment. We demonstrate FI's potential to enhance agricultural IoT system performance through a simulated case study on improving insect pollination efficacy (entomophily).

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