Spotify’s acquisition of The Echo Nest was not entirely surprising since the two companies have a common core: music (and Spotify APIs). With Echo Nest, it is expected that Spotify will be able to deliver better content recommendation to users, as well as better music discovery.
The financial aspect of the deal was not disclosed but recent reports stated that the deal is valued at $100 million, in which 90 percent of that is Spotify equity.
Though the acquisition centers on music, there is much more than meets the eye.
Smarter data, better business
Matrix Partners’ Antonio Rodriguez, an early investor in The Echo Nest, stated in the blog post that he was quite elated with what the company has accomplished. He noted the huge improvement in Spotify which started when Big Data was still not a buzzword in the tech industry, back when only Google was using machine learning to improve its services. With it’s acquisition of The Echo Nest, Spotify is equipping itself with machine learning.
“Today, as this acquisition shows, no meaningful end user service can exist at scale without a deep bench in Machine Learning— whether it is Facebook, Twitter, or as of now, Spotify,” Rodriguez wrote.
Machine learning is a branch of artificial intelligence which focuses on the construction and study of systems so it can learn from the gathered data. It plays a huge role in making sense of Big Data.
As Rodriguez pointed out, machine learning is quite important if you want to get ahead of the curve, and remains the reason why companies are now highly invested in related solutions as they apply to their businesses’ bottom lines.
Here’s some recent developments from the tech world regarding machine learning:
Machine learning goes to work
Fujitsu Laboratories Ltd. announced that it has developed a model predictive control (MPC) technology, based on multiple long-term forecasting scenarios intended for the supply chain management. The technology will allow the company to make better decisions for production quantities and order quantities in situations wherein forecasting is unreliable and uncertain such when demand changes due to factors such as discount sales promotions or new product introductions.
At Microsoft’s SharePoint 2014 conference, the company introduced Office Graph, a machine learning solutions that analyzes content, user interactions and activity streams to map the relationships across these outlets so that it can provide the most personalized content for users of Office 365.
“Graph is what moves us beyond people and docs,” said Jeff Teper, Microsoft’s Corporate Vice President of Office Server Services. “We want software to learn from an organization and show you what’s relevant to you.”
Investors are also seeing the value of machine learning as seen in the recent round of funding raised by Emotient, a startup that specializes in facial expression recognition. The startup raised $6 million in a round led by Seth Neiman, formerly a general partner at Crosspoint Venture Partners and now leading new VC firm Handbag, with the participation of existing investor, Intel Capital
“We believe our technology is differentiated in its ability to deliver sentiment and emotional insights in real-time and in its accuracy in uncontrolled environments, such as a crowded store,” Emotient spokesperson Vikki Herrera said.