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Business News/ Industry / Infotech/  Robo Brain’s Saxena launches firm to mine audience insights
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Robo Brain’s Saxena launches firm to mine audience insights

Predict Effect uses its own algorithm to find similar web documents, users, actions, etc. for a given tweet, Facebook post or user review on Amazon

Predict Effect is using the software-as-a-service (SaaS) based model to make money. Premium
Predict Effect is using the software-as-a-service (SaaS) based model to make money.

Mumbai: Ashutosh Saxena, an assistant professor in the computer science department at Cornell University, is mostly known for his work with robots and as director of the open-source Robo Brain project—a multi-university large-scale computational system that learns from Internet resources, computer simulation and real-life robot trials.

The 30-year-old India-born scientist has now launched his own company along with Aditya Jami—a Stanford graduate who is also the chief software architect of Robo Brain, to help clients mine users insights with the help of Machine Learning algorithms that can sift through mountains of data that reside on the Internet in sources like social networks, reviews and blogs.

Machine learning is based on algorithms that can learn on their own from data without relying on rule-based programming.

“The company has been built with many from the team that built the Robo Brain (robobrain.me). Our technology parses billions of Internet activities—web and social—everyday in order to build a collective social intelligence engine. Unlike static knowledge sources such as Wikipedia, Freebase, or RoboBrain, PE’s social intelligence is focussed on people’s opinion about the events around the world and the influence of blogs and news articles," Saxena, chief scientist and co-founder of Predict Effect, said in an email interview on Wednesday.

Saxena, who did his BTech from Indian Institute of Technology Kanpur and got his doctorate from Stanford, says his new company Predict Effect has built a “robust collective social intelligence graph" that captures all social interactions and the web, and organizes them into topics.

Aditya Jami, who was also the chief software architect of Robo Brain when he was a visiting scientist at Cornell University, cited the example of a map where you can insert a pin for every document, website, user, image or social media interaction such that two pins are closer in the map if they are related in some way.

“For example, the pins corresponding to Hillary Clinton and Donald Trump will be close to each other since both of them are running for the next presidential election. Similarly, both of these pins will be close to pins corresponding to news articles covering election news," explained the 30-year-old Jami, who is now co-founder and chief technology officer of Predict Effect.

Having created this map, Predict Effect uses its own algorithm to find similar web documents, users, actions, etc. for a given tweet, Facebook post or user review on Amazon, he added.

The exercise involves complex engineering systems and processing of a large volume of data. “We handle around 300-400 million documents every day and we have tailored our machine learning algorithms to do it in a practical and timely manner," Jami said.

He insisted, though, that user privacy is not being compromised while “doing all this".

“Because we use collective intelligence and machine learning, we don’t need to track a specific user but instead collect a wide variety and high volume multimodal public data from various channels. We then derive audience insights, behaviours and marketing intelligence for individual advertisers using deep representations. This resultant data more accurately represents the real-time actions, behaviours and interests of all the activity footprint," he said.

Predict Effect is using the software-as-a-service (SaaS) based model to make money. “We will have a tiered-pricing based on number of marketing channels Facebook, Twitter, etc), monthly spend, volume, etc," said Jami.

Jami added his company works with a “few Indian clients in online education, and healthcare service space" but did not divulge any names.

Saxena, meanwhile, is also drawing some synergy with his Robo Brain project.

RoboBrain integrates data from different sources as well, but it is focussed on the type of knowledge that robots need for performing their tasks, he said.

“For Predict Effect, the knowledge is more focussed on events, sentiment, and user behaviour. RoboBrain’s language knowledge actually shares some information with PE’s information. This is because both of them learn from sources such as Wikipedia, Freebase, etc," he added.

To be sure, established technology companies like Google Inc., Microsoft Corp., Facebook Inc., Amazon.com Inc. and International Business Machines Corp. (remember the IBM Watson machine that beat Jeopardy players in 2011?) use machine algorithms extensively to sift and contextualise mountains of data for things like facial recognition or with view to placing ads intelligently, understanding consumer behaviour, and cross-selling.

Microsoft, for instance, uses Machine Learning APIs (application programming interfaces), real-time streaming analytics and real time BI (business intelligence) for facial recognition. And while Facebook’s facial recognition research project, DeepFace, now works almost like the human brain, Google claims its FaceNet system recognises the right person 99.96% of the time on LFW (Labelled Faces in the Wild dataset).

That’s not all. According to a June 2015 article in McKinsey Quarterly, contenders for the US National Basketball Association championship relied on the analytics of Second Spectrum—a California machine-learning start-up. By digitizing the past few seasons’ games, it has created predictive models that allow a coach to distinguish between “a bad shooter who takes good shots and a good shooter who takes bad shots", and to adjust his decisions accordingly.

The article also pointed out that “...Colin Parris, who joined GE Software from IBM late last year (2014) as vice president of software research, believes that continued advances in data-processing power, sensors, and predictive algorithms will soon give his company (General Electric) the same sharpness of insight into the individual vagaries of a jet engine that Google has into the online behavior of a 24-year-old netizen from West Hollywood".

In Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques, the article added.

Saxena acknowledged the competition but insisted that his unique selling proposition is that Predict Effect uses “all different types of impressions that people make online (irrespective of the source), including text, images, videos, and we embed them into the same representation space. This allows us to build a richer representation of audiences even with sparse activity signals".

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ABOUT THE AUTHOR
Leslie D'Monte
Leslie D'Monte specialises in technology and science writing. He is passionate about digital transformation and deeptech topics including artificial intelligence (AI), big data analytics, the Internet of Things (IoT), blockchain, crypto, metaverses, quantum computing, genetics, fintech, electric vehicles, solar power and autonomous vehicles. Leslie is a Massachusetts Institute of Technology (MIT) Knight Science Journalism Fellow (2010-11), author of 'AI Rising: India's Artificial Intelligence Growth Story', co-host of the 'AI Rising' podcast, and runs the 'Tech Talk' newsletter. In his other avatar, he curates tech events and moderates panels.
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Published: 10 Sep 2015, 01:41 AM IST
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