Author: expi_4oeorq

On May 2nd, 2017, Free Ventures hosted its semi-annual Demo Day showcasing six startups at The House near UC Berkeley. Unlike past semesters, this year’s Demo Day was closed to the general public to allow Berkeley students, investors, mentors and leaders of Silicon Valley to network in a more private setting.

Free Ventures incubates startups in various industries, each whom will continue to move forward with their companies in the summer.  Managing Director, Blake Lafayette, opened the night with a brief introduction of what Free Ventures does and what each startup has accomplished this past semester. Greylock Partners, Early Growth Financial Services, The House Fund and Pear VC sponsored Free Ventures this semester and accelerators like Y Combinator has invested in a few of their startups as well.

 

 

About Free Ventures

Free Ventures is UC Berkeley’s student run startup incubator that allocates resources and mentorship to help student translate an idea into a company. Founded in 2013, Free Ventures has helped over forty teams and has successfully created a student run accelerator within the UC Berkeley community. Free Ventures continues to invite new teams and startups into incubation every semester to help diversify the types of companies it hosts. Meetings are held on bi-weekly and sometimes more frequently where each team gets to develop their companies with the help from VC mentors.

 

 

Teams

PeerEditr

 

PeerEditr is a feedback and collaboration tool that is the quickest way for someone to receive feedback. It is designed for any documents like resumes or marketing presentations that allows the user to get feedback with one click. It creates a feedback room with annotation tools that can be shared with anyone allowing for edits to be made quick and easy. PeerEditr will be working with Pear VC for the summer.

 

Free Ventures has provided access to our team from the beginning through sponsorship and mentorship and has also taught us what it takes for someone to invest in our company.

– PeerEditr

 

Memory

Memory is an Alexa-based voice assistant for individuals with Alzheimer’s. It uses information given by the patient’s family members and caretakers to collect data on the best way to respond to a patient’s question or concern. Memory’s goal is to lower the difficulty on caretakers and to allow physicians to provide customized treatments to each and every patient. Memory will be working with Lightspeed Venture Partners for the summer.

 

Acuity for Moms

 

Acuity for Moms is an online program that gives the opportunity for moms to re-enter the workforce by providing various resources and skill training. The platforms creates a customized path in order for each client to have their own unique experience depending on their skill set, time availability, interests etc.

 

 

Genetic Foresight

Genetic Foresight provides a genetic test that will be able to determine how an individual will react after taking a certain kind of medication depending on their genetic code. Doctors will then be able to make safer and more effective prescriptions to each patient preventing the risk of any medical interference with the patient’s genetics. Each report will be evaluated biannually from new research, new drugs and new medications so that the individual is always kept up to date.

 

 

Paladin Drones

 

Paladin Drones are drones that provide safety to the public, which decreases the response time by two minutes by providing situational awareness before first responders arrive at a scene. Paladin Drones is now a finalist for Berkeley Big Ideas.

 

 

 

Layer Six

Layer Six is the only research backed cognitive solution to help solve the problem of information overload and reduction of high performance. It brings data-driven diagnostics, software on how our brain already works through cognitive assessments and personalized VR therapeutics to its consumers.

 

 

After hearing from all six startups, the audience was able to interact with the teams, investors and Free Ventures Team to learn more about the programs, development processes, and companies.

To learn more about Free Ventures please visit their website.

For questions please feel free to email Blake Lafayette, Jeffrey Feng, or Pranav Gulati!

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This semester, The Sutardja Center created a new course, Data-X, which allowed students to team up to tackle technically complex problems. In the course, students identified real-world problems, collected data, and created applications to find solutions.

Click on the picture below to check out this year’s Data-X course!

 

Data-X was taught by Ikhlaq Sidhu and student instructors Kevin Bozhe Li, Alexander Fred Ojala, Nathan Cheng and Sam Choi. Teams used what they learned throughout the course to present their Final Project Demo on May 11th, which ranged from topics such as estimating stock prices to automated thickness detection for 2D materials.

 

 

The course model was outlined for students to:

• Brainstorm a problem
• Propose a low tech solution
• Execute and Iterate BMoE (Berkeley Method of Entrepreneurship) Reflections- To help students get in the right mindset of incorporating innovation with technology
• Present Final Presentation

 

After just these fourteen weeks, students used their understanding of concepts such as filtering, prediction, classification, decision making and much more to demonstrate their team-based data application project.

 

 

During the Final Demo, all teams first posed a problem that could be solved using Data-X analysis. Following this, they showcased what their research and collection of data led them to and how they were able to then find a solution. Whether the approach was a one-step or two-step problem, the teams each recorded their project outcomes and what they intended to use as a user interface.

The teams were then able to set up an architecture for their solution by compiling and scraping data, processing it, and finding a regression model that tested their outcomes. As a result, each team summarized what their results were and some even evaluated future predictions of how their solution can be used in a more effective way.

Several teams expressed that there was a learning curve while taking the course. Because each team was trying to solve computationally intensive problems, they had to find other ways of using what they learned in the class to move forward with their data collection.

 

 

All in all, each student was able to grasp an understanding of what Data-X is and how it can be used within a short time frame of only three months. On the May 11th Final Demo, students successfully applied the frameworks they learned throughout the course.

 

“Sidhu’s class is great because he shows you all the cool things that people are doing with data science and the goal is not really for you to understand a 100% of the material like we traditionally do in Berkeley classes, but to understand as much as you can and try to contextualize it. You don’t have to be an expert to take this class but you will learn a lot of it” 

– Rohan Punamia

 

To learn more about Data-X please see:

https://data-x.blog/

http://scet.berkeley.edu/data-x-course/

http://scet.berkeley.edu/data-strategy-working-hard-enough/

 

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This article is will be published in the 2017 Sutardja Center for Entrepreneurship & Technology’s annual journal AIR (Applied Innovation Review) in June 2017. You can see the 2016 version here.

Shomit Ghose is a UC Berkeley alum and mentor, venture capitalist, and partner at ONSET ventures.

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Cognitive Irredentists Arise!

A dystopian world: sentient machines manipulating human consciousness to harvest our energy.  All while we humans remain docile, unwitting and oblivious.  This was the world depicted in the 1999 Hollywood blockbuster, The Matrix¹.  How far in the future might such a world be?  Er, perhaps not so distant.  Precursors of The Matrix may already be upon us.

Human beings today carry with them a loosely-attached (so far²) second brain, also known as your mobile phone.  This second brain serves as a quiet collector of all manner of 60x60x24x7 information about us, passes that information to vast banks of computers in the cloud which process it in opaque ways, and our subsequent actions – whether it’s to forgo that chocolate croissant, read a specific piece of content, buy a specific product, turn left at the intersection, or mete out a jail sentence³ – are governed by the results of that processing.  In effect, computer algorithms that a small group of humans have programmed are now subtly directing and “programming” the actions of the broader human population.

Is this a concern?  It should be.  Today, for the first time, the mobile Internet makes it possible for individual actions to be tracked and assessed in real-time, at population scale, and for data-driven algorithms to be deployed to influence our future actions.  All of this is made possible by the track-your-every-move⁴ nature of the Internet, smart phone ubiquity, the ability to use machine learning to build statistical correlations on huge volumes of “weak” data, an “asymmetricity of information” advantage in favor of those who collect the data, and the science of behavioral economics.

 

Data Signals Everywhere

Until recently, due to the limitations of our computing infrastructure, data-driven applications were based principally on “strong” data.  I.e., data that was fairly finite in volume, very specific, and fit neatly into a relational database: your electronic medical record indexed by your name and birthdate; your driving record indexed by your driver’s license number; your salary and tax records indexed by your social security number; your purchase history indexed by your credit card number; etc.  By and large, strong data has been (relatively) well regulated from a privacy perspective and been (somewhat) well protected.

Weak data, by comparison, is data that is vast in volume, and by itself is very fuzzy and ambiguous; historically, it’s been next to impossible to make any sense of weak data because it’s, well, so weak.  Strong data, for example your birthdate and the make of your car, will be predictive of your future auto insurance claims.  But what do we make of weak data that tells us that you like to eat meat and drink milk other than you’re probably not a vegan?  While strong data could be used to understand you individually, weak data could not.

But enter limitless amounts of cheap storage and computing in the cloud, fold in machine learning algorithms of the unsupervised variety, and weak data can now be statistically correlated and stitched together to yield individualized results that may be just as predictive as those from strong data.

Want to find a college educated professional who’s likely a Republican?  Talk to a surgeon or anesthesiologist⁵.  Psychiatrist or infectious disease physician?  Likely a Democrat.  Do you eat lots of red meat and drink lots of milk?  Without seeing your driving record we now have a good idea about your auto insurance risk⁶.  Properly harnessed and correlated, weak data can yield all manner⁷ of intimate insights⁸ about individuals.  Are you an extrovert⁹ or an introvert?  Can your browsing behavior be used to gain insights on your specific personality¹⁰ traits or your age and gender¹¹?  Ethnicity¹²?  Are you gay¹³?  What does your streaming music¹⁴ playlist say about your cognitive abilities?  Suffering from depression¹⁵?  Psychopathic¹⁶ tendencies?  And is your Echo or Alexa¹⁷ device currently listening to the conversations¹⁸ in your home?

 

The Ghost in the Machine

As insightful – and more importantly, as intrusive – as these individualized conclusions may be, weak data has not been regulated, nor is it well protected for privacy.  Weak data conveys a powerful advantage from a business point of view because companies and organizations can utilize unsupervised machine learning techniques to comb through huge, heretofore intractable amounts of data and find correlations and insights that would otherwise be unperceivable and unknown to the human mind.  From a competitive standpoint, data sources and machine learning algorithms have been weaponized by companies at business’ leading-edge.

An obvious weakness of the data-driven model is that machines learn from the underlying data – e.g., girls become teachers, boys become engineers – and the underlying data may already contain biases¹⁹, thereby perpetuating inequity, or can be mis-trained²⁰ by social engineering attacks.  How can we trust the accuracy of a machine’s decisions if we cannot vouch for the validity and balance of the data on which it was trained?  Furthermore, the companies that control the data apply their own proprietary algorithms to their data asset for one purpose only: to maximize their profit.  This is the rightful goal of any business, of course.  But the consumer must be aware that if there are a large number of product pages, or news articles, or routes to a physical destination available, a data-driven business will not be curating/selecting the choices presented to you based on what optimizes your benefit but on what optimizes the business’ profit²¹.

In this way, those that control the data enjoy an advantage in asymmetry of information: we consumers don’t know on what basis a decision is made on our behalf.  We know neither the body of the underlying data – the full range of our democratically available choices – nor have any understanding of the opaque algorithms being used to process that data.  And all of this data is increasingly concentrated in just a few hands: Google, Facebook, Amazon.  Facebook famously experimented²², through selective content exposure, with manipulating the emotions of almost 700,000 of its users to show

“…that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. [Facebook provided] experimental evidence that emotional contagion occurs without direct interaction between people…”

Matrix-like, information asymmetricity enables our actions to be programmed and manipulated by a handful of organizations²³ that have access to, and an understanding of, data and correlations that we individuals do not.

 

Who Gets to Choose?

Behavioral economics is what finally ties weak (and strong) data, machine learning, and information asymmetry into a neat little cybernetic loop: data is collected, data is analyzed, and the “correct” (from the business’ point of view, at least) set of actions are fed back to us to inform our ongoing behavior, which will then also be collected and analyzed.

Behavioral economics is a potent tool for those holding an information asymmetry advantage because the Internet presents a seemingly infinite set of choices to individuals.  But humans dislike choosing from large sets of choices.  We much prefer choosing from finite sets²⁴.  Whether it’s the forward-decisioning of the “Keep watching” default²⁵ that keeps you bingeing on a streaming video service, or presenting what brand of drug²⁶ a physician might prescribe, we humans like the range of our possible decisions to be defined or limited.  Too much choice is seen as being a “tyranny of choice²⁷”.

So if you’re a large company, sitting on a massive trove of data, with highly-predictive machine learning algorithms whirring away, and hundreds of possible news stories or products or promotions to present to an individual consumer (information asymmetry in action), which half-dozen options do you choose to show?  The half-dozen that would be of maximum benefit to the individual, or the half-dozen that would most benefit your profits?  It’s likely that the company’s “choice architecture²⁸”, indeed its duty to its shareholders, is to optimize for profit, even at the explicit expense of the benefit to the individual consumer.  Through choice architecture the actions of the individual become programmed²⁹ by the data and the algorithms.

 

The Best of Times, the Worst of Times

Needless to say, all human inventions, Big Data included, can be used for beneficent or maleficent purposes: you can use a brick to build a house or you can throw it at someone’s head; you can cut your food with a knife or stab your fellow diner; etc.  The same is true of data and algorithms; and Pandora’s Box will yawn ever wider with the volumes and sources³⁰ of data continuing to skyrocket.  But just as we should not outlaw bricks, or butter knives, or cars for that matter, neither should we proscribe Big Data.

Data and algorithms can be – and are –  used today to level the playing field and bring benefit, at scale, to underserved³¹ or under-resourced segments across the human population.  Everything from financial services³², to education³³, to medical care³⁴ can be delivered at low cost, scalably, and across geographic boundaries by harnessing data, machine learning, and even behavioral economics.  In this way, data and algorithms can provide a “status hack” to improve access to the resources people need.  In no way should such uses of data and algorithms be fettered or circumscribed.

What does need to happen is to bring awareness of the opportunities for light and risks of darkness inherent in our (inescapable) data-driven future; we must not resign³⁵ ourselves to the latter fate.  There must instead be an awareness of the many dangers — to privacy at a minimum, and to our ability to freely choose at a maximum — posed by the mass collection and analysis of what seems to be even the most trivial shreds of data.  We must therefore always be acutely aware of the privacy implications of our intentional and unintentional data trails, and we must demand transparency from those who control and act on our data sources.  We must always have an awareness and skepticism of opaque algorithms deployed by the few for the “benefit” of the many.  We must swallow Morpheus’ red pill.

 

 

1. “The Matrix”.  2017.  Online.  https://en.wikipedia.org/wiki/The_Matrix
2. Constine, Josh.  “Facebook is building brain-computer interfaces for typing and skin-hearing”.  TechCrunch.  April 2017.  Online.  https://techcrunch.com/2017/04/19/facebook-brain-interface/
3. Angwin, Julia.  “Machine Bias”.  ProPublica.  May 2016.  Online.  https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
4. Englehardt, S., Narayanan A.  “Online Tracking:  A 1-million-site Measurement and Analysis”.  http://randomwalker.info/publications/OpenWPM_1_million_site_tracking_measurement.pdf
5. Sanger-Katz, Margot.  “Your Surgeon Is Probably a Republican, Your Psychiatrist Probably a Democrat”.  New York Times.  October 2016.  Online.  https://www.nytimes.com/2016/10/07/upshot/your-surgeon-is-probably-a-republican-your-psychiatrist-probably-a-democrat.html?_r=1
6. Evans P., Forth P.  “Navigating a World of Digital Disruption”. Boston Consulting Group.  2017.  Online.  http://digitaldisrupt.bcgperspectives.com/
7. Kotikalapudi R., Chellappan S., Montgomery F., Wunsch D., Lutzen K.  “Associating Internet Usage with Depressive Behavior Among College Students”.  IEEE Technology and Society Magazine.  Winter 2012.  http://web.mst.edu/~chellaps/papers/TSM.pdf
8. Quercia D., Kosinski M., Stillwell D., Crowcroft J.  “Our Twitter Profiles, Our Selves: Predicting Personality with Twitter”.  https://www.cl.cam.ac.uk/~dq209/publications/quercia11twitter.pdf
9. Gosling S., Augustine A., Vazire S., Holtzman N., Gaddis S.  “Manifestations of Personality in Online Social Networks: Self-Reported Facebook-Related Behaviors and Observable Profile Information”.  September 2011.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3180765/
10. Kosinski M., Stillwell D., Kohli P., Bachrach Y., Graepel T.  “Personality and Website Choice”.  June 2012.  https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/person_WebSci_final.pdf
11. Hu J., Zeng H.-J., Li H., Niu C., Chen Z.  “Demographic Prediction Based on User’s Browsing Behavior”.  2007.  https://www2007.org/papers/paper686.pdf
12. Kosinski M., Stillwell D., Graepel T.  “Private traits and attributes are predictable from digital records of human behavior”.  PNAS.  October 2012.  http://www.pnas.org/content/110/15/5802.full
13. Jernigan C., Mistree B.  “Gaydar:  Facebook friendships expose sexual orientation”.  First Monday.  October 2009.  http://pear.accc.uic.edu/ojs/index.php/fm/article/view/2611/2302
14. Rentfrow P., Gosling S.  “The Do Re Mi’s of Everyday Life: The Structure and Personality Correlates of Music Preferences”.  Journal of Personality and Social Psychology.  2003.  https://pdfs.semanticscholar.org/1364/53addebb04b046e06a524c19fa4e891ea7ae.pdf
15. “How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos”.  MIT Technology Review.  August 2016.  Online.  https://www.technologyreview.com/s/602208/how-an-algorithm-learned-to-identify-depressed-individuals-by-studying-their-instagram/
16. Hancock J., Woodworth M., Porter S.  “Hungry like the wolf: A word-pattern analysis of the language of psychopaths”.  The British Psychological Society.  2011.  https://pdfs.semanticscholar.org/f7b9/cddeb56741f5bae0e9ffec7a901967cdd03d.pdf
17. Google.  “Tomato, tomhato.  Google Home now supports multiple users.”  April 2017.  Online.  https://blog.google/products/assistant/tomato-tomahto-google-home-now-supports-multiple-users/
18. Schwartz H., Eichstaedt J., Kern M., Dziurzynski L., Ramones S., Agrawal M., Shah A., Kosinski M., Stillwell D., Seligman M., Ungar L.  “Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach”.  PLOS One.  September 2013.  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073791
19. Caliskan A., Bryson J., Narayanan A.  “Semantics derived automatically from language corpora contain human-like biases”.  Science.  April 2017.  http://science.sciencemag.org/content/356/6334/183.full
20. Victor, Daniel.  “Microsoft Created a Twitter Bot to Learn From Users. It Quickly Became a Racist Jerk.”  New York Times.  March 2016.  Online.  https://www.nytimes.com/2016/03/25/technology/microsoft-created-a-twitter-bot-to-learn-from-users-it-quickly-became-a-racist-jerk.html
21. Titcomb, James.  “Facebook showed advertisers it could tell when teenagers were emotionally vulnerable”.  The Telegraph.  May 2017.  Online.  http://www.telegraph.co.uk/technology/2017/05/01/facebook-exploited-emotionally-vulnerable-teenagers-sell-adverts/
22. Kramer A., Guillory J., Hancock J.  “Experimental evidence of massive-scale emotional contagion through social networks”.  PNAS.  October 2013.  http://www.pnas.org/content/111/24/8788.full
23. Stanley, Jay.  “China’s Nightmarish Citizen Scores Are a Warning For Americans”.  American Civil Liberties Union.  October 2015.  Online.  https://www.aclu.org/blog/free-future/chinas-nightmarish-citizen-scores-are-warning-americans
24. Iyengar S., lepper M.  “When Choice is Demotivating: Can One Desire Too Much of a Good Thing?”  Journal of Personality and Social Psychology.  2000.  https://faculty.washington.edu/jdb/345/345%20Articles/Iyengar%20%26%20Lepper%20(2000).pdf
25. Pittman M., Sheehan K.  “Sprinting a media marathon:  Uses and gratifications of binge-watching television through Netflix”.  First Monday.  October 2015.  http://firstmonday.org/ojs/index.php/fm/article/view/6138/4999
26. “Changing default prescription settings in EMRs increased rates of generic drugs, study finds”.  Science Daily.  May 2016.  Online.  https://www.sciencedaily.com/releases/2016/05/160509191841.htm
27. Schwartz, Barry.  “The Tyranny of Choice”.  Scientific American.  April 2004.  https://www.swarthmore.edu/SocSci/bschwar1/Sci.Amer.pdf
28. Thaler R., Sunstein C., Balz J.  “Choice Architecture”.  https://www.sas.upenn.edu/~baron/475/choice.architecture.pdf
29. Rosenblat A., Stark L.  “Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers”.  International Journal of Communication.  2015.  https://starkcontrastdotco.files.wordpress.com/2016/08/4892-21331-1-pb.pdf
30. Ghose, Shomit.  “Securing Your Largest USB-Connected Device: Your Car”.  ODBMS.org.  March 2016.  Online.  http://www.odbms.org/2016/03/securing-your-largest-usb-connected-device-your-car/
31. McClelland, Colin.  “Phone Stats Unlock a Million Loans a Month for Africa Lender”.  Bloomberg.  September 2015.  Online.  https://www.bloomberg.com/news/articles/2015-09-23/phone-stats-unlock-a-million-loans-each-month-for-african-lender
32. Lohr, Steve.  “ZestFinance Takes Its Big Data Credit Scoring to China”.  New York Times.  June 2015.  Online.  https://bits.blogs.nytimes.com/2015/06/26/zestfinance-takes-its-big-data-credit-scoring-to-china/
33. Bienkowski M., Feng M., Means B.  “Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics”.  US Department of Education.  October 2012.  https://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf
34. Ghose, Shomit.  “Continuous Healthcare:  Big Data and the Future of Medicine.”  VentureBeat.  June 2015.  Online.  https://venturebeat.com/2015/06/21/continuous-healthcare-big-data-and-the-future-of-medicine/
35. Turow J., Hennessy M., Draper N.  “The Tradeoff Fallacy: How Marketers Are Misrepresenting American Consumers And Opening Them Up to Exploitation.”  Annenberg School for Communication – University of Pennsylvania.  June 2015.  https://www.asc.upenn.edu/sites/default/files/TradeoffFallacy_1.pdf

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On May 3rd, Team Dory (Kimberlie Le and Joshua Nixon) cooked up a flaky, fatty, fishy plant-based salmon to win the plant-based seafood collider finals.

The challenge for this collider was to develop a roadmap for a company selling plant-based seafood that tastes, smells, and cooks like fish. Prizes and advising for the collider were sponsored by SeaCo with further mentorship and support provided by the Good Food Institute.  The final pitches were judged by Renée Loux, co-founder of SeaCo, Bryan Chang from the Collaborative Fund, Christie Legally from the Good Food Institute, and Ben Roche from Hampton Creek.

Team Dory took home the prize by finding plant-based solutions to replicate the characteristics that people love about seafood: healthy fats, flaky texture, and a fishy taste that melts in your mouth. Additionally, the group worked to find solutions that would be non-GMO and have low environmental impact.

“When we first heard of the plant-based seafood challenge, we were really excited because it fit well into our academic domains, myself with bioengineering and Kim with environmental studies,” said Joshua Nixon from Team Dory, “We really saw this as a biological challenge, not so much as one of a mechanical or informational nature. I think this led us in a very different direction than the other teams, largely due I think to our academic backgrounds.”

Through their research, Team Dory decided that fungi would be the best choice to be the base for their plant-based seafood. Fungi have properties that are much more similar to meat than vegetables. Its texture is more like meat when cooked; it has a filamentous structure which replicates the muscle fibers of meat; and it matches the nutritional profile of meat better than vegetables. It is also very sustainable in terms of land and water usage.

Fungi also naturally contain a meaty (umami) flavor that Team Dory sought to enhance by using microalgae which also contain Omega-3 Fatty acids to make the plant-based seafood as healthy as the real thing.

 

“The things we have learned from SCET are very practical for us post-graduation; we feel confident in knowing how to go about entrepreneurship,” said Joshua, “The lessons in teamwork, communication, and grit, as well as start-up know-how, are hard to describe because they aren’t taught from a book, you learn them viscerally through experience.”

Team Dory wasn’t the only one with innovative solutions for creating plant-based seafood.

Taking 2nd place was Team 5 Star (Aaron Hall, Isabella Huther, Aaron Jauregui, Vin Lay, Etisha Lewis, and Max Yen) which worked on replicating salmon and tuna. Team 5 Star’s key insight was that they could replicate many of the flavors created by heme (a key element to creating a “meaty” flavor), by adding metals such as copper and iron. Adding iron to a plant-based broth improved its flavor match (with shrimp) by nearly 25% (which they verified by using mass spectrometry).

Team 3 (Maddie Huber, Judy Shan, Tejal Gala) focused on emulating sardines and tilapia and took 3rd place. One of their creative innovations was a “collagen-protein matrix” to replicate the texture of fish. The matrix is composed of agar (a plant-based alternative to collagen), textured vegetable protein, and algae layered in a brick-like pattern to help create the signature flaky texture and melt-in-your mouth feeling of fish.

All the teams in the collider brought useful insights to the problem of creating a roadmap for a plant-based seafood company selling fish that closely resembles the real thing.

“We were thrilled to sponsor the collider project’s synergy of thought leaders at SCET, the Good Food Institute and a dynamic group of teams compelled to tackle a unique and complex challenge,” said SeaCo co-founder Renée Loux, “At SeaCo, we’re committed to utilizing innovation and commerce to help solve our oceans’ issues and the range of inventive presentations and critical and creative thinking illustrates great promise for the next generation of the plant-based industry.”

With the world’s population growing, demand for seafood rising, and fish populations dwindling, this collider has helped move the world closer to providing seafood that will be more sustainable, ethical — and very importantly, just as tasty — as the real-thing.

 

 

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