Schedule

Diversity leads to more innovation and productivity. McKinsey estimates that firms with greater gender diversity outperform others in profitability by up to 21%. AI is a space where this is particularly relevant. Gender and neurodiversity become key ingredients that make our algorithms less homogeneous, more balanced and innovative. Our C-suit leaders need to do a lot more to drive towards a better gender ratio at workplace, on the floor and in the leadership team. We learnt a lot in the past two years, moving our gender ratio from 72:28 to 65:35, and creating a leadership team where women leaders constitute 38%. There is still a long way to go, and our endeavor should be to mimic society at large, where the gender ration is ~ 50:50. It's not going to be easy, but accelerated improvements need to be made in our academic system to get more women in STEM, in hiring practices to remove biases, and in HR policies to create an inclusive environment.

What is more important for any e-wallet than getting millions of monthly transacting users? To inculcate a sense of security and trust in users that their hard-earned money is safe in their wallets. Data Science models play an important role to achieve this. The problem of fraud detection is very interesting and equally challenging because of the ever-evolving nature of the problem. Adding to this, building a robust solution in this domain is quite difficult because it is nothing less than searching for a needle in a haystack. In this talk, Sruthi will walk the attendees through the lessons learned from building an ML model to predict the fraudulent logins to hijack wallets. In particular, handling insufficient labels, data drift and selecting a sustainable model for monitoring metrics.

Success of a Digital Transformation is in its ability to deliver greater business agility with enhanced stakeholder experience and superior intelligence quotient of its business processes. Artificial Intelligence is at the core of any digital transformation initiative driving the experience and intelligence quotient of its processes through infusion of intelligence and automation. The session will focus on the journey of some of the largest organizations in their digital transformation and how Artificial Intelligence has been a key enabler.

In today’s digital world, Data & AI is at the heart of the business transformation, helping organizations to reimagine themselves in a way that was not even possible just a few years ago. But at the same time, organizations are struggling to hire, nurture and retain the right talent to realize their data & AI-driven transformational journey. On one side, organizations need to assemble the right team with a myriad of roles such as Data Strategist, Data Analyst, Data Scientist, Citizen Data Scientists, ML Engineers, AI Researchers. On the other side, they also need to figure out the right team structure - centralized, federated, distributed. On top of it, they need to ensure diversity in the team to be most effective. In this talk, we will share some of the best practices and lessons learnt from our journey with numerous businesses across industries that are in pursuit of leveraging the power of Data and AI in an effective and sustained manner.

Owing to their powerful cognitive abilities, humans have achieved mind-boggling milestones - from inventing everyday conveniences like cars and computers to exploring the vast frontiers of space and discovering renewable sources of energy in the interests of sustainability. With this tremendous ability to drive positive change and improve the quality of human life and the natural habitat around us, what has predominantly been out of our cognitive control are our inherent biases, which (unfortunately) are formed during the process of our transformation and development since the stone age, thus almost evolutionary. Today, we live in a world that is run by data, driven by data, and is thriving on data. It is all around us, in anything and everything we can perceive. We predominantly experience data firsthand through our senses, and then a significant amount of data is captured through technology for further rummaging. Therefore, it is natural that our inherent biases are unknowingly seeping through the data we collect. Once in the data, the data analytics and AI designed to synthesize this information are also flooded with the same bias, owing to how these systems have been developed, implemented, and channelized. However, when human fallacies skew the data itself, so the inferences drawn from it follow suit, which is why we must be wary of bias. A prominent bias in AI is gender bias. There is a need to drive action at a grass root level to increase awareness in society of this bias, the potentially grave consequences it could bring, and how we can limit its propagation. Only reducing bias in society can improve the gender balance in the data we process. Every step we take to mitigate this bias becomes a much-needed leap for women in society, giving them room to aspire and achieve, allowing them to not just survive, but thrive and rise in the world of AI.

We are currently living in the greatest advancements of Artificial Intelligence in history. It has emerged to be the next best thing in technology and has impacted the future of almost every industry. We’ve reached the point where there’s an intersection of data, technology and business need which are moving from proof of concepts to large scale production deployments in real time world. Rakuten Group has 70+ businesses and almost 1.5 billion members across the world that serves users worldwide through businesses based in 30 countries and regions. As Rakuten members engage across many facets of their lives with Rakuten Group services (such as e-commerce, fintech, advertising, investing etc) massive volumes of data are generated every minute. By deriving deep insights from wealth of data Rakuten is able to drive Personalization and improve the experience of all of its members. This is something clearly evident for Rakuten Ichiba ecommerce digital service that brings together diverse and unique merchants to create a vibrant and lively online shopping mall. Rakuten’s innovations and competitive services are driven and guided by the passion to empower people and society. Rakuten Institute of Technology (RIT) is the core AI research wing of Rakuten. At Rakuten the AI Innovation & strategy is aligned to the business strategy. Some of the areas where RIT innovation is shaping major verticals, namely, Customer Program whose aim is to acquire a deep customer understanding for Rakuten membership, Natural Language program that deals with automating product catalog & machine translation, Voice AI to empower users of Rakuten Mobile with voice commands, Conversation AI to self serve the customer, Vision program whose mission is to provide frictionless image-based experience, and moonshot initiatives programs like curing cancer using AI, a fully autonomous mobile network and explore real use cases for quantum computing. The future of AI in the next decade looks quite promising and eventful. There will be surge in the use of this technology in everyday life. The transformation brought by AI has been so pervasive that it is deeply influencing user experience and how humans interact with brands and technologies. AI would be able to suggest the perfect product to shoppers and to make sure that the product is stocked up, back at the warehouse. One will use AI to understand and detect life-threatening diseases in the early stage. More and more new algorithms will be developed to handle complex data, learn deeper with the lesser size of annotated data sets. Most of the NLP tasks will be supported by large pertained transformer BERT/BART models along with Neurosymbolic AI study to bring more context-awareness supporting reasoning layer. This shall reduce the effort of training text or images through all possible orientations, explaining causation effects and reasoning beyond learning. Rakuten is fully invested on these varied initiatives to ensure it is always ahead of the innovation curve. Rakuten consists of AI research scientists and AI engineers who have in-depth specialization in machine learning, deep learning, natural language processing, computer vision, knowledge discovery and data mining. RIT being a research institute, it has collaborated with top notch academic and scientific institutes around the globe. By working on collaborative research projects, the bridge between academia and industry is established. While the opportunities of AI are great, there are risks that need to be addressed. Datasets and algorithms can reflect or reinforce gender, racial or ideological biases. When the datasets (fed by humans) that AI rely on are incomplete or biased, they may lead to biased AI conclusions. Moving forward, we need to have a very good understanding on international standards, technical specifications and requirements which will help to build AI technologies and solutions that perform well which are reliable and transparent especially in the highly regulated medical and Finance areas. Rakuten will continue to invest in its core strength producing not only the latest cutting-edge AI solutions which can transform business and Human life but they can be trusted and explainable. This, in turn, will encourage people to use their expertise, experience and intuition to validate conclusions or make a different decision than the one proposed by the machine.

AI is one of the fields in which women can experience tremendous success, especially with the right push towards female participation in the industry. Women are a necessary force that organizations must integrate in order to accelerate the AI maturity of enterprises. In specific, a heavy emphasis on the female workforce within the artificial intelligence setting can help alleviate some of the biggest problems that enterprises face in the eyes of machine learning technologies, such as selection bias. This ultimately means that organizations will always fail to harness the fullest capacity of their digital innovations without including women, as machine learning technologies will be fed a constant stream of biased data, producing junk results that are not reflective of the full picture, causing potentially catastrophic harm to organizations. Therefore, in order for organizations to achieve the highest AI maturity levels, it is necessary to mobilize women on a mass scale and include them as part of all enterprise endeavors in artificial intelligence, from research to product launch. As of Dec’21, studies have found that only 10-15% of machine learning researchers in the leading technology companies are women; less than 14% of authors of AI research papers are women; and women are under-represented at 17-18% across the largest online global data science platforms. About 55% of university graduates are females, but only a little over one-third of those degrees are in STEM (science, technology, engineering and math) And that lack of diversity is a serious issue. AI algorithms are susceptible to bias, so building them requires a team that includes a wide range of views and experiences.

Enterprise AI in the 2020s is shifting from Analytical ML to Operational ML. Every enterprise aspires to be a data-driven enterprise. It is imperative for business leaders to actuate data insights derived from Artificial intelligence (AI) and machine learning (ML) initiatives for business resilience and growth. But studies reveal that roughly only half of all AI proof of concepts are ever scaled to production. Hence the question arises as to what the challenges are driving such production initiatives. This session will provide an understanding of the Machine Learning Operations (MLOps) Strategy, the ecosystem enablement, and the key levers that need to be adjusted to drive Enterprise-Wide AI/ML adoption. The session will focus on defining the elements required by business leaders, AI, and Software leaders to achieve the following: Creating production-ready, scalable AI solutions. Delivering Business Value by leveraging AI/ML initiatives. Organization-wide cohesive Data Management, ML Model Governance, and Intellectual Property Platforms. Focus on ML Use-Case Prioritization, channel investments in production-sizing AI, reduce stakeholder conflicts, and propagate pragmatic business impact of ML operationalization.

Augmented intelligence is an offshoot of artificial intelligence that focuses on human-centric assistance, operating on the principle that cognitive technology is designed to enhance human intelligence rather than replace it. Investing in advertising technology is increasingly becoming more attractive to businesses seeking profitability faster and sooner. What is also true is that it is embroiled in controversy around user privacy. Can augmented intelligence be the gatekeeper for people’s interests while being the cheerleader for businesses? Can women in tech contribute differently in shaping a double-edged sword of a technology that this is emerging to be?

Statistics show how women continue to be underrepresented in the tech field. For instance, the Big Five tech giants (Amazon, Apple, Facebook, Google and Microsoft) have only 34.4 percent women in their workforce. In this panel, we will discuss how women can get into roles typically labelled as ‘not suitable for women’, and how we can change the narrative for women in tech.