The MassTLC Leadership Awards recognize and honor “the best and the brightest” of the Massachusetts tech ecosystem each year. On July 18, after months of nominations and judging, the finalists in each of fifteen different categories were announced (learn more about the selection process, here).
Who will be crowned the winners? Find out on October 3, 2018, at the 21st annual Mass Tech Leadership Awards Gala.
In the meantime, let us introduce you to our finalists and the great work that they are doing here in Massachusetts.
In the words of their nominators, meet the 2018 “Machine Learning in Action” finalists.
___
Crimson Hexagon for BrightView
BrightView for Customer Care drastically reduces the time it takes global brands to respond to customer issues by using advanced machine learning to surface and classify inquiries. Instead of manually sorting each post or relying on imprecise keyword tools, BrightView uses customer posts to train the algorithm to categorize inquiries at massive scale.
BrightView for Customer Care helps global consumer brands with high volumes of customer care inquiries across many channels drastically increase the efficiency of their customer care programs by reducing the number of irrelevant posts they encounter and, therefore, the time it takes them to respond to important customer inquiries. Additionally, by categorizing incoming customer inquiries, BrightView for Customer Care helps brands better understand the common issues experienced by their customers.
By using machine learning to identify and categorize social care issues in real time, global brands can greatly improve the time to response for social care. By optimizing this process, brands can respond to more care issues more quickly from more channels (both earned and owned), as well as reduce the number of agents they need providing care and/or expand their hours of service.
DataRobot for DataRobot
DataRobot provides new capabilities in multiclass classification and anomaly detection, extending the power of its automated machine learning platform to address complex business problems with machine learning, while maintaining the platform’s ease-of-use for business analysts, software engineers, data engineers, and other professionals without traditional data science training.
The DataRobot platform allows a greater number of business challenges to be solved through automated machine learning while providing greatly enhanced visibility into the modelling process.
While many vendors offer machine learning capabilities, DataRobot is the first to offer comprehensive automation for the data science process, and these advancements further extend the number of AI challenges that users are able to solve with the platform.
Edgewise for Edgewise
Edgewise verifies the identity of applications before allowing them to communicate and automatically builds policies to enforce least-privilege access.
While most network security tools are predicated on addresses, ports, and protocols, Edgewise’s Zero Trust product builds security policy on secure application identities, allowing for a 98% reduction in attack surface within the network.
Security operations professionals gain visibility into the communications patterns of their applications, as well as, machine learning-suggested policies to protect them, simplifying cloud security dramatically.
Sophos for Intercept X
Sophos Intercept X with deep learning offers predictive protection that allows companies to stay one step ahead of would-be attackers. Intercept X is a next-generation endpoint security product that stops zero-day malware, unknown exploit variants and stealth attacks, and unknown ransomware attacks. Intercept X learns the observable threat landscape, processes hundreds of millions of samples, and makes more accurate predictions at a faster rate than traditional machine learning solutions.
With the new deep learning model, Intercept X is able to perform a signature-less pre-execution evaluation of any executable file and determine if it is malware, potentially unwanted software, or a legitimate application.
Sophos believes protection against evolving attacks does not need to be complex or difficult to manage. With Intercept X, all organizations can access advanced technology previously only available to large, well-resourced enterprises.
Spotify for “This Is” Playlists
Artists seeking an audience on Spotify rely on Spotify’s career-spanning “This Is” playlists, which generate mixes at scale by combining machine learning, listening data, and human curation. This approach gives thousands of small artists an opportunity for success, while retaining the human element at the core of artistic connection.
Unlike purely algorithmic or purely manual music playlists, “This Is” uses Spotify’s new “algotorial” approach to building playlists. Both humans and ML mix with each other to produce an experience where millions of people are delivered a playlist is perfectly crafted for them.
As the world’s largest streaming service, Spotify has been able to use “This Is” and similar initiatives to raise the visibility of thousands of artists in dozens of markets, helping them make progress towards their long-term goal of creating a thriving “middle class” of artists living off their work. Spotify listeners are hearing more artists than ever. Since 2014, the average number of artists each listener streams per week has increased 37 percent, from just under 30 to about 41 artists per week in 2017.
