cr8: the world’s first autonomous content creation platform for visual images and video

Problem:

World-class Brands work with platforms such as Instagram and YouTube to create, host, and share their respective content. However, it is unclear to those companies how much of their brand content affinity social media metrics (likes, loves, and upvotes) actually point to user affinity for the brand. In addition, it is challenging for these Brands to turn those social media metrics into targeted and actionable marketing strategies. For example, it would be hard for Nike to determine what type of Nike shoes a certain consumer would be most likely to want to buy next simply by the fact that the user liked the Nike page on Facebook.

What is cr8 and How it Helps Consumer-Brand Companies:

cr8 is the world’s first autonomous content creation platform for visual images and video. Our proprietary algorithms allow users to create engaging images and video in real-time. We help capture and monetize the “creative graph” for the most valuable demographics, globally by giving Brands a novel way to access and engage consumers on these platforms. We do so by reversing the content affinity process by becoming user generated content creation and distribution platform. Fundamentally, we believe that user generated content (leveraging brand assets such as a Nike shoe) is the most valuable measure of consumer affinity. We give brands access to new data streams, for example, how consumers are leveraging their brand assets to create content for the internet or communicate with friends. This level of visibility and understanding of consumer preferences will help us establish true brand affinity.

Additional example: Using the same Nike example as above, if a user creates content using specific Nike clothes and accessories, the company Nike will be able to more effectively market specific products to that user based on current and previous content generated by that person.

How We Do It

By using machine learning, we are able to train algorithms on millions of visual datasets within specific brand verticals. Users have access to our proprietary trained algorithms and can apply it to their targeted image or video to generate new content. This new content is a merge version of the two images or videos to create the autonomous user-generated content.

Users then have the opportunity to share their content across multiple platforms (i.e. Facebook, Instagram, Pinterest, Snap Chat, or text messages). cr8 then captures the creative data graph of the user base and leverages this data to better understand the “true” brand interests of the most valuable demographics, globally and at scale.

Competitive Landscape & How We Are Different

Content creation and sharing platforms are a massive market, worth billions of dollars (i.e. Facebook, YouTube, Giphy, etc.).

Our competitive advantage stems from our proprietary technology that creates a sticky user generated content that increases barriers to entry. Our first-mover advantage and open platform model, position us to generate high-value to Brands who spend significant billions on advertising, sometimes without understanding the efficiency of their marketing spend. Reinvented content drives key insights into consumers’ true brand affinity increasing value to brands in ways no other company can do. We provide this valuable insight to Brands which, in turn, encourages Brands to deepen their relationship with us, resulting in more content and stronger deeper learning algorithms.

Business Model

We generate profits through three revenue streams:

1. Content licensing fees for user generated content leveraged by 3rd parties
2. Subscription fee charged to brands for access to cr8 data analytics platform
3. 20% fee charged to content creators that generate ad dollars from content created on platform

Our Ask

Our team is asking for $175K pre-seed investment. The money will be used for:

1. MVP development, which requires one machine learning contractor
2. Design needs, Front-end UX/UI design contractor
3. Initial growth marketing budget

Opportunity:

The concept of a virtual personal assistant has progressed rapidly in recent years. Historically, real-world implementations have included a virtual receptionist directing customers (the digital phone tree), voice-typing software transcribed audio recordings. Apple’s release of Siri in 2011, was the first commercially viable and dynamic personal assistant product directed towards consumers. Since Siri’s debut, large technology companies have released their own versions of broad, voice-based consumer facing personal assistants, including: Cortana (Microsoft), Alexa (Amazon), and Google Voice/Now (Google). On the startup side, leading technologies are text-based and focused on specific verticals – notable companies include: X.ai, Clara Labs, and Julie Desk.

IT research firm Gartner predicts that many touch-required tasks on mobile apps will become voice activated within the next several years. The voices of Siri, Alexa and other virtual assistants have become globally ubiquitous. Siri can speak 21 different languages and includes male and female settings. Cortana speaks eight languages, Google Assistant speaks four, Alexa speaks two. For the first time ever, parity with the human voice has been reached amongst conversational speech recognition and AI systems at an accuracy level of 93.4%. The consumer opportunity for voice technologies is huge, as the number of people worldwide using AI voice assistants is projected to increase to 1.8 billion by 2021.

The even larger opportunity is the market for voice AI products within enterprises. In a joint announcement, Amazon and Microsoft have said that the companies will be combining their technologies to unlock new opportunities within the enterprise. Research firm Tractica estimates that more than 23,000 AI voice assistants will be deployed for customer service applications between now and 2022. The most impactful technologies will deployed upon datasets that are constantly streaming and standardized.

Solution (james.ai):

James.ai is a enterprise AI assistant that leverages artificial intelligence to become the center of the efficient enterprise. By focusing in this space, our company could become the dominant enterprise interface, an claim a powerful dataset. Voice has yet to be digitized and the product could leverage team meetings as a beachhead to build a valuable data asset centered on voice before expanding into broader enterprise use cases.

Specifically, james.ai is an AI-powered note-taking personal assistant that attempts to optimize business meetings by joining scheduled calls and taking note of key action items and follow ups. The technology then circulates a summary of those key moments to participants following the meeting. Over time, the technology will integrate with and automatically update enterprise systems (e.g., Salesforce, slack, trello, etc.), addressing the ongoing challenge of end-user adoption. In the long run, the company will become an open platform for other vertically-focused AI agents to integrate with and the product will become capable of emotion and behavior analysis to further optimize enterprise workflows.

Commercial value:

The Company’s initial product experience would be straightforward. The user journey involves four main steps:

– A subscriber schedules a meeting and adds james.ai (via an email address) to the meeting invite

– james.ai dials into the group meeting and announces his presence

– james.ai silently participates in the meeting by taking note of key action items and follow ups, leveraging natural language processing

– james.ai emails a summary of key moments noted to meeting participants and updates enterprise systems

From a go-to-market perspective, we would leverage a consumerized B2B sales model and focus on sales teams. Client meetings are core to the function and sales professionals are highly dependent on CRM systems, making the group an attractive initial target for an AI personal assistant. The company would greatly benefit from the natural network effects and virality elements driven by group meetings – one subscriber invites multiple attendees to a meeting, naturally exposing other potential customers to james.ai.

Product promise:

To jumpstart the product development process, we could leverage public domain NLP technologies and deploy these technologies through group meetings that are tied to classes that we are taking at Booth. Upon receiving feedback from this process, we can begin to leverage the learning to deploy NLP technologies on larger datasets – for example club meetings.

Upon establishing an accuracy baseline, we can then run A-B tests, applying the NLP technology to different types of meetings that stretch the accuracy of the product. As we experience when and how accuracy declines, we can then better understand which vertical within types of enterprise meetings is best to build an MVP.

Sources:

https://www.engadget.com/2018/04/09/in-pursuit-of-the-perfect-ai-voice/

https://blogs.3ds.com/northamerica/the-rise-of-ai-voice-assistants-in-the-enterprise/

 

 

Opportunity: the incredible decrease in gene sequencing cost and the promise of blockchain could create a new era of precision medicine.

The dramatic decrease in the cost of sequencing a human genome may well end up the most compelling and consequential technology advance of the past decade. Ten years ago, it cost $10M to sequence a genome. Illumina, the leader in sequencing, promises a $100 genome in the next few years and several promising startups are pursuing credibly higher accuracy and long reads at $100 or less.

Cataloguing and understanding the genome promises a wide range of significant, valuable advancements in human health. With better genetic information, personalized, precise therapies can be developed to treat diseases. It can be used to help people make optimal choices about medical treatments and daily health decisions, such as diet and exercise. Massive genomic datasets can be used to help pharma companies develop better, more effective drugs.

While many of these opportunities are not yet realized, pharma companies are currently paying large sums for genomic datasets to help with drug development. In 2012, Amgen acquired the company deCODE and its 2,600 sequenced genomes for $415M. Genentech paid 23&Me $60M for access to its database. AstraZeneva and Regeneron have both launched multi-million dollar programs to sequence 2M and 500K genomes, respectively.

Solution: use blockchain’s augmented agreements to create a marketplace for genomic data

Nebula Genomics aims to seize this opportunity by creating a marketplace for genomic and phenotypic data. While the cost will fall soon, sequencing currently costs about $1,000 per genome; luckily pharma companies are willing to pay significantly more per genome than that.

A thornier problem is privacy. A person’s genomic sequence is critical, private information. Bank accounts and social security numbers can be changed; genetic makeup cannot. Luckily, the emergence of blockchain protocols allow for a secure, distributed transaction that compensates the person for the data they contribute and gives participants a stake in the network value created by Nebula’s platform.

Nebula’s marketplace:

Pharma companies pay fiat money for tokens, which they pay to Nebula Participants. Nebula Participants pay in tokens to get their genomes sequenced by Nebula, which plans to sequence through partners.

Importantly, Participants store that data themselves, using encryption tools provided by Nebula. This ensures control over data and security as the data isn’t pooled in a central location.

Participants can use Nebula’s genomic self-analysis tool to gain insights about their genome. As more large-scale genomic studies are done, this analysis tool will improve over time, making the platform more valuable to users.

Pharma companies then buy data from users using the Nebula platform. This can be one-time purchase of genomic and phenotypic data or requests for more longitudinal data, such as health records of fitbit data. Participants are paid in tokens, ensuring anonymized transactions. Finally, Participants can sell tokens for fiat money on the market as Pharma companies will regularly pay fiat money for tokens.

Personal Genomics companies are middlemen:

Nebula is a data marketplace, facilitated by sequencing and a blockchain protocol for distributed, anonymized compensation:

Feasibility, risks:

Feasibility relies in large part on acceptance, adoption, and regulation of cryptocurrency. Risks are tied to the booming, but imperfect blockchain and cryptocurrency market. Considering the large amount of money recently invested in cryptocurrency and blockchain companies, it appears that market is here to stay — but, in the event of a serious falling-out of the market or adverse regulatory intervention, Nebula will face challenges. To Nebula’s credit, they aren’t a speculative currency and their business model is greatly enhanced by blockchain protocols.

 Competitors

23&Me and Ancestry are two leading personal genomics companies. They only sequence a person’s exome, or the 1.5% of the genome that we know codes proteins. At this stage, we think the rest of the genome plays little role in our biology, but most scientists admit that the rest of the genome is likely more important than we know. Many now believe — and Nebula’s asserts — that sequencing the entire genome is important. Critically, Nebula’s collection of phenotypic data and structure that compensates participants clearly differentiate it from all personal genomics companies.

LunaDNA is another competitor that adopts a similar model to Nebula, but serves as a middleman and lacks Nebula’s quality team.

 

Sources

Nebula White Paper: https://www.nebulagenomics.io/assets/documents/NEBULA_whitepaper_v4.52.pdf

MIT Tech Review: https://www.technologyreview.com/s/610221/this-new-company-wants-to-sequence-your-genome-and-let-you-share-it-on-a-blockchain/

Fortune: http://fortune.com/2018/02/09/blockchain-genetic-testing-nebula/

Wired: https://www.wired.com/story/solve-genomics-with-blockchain/

CNBC: https://www.cnbc.com/2018/02/08/harvard-genetics-pioneer-will-monetize-dna-with-digital-currency.html

Exome vs Genome: https://www.jax.org/news-and-insights/jax-blog/2016/september/genomes-versus-exomes-versus-genotypes

 

 

 

 

 

Opportunity: the growing EV market exacerbates existing challenges for electric utilities.

 

Electric Vehicles (EVs) are a rapidly growing market that’s expected to grow to significant size in the next decade. Below charts are from the International Energy Agency’s Global EV Outlook 2017 report and Edison Electric Institute’s prediction that the US will have 7M EVs by 2025.

 

Energy Utilities are taking notice. Utilities actively encourage EV adoption as they view it as an important future revenue stream. Southern California Edison’s report on EV adoption notes that utilities care about the location, charging schedule, and habits of EV owners. And, for good reason — EV households have significantly different energy use profiles:

Finally, the rise of renewable energy production in the grid has created new challenges for utilities. Utilities have long faced a deepening “duck curve,” which is greatly exacerbated by the above EV charging profile.

The electric utility market is a government-sanctioned geographic monopoly, and EV charging is fast becoming an important part of that market. We propose a solution to augment that market through tracking EV user habits and incentivizing behavior to flatten the duck curve.

Solution: Platform to augment the EV charging-Utility market and flatten the duck curve

We take inspiration from the NREL researcher that predicted and studied the duck curve: Paul Denholm. In an interview with Vox, he states that if utilities could provide the right price incentives, EV charging could be used to flatten, rather than exacerbate, the duck curve.

We propose creating a platform that connects the electric vehicle’s system to the cloud and use the data collected to collaborate with the utility companies in order to optimize the value for all parties involved. The platform would analyze the driver’s habits and calendar, predict future behavior, then advise the driver when to charge based on both optimized pricing and predicted future driving requirements. It would provide utilities with a representative data set with which they could predict demand and optimally price to encourage flattening the duck curve.

Because this platform would require both widespread deployment of software on EVs (which isn’t yet easily done) and cooperating with an electric utility, we think an EV OEM is best positioned to develop a joint venture with a utility and implement our platform. Therefore, we are proposing this solution for Tesla to incorporate into the software for its electric vehicles. This would be mutually beneficial for drivers, utility companies, and Tesla:

• Drivers: the application would help drivers remove the decision-making process of when and where they should charge their electric vehicle. Using a surge-pricing model, drivers would be incentive to charge their vehicles at locations that best fit their specific priorities (i.e. cost, time, range anxiety). For example, drivers would be able to plug their car in overnight and the system would then optimize at which times that night the car would be charge (i.e. 1am-3am and 5-6:30am).
• Utility companies: By leveraging the data generated by the system, the utility companies would be able to predict electricity usage and optimize energy production and maintenance of its assets in different areas. It would also help them set pricing to encourage smoothing of demand surge throughout the day. Lastly, eventually, utility companies will be able to optimize where to expand when and where to invest in the CapEx of expanding charging stations throughout a particular current city or expand to a new one.
• Tesla: the main benefit for Tesla is the differentiating advantage that it gives them over competitors. For example, Tesla could use the data to negotiate a cheaper electricity rate with utility company, attracting consumers to choose their EV rather than competitors.

Feasibility is increased if an OEM implements this solution.

As the density of EVs which have the solution installed increases within a specific area, the platform will gain more profound insight into the usage patterns of geographically clustered consumers. By understanding their preferred routes, planned trips and holidays, and charging habits, the platform will suggest the most appropriate time for the consumer to charge their EV based on their preferences (i.e. convenience vs pricing).

That considered, the feasibility of the solution rests primarily on the ability for the platform to be scaled. For success, there must be a representative sample in any and all given geographical locations that the solution is marketed for the necessary insights to be achieved.

Given that a lack of scale is the most challenging obstacle preventing a dataset significant enough to draw insights, the implementation will be best suited to rollout from an EV manufacturer. The OEM’s willingness to adopt the software and install as standard in all vehicles will provide the scale required, increasing the utilisation percentage as the EV market booms in the coming decade.

Pilot: Tesla uses OTA to implement a test-run in a small market

Tesla is currently the EV manufacturer best positioned to implement this solution. To begin the pilot, Tesla would implement the algorithm by releasing it as part of an over-the-air (OTA) update to a portion of its existing customer fleet. Data is collected both on the usage of each vehicle and the recharging preferences manually entered by end users. This approach will build on its existing data set of travel routes, providing a deeper understanding of consumer preferences for recharging based on their disclosed preferences and inputs on future travel plans by end users.

Once the customer-specific usage patterns of all Teslas within each specific geography is understood, Tesla can predict future usage patterns for where and when all Teslas are expected to travel and recharge, based on machine learning and AI modelling. This data will identify periods of high demand, which will be of particular interest to utility companies.

Tesla can then share these predictions with utility players, encouraging them to charge variable pricing within specific time periods of geographies. Variable pricing will allow utility providers to smooth out their demand curve and increase margins. In return, Tesla may wish to negotiate lower kWh rates at public charging locations

Competitors & Risks

There are many applications that help EV drivers find charging stations, but none have the ability to predict pricing or analyze a driver’s behavior to recommend charging time.

GM is the main Tesla competitor for plug-in EVs in the US; GM could implement a similar solution. However, this would probably help — it would get more utilities on-board with dynamic EV charging pricing to influence demand curves.

One critical risk is driver adoption of the service. Drivers face significant range anxiety, which is a key barrier to EV adoption (despite the irrationality of this fear). The temptation to plug in the EV and charge it as soon as the driver gets home from work is pretty strong. The application and utility can defeat this in three ways:

• Set prices to significantly incentivize changing charging behavior
• Track savings accrued — recognize good behavior
• Reliably predict the driver’s schedule to avoid an under-charged emergency

Sources

Global EV Outlook 2017 report: https://www.iea.org/publications/freepublications/publication/GlobalEVOutlook2017.pdf

SC Edison report on EVs: https://newsroom.edison.com/internal_redirect/cms.ipressroom.com.s3.amazonaws.com/166/files/20136/SCE-EVWhitePaper2013.pdf

SEPA report on energy grid preparedness for EVs: https://www.utilitydive.com/news/time-is-not-on-their-side-utilities-ill-prepared-for-ev-demand-sepa-finds/519530/

Vox Article with Denholm, discover of the duck curve: https://www.vox.com/energy-and-environment/2018/3/20/17128478/solar-duck-curve-nrel-researcher

Recursion Pharmaceuticals uses augmented perception to speed up the initial stages of drug discovery.

 The problem. Matching thousands of drugs to thousands of diseases:

There are thousands of rare diseases and thousands of FDA approved drugs that may have a positive impact on those diseases, but those drugs, both singly and in combination, on the rare diseases is time-consuming. Rare diseases are diseases that affect less than 200,000 people.  Pharma companies are less likely to pursue those smaller markets. Despite the small market of any single disease, it’s estimated that rare diseases affect 10% of Americans. If Recursion’s platform can help access this large market of 10% of Americans, it could be massively valuable.

Recursion was founded by an experiment to find a drug that impacts a rare disease which weak blood vessels lead blood into the brain and cause strokes. The experiment applied 2,000 different drugs to a diseased cell sample. Then, a pair of cell biologists looked at the 2,000 experiments to evaluated the phenotypic impact – or how the drugs appeared upon visual inspection to impact the cell sample. A computer algorithm developed by Anne Carpenter’s group at the Broad Institute also looked at the images from the experiments. The human team selected 39 drugs that appeared to have a positive impact on the diseased cells.

Here’s the interesting par: the computer program also selected 39 drugs – but the computer-selected and human-selected sets didn’t overlap at all. The computer selected 39 different drugs than the people did.

And, after closer study, only one of the people-selected drugs continued to appear to have an impact while 7 of the computer-selected drugs were impactful enough to merit further study.

The solution. Recursion’s automated phenotypic drug discovery platform:

Phenotypic drug discovery involves testing a drug in vitro (in a test tube) by applying a drug compound to a diseased cell sample. Researchers can judge the impact of the test by observing phenotypic changes in the cell sample.

Recursion’s platform has automated microscopes that sends thousands of images of in vitro tests each week to image recognition software. The software, armed with image data of healthy tissue, looks at the images and determines if the tested drug makes the cells look healthier.

Initial results. Recursion has made significant progress toward their goal of treating 100 genetic diseases by 2025:

Thus far, Recursion has identified promising compounds for 34 different rare diseases. Seven have progressed to in vivo tests and two are nearing applying to enter FDA trials. Their success has generated investor interest; Recursion has raised nearly $80M since February 2017.

 

 

Critique: the pharma value chain is challenging: 

The main issue that I can see with Recursion’s platform is that, because of the nature of the value chain in drug discovery, it fails to extract much value. Simply identifying that an approved drug has in vitro phenotypic impacts on a cell sample isn’t worth much. From there, millions of dollars must be spent to test, optimize, and characterize the lead compound in animal models. Then the drug must be tested in people in FDA clinical trials, which takes many years and many $Ms.

Recursion simply produces the early lead. Now, that drug is FDA-approved, so safety tests would often (but not always) less intensive. And, many pharma companies that own these drugs just have them sitting on the shelf, not being used or sold. So it could be a compelling tool for large pharma companies that have patented compounds that are failing to generate good data in the clinic – sort of a second-life option for those drugs.

 

But the dataset could be valuable in the long run:

However, Recursion also seems focused on generating a massive dataset of cellular models, which grows by 20 TB each week. This data could be a valuable tool for a large pharma company that develops its own drugs, such as Recursion partner, Sanofi. I would suggest focusing on collecting that data. Perhaps the company could develop a portable version of their platform and partner with CROs (contract research organizations) to collect data from clinical trials.

 

However, in the long run, competition from AI drug design companies may be problematic:

One general category of competitors are general AI-enabled drug development companies, such as Insilico Medicine and Atomwise. These companies promise to perform rational design of drugs from computer models. However, they haven’t yet produced compelling results.

 

Sources

Recursion: https://www.recursionpharma.com/

NIH profile: https://sbir.nih.gov/statistics/success-stories/recursion

NIH rare diseases: https://rarediseases.info.nih.gov/diseases

Anne Carpenter’s group at the Broad: https://personal.broadinstitute.org/anne/

Investor blog post about Recursion: https://medium.com/@CRVVC/recursion-pharmaceuticals-ai-enhanced-drug-discovery-fdb8d7aad64c

Tech Crunch article about $60M Series B: https://techcrunch.com/2017/10/03/drug-discovery-startup-recursion-raises-60-million-in-series-b-from-dcvc/

FierceBiotech profile: https://www.fiercebiotech.com/special-report/recursion-pharmaceuticals

 

 

Team Members: Brentt Baltimore, Moises Numa, Corey Ritter, Mitchell Stubbs