Sophya

Future of work skills are becoming increasingly important, and technological ‘whiplash’ disproportionality affects the disempowered. With the current education infrastructure, billions of learners will inevitably be left behind without access to basic education or critical upskilling - unable to keep up with education/skill demand, despite education being key to inclusive economic growth.

The internet holds the answer to scalable learning, but is too unstructured for learners who need it the most. We’re predicting that internet content will continue to become more open, free, and germane to skills of tomorrow, and, informed by data science analysis of internet learners, we're building the 'Spotify' of learning, where learners no longer have to go find the right content - the right content will come find them. By identifying web learning content they find helpful, communities can pool their brainpower to help local peers learn in a free, region-specific way that creates shared prosperity.

According to the World Economic Forum’s latest report on Future of Jobs, by the mid-2020s, 75 million jobs may be displaced by technology trends, while 133 million new roles may emerge. Separately, as the world’s population continues to increase, brick and mortar educational institutions cannot keep up with global demand for basic education or these new technological skills. 

Both massive problems above will disproportionality affect underserved populations. This is partially because underserved communities fill many of the jobs that will be replaced, and partially because these same communities suffer structural disadvantages in pursuing the basic education and further training that the new roles require. 

We are tackling the problem of inaccessible education and training that frequently displaces underserved communities and thus bars this group from equitable prosperity.

Through user research, we realized that learners sink too much time into Google searching for the right content, and don’t get any benefit or insight from learners who came before them. In underserved areas we particularly found that aspiring and current students were often confused about how to reach their professional goals. (E.g. “I want to be a programmer/doctor, but I don’t know how”). We solve these problems.

We ultimately aim to serve learners from ‘pre-K to gray’, addressing the needs of the entire education and workforce sector. But initially, we’re starting in health science and computer programming for students who are either in school, or simply have an internet connection. We chose these starting points because skills in health and programming are sought-after and ‘future-proof’. There is also a shortage of millions of healthcare and technical workers that will worsen indefinitely without a scalable system for learning these skills.

We’re actively working with students in these fields to crowdsource identify the best free and/or open learning content on the web that is actually meaningfully helpful to them in career or learning progression. I.e. the internet content they actually use to learn. We’re also working with experts and faculty in these fields to see what is recommended from the ‘top down’, to cross reference with the material that students are organically finding, sharing, and using on their own.

Using this data, we construct recommendation systems that give similar learners exactly what they need, in the right order that they need it. No more wasted time, confusing career pathways, or barriers to learning desired skills.

We describe what we’re building as ‘the Spotify of Learning’, because instead of having to go out on the web and sink hours into finding the right content, we built a system where the right content instead comes to the learner. At the right level, and in the right order to learn best. This is informed by data from how community-members are learning.

Ultimately, Sophya enables anyone, anywhere, to skill-up to become anything they want, for free, using the power of the Internet. Any person can input their level of education  (e.g. Grade 7), and where they want to go (e.g. ‘be a doctor/engineer/data scientist/roboticist’). Like a GPS for learning, Sophya displays an interactive pathway to take, with each step being a learning node containing the content needed to progress. This content is crowdsourced from the web, comprising the most effective, up-to-date resources that similar learners organically use. This enables learners to flexibly move through learning, on or offline, from any device, without having to be actively in attendance at school, anywhere in the world. Because content is crowdsourced, Sophya will be able to return location- and language-specific content that is most likely to be helpful for a given learner in their specific niche. When people in given communities learn in Sophya, it provides a network effect to local learners: serving up content likely relevant given their proximity to other learners nearby.

Sophya currently also provides AI tools that enhance learning. For one, we enable learners to curate and organize their learning content into solo or collaborative Learning Paths (where we also use machine learning algorithms to recommend helpful ‘missing’ content). We also provide tools to enable learners to interact with their content to enhance learning. For example, our computer vision and natural language note-taking tools allow learners to write notes or draw directly onto video they are watching (live!), and to export their results. They’re thus able to create strong memory anchors and learn better. 

Part of improving learning is improving student engagement. We provisionally patented our highly engaging 'foreshadowing' technology, that alerts learners of content that will occur in the _future_ of a video, so when they write notes onto the video, they know where they should or shouldn't write.

New processes:

• Application of data-driven crowdsourced recommendation for learning. 

  • We built a recommendation system that aggregates content from students and teachers in the content area it is relevant to (e.g. recommended health content is derived specifically from health students/teacher).

  • Statistical models applied to algorithms, weighting content appropriately. This ensures that expert-curated content is more likely to be shown to learners, but despite this, content that students frequently use/find helpful is also shown.

  • Pathways displayed to Learners update in real-time, based on use by target learner group. This ensures that learners have access to the most useful content.

New technologies:

• We created (and have new patents pending on) several novel tools that allow learners to engage deeply and interactively with existing content on the web.

• Includes our video note-taking tools (which allow students to write their notes directly onto live video and export the results), our video-object detection tools, our flashcard tools, and our automated assessment tools.

Existing technologies and processes:

• Algorithms that identify and group users into clusters based on content they engage with (i.e. Spotify-like, but applied to learning).

• Pinterest-like boards where content is aggregated and displayed.

  
  

  NB: Why does this all matter? For lowering barriers to learning. Briefly: i) Learning paths/boards with recommendations take fatigue/confusion out of finding the right stuff to get you from point A to B. ii) Tools to increase engagement and learning result in more stickiness/pleasure for the user, which drives learning longevity.
  

AI is a critical piece of Sophya. It is the core of our technology, powering our computer vision, natural language processing, and machine learning. 

Our novel computer vision tech allows learners to interact much more actively with video content they use, and creates strong network effects by allowing the system to give other learners insight into what segments of videos their peers have found the most useful. 

Our NLP allows users to demonstrate mastery of topics so they can progress at their own pace, can demonstrate that they are learning, and can certify.

Our machine learning is key for recommending the right content to the right users at the right time. Building the algorithms well ensures that we can segment users finely and train on specifically narrow datasets for those narrow groups.

An important ingredient if you seek better learning outcomes is ensuring that your education solution is as engaging as possible. For example, early childhood education was seen as an uncrackable nut until Sesame Street made it engaging. To this point, our platform will include gamification (done by actual gamers - each person on our 8 person team is a veteran videogamer), engaging UX, and a reputation system. None of this is new, but it’s important to mention that we’re deliberately applying principles of engaging UX to enhance stickiness and the learning experience.

Sophisticated AI has become the biggest disrupter of jobs. Sophya allows communities to “use AI to fight AI”. We do this by building a community-driven system for learning, that creates a peer-driven network effect to enable everyone to access opportunity. Underserved communities work hard to skill-up in future of work competencies, but no network effect is being created: it’s equally difficult for each new member to skill up. We’re building a community-driven flywheel system that enables learners to benefit from learners who came before them.

Theory of Change: 

1. People in any given community use Sophya to have a data-validated, peer-curated stream of upskilling resources provided to them, for free.

2. Users jump onto Sophya, input their current and desired levels of education or upskilling. 

3. Sophya provides an interactive pathway of resources from the web, in the right order needed to learn the desired skills.

4. Peoples’ use of resources within the platform helps Sophya to know whether or not to keep that material in the system for subsequent learners. 

5. Because of peers in the community who use Sophya to learn similar skills, new learners get resources more closely matched to their local needs.

6. Learners don’t have to be in school or pay tuition. Therefore, underserved communities gain access to upskilling immediately.

7. Upskill → better work opportunities, or can choose entrepreneurship to create wealth and jobs → change socioeconomic status → reinforce positive cycle for community.

Our general principle is to build software that encourages collaborative, peer-enhanced learning. This is particularly important because there is no way that educational institutions are going to be able to keep up with the demand by students for basic or advanced future of work skills, and if peers do not have a platform in which to reliably learn together scalably, billions of students/student hopefuls globally will inevitably be left behind. Furthermore, encouraging peer-collaboration helps scale to more users at lower cost to the company, which aids in sustainability. 

Currently, we serve approximately 4,000 students within our beta. We are opening to the public in ~1 month, and have close to 60,000 students in our pipeline to join the platform (via existing relationships with institutions). This lines up well with our growth projections to have 100,000 students using the platform within 1 year, and close to 4 million students within 5 years. 

Note that our non-linearly accelerating rate of growth reflects our continually improving models that consistently will serve learners better (flywheel model).

We generally divide our goals up into a few buckets: overall/mission, growth, technical, partnerships.

Next year:

• Overall: Plant the seed in the minds of communities without easy access to good schools that free, efficient learning on the web can be done with the right system in place (Sophya).
• Growth: We’re aiming to have 100,000 students/lifelong learners on the platform within the next year.
• Technical: Within the next year we aim to have full-scale recommendation systems for healthcare and technical ‘Future of Work’ content (e.g. robotics, programming, data science).
• Partnerships: We’re aiming to partner with at least 5 leading public institutions who can help endorse content and progression for open access Learning Paths.

Five years:

• Overall: Be the go-to platform for people around the world to use whenever they want to learn anything in the most efficient way. Give everyone on the planet the ability to make maximal use of their ‘brain capital’.
• Growth: We’re aiming to have close to 4 million students/lifelong learners on the platform within 5 years. 
• Technical: Within five years, we aim to have recommendation systems for K-12 education, several undergraduate programs (including all available STEM education pathways), and much more robust healthcare and ‘Future of Work’ learning pathways.
• Partnerships: We’re aiming to partner with at least 50 leading public institutions who can help endorse content and progression for open access Learning Paths.

Next year:

• Model training: We want to ensure our models for recommendation are neither over or underfitted, but serve the initial beachhead market of future of work training well. 
• Distribution: Once product-market fit is reached, our key task will be distribution.
• Legal: We have to ensure that content being embedded or uploaded isn’t posing any copyright issues. We can follow in YouTube/Pinterest’s footsteps (both multi-billion dollar companies, so they clearly have Terms of Services that the law is okay with).
• Financial: Have to hit our milestones without running out of cash.

Five years:

• Model training: continually improving, but also new skills will become available 
• Distribution: Similar as before - but instead of distributing through institutions, we’re able to deliver directly to students who ideally share with each other.
• Market: The market will evolve as competitors enter and required upskilling changes.
• Culture: As we expand geographically - we’ll have to contend with places that don’t see education for women of color or particular class systems as important as for the ‘dominant’ group. We need to break through this.

Each can be difficult, but is doable. We’ll lean on our resources, which include a diverse and accomplished advisor board, our partners, data, government and non-government organizations, and our ‘smart creative’ teammates.

• Model training: We’ll ensure we have a diverse set of users and narrow datasets on which to train our learner-level-specific algorithms. We’ll ensure enough learners are on the platform so that the algorithms are solid.
• Distribution: We have MOUs with multiple schools with student numbers that total in the hundreds of thousands. We also have social media growth hacks to help with distribution. We will make the software as shareable and fun as possible to encourage distribution by our users.
• Legal: Why, lawyers of course! Get creative with building high value to the user without IP issues.
• Financial: Raising enough now to get to sustainability and then profitability. Then we have the option of being revenue growth or can raise a venture round to scale as fast as needed. 
• Market: Ensure we keep an ear to the ground re: market dynamics, and new future of work skills needed.
• Culture: Still working this out. But from now we’re building relationships with two major NGOs that could help us enter culturally different markets in a sensitive way.

We have 7 full-time employees (CEO/COO and five engineers). In addition, we have 3 interns (UX design, market research, and growth). In the next 6 months, we plan to hire 2 more engineers, and 2 growth hacker marketers.

We have experience in education, finance, growth, programming, data science, research, public health, and medicine. These competencies are strongly aligned with building a sound, scalable system of education. 

Our leadership team is composed of a medical doctor (Vishal) and a PhD student (Emma) at Harvard. Each were popular Teaching Fellows at Khan Academy, and have worked on building education/data platforms at the World Health Organization and at Harvard Medical School. Vishal (CEO) helped scale a prior startup’s userbase from tens of thousands to hundreds of thousands of students. Emma (COO) is an insanely talented co-founder who knows how to product manage, fundraise, manage the team, hire, and code. Our CTO Mark has 20 years of software engineering experience and leads the engineering team with care, humor, and great mentorship.

We care about the learner’s experience with the software. When people can personalize, collaborate, or just enjoy interacting with an app, they spend more time with it. We’re user-centered-design focused, and weight user experience on par with desired outcome. We have an in-house design team, front-end specialist, and UX designer. Our short iteration cycles center around testing with students.

We’re surrounded by an incredible team of advisors/investors from Harvard, MIT, Google, Stanford, Carnegie Mellon, and Facebook, who span several relevant verticals including education, artificial intelligence, data science, and business operations. We’re in Harvard’s top incubator (Launch Lab X), and are current MassChallenge Finalists.

Sophya is currently partnering with 4 large universities across 2 countries for algorithm training and initial piloting. Three of these universities are using Sophya primarily in their health sciences schools, and one is preparing to deploy Sophya university-wide in the next 8 weeks (21,000 students). The pilots are focused around improving recommendation algorithms for students, and around user experience testing, feedback, and iteration.

In the spirit of improving health education access, health science faculties are helping to train the ML recommendations on appropriate content that should be distributed to all health science learners - both in school and out. The university-wide deployment is aimed at ‘not overfitting’ - ensuring that the tools and platform are suitable for many types of learners. 

The total number of students who will be able to access the software from these pilots is approximately 100,000.

We’re SaaS. We provide some of our services to students/learners for a monthly or annual subscription fee, and some of our services for free.

Because of its potential importance in creating sustainable education and skills equity, we provide our learning recommendation technology for free, worldwide. But, students can opt to upgrade to premium accounts, which cost $5-7/month/student. This allows them to have unlimited use of our previously described AI tools to enhance their learning experience and to save time, and allows them to upload learning content for personal use, and to share content that they have permission to share.

Institutions can pay us to provide insight into their students/employees skills analyses, skill gaps, and assessment metrics.

While we’ve currently raised enough investment capital to sustain us for 1 year from today at our current burn rate, we aim to be a revenue driven company, and should become cashflow positive from user revenue by January 2020.

To be clear, our current model is fee-for-service, and our customers are students (subscribing to our enhanced learning tools), and institutions (paying for analytics and insight). In the future, we may explore an additional market-linkage model, allowing students to flag topics they need help with and having tutors join the platform from anywhere in the world, to provide help for a fee.

The more we can understand what free and open content on the web students are using to learn, the more we can do a good job of constructing hot-updating learning paths and making career pathways visible to underserved communities, at scale. While we’re tackling this problem by partnering with institutions around the world, we would be able to build the best models primarily by accelerating partnerships with institutions. Solve could help in the following ways:

1. Assistance with partnerships: This piece is CRITICAL and working with Solve would be incredibly helpful. As described above, users of Sophya benefit from a strong network effect. And, our algorithms rank most highly the educational content that is recommended by local experts (e.g. teachers in a given space or students who are doing well). Therefore, Solve could be incredibly helpful by helping us build relationships with educational institutions around the world whose experts could help validate the internet content that their local students would be using to learn. We then can much more quickly train algorithms to provide location-helpful educational pathways to any learner.
2. Community/network: This is also a very valuable piece. Communities of early-stage entrepreneurs are incredibly powerful because we each deal with similar issues, and can help each other get through them.
3. Funding: Money is any startup’s oxygen. The grant and access to other funding is great.
4. Personalized support: Mentorship, a brain trust of advisors, and specific help with PR would all be very helpful.

In general, we’d like to partner with educational institutions (at any level) to help validate and train models to be helpful for local learners who may or may not be in school.

We don’t have specific organizations in mind, though we are over-indexing for partnering with educational institutions in underserved areas. That way we can do two things: i) ensure that local community-members who aren’t able to enroll in those schools can get vetted educational internet content germane to their region, and ii) ensure that we can cross-reference the internet material that is being validated at schools in underserved areas with schools in high economic status areas to look for significant differences in material. We can then recommend content in a more equitable way. 

Increasingly sophisticated AI has become the biggest disrupter of jobs. Sophya enables communities to “use AI to fight AI”, by building a community-informed system for basic education and upskilling that uses strong network effects to enable everyone to have opportunity. Scarily, the rate at which people requiring retraining, coupled with the rate at which the population is growing, severely outstrips how fast current education systems can train. So we’re using AI to build a scalable system of learning. 

AI is the core of our technology, powering our computer vision, natural language processing, and machine learning. The prize is useful in name and in the funds it would provide (namely, for our engineering costs!).

Our novel computer vision tech allows learners to interact much more actively with video content they use, and creates strong network effects by allowing the system to give other learners insight into what segments of videos their peers have found the most useful.

Our NLP allows users to demonstrate mastery of topics so they can progress at their own pace, can demonstrate that they are learning, and can certify - even if they never attended school, or live rurally.

Our machine learning is key for recommending the right content to the right users at the right time. Building the algorithms well ensures that we can segment users finely and train on specifically narrow datasets for narrow groups.

I grew up in government housing with awful role models everywhere around me. The only way I was able to do something great was because of STEM. I ultimately went to medical school, which has been incredibly helpful for my family, and will be for my kids too. 

I was on a rural emergency medicine rotation, and saw many kids who seemed to be growing up in similar circumstances that I did. Surrounded by cigarette smoke, drug use, and crime. I realized that the most concrete way to a better life for these kids was if they were able to engage in STEM education early, and in as engaging a way as possible. That’s a big part of why I started Sophya - so that kids (and people of any age) everywhere could hop on, tell Sophya what they want to become, and Sophya would help them get there. All the right content, in the right order, free, with help when they needed it. Then they would be able to learn on their own time, not needing to be enrolled in expensive or faraway schools, and would be able to upskill in a way that could change their futures.

Our beachhead is in STEM education. We’re using data science to recommend the most helpful STEM learning resources are on the web, in the right sequence, at the right level, so no matter what their circumstance, learners everywhere get access to the same content that more privileged contemporaries use to get ahead.

One of our personal battles at Sophya is to empower the disempowered - and on a global scale this is disproportionately women of color. 

Access to visual career paths as well as all the required education to get there will be a boon to women and girls around the world, and could enable engagement in STEM and Future of Work education as early as possible, and in as engaging a way as possible. That’s a big part of why my cofounder Emma and I started Sophya - so that girls (and people of any age) anywhere could hop on, tell Sophya what they want to become, and Sophya would help them get there. All the right content, in the right order, with help when they needed it. Then they would be able to learn on their own time, at home or not - not needing to be enrolled in expensive or faraway schools, and would be able to upskill in a way that could change their futures.

Our beachhead is in STEM education. We’re using data science to recommend the most helpful STEM learning resources are on the web, in the right sequence, at the right level, so no matter what their circumstance, learners everywhere get access to the same content that more privileged contemporaries use to get ahead.

I grew up in government housing with many refugee friends, and we had awful role models around us. The only way I was able to do something great was because of STEM. I ultimately went to medical school, which has been incredibly helpful for my family, and will be for my kids too. 

I was on a rural emergency medicine rotation, and saw many kids who seemed to be growing up in similar circumstances that I did. Surrounded by cigarette smoke, drug use, and crime. I realized that the most concrete way to a better life for these kids was if they were able to engage in STEM education early, and in as engaging a way as possible. That’s a big part of why I started Sophya - so that kids (and people of any age) everywhere could hop on, tell Sophya what they want to become, and Sophya would help them get there. All the right content, in the right order, free, with help when they needed it. They could learn on their own time, not needing to be enrolled in expensive or faraway schools, and would be able to upskill in a way that could change their futures.

Refugees are in a particularly precarious situation because they lack empowerment in multiple dimensions. Proving credentials can be difficult, language skills vary, and parenting is tough because of long hours in low-skill work. We’re building Sophya so that no matter what their circumstances, learners can upskill to improve their lives.

AI is the core of our technology, powering our computer vision, natural language processing, and machine learning. The prize is useful in name and in the funds it would provide (namely, for our engineering costs!).

Our novel computer vision tech allows learners to interact much more actively with video content they use, and creates strong network effects by allowing the system to give other learners insight into what segments of videos their peers have found the most useful.

Our NLP allows users to demonstrate mastery of topics so they can progress at their own pace, can demonstrate that they are learning, and can certify - even if they never attended school, or live rurally.

Our machine learning is key for recommending the right content to the right users at the right time. Building the algorithms well ensures that we can segment users finely and train on specifically narrow datasets for narrow groups.

As far as data privacy and ethics, we completely de-identify learner personal information, and only use collected data for statistical analysis to inform our machine learning algorithms (to improve recommendations and assessment for learners). Content metadata is not identifiable and is solely used to improve recommendations for the learner. Data is stored in accordance with GDPR guidelines, on AWS.

One of the biggest disruptors of blue-collar jobs is artificial intelligence applications that are rapidly increasing in sophistication. With this in mind, Sophya allows communities to “use AI to fight AI”, so to speak. We’re doing this by building a community-informed system for learning Future of Work skills, that takes advantage of peer-driven network effect to enable everyone to have opportunities. 

The rate at which employees are requiring retraining, coupled with the rate at which the population is growing, severely outstrips the rate at which the current systems of learning allow people to be educated or trained. A scalable system of learning is desperately required - this is what we’ve built.

Specifically, users in a given community who are skilling up in ‘Future of Work’ knowledge - principally healthcare and technical skills (robotics, programming, data science, etc.) - pull those resources into Sophya to do their learning. We then check IP/copyright/shareability status of these resources, and provide the best of these resources to help train/education those in the community who are most at-risk of displacement.

With the prize money, we would fund the optimization of algorithms that allow communities to, at scale, easily input learning materials they are using, which are then recommended in an optimized order to fellow community-members who are trying to learn the same skills. We would also cross-reference materials each community is using with content used by similar communities, to ensure that the highest value content is made available to everyone.