Schedule

We are in the process of finalizing the schedule. Please check this page again. Here's the schedule from last year.
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  • Day 1 - Keynotes

    3rd July 2020

  • Now more than ever, business and government leaders declare a need to adopt AI, but AI also needs people. The technology is usually what gets the attention, but for AI to succeed and make a positive impact in business and society we must place the human at the center. Math is a tool to augment human intelligence, not replace it, because the combination is far stronger than either alone. AI is also a powerful tool but one with risk and complications, so to harness it most effectively we need humans to hold it accountable. And as AI increasingly shapes our world we need to ensure that it is reflective of those who live in it to avoid bias, but women and people of color are woefully underrepresented in AI research and roles.

  • The increase in AI has led the organizations to now focus on making data-driven decisions although they struggle to integrate the AI solution with existing business processes. Currently, the external world focuses on how to create a model and often forgets on preparing how to deploy and manage it. This talk focuses on the most challenging part of AI which is often called ‘The Last Mile Challenge’. Here it highlights the end to end the journey of an AI solution, that is, how can we make AI implementable and the challenges faced during deployment which could have been avoided in the development stage itself. This is explained in more detail by walking the audience through a business use case from Ericsson Global Ltd. Different aspects come into play when we talk about deployment. Model deployment is not just a stand-alone model or a Jupyter notebook or a pickle file. Starting from collecting the data to industrializing it, there are many do’s and don’ts that one needs to be aware of and the amount of effort that goes into this is generally underrated. The talk will start with understanding the business, formulating the problem statement, how to approach the problem and build the model and then discuss about the deployment, how to package the AI solution, building data pipeline, containerization, versioning, and continuous integration and deployment.

  • Diversity is in our roots and the core purpose of HSBC. We were founded more than 150 years ago to facilitate trade between Asia and Europe. HSBC has always brought different people and cultures together ..its what our brand promise is ….Together we Thrive. We aspire to have a diverse workforce that’s representative of our customer base and reflects the communities that we operate in. HSBC has a large captive analytics Centre, GAC CoE at India. Given the availability of highly talented women in analytical workstreams, we would like to leverage this talent to create more value to HSBC globally. This also brings in different ideas and perspectives, help us innovate, manage Risk and grow sustainably. Three pronged approach to help drive the diversity numbers for us and Analytics at large o Solving for the Leaky pipeline o Sticky floor o Glass ceiling

  • In this age of intelligent machines powered by sensors, controllers and actuators, data science is all set to play an equally important role in the 4 th industrial revolution. The digitisation of industries and introduction of Industrial IoT has provided unprecedented opportunities to understand the machines and their behaviour. The vast amount of data that gets generated from the numerous sensors mounted on these machines provide a wide scope of analysis that augments the work being done by engineers who operate and maintain these machines. The aim of this talk is to show the possibilities that two of the most impactful technologies, namely IoT and AI, can create when used together. The examples of use cases are Descriptive analysis; Vibration analysis with PLC data for rotating machines; Failure prediction of components in highly sensitive environments; visual analytics. The talk would also highlight how translation of the business problem into an appropriate ML problem plays a very crucial role for creation of a successful solution.

  • The face of science and technology is changing at a very fast pace in this age of artificial intelligence and robotics. If we do not keep ourselves updated with the technological advancement, we easily become obsolete and so do our skills. The time when one used to get a degree and had same job for life is sadly over, these days our skills need routine updates and upgrades. We cannot afford to get satisfied with same skills, and the importance of continuous learning has increased like never before. Unlike in the past, the learning starts after we leave the school, we learn from the real-world problems, interdisciplinary is the new branch of science, and collaboration —not always the competition —is the way we advance.

  • Why do we need to constantly monitor medicines, from development to post-market and why is it so complex? The use of medicines changes considerably from well controlled clinical trials to when it hits the market and patients start taking medicines in an “uncontrolled” manner. The constant flow of information – real world data is extremely valuable, but makes monitoring medicines very laborious, costly and complex. This webinar will cover how the use of artificial intelligence can tackle the cost and complexity of how pharmaceutical/biopharma companies monitor medical literature for safety, efficacy and quality of medicinal products. Real world data comes in various shapes and forms, including published literature articles. By simplifying and speeding the detection of adverse events from drug development to post-market, we can help keep patients safe. Outline: Using AI to predict accurately and faster the benefit/risk profile of medicines Learn how to mitigate the risk and protect patients using technology Simplify and lower the costs of monitoring of medicines

  • As artificial intelligence becomes an increasing part of our daily lives, from the image and facial recognition systems popping up in all manner of applications to machine learning-powered predictive analytics, conversational applications, autonomous machines, and hyperpersonalized systems, we are finding that the need to trust these AI based systems with all manner of decision making and predictions is paramount. AI is finding its way into a broad range of industries such as education, construction, healthcare, manufacturing, law enforcement, and finance. The sorts of decisions and predictions being made by AI-enabled systems is becoming much more profound, and in many cases, critical to life, death, and personal wellness. This is especially true for AI systems used in healthcare, driverless cars or even drones being deployed during war. However most of us have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning is being applied. Many of the algorithms used for machine learning are not able to be examined after the fact to understand specifically how and why a decision has been made. This is especially true of the most popular algorithms currently in use – specifically, deep learning neural network approaches. As humans, we must be able to fully understand how decisions are being made so that we can trust the decisions of AI systems. The lack of explainability and trust hampers our ability to fully trust AI systems. We want computer systems to work as expected and produce transparent explanations and reasons for decisions they make. This is known as Explainable AI (XAI).

  • Artificial Intelligence (AI) enhances and amplifies human expertise, makes predictions more accurate, automates decisions and processes, optimizes employees’ time to focus on higher value work, improves people’s overall efficiency, and Will be KEY to helping Humankind travel to new frontiers and solve what feels now like insurmountable problems. But- We’ve got to get AI right… Can you trust the decisions made by an AI? Just because a decision is made by an AI does not mean that the results are morally or ethically squeaky clean. This talk will introduce listeners to the very real dangers of unmitigated bias in AI, told in a very personal way through storytelling. The talk will conclude by giving very tangible steps that organizations can take to best leverage this technology to your advantage WHILST mitigating the risks.

  • Everyone talks about the importance of developing AI skills. But what does this really mean? And how do you get started? AI skills are a collection of competencies that go from understanding AI capabilities to be able to identify opportunities for new AI in your organization, to having the ability to use sophisticated specialized technologies with focused outcomes, to having the capacity to build your own AI models through deep technical programming skills, and a solid mathematical foundation.

  • Although machine learning is a fairly new concept born in the 1950s, it shares a lot of similarities with one of India's most ancient meditation techniques practiced for over 2500 years. Understanding that your brain is an extremely sophisticated machine learning system can help us acknowledge and come to peace with whatever happens to us. Everything we experience with our senses is our input data, everything we think and do is an output. What happens inside of the ‘black box’ which we call our brain is still a mystery, and vipassana can give us an insight into these complex algorithms that are trained in the brain throughout our lifetime. The simplicity of the input – output concept that describes our actions can help us find the path to equanimity.

  • Random forests (Breiman, 2001) are popular in statistical data science but less well known to computer scientists. They are particularly appealing because they are accurate predictors that often require little or no tuning. This talk will introduce the method and describe some of its strengths and weaknesses.

  • AI is one of the hottest field for entrepreneurs to build companies in. This group consists of 250+ AI professionals. In this talk, Sramana Mitra will cover bootstrapping techniques for launching an AI startup.

  • Day 2 - Workshops

    4th July 2020

  • Demand forecasting is an age old concept being practiced by businesses and conventional models were designed to forecast based on historical data but businesses keep discovering newer internal and external influencers to demand which they might not have experienced in past to learn from. This raises a need for AI models to be designed to handle unprecedented exogenous factors to enable better forecasting to varied demand. We would like to share our experience of designing such models along with its impact at some of clients. We will be using google colab to share and execute the code snippets. Be ready with your google account to do hands-on with us.

  • Computer vision is a trend nowadays in the field of data science and machine learning. With a hands-on and interactive approach, we will understand essential concepts in the computer vision along with the extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying computer vision tools to solve real-world problems. We will leverage machine learning, deep learning and transfer learning to solve some popular tasks in computer vision including the following: Introduction to Image Processing and Computer Vision The goal of computer vision Image formation Representation of images Basic image transformations Deep Learning Frameworks in Computer Vision Introduction to deep learning Convolutional Neural Networks Transfer Learning frameworks Image Classification and Object Detection Getting image dataset Preprocessing the image dataset Data Augmentation and Image data generation Image classification on the benchmark datasets Advanced Computer Vision

  • Celebrating Tech Leaders Driving Disruption & Innovation in Data Science & AI