The author of this paper defines the fundamentals of a connected supply chain and the challenges for adoption and implementation. He goes on to illustrate the objectives and approaches to deploying a connected intelligence framework and generating business value. The author also advocates the integration of AI-powered platforms for streamlining functions within the supply chain and optimizing operations.
The authors of this paper define a robust framework for continuous modernization of the Insights function. They explain why Insights organizations need to begin with a North Star goal and clear strategy aligned to business goals and what aspects across technology, people, and process they need to address to power the organization’s business goals continuously.
Traditionally, for video-based Action Recognition task, 3D CNN are used to extract temporal information and Temporal Global Average (TGAP) layer to summarize this information. In this work, we replace the TGAP layer with the attention mechanism of BERT as it has been state-of-the-art for many sequence-based tasks. BERT’s bidirectional attention mechanism gets a better representation of Temporal information with respect to TGAP.
Having an accurate dataset is a very important aspect for training neural networks. A model trained on corrupted and faulty data may result in poor performance and getting a clean dataset is not always possible.
Text classification is a common problem in natural language processing, and its task is to assign a pre-defined category to a given text sequence. The key step in solving it is to learn the text representation. Mainly, there are two solutions: to use a pre-trained model or not …
The past decade has seen rapid advancements in the area of computer vision, with deep neural networks, particularly convolutional neural networks (CNNs), achieving remarkable results in multiple areas of image processing. The breakthrough success of deep learning for images has sparked more recent…
Organizations have been investing in Analytics and Business Intelligence (BI) for many years now. However, a common problem they are facing is that they are still trying to solve business problems on an ad-hoc basis. For instance, when a senior executive asks his BI analyst the reason behind the drop in sales, the analyst goes back, builds a regression model and then presents the top two factors that influenced the drop.
Deep Learning has huge implications for enterprise business applications that remain largely unexplored. This paper identifies four areas of impact – Data Pre-Processing, Disaster Recovery, Application Software Delivery, and Change Management.
Between macro strategies for segmentation and individual-level personalization, lies a huge area of opportunity for businesses to create live, shifting micro-segments of consumers based on their immediate or recent behavioral characteristics. And the source of data for these micro-segments is not necessarily an external social or digital space
Data is growing in volume, speed and in types. This is creating challenges for storing data and using it when required. To overcome this challenge, organizations are embarking on data lake journeys.
As digital media data analytics and allied digital cognitive capabilities evolve, brand tracking goes live – straight from the consumer’s consciousness. Social data, combined with Web Analytics data and customer data available to an organization, provides compelling snapshot of consumer opinion, experience, and engagement with a brand.
Organizations with top-down decisions have often found that leveraging of data to drive the decision-making process is lower. The concept of Enterprise Data Marketplace propels employees to actively seek and use data…
Product offering has no less part to play in building market share, brand image and the very much sought after, customer loyalty. A product is nothing but collection of multiple attributes to serve the functional, emotional and aspirational needs of users.