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2. Symbolab - Wikipedia

   en.wikipedia.org/wiki/Symbolab

   Symbolab is an answer engine [1] that provides step-by-step solutions to mathematical problems in a range of subjects. [2] It was originally developed by Israeli start-up company EqsQuest Ltd., under whom it was released for public use in 2011. In 2020, the company was acquired by American educational technology website Course Hero. [3] [4]

3. QANDA - Wikipedia

   en.wikipedia.org/wiki/QANDA

   QANDA (stands for 'Q and A') is an AI-based learning platform developed by Mathpresso Inc., a South Korea-based education technology company. Its best known feature is a solution search, which uses optical character recognition technology to scan problems and provide step-by-step solutions and learning content.

4. Microsoft Math Solver - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Math_Solver

   Microsoft Math contains features that are designed to assist in solving mathematics, science, and tech-related problems, as well as to educate the user. The application features such tools as a graphing calculator and a unit converter. It also includes a triangle solver and an equation solver that provides step-by-step solutions to each problem.

5. Photomath - Wikipedia

   en.wikipedia.org/wiki/Photomath

   Photomath is an educational technology mobile app, owned by Google.It features a computer algebra system with an augmented optical character recognition system, designed for use with a smartphone's camera to scan and recognize mathematical equations; the app then displays step-by-step explanations onscreen.

6. WolframAlpha - Wikipedia

   en.wikipedia.org/wiki/WolframAlpha

   WolframAlpha (/ ˈ w ʊ l f. r əm-/ WUULf-rəm-) is an answer engine developed by Wolfram Research. [3] It is offered as an online service that answers factual queries by computing answers from externally sourced data.

7. OpenAI o3 - Wikipedia

   en.wikipedia.org/wiki/OpenAI_o3

   Reinforcement learning was used to teach o3 to "think" before generating answers, using what OpenAI refers to as a "private chain of thought".This approach enables the model to plan ahead and reason through tasks, performing a series of intermediate reasoning steps to assist in solving the problem, at the cost of additional computing power and increased latency of responses.