Artificial Intelligence & Machine Learning
Tuesday, August 17, 2021
Wednesday, August 18, 2021
When banks and lenders speak about alternative data they usually limit their range of options to a limited number of sources including open banking data, Telco data, or psychometric data.
We will talk about how financial institutions can improve their performance by combining different sources of data (behavioral and transactional) with credit bureau data (wherever available) via embedded technologies.
We will see how the combination of traditional, transactional, and behavioral data helps solve for major pain points including checking affordability and assessing the probability to default.
We will also talk about how to implement a framework of alternative data with which financial institutions can get a holistic view of their applicants without adding any friction to the process.
Lastly, we will explore how to achieve a higher predictive power of credit risk models which, in turns, allow for higher approval rates, lower risk rates, happier and more loyal customers.
Based on Gartner's research, 85% of AI projects fail. In this talk, we show the most typical mistakes made by the managers, developers, and data scientists that might make the product fail. We base on ten case studies of products that failed and explain the reasons for each fail. On the other hand, we show how to avoid such mistakes by introducing a few lifecycle changes that make an AI product more probable to succeed.
AI is breaking through in more and more domains. It is better than us at playing go and even recognizing faces. One of the last strongholds that seems impossible to crack is human language. In this talk I will go deeper into the most recent breakthroughs in AI. Why did they occur and how were they achieved? Following this reasoning I will show why language is the next frontier.
You have a lot of customer feedback data that is all over the place. How are you going to get it all into one centralized database?
Wonderflow, founded in 2014, is a unified VoC analytics platform designed to help ease all of your feedback-analysis headaches. It analyzes all unstructured consumer feedback in a single place using AI while presenting results in the simplest way.
In this talk, you will learn the basics of how NLP and predictive technologies are used to extract value from lots and lots of feedback data. You will see how simple yet effective our Wonderboard user interface can be and the ease with which even complex problems can be tackled.
There is no reason to allow a ton of customer feedback to sit there uselessly or a data mess to ruin your day. After attending this session, you'll have the necessary VoC analytics knowledge to make winning decisions based on your customers' feedback.
Carbon and silicon while they have similarities are not the same thing. Understanding the impact of training datasets on AI and machine learning is increasingly a political as well as a technical issue. Issues of epistemic justice and wider ethical issues about the adoption of AI require us, to quote Lincoln, to "think anew, act anew". This presentation will look at both the theoretical framing of the issues and the practical implications.
Did you know that the way you type is unique?
Join this session to find out how typing biometrics evolved, its current use-cases in CyberSecurity, and get a glimpse into its future applications for Personal Productivity and beyond.
AMP Robotics, a pioneer in AI, robotics, and infrastructure for the waste and recycling industry, builds and deploys robots in recycling centers at scale to enable a world without waste. Innovative technologies are fueling the modernization of recycling; AI-guided robotics address many of the challenges facing the industry, and the ability to transform recoverable material into data opens up vast possibilities to continue to improve the economics and efficiency of recycling. Learn more about these technologies and the road ahead for next-generation recycling infrastructure.
Data Science and Machine learning use cases are multi-faceted, encompassing multiple stages till adoption and productionization. Each stage comes with its own challenges and has equal importance in the overall adoption of data science use cases for business. A step-wise and agile approach to tackling data science use cases is advantageous across industries like financial services, healthcare, retail etc. The nature of use cases can be peculiar to the industry, however, a data science methodology which starts from design thinking workshops to data engineering, machine learning model building and monitoring, along with business buy-in from stakeholders is the high level state of operation. Following a structured and agile approach to build your data science use cases can help you move more of your machine learning models from silos and workbenches into production.
Learn more about the end to end journey of a data science use case to see how you can get the most out of your next data science project and drive value for your business.
$3T of Food, Pharma, and Data are stored across the global supply chain, growing at 8% CAGR, but as recently as the 1990s, the cold chain still relied on styrofoam containers for temperature control in major products. And even today, 90% of refrigeration is “dumb” and provides no real-time monitoring or data-driven optimization. This leads to massive product waste and quality issues. How can we successfully get our products from point A to point B? What we need are smart cold chains that leverage data-driven technology to monitor products in real time to ensure quality. We are seeing a lot of novel solutions emerging around IoT sensors, tracking systems and AI technology that are adding value to the cold chain, but these technologies are still in their infancy. In this session, Manik will take us on a journey of how the cold chain may look in 2040 with these technologies. For example: AI enables fulfillment at the point of sale, Automated cold chain technology in central warehouses, Cloud integrates cold chain technologies and data for different stakeholders for full visibility and Continuing evolution of efficient transportation.
Thursday, August 19, 2021
The process of hiring has always been simple: candidates apply, they are interviewed, sometimes given a task or test to complete, then they are hired. But what if the future of hiring was still simple, but used complex AI to find the perfect candidate? Vivek Ravisankar says AI in hiring is now becoming essential, but that’s not all. In order to scale your teams successfully, companies need to start utilizing new hiring tools, but they also must be using those tools correctly. Right now, we need to rethink AI’s current and future role in hiring, so Vivek will dive deeper into the benefits and challenges of using AI while recruiting and how AI will play an important role in sourcing and hiring as we move forward in an increasingly digital world where face-to-face options aren’t readily available anymore