Stay on top of global coronavirus cases with Fractal’s AI Augmented Analytics platform.

How AI Guides Reckitt Benckiser’s Marketing Strategy
Problem solving at internet scale

It is critical to build a strong engineering capability that can mine big data in real-time, operationalize the solution and implement it at scale. This session will focus on how to build and deliver the essential capabilities (both technical and otherwise) to enable AI and scale algorithms to power decisioning in the enterprise.

Natali Mohanty

Senior Vice President, Data & Analytics

Global Head of Product Growth & Analytics, YouTube Marketing

Face recognition can detect and identify faces in real time for use in security surveillance, attendance management, criminal identification, and more. It can track people and log their attendance, identify suspicious people, and reveal insights such as demographic information and facial expressions.

AI algorithms can automatically identify actions performed in a video to create better customer experiences. For example, using video to see how a customer interacts with a product can help manufacturers design better quality products.

Detecting and recognizing objects in images and videos can reveal insights that drive value. Face-recognition systems on real-time video feeds can assist with security surveillance or managing attendance. AI can also identify brands and logos to detect specific products in shopping aisles.

Can you detect and identify suspicious person in an image or video? Can you track people and log their attendance using their face data? Our deep learning based face recognition algorithm can detect and identify faces in real time which can be applied to many real life application including security surveillance, attendance management, criminal identification etc. The solution can be extended to extract sentiments (happy, sad, angry etc.) and demographic information (age, gender etc.) attendance management, criminal identification etc. The solution can be extended to extract sentiments (happy, sad, angry etc.) and demographic information (age, gender etc.)

Create the optimal vision and plan for identifying and driving new growth at your enterprise

Hosting on September 28-29 in Chicago at the Swissotel Chicago. This private, invitation-only event comprised senior executive analytics thought leaders among Fractal’s strategic clients, representing some of the world’s most valued brands representing, in aggregate, over $600 billion of annual revenue.Hosting on September 28-29 in Chicago at the Swissotel Chicago.

Fractal hosted our 4th annual Client Advisory Board event on September 28-29 in Chicago at the Swissotel Chicago. This private, invitation-only event comprised senior executive analytics thought leaders among Fractal’s strategic clients, representing some of the world’s most valued brands representing, in aggregate, over $600 billion of annual revenue.

The event content was comprised of a keynotes, a panel and workshops with the following themes:

  • Shared key trends in analytics and artificial intelligence (AI) and how these approaches are transforming our world
  • Revealed new applications of Deep Learning (form of AI) among Member industries to shift from gut decisions to algorithms that
    drive faster, more accurate, and more effective decisions
  • Share their views on the key drivers of analytics success within Member enterprises including how to organize and which people
    are needed, the importance of data acquisition, influencing the C-Suite to drive the analytics mandate, and influencing business
    users to adopt analytics, by considering:
  • Members shared case studies on successful analytics implementations and lessons learned
  • Discussed the key considerations in building an analytics roadmap and adoption plan

Our analysts used advanced tools and text-mining techniques such as pattern matching, phonetic matching and approximate string matching to develop an algorithm for de-duplicating records and identifying unique customers and households.

We recommended a scorecard-based approach for extending credit, to score retailers’ behavior on PAE business transactions.

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Our analysts used advanced tools and text-mining techniques such as pattern matching, phonetic matching and approximate string matching to develop an algorithm for de-duplicating records and identifying unique customers and households.

On Deep Learning, Healthcare and Qure.ai

At Qure.ai, our mission is to make healthcare more affordable and accessible using the power of deep learning. Deep learning is transforming how machines learn – from having to hand… -craft features to machines automatically learning features from labeled data

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