Author: ssteiny

Knowhere

ssteiny

Opportunity:

The newspaper industry represents a huge opportunity, with 2.7B print readers and 1.3B digital readers worldwide. In the U.S. alone, newspaper publishing was a $24B industry in 2018 and magazine and periodical publishing was a $28B industry in 2017, and this doesn’t even take into account news from other sources such as online-only publications. However, people have been increasingly concerned with bias in the news, with 72% of U.S. adults stating that news organizations “tend to favor one side” when presenting the news on political and social issues. Only 20% and 5% of U.S. adults trust information “a lot” from national news organizations and social media, respectively. The desire for unbiased news carries beyond the United States, as globally a median of 75% across 38 countries state it is “never acceptable” for a news organization to favor one political party over others.

 

Solution:

To solve news bias issues, Knowhere uses machine learning and artificial intelligence to ultimately produce news stories written by machines. To do so, Knowhere’s algorithm selects topics based on what is most popular on the internet. The system then reads thousands of articles on the topic (from a combination of left wing and right wing sources) to gather data and writes an article on the topic based on what it finds. The technology strives to removes any bias, and presents information based solely on facts, while still reading like an article written by a journalist. Knowhere also weighs the accuracy of the sources it is using, making sure to weigh more accurate sources more heavily.

 

For political topics, Knowhere also produces “left” and “right” versions of each article so users can pick which they prefer, as well as compare different tones and viewpoints. Ultimately, two human editors review each article before it is published by Knowhere.

 

Challenges and Competition:

Most competitor companies (such as Google and Wikipedia) are focusing on mitigating the issue of fake news and increasing accuracy, while Knowhere is focused on the issue of removing bias. One of the challenges Knowhere may face is the fact that people often like to read biased information and fall privy to confirmation bias. They may not be open to trusting sources that offer perspectives different from theirs. Additionally, while Knowhere is taking strides to reduce bias, the company as a whole and its algorithms were created by humans, which means that there is inherently bias in the structure of the system.  Finally, Knowhere may face challenges in respect to making money. Most news sites make money through targeted advertising, but theoretically, Knowhere is looking to reach an audience through unbiased information, which makes targeting toward a specific group less useful.

 

Suggestions/Improvements:

Knowhere aggregates and filters news but does not create original journalism and relies completely on the work of others. It is currently not clear how Knowhere can best monetize their service. Objective news feed that targets all internet users will not generate individual user data that can be used for advertising targeting. Knowhere can offer paid consulting services that will help other news providers become more accurate or at least make them aware of how they stack in comparison with their competitors. That service may be of interest to chief editors who can benefit from understanding the biases of the journalists who work for them. As such, it can aid hiring decisions in the publishing industry. Finally, publishers can make the service available to their writers to increase their productivity.

 

Another challenge is to make the Knowhere content more widely available. To increase consumer awareness for their service, Knowhere can publish an index of accuracy for news sources. It can publicize the index through partnering with parties such as Wikipedia, Firefox, Google, or other interest and advocacy groups. Further, Knowhere should develop an app with the ability to set alerts for different topics. One of their areas of focus are complicated issues that are likely to have follow up coverage. The alerts can help retain the attention of users who liked a Knowhere article on the topic. Knowhere can add key word labels and a “Topics” section. Thus, a user who is newly interested in a topic, can easily access the universe of articles that Knowhere has already published on that topic. They can also make sharing easier and add buttons that allow users to email articles (the current options are only to share through Facebook and Twitter). Finally, they can add a “Local News” section that can draw in additional users.  

 

Sources:

 

Duffle

ssteiny

Opportunity:

Travel and tourism is a huge sector, with direct contributions of $2.57 trillion to the global economy in 2017 and total contributions of $8.27 trillion [1]. Travel agencies (including companies such as Expedia and Priceline) generated $40.5 billion dollars in 2017, with annual growth of 5.3% from 2012 to 2017 [2]. Given the size of this industry, even small improvements in helping consumers decide on and book travel arrangements would have a huge impact. Many consumers are looking for more tailored and personalized recommendations. Yet as the amount of options and information on travel increases, it becomes ever more time consuming to sift through all of the possibilities to find the unique and perfect fit for any given individual. This creates a large opportunity to go beyond the traditional and old-fashioned ranking systems.

 

Solution:

Duffle is a response to the increasing demand for unique experiences. It facilitates travel discovery and booking by analyzing personal preferences (stated and based on past reviews), airline, restaurant, car rental, tourist activities, weather, and social media data. AI-powered chatbots consider budget, frequent flyer and other reward membership information, location, and availability. Users are able to swipe left or right trip and activities recommendations. The algorithm learns over time from the user response to recommendations and can improve them when users input personal information such as pet or kids joining for the trip. Fellow travelers can also be added to a trip so Duffle and not the main user is the Q&A point for all people on the trip. For solo travelers, Duffle can connect users to other travelers considering the same destinations and dates.

 

Once a trip is booked, Duffle compiles a list of recommended attractions or activities and historical information about sites the user can visit. The service includes automatic check-in, 24/7 travel assistance app, and on the ground guide.

 

Duffle assists users past the initial booking by providing packing list recommendations, local sim card options, or advice on recommended vaccinations. It can also alert to security concerns or unfavorable weather changes and offer quick assistance with rebooking. The platform benefits from network effects on the client side since the algorithm learns from the likes and dislikes of the specific user and those of other similar users and can connect travelers with similar interests, budget, and desired travel dates.

 

Pilot:

Our pilot would require the initial aggregation of source data needed for the curation algorithm. Thus, we would begin to create the network effects within the university format at the University of Chicago or University of Illinois at Chicago (or any comparable university) that have a population of active social media participants that engage with rich multimedia content which can provide travel preference data. We would initially pool this data to create curated content which our data scientists and travel agent partners would initially evaluate for relevance and feasibility. After 2-3 months of the aggregation process, we would look to merge the data and algorithmic learnings to create a machine learning software that can input new data sets, curate existing ones, and accept continuous inputs that will allow it to learn continuously.

 

Post this initial data collection and curation period, we will engage our university volunteers in person and present them our recommendations. We will conduct online surveys and in-person interviews to gauge their WTP for the trips recommended to them and their relative interest levels. Additionally, we will compare our recommendations vs those of our competitor set (see below), using the data we collect on the student’s interests, geography, and finances. We would also have them rate their interest levels relative to our recommendations and those of competitors on a blind basis.

 

While the student population is ideal to begin with, they have limited income levels and travel abilities. We would lastly roll out this final pilot stage to all members of the university (faculty, staff, etc.) in order to maintain a comprehensive set of age and income ranges for our algorithm that encompass all market segments. We will look for our pilot to confirm/negate our hypothesis that our recommendation platform with be 1) more robust and comprehensive than competitors, 2) more thorough in its presentation, 3) provide more value for each budget, 4) have a higher probability of booking than competing platforms.

 

Competitors/Risks/Feasibility:

Currently, there are various companies offering services that Duffle will offer, but none that have successfully combined the data that we plan to combine, and utilized artificial intelligence to optimize the service. Hopper, a discount flight platform, has launched an “AI driven travel agent” that allows users to input preferences about their ideal trip (i.e. duration, time of year) and get recommended flight deals. The platform learns from which deals the user does or does not accept. We plan to utilize AI in a similar way, but through connecting a user’s social media and allowing for more preference information, can offer a more customized result, beyond just flight deals. Mezi, a corporate travel website acquired by American Express, uses AI to learn about a traveller with every additional trip – when they book, what type of ticket they prefer, etc, and provides a platform for booking and managing work travel. Our goal is to provide a similar platform for booking and managing consumer travel and combining it with recommendations for activities, hotels, etc while on the vacation.  

 

While feasibility of the product is reasonable given we have seen companies utilize different aspects of the technology, and are just looking to aggregate these features, there are risks associated with Duffle. Travel is a popular industry with many companies looking to disrupt or own the booking and planning process. Given that, we will need to rely on network effects and push marketing while continuing to innovate and improve the product.

 

Sources:

[1] World Travel and Tourism Council. Total Contribution of Travel and Tourism to the Global Economy from 2006 to 2017. Reported through Statista.

 

[2] IBIS World Industry Report. Travel Agencies in the US. September 2017.

http://www.hopper.com/corp/announcements/hopper-can-now-predict-where-youll-want-to-go-on-vacation

https://mezi.com/

Team Members:

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez

Descartes Labs

ssteiny

Opportunity:

Descartes Labs is a startup founded in New Mexico in 2014 that is building data refinery for satellite imagery to better understand the planet. Currently, even with so much data and imaging of the plant available, it is difficult for companies and government agencies to successfully predict crop yields and potential shortages. This poses a challenge when preparing in advance for the changes year over year and increases the concern for climate change and food scarcity around the globe. Descartes claims that it can accurately predict crop yields, beating out the accuracy of the US Department of Agriculture, which is currently the only alternative for information.  

Solution:

Descartes uses the increase in the availability of large data sets accumulated by the increase in shrinking and cheaper sensors, as well as the rise in popularity of nanosatellites to determine how healthy the corn crop is on the planet from space. The company uses spectral information (non visible to the human eye) to measure chlorophyll to make these predictions and analyzes satellite data of every single farm in the US on a daily basis to update its predictions and deliver local estimates. 

Effectiveness, Commercial Promise, and Competition:

In terms of effectiveness, Descartes Labs states that it “can predict the yield of America’s 3 million square kilometers of cornfields with 99% accuracy.” Additionally, in 2015, the predictions made by Descartes beat those of the United States Department of Agriculture by 1% and the algorithms of the company continue to improve year over year.

Descartes Labs presents an opportunity for a wide range of groups, including corporations, government leaders, and humanitarian groups. For example, Cargill, an agricultural conglomerate, is a customer of and investor in Descartes Labs. The technology likely helps Cargill understand crop yields for a given year. Descartes Labs also received a grant of $1.5 million from the U.S. Defense Advanced Research Projects Agency, which uses the technology to anticipate food shortages, and thereby predict areas of sociopolitical conflict, in the Middle East and North Africa.  

Another application of the technology is disease forecasting and prevention. The high resolution private and public satellite data can help identify high risk environments such as areas with stagnant water conducive to mosquito proliferation. Those leads can be combined with medical and social media data to predict and backtest the spread of diseases. Such information will be valuable for epidemiologists and local governments.  

Several competitors include Orbital Insights, Gro Intelligence, and Tellus Labs. Orbital Insights covers a much wider range of industries – for example, it can help retail companies understand vehicle counts and traffic monitoring.

Suggestions / Improvements:

To improve, Descartes could utilize it current data and connect with various sensors on other devices, to triangulate the information it has and make more accurate predictions. This is a direction that CEO and Co-Founder, Mark Johnson, wants to go, given the vast amount of “potential sensor data we’ll be getting from combines, tractors, cars, boats, barges, trains, ships, grain silo. Everything is going to have sensors on it, so making sense of all that data is the sort of challenge we’re aiming toward” (Mark Johnson in Fast Company).

Descartes can also explore ways in which their data and capabilities can benefit individual farmers in addition to commercial clients such as Cargill. Descartes can partner with NGOs, consultants, and local governments to enable subsistence farmers with its data and technology.  

Another potential application is to tackle wildfires. Descartes can combine weather, geo imaging, and historical data from previous wildfires to identify high risk areas and potentially suggest effective ways for wildfire suppression once fires break out.

Sources:

https://www.descarteslabs.com/

https://www.fastcompany.com/40406046/this-startup-is-building-a-fitness-tracker-for-the-planet

https://medium.com/@thephilboyer/announcing-our-investment-in-descartes-labs-9dca8257d0d9

https://www.forbes.com/sites/themixingbowl/2017/09/05/can-artificial-intelligence-help-feed-the-world/#110f4bd346db

https://blog.nationalgeographic.org/2018/02/21/forecasting-diseases-one-image-at-a-time/

https://venturebeat.com/2017/08/24/descartes-labs-raises-30-million-to-better-understand-earth-with-ai/

https://www.theverge.com/2016/8/4/12369494/descartes-artificial-intelligence-crop-predictions-usda

Clients can integrate their data with the Descartes Platform to create their own solutions, models, and forecasts:

Team Members:

 

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez