Opportunity

Similar to other creative arts like television and film, music is at a very early stage of incorporating AI / machine learning-created content. Current AI / ML applications have been primarily limited to streaming services, where companies like Spotify will, among other techniques, use AI / ML to analyze actual song content to find related music.  One of the primary barriers to entry for companies looking to create content through AI / ML is that such content may lack the appropriate “star power” (ie, brand) to ensure widespread adoption: mainstream artists will likely not be the ones to source material from AI / ML, nor can AI / ML-composed music stand on its own outside of a few select genres.

However, when looked at as a collaborative tool, AI / ML composition could provide a royalty-free source of creative input for artists, yielding new music in a crowded marketplace. Moreover, certain genres will be very conducive to AI / ML-created material, such as electronic dance music (EDM). Lastly, AI / ML-sourced music can decrease costs in music applications where a brand is of little or no importance, namely movie soundtracks and advertising music.

 

Solution

Amper Music is a cloud-based, supervised AI / ML music creation tool that allows musicians and composers to develop music through a variety of inputs. More specifically, musicians define the objective function that the creation tool will solve for by specifying parameters like mood, style, instrumentation, tempo, and song length. The software will produce content through AI / ML, at which point artists can modify the AI / ML generated content to produce unique, royalty-free content. Amper can also be accessed through existing Adobe software as a downloadable panel, further reducing artists’ switching costs.

 

Commercial Promise

The global music industry is clearly a large market, totaling nearly $50B in revenue in 2017 and projected to grow to almost $60B by 2022. Amper has successfully navigated several stages of VC funding and, as of March 2018, has a valuation of $30M, so investors see a promising product.  It is clear that there is a market for the type of music Amper is generating. Developing new music to accompany a project in the traditional manner, or even licensing it, is a more costly option than that which Amper offers. The process of generating music for a project with Amper is very simple and easily customized to suit the content while still very low cost.  

 

The primary threat to Amper is the relatively low financial barrier to entry.  If the market were large enough to garner major interest from the business world, then financial barriers to entry would be non-prohibitive. That said, there is an unknown amount of time required to refine the ML algorithms suitable for content creation, which may contribute to a higher barrier to entry.  Many researchers and even other companies have experimented with this type of music generation to varying degrees of success. In fact, a company called Jukedeck tried this very business model several years earlier and has made little progress. One clear advantage that Amper has is a point of entry into a creative marketplace.  Amper has a developed a “panel,” which Adobe Premier users can download to easily incorporate Amper into their projects.

Alterations

While AI / ML-created content has nearly universal applicability within the music industry in terms of output genre, in the early stages it may prove more beneficial for Amper to specialize in a given application or genre, even if it makes its software available to other applications. Amper could specialize in genres like EDM, where artists are the most likely to adopt Amper Music’s tool as a creative partner, which would allow Amper to further refine its creative algorithms and gain traction with notable EDM artists. At that point, Amper could use the success from this venture as a launch point for new genres. While simultaneously building credibility within the music industry through EDM, Amper should also prioritize artist-agnostic applications like advertising in order to provide reliable sources of revenue. Once sufficiently established within the music industry, Amper could round out its offerings as a complete music collaboration tool by partnering with Narrative Science or another natural language processor to develop lyrical content to pair with the audio.

 

Sources

Amper Music

https://www.ampermusic.com

 

Musical Artificial Intelligence – 6 Applications of AI for Audio

https://www.techemergence.com/musical-artificial-intelligence-6-applications-of-ai-for-audio/

 

Global Music Industry Revenue 2012-2022

https://www.statista.com/statistics/259979/global-music-industry-revenue/

 

Audio Synthesis at Jukedeck

https://research.jukedeck.com/audio-synthesis-at-jukedeck-788239d65494

 

Jukedeck Financial Statements

https://beta.companieshouse.gov.uk/company/07953149/filing-history

 

How Does Spotify Know You So Well?

https://medium.com/s/story/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe

 

Pitchbook: Amper Music

https://my.pitchbook.com/profile/122486-41/company/profile#timeline

 

Pitchbook: Jukedeck

https://my.pitchbook.com/profile/60158-44/company/profile#timeline

 

Team Members

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer

 

• Opportunity

 

The recent increase in the number of satellites, providing multi-spectral, full-earth coverage at rapidly decreasing cost, provides significant opportunity to achieve business insights through the analysis of real-time or near real-time high-resolution satellite imagery. When coupled with significant increases in computing power and cloud computing networking, companies can use machine learning algorithms to interpret, analyze, and process imagery data to solve business problems across a large number of applications. At a simplistic level, “picture-takers,” or companies that operate a constellation of satellites, capture images in multiple spectrums (visible, infrared, radar, etc) of the earth at high resolution (down to 30 cm) for further processing. Next, a “sense-making” company accesses the data and analyzes it by comparing different images of the same object, aggregated across multiple objects and periods of time, in order to provide business insights for the given application. One of the leading companies in analyzing this satellite imagery is Orbital Insight.

 

 

• Summary of Solution

 

While some satellite companies also perform the subsequent analysis, Orbital Insight is purely a “sense-maker.”  They are experts in getting useful aggregate information out of the satellite images. In order to do this, they employ proprietary machine learning and computer vision algorithms to observe changes in specific activities or resources which are visible in or can be deduced from the satellite images.  The algorithms are trained to identify specific features in the images, like cars in a parking lot, crops in a field, or oil tankers at sea, and quantify them. In doing so, Orbital Insight can preempt commodity trends or estimate non-public information regarding a company’s performance. They partner with several key satellite constellation owners to gain access to higher quality, more complete, or more frequently updated satellite imagery.

 

 

• Evaluate Effectiveness / Commercial Premise

 

Orbital Insight solves business problems through both industry and product-specific channels. While Orbital Insight currently does the majority of its business through product-specific channels, analysis gleaned from satellite imagery can be applied to almost every industry where insight is possible through monitoring global trends or competitor performance. Among others, notable consumers of Orbital Insight’s analysis include retail, energy, financial services, agriculture, insurance, and government. By using satellite imagery, for example, Orbital Insight is able to forecast U.S. corn and soy production with predictive crop yield analytics through merging rich satellite data, weather and historical data in real time to provide investment-grade insights to a variety of clients including hedge funds, asset managers and financial data service providers before commercial and government statistics are available. In another application, Orbital Insight used satellite imaging in the aftermath of Hurricane Harvey to refine their model predicting flooding for their insurance company clients. Orbital Insight’s use of machine learning and data analytics applied to satellite imaging can provide customers with both valuable investment insights and business solutions.

 

• Competitive Landscape

 

As mentioned previously, the competitive landscape in the satellite imaging market is largely divided between “picture-takers” and “sense-makers,” and some companies try to span both business models. As a result, Orbital Insight competes against both imaging companies that perform analysis (e.g. Planet Labs) and companies with no satellites who focus solely on analysis (eg, Descartes Labs). While the satellite imaging industry (i.e. the “picture-takers”) is projected to be a $6.8B industry by 2023, the satellite imagery analysis industry in which Orbital Insight competes is less mature and composed largely of private companies, spanning industries that produce trillions of dollars in annual revenues. There are very few barriers to entry for imagery analysis, and thus Orbital Insights achieves its competitive advantage through partnerships with a number of the “picture-takers,” where the quality of analysis produced from its algorithms is higher due to Orbital Insight having access to more timely images sourced from different data streams (i.e. multi-spectral), and its leading proprietary machine learning algorithms. While Orbital Insight is a private company that was founded very recently, with a valuation of approximately $230M, DigitalGlobe, a leading public imaging and analysis company, provides an idea of the size of potential revenues, achieving $725M in revenue in 2016.

 

 

• Proposed Alterations to Increase Value

 

Orbital Insight has a strong business model with a clear value proposition, but operates in a competitive field with low barriers to entry.  They are entirely at the mercy of the “picture-takers” and are squeezed at both sides of the stack with little likelihood to succeed through vertical integration.  In order to succeed in the space, they should differentiate themselves on algorithm performance and take measures to protect their methods. If they are known as the player that can most accurately predict commodity trends and extract the most value from satellite images, then they will have a place in the ecosystem. Since the software patent landscape has varying levels of effectiveness based on the applicable country, is complicated, and in a state of flux, Orbital Insight should be more proactive than the average company in protecting its IP. Lastly, in order to not be beholden to any single upstream firm which may itself try to vertically integrate, they should also make efforts to expand the number of partners in the satellite imaging industry.  This will ensure that satellite imagery continues to be a widely available product, allowing companies like Orbital Insight to benefit from an ever-expanding amount of data.

 

Sources

Newsweek

http://www.newsweek.com/2016/09/16/why-satellite-imaging-next-big-thing-496443.html

AI Applications for Satellite Imagery and Satellite Data

https://www.techemergence.com/ai-applications-for-satellite-imagery-and-data/

How AI Could (Really) Enhance Images from Space

https://www.wired.com/story/how-ai-could-really-enhance-images-from-space/

Global Commercial Satellite Imaging Market Size, Share, Development, Growth and Demand Forecast to 2023 – Industry Insights by Application, and by End-User

https://www.researchandmarkets.com/research/dq6sc4/global_commercial

Orbital Insight Sees the Big Picture with AI

https://www.nanalyze.com/2017/01/orbital-insight-artificial-intelligence/

How Orbital Insight Measured Hurricane Harvey’s Flooding Through the Clouds

https://www.forbes.com/sites/alexknapp/2017/09/26/how-orbital-insight-measured-hurricane-harveys-flooding-through-the-clouds/#5a7fe27b676c

DigitalGlobe Form 10-K

https://www.sec.gov/Archives/edgar/data/1208208/000155837017001064/dgi-20161231x10k.htm

Patent Protection for Software-implemented Inventions

http://www.wipo.int/wipo_magazine/en/2017/01/article_0002.html

 

Team Members

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer