MIT Solve

Solution Overview

Solution Name:

Recipe Machine Learning

One-line solution summary:

Using recipes for cooking as way to teach the basics of computer programming as well as the machine learning in terms of "Perceptron".

Pitch your solution.

What if women who prepare meals for their families in villages can be introduced to analogies of cooking to the cutting edge in computer technology. There is a digital divide between those who have digital technology skills and those who don't. The proposed solution is an online course which would teach computer programming by means of analogies to cooking to programming as well the perceptron, a key part of neural networks. By introducing the cutting edge of machine learning with analogies to a subject a homemaker may be familiar with, women in remote areas of the world may gain a familiarity as well as an interest in learning more about this job skill.

Machine Learning: The Opposite of Prejudice Work Best

The course presents how machine learning with a focus on training/testing data, and how the opposite of prejudice works best in ML.

Film your elevator pitch.

What specific problem are you solving?

Research has shown that, among men and women, women tend have less of an interest in computer science degrees. However, women tend to vulnerable to disruptions in the job market. Women in rural, remote communities in various parts of the world are often traditionally preparing food for their families. What if this experience with food preparation can be a resource for gaining digital workforce skills?

What is your solution?

There is a digital divide between those who have digital technology skills and those who don't. The proposed solution is to teach computer programming and data analytics by means of an online course.

The method of teaching is by means of analogies from cooking and similar activities to data analytics. For example, both require substantial time for preparation. Along with this, ingredients for cooking are inputs added in specific measurements or portions. With machine learning and what is called the "perception", there is something analogous to this in terms of inputs and the weighted input (essentially measured inputs). Please the video above in the Film Your Elevator Pitch section.

Who does your solution serve, and in what ways will the solution impact their lives?

The target population is for those who prepare food, especially housewives, interested in learning computer science, especially the field of data analytics, as a new job skill. By relating to analogies to food preparation, a new skill can be presented based upon something the students are familiar with. This addresses the needs for those in this demographic who seek digital workforce job skills.

Which dimension of the Digital Workforce Challenge does your solution most closely address?

Reduce inequalities in the digital workforce for historically underserved groups through improved hiring and retention practices, skills assessments, training, and employer education and engagement

Explain how the problem you are addressing, the solution you have designed, and the population you are serving align with the Challenge.

It assists in creating jobs in the digital workforce. It reduces inequalities in the digital workforce for historically underserved groups. Computer science and specifically machine learning is presented to housewives and those who prepare food by means of analogies of recipes and cooking topics to machine learning. The goal is allow the skill of food preparation to be a resource for digital job skills.

In what city, town, or region is your solution team headquartered?

Fort Lauderdale, FL, USA

Is your solution already being implemented in one or more of the following ServiceNow locations (Australia/New Zealand, Canada, France, Germany, India, Ireland, Israel, Japan, the Netherlands, Singapore, the United Kingdom, United States), or are you planning to expand your solution to one or more of these countries?

I am planning to expand my solution to one or more of these ServiceNow locations

Please provide an overview of your expansion plans. What is the market opportunity for your business or product here?

The plan would be to start in the Southeast region of the US.

What is your solution’s stage of development?

Prototype: A venture or organization building and testing its product, service, or business model.

Explain why you selected this stage of development for your solution.

The online course has been partially developed and is being tested

Who is the Team Lead for your solution?

Winston Grace

Do you qualify for and would you like to be considered for the ServiceNow US Racial Equity Prize? If you select Yes, explain how you are qualified for the prize in the additional question that appears.

Yes, I wish to apply for this prize

Explain how you are qualified for this prize. How will your team use the ServiceNow Digital Equity Prize to advance your solution?

There is a component in the course on how machine learning functions best when it functions without a prejudgement of other people. Stereotypical thinking impairs machine learning, which can be found when training data has a higher score than testing data. This will be explained in more detail in the online course.