Future of Technology covered trends in technology across the globe and innovation changing the future

 
 

Outline/Structure of the Talk

nill

Learning Outcome

great

Target Audience

All who loves technology

Prerequisites for Attendees

nill

schedule Submitted 10 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Usha Rengaraju
    By Usha Rengaraju  ~  9 months ago
    reply Reply

    Dear Dr.Hari , 

    Thank you for the proposal submisssion. Could you please provide more insights in to the learning outcome and update the proposal with complete abstract.

    Thanks and Regards,

    Usha Rengaraju

     


  • Liked Suvro Shankar Ghosh
    keyboard_arrow_down

    Suvro Shankar Ghosh - Learning Entity embedding’s form Knowledge Graph

    45 Mins
    Case Study
    Intermediate
    • Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object
    • Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.
    • With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.
    • Knowledge Graph infoboxes were added to Google's search engine in May 2012

    What is the knowledge graph?

    ▶Knowledge in graph form!

    ▶Captures entities, attributes, and relationships

    More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content

    ▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.

    ▶Structured Search & Exploration
    e.g. Google Knowledge Graph, Amazon Product Graph

    ▶Graph Mining & Network Analysis
    e.g. Facebook Entity Graph

    ▶Big Data Integration
    e.g. IBM Watson

    ▶Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati

  • Liked Srivalya  Elluru
    keyboard_arrow_down

    Srivalya Elluru - A Robust Approach to Open Vocabulary Image Retrieval with Deep Convolutional Neural Networks and Transfer Learning

    20 Mins
    Talk
    Beginner

    Enabling computer systems to respond to conversational human language is a challenging problem with wide-ranging applications in the field of robotics and human computer interaction. Specifically, in image searches, humans tend to describe objects in fine-grained detail like color or company, for which conventional retrieval algorithms have shown poor performance. In this paper, a novel approach for open vocabulary image retrieval, capable of selecting the correct candidate image from among a set of distractions given a query in natural language form, is presented. Our methodology focuses on generating a robust set of image-text projections capable of accurately representing any image, with an objective of achieving high recall. To this end, an ensemble of classifiers is trained on ImageNet for representing high-resolution objects, Cifar 100 for smaller resolution images of objects and Caltech 256 for challenging views of everyday objects, for generating category-based projections. In addition to category based projections, we also make use of an image captioning model trained on MS COCO and Google Image Search (GISS) to capture additional semantic/latent information about the candidate images. To facilitate image retrieval, the natural language query and projection results are converted to a common vector representation using word embeddings, with which query-image similarity is computed. The proposed model when benchmarked on the RefCoco dataset, achieved an accuracy of 68.8%, while retrieving semantically meaningful candidate images.

  • Liked Aakash Goel
    keyboard_arrow_down

    Aakash Goel / Ankit Kalra - Detect Workout Pose for Virtual Gym using CNN

    45 Mins
    Talk
    Beginner

    Approximately 80% of the people across globe do not use gym, yet they pay $30 to $125/month.Attrition from gym is linked with discouraging results and lack of engagement. Traditional gym users don’t know proper exercise regimen and users prefer workout regimens that are fun, customizable and social.

    To combat above problem, we came up with idea to provide customized fitness solutions using Artificial Intelligence. In this talk, we showcase how we can leverage Deep Learning based Architectures like CNN to develop "Workout pose detection" that tracks user movement and classify it corresponding to specific trained workout and will determine whether the performed pose is correct or wrong.


    Keywords: CNN, Deep Learning, Image classification Model, Computer Vision.

  • Liked Karthik Bharadwaj T
    keyboard_arrow_down

    Karthik Bharadwaj T - 7 Habits to Ethical AI

    Karthik Bharadwaj T
    Karthik Bharadwaj T
    Sr. Data Scientist
    Teradata
    schedule 10 months ago
    Sold Out!
    45 Mins
    Talk
    Beginner

    While AI is been put to use in solving great problems of the world, it is subjected to questions the morality of how it is constructed, used and put into use. Karthik Thirumalai addresses the 7 habits of building ethical AI solutions and how it could be put to use for a better world. These habits Data Governance, Fairness, Privacy and Security, Accountability, Transparency, Education help organizations to successfully implement their AI strategy which reflects fundamental human principles and moral values.

  • Liked Akash Tandon
    keyboard_arrow_down

    Akash Tandon - Traversing the graph computing and database ecosystem

    Akash Tandon
    Akash Tandon
    Data Engineer
    SocialCops
    schedule 10 months ago
    Sold Out!
    45 Mins
    Talk
    Intermediate

    Graphs have long held a special place in computer science’s history (and codebases). We're seeing the advent of a new wave of the information age; an age that is characterized by great emphasis on linked data. Hence, graph computing and databases have risen to prominence rapidly over the last few years. Be it enterprise knowledge graphs, fraud detection or graph-based social media analytics, there are a great number of potential applications.

    To reap the benefits of graph databases and computing, one needs to understand the basics as well as current technical landscape and offerings. Equally important is to understand if a graph-based approach suits your problem.
    These realizations are a result of my involvement in an effort to build an enterprise knowledge graph platform. I also believe that graph computing is more than a niche technology and has potential for organizations of varying scale.
    Now, I want to share my learning with you.

    This talk will touch upon the above points with the general premise being that data structured as graph(s) can lead to improved data workflows.
    During our journey, you will learn fundamentals of graph technology and witness a live demo using Neo4j, a popular property graph database. We will walk through a day in the life of data workers (engineers, scientists, analysts), the challenges that they face and how graph-based approaches result in elegant solutions.
    We'll end our journey with a peek into the current graph ecosystem and high-level concepts that need to be kept in mind while adopting an offering.

  • Liked Indranil Chandra
    keyboard_arrow_down

    Indranil Chandra - Data Science Project Governance Framework

    Indranil Chandra
    Indranil Chandra
    Assistant Manager
    CITI
    schedule 10 months ago
    Sold Out!
    45 Mins
    Talk
    Executive

    Data Science Project Governance Framework is a framework that can be followed by any new Data Science business or team. It will help in formulating strategies around how to leverage Data Science as a business, how to architect Data Science based solutions and team formation strategy, ROI calculation approaches, typical Data Science project lifecycle components, commonly available Deep Learning toolsets and frameworks and best practices used by Data Scientists. I will use an actual use case while covering each of these aspects of building the team and refer to examples from my own experiences of setting up Data Science teams in a corporate/MNC setup.

    A lot of research is happening all around the world in various domains to leverage Deep Learning, Machine Learning and Data Science based solutions to solve problems that would otherwise be impossible to solve using simple rule based systems. All the major players in the market and businesses are also getting started and setting up new Data Science teams to take advantages of modern State-of-the-Art ML/DL techniques. Even though most of the Data Scientists are great at knowledge of mathematical modeling techniques, they lack the business acumen and management knowledge to drive Data Science based solutions in a corporate/MNC setup. On the other hand, management executives in most of the corporates/MNCs do not have first hand knowledge of setting up new Data Science team and approach to solving business problems using Data Science. This session will help bridge the above mentioned gap and help Executives and Data Scientists provide a common ground around which they can easily build any Data Science business/team from ground zero.

    GitHub Link -> https://github.com/indranildchandra/DataScience-Project-Governance-Framework

  • Liked AbdulMajedRaja
    keyboard_arrow_down

    AbdulMajedRaja - Become Language Agnostic by Combining the Power of R with Python using Reticulate

    AbdulMajedRaja
    AbdulMajedRaja
    Analyst (IC)
    Cisco Systems
    schedule 10 months ago
    Sold Out!
    45 Mins
    Tutorial
    Intermediate

    Language Wars have always been there for ages and it's got a new candidate with Data science booming - R vs Python. While the fans are fighting R vs Python, the creators (Hadley Wickham (Chief DS @ RStudio) and Wes McKinney (Creator of Pandas Project)) are working together as Ursa Labs team to create open source data science tools. A similar effort by RStudio has given birth to Reticulate (R Interface to Python) that helps programmers combine R and Python in the same code, session and project and create a new kind of super hero.