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Getting complex mathematical and scientific problems solved using freelancers

A complex math and science problem shouldn't stop you from growing your business. We show you how to solve your complex problems with freelancers.
31. mar. 2020 • 6 minutt lesetid

Don't let complex math and science problems slow you down. 

Your business is limited by the problems it's capable of solving. So if your business is capable of solving every complex problem it encounters, wil that make it limitless? Theoretically yes.
While we can't promise that this article will make your business limitless, it will help you identify the specific solution processes you need to solve complex math and scientific problems. We'll even show you how to save a tonne of money getting freelancers to help solve these problems for you. 

General programming

Many complex mathematical and scientific problems can be solved with programming languages. Python and C++ are often used to solve complex problems.
If your problems consist of differential equations, probability analysis, data rearrangement or any other solution processes heavily dependent on manual human input and interpretation a programmer can help you solve them. 
Here is an example of how you can solve differential equations with Python:

Machine learning

Machine learning is the application of algorithms and statistical modelling to teach computers how to solve problems autonomously. So you're basically telling algorithms what your problem is, feeding them a whole bunch of data to give them context and then sitting back and waiting for them to come up with your desired results.
The fascinating thing about machine learning is, as the name suggests, machines actually learn how to think for themselves. They learn from past experiences and continually adjust their methods until their results are as accurate as possible.
There are two primary branches of machine learning, supervised learning and unsupervised learning. 
Supervised machine learning creates predictive models based on the data you feed into the algorithms. So if you wanted to create software that identifies parrots in photos, you would first train the algorithms to identify the key features of a parrot by exposing them to a whole heap of parrot photos. 
Then, each new photo the software analyzes is compared against these original parrot photos to determine the likelihood of a parrot being in the picture. The outcomes will not be perfect to begin with, but the supervised machine learning algorithms will keep adjusting the solution process until the variables identifying a parrot have an optimal weight distribution and the final prediction is as accurate as possible.
Unsupervised machine learning identifies patterns in a dataset, this is usually achieved with a technique known as cluster analysis. Cluster analysis groups related data points together to form a 'clearer story' of what the overall data set is representing. It's great for giving insights on data distribution, such as optimal electrical charging station locations based on the distribution of electric car owners in a region. 

Solving scientific problems with machine learning

At the time of writing this article, currently the most popular application of machine learning in the scientific industry is assisting with the response to the Coronavirus.
Here are just a few example of how machine learning is helping with this:
Forecasting infection rates
Predicting the effectiveness of different social distancing options
Predicting the interaction of chemical compounds with different protein structures to recommend the most effective vaccine.
Here are some other machine learning applications in science.
Cancer prognosis and prediction.
Medical imaging, to track the real time progression of cancerous cells
Remote monitoring solutions to monitor the wellbeing of seniors and discharged patients.
Radiotherapy to identify and classify medical conditions with a higher degree of accuracy.

Solving mathematical problems with machine learning

Here are some way machine learning is used to solve complex mathematical problems:
Symbolic integration and solving differential equations.
Mathematical word problem solving using transformer models. 
Here is a great video from Google summarizing mathematical problem solving capabilities of machine learning:

Artificial Intelligence

Though often used interchangeably, artificial intelligence and machine learning are two different things. Machine learning is a subset of artificial intelligence.
machine learning vs ai
So in other words, artificial intelligence decides how machine learning algorithms should be best implemented.
The mechanism of artificial intelligence was inspired by the function of the human brain, this is especially evident in areas such as deep learning and artificial neural networks.
If this field of technology is foreign to you, the following video is a great introduction:

Solving scientific problems with artificial intelligence

Since machine learning is a subset of AI, Artificial intelligence is also being leveraged to aid the fight against Covid-19. A number of crowdsourcing projects have been launched to tap into the global data science community to predict key Coronavirus trends with AI. 
Here are some other ways AI is being used to solve scientific problems:
To test hypotheses about different astronomical processes in the universe
Autonomous driving technology.
To predict weather patterns and autonomously adjust heating and cooling to user requirements.
Modelling and computation of biological systems.
The following videos summarizes some of the other exciting applications of AI in science and technology.

Solving mathematical problems with artificial intelligence

Here are a couple examples of the application of AI in mathematics.
Visual analysis is an exciting application of AI that allows humans to effectively work alongside computers by visualising the data being processed. This level of transparency allows humans to manage their data queries in a highly efficient manner.
Reinforcement learning is a more sophisticated modification of the supervised learning branch of machine learning. With the assistance of AI technology, computers can be trained to apply their machine learning training to unrehearsed situations, such as drone rescues in foreign evenements.

Augmented reality

Since it's worldwide recognition from the mobile game sensation Pokemon Go, augmented reality continues to evolve in the fields of science and mathematics transforming problem solving into an immersive and interactive experience

Solving scientific problems with augmented reality 

The most popular application of augmented reality in science is as a teaching tool.
The video below outlines the incredible capabilities of AR to help explaining scientific concepts to young children
AR technology can be used as a teaching aid for just about any area of science and technology, no matter how complicated. The following video demonstrates how AR is used at UCLR to give their students a highly interactive learning experience.

Solving mathematical problems with augmented reality 

Though this isn't an example of solving a complex math problem, it demonstrates the incredible  possibilities of this technology as it continues to develop. Watch as this app solves a math problem just by capturing an equation through a mobile camera.
Augmented reality is expected to completely rredefine the way maths is taught so not only will this technology be capable of eventually solving complex equations by simply pointing a mobile device at it, it will aid in the explanation of complex mathematical concepts to the layperson.  

Hiring freelancer to solve complex math and science problems

The humble calculator might not strike you as a very innovative solution because we've grown accustomed to it. If you can't mentally calculate your shopping list total, you can just flip out your phone, open the calculator app and start poking at it. We don't even take note of this natural reaction anymore, yet only 50 years ago, before the invention of the digital calculator, problem solving habits looked very different.
Basic calculators on mobile devices can now be instantly transformed into scientific calculators by just changing the orientation of the phone, so while standing in line at the checkout you could solve complex differential equations if you wanted to. 
With that amount of calculative power at our fingertips at all times, we have a lot more cognitive power than our relatives 50 years ago. 
Scientific and mathematical organisations should follow this adoption trend by integrating the complex solution models mentioned in this article. By doing so, your business will become capable of solving any complex problem you encounter, much faster than your competitors that refuse to innovate.
To capitalize on the potential of these complex problem solving mechanisms, an efficient means of implementation is necessary. 
The solution?
Freelancers experts in machine learning, AI and augmented reality can now be sourced from anywhere in the world within a matter of minutes, and they can be hired on a project by project basis so you can strategically build out your complex solution options as you need them.
If you're unsure about which specific talent you might need to create your unique problem solving mechanism, a Technical Co-Pilot will help point you in the right direction. 

Final thoughts

Technology has evolved to a point where there is a possible solution process to almost every problem. Bridge the gap between your complex mathematical or scientific problems and their solutions with expert freelancers today!
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