RPA, AI & ML Summit

How to embed trust into Intelligent Automation operations . I can elaborate on the various AI frameworks in to the operating model ( for e.g. MAS FEAT framework - i can discuss a real life case study for a regional bank who has adopted this framework with our help.

This talk reviews some the more useful resources on Machine Learning, Deep Learning and other subsets of Artificial Intelligence available to an R user today.

Dr. Andrey Kostenko is a Senior Data Scientist at IAG InsurTech Innovation Hub, also known as Firemark Labs, where he applies insights and cutting-edge techniques in computer vision, machine reading and advanced analytics on text, images and other forms of data towards new products and services that redefine insurance, as enabled by recent advances in machine learning, deep learning, reinforcement learning etc.

Before moving to Singapore in 2018, Andrey was a Data Scientist at trafficguard.ai, an Australian AdTech start-up developing novel data-driven algorithms for mobile ad fraud detection. In 2013, Andrey received his Doctorate Degree in Business Statistics and Econometrics from Monash University, Australia. By then, he had already had an MBA degree from the UK (thanks to the scholarship of the President of the Russian Federation to study overseas) and his first university degree from Russia.

When not at work, Andrey is active on Kaggle, the best platform for testing various ideas and learning new skills in machine learning. In his leisure time, he enjoys travelling, solving puzzles, as well as reading on mathematicians and scientists.

Integrating AI technologies have observed to make automation more efficient and have improved business workflows in the market. However, identifying the correct automation processes to be integrated with AI is still a challenge. For this talk, I will be sharing one of the ways to identify potential AI integrations to your existing RPA processes with a few example use cases.

The power of machine learning, data science and big data is no longer considered a hype or fad. It’s a reality and it’s impacting the bottom lines of businesses. Data has emerged as a real competitive edge and disruptive force for enterprises. Companies that make the most of data by using machine learning and data science will win and outlast in this digital world.

Sonal is a Transformational business program manager for Asia Pacific in Microsoft. She is at helm of formulating and implementing next generation processes enabled by Data and Artificial intelligence to take organization to next level of efficacy and effectiveness. She is strong advocate of growth mindset and inclusive culture.

Karan is the Asia Pacific leader for IBM Cloud Private for Data(ICPD). He is responsible for all aspects of ICPD business; from building the team, driving GTM strategy and delivering growth across APAC region. His current mission is helping customers unlock value from their data using modern data science and AI technologies with the combination of IBM platforms, Partner ecosystem and open source technologies.

Prior to joining IBM, Karan was the first external leadership hire for Cloudera in Asia Pacific. He was part of the senior leadership team that inspired customers to adopt and deliver value through revolutionary technologies like Hadoop, Spark, machine learning and data science technologies. With over a decade of experience in Data and AI senior leadership and executive management positions, Karan has delivered sustained growth consistently in business through innovative customer and channels strategy. Prior to IBM and Cloudera, Karan also held executive level positions at Informatica and HCL Technologies. A technology and business thought leader, Karan has been the keynote speaker for many events across Asia Pacific and USA. Karan has been named IBM cloud engagement champion to inspire and engage more than 10000+ employees of IBM Cloud Asia Pacific. He is the founding member of Data and AI forum and has been featured in leading company blogs and publications.

Automation is a key aspect of digital transformation strategy, as it lays the foundation for organizations to learn faster and reap the benefits of cognitive computing. While there are multiple ways to approach automation, IPA as an approach & set of tools, has a key role to play in accelerating this transformation. It's versatility makes it ideal for building low risk demonstrative precursors for large scale system level solutions, and equally useful for automating the long tail of manual data flows 'from the ground up'. In this talk, I will attempt to use examples to make the case for why having IPA expertise in your organization is necessary regardless of the current state of automation.

Himavad has over 7 years experience in the semiconductor manufacturing industry, owning critical processes and leading key projects. Most recently, at Micron, he led his department's automation journey from scratch, using a combination of tools including RPA to achieve 6 headcount savings in 6 months.

Vinit Mishra works with United Parcel Service (UPS) as a strategy manager and lead data scientist. He has over 13 years of experience in supply chain and logistics. Prior to joining UPS, he was managing supply analytics at Anheuser-Busch Inbev where he rolled out some of the very high-value data products and services involving ML and AI. He is passionate about making a difference in logistics industry with the power of AI and ML

Every single business in the world is data-driven. As a result companies are investing hundreds of thousands of dollars into process mapping. This has resulted in organizations hiring consultants to watch their staff and document their work processes. However it is becoming apparent that there is simply too much data for organizations to process using humans alone.
This has led to the development of visual process mapping tools. These tools have proven useful in capturing user processes in a less invasive manner, but have their system limitations and still require a human to review the process data.
Artificial Intelligence offers a possible solution to the above challenges, because it is well suited at capturing and processing large volumes of data. Gaining access to this data empowers organizations to map all of their organizational processes and then select the ones that lend themselves to RPA based on process complexity and transactional volume.

Ensuring that an organization has accurately mapped their process before their RPA journey has the further benefit of allowing organizations to compare costs and process times before and after the implementation of RPA.

Former US Air Force Officer and consultant, Jeremiah has managed large scale programs for the USA government for over 12 years. Managing the development of state-of-the-art technologies with budgets exceeding 100s of millions of dollars. Leveraging on that experience, he has built the world's first AI enabled Intelligent Process Discovery solution.

What is RPA & how impressive is it to use for daily business processes

ii. Organizations dealing with RPA for their intelligent business processes
iii. Role of ML in RPA and intelligence Automation by using UIPath
iv. Migration of RPA to IPA
v. User Adaption to IPA

i. RPA Consultant , UIPATH Practitioner
ii. He has also provided RPA Training on UIPath Tool

The growing demand for social networks has attracted hundreds of millions of web users around the world. The growth of social networks has resulted in has proven irrefutable interconnection between users by providing huge rate of exchange of information and knowledge.

The main aim of the proposed algorithm is to analyse the news popularity dataset. The nature and type of news articles which has maximum attention by the audience are known as News popularity.

There are several factors which lead to the popularity of the news articles. Various means of reading news includes social media sites and news sites. Online news popularity depends upon various factors such as the number of shares on social media, number of comments by visitors, number of likes etc.

An automated system needs to be developed in order to identify the attributes which play a vital role in making the news to be popular and what type of news becomes popular over a period of time. In this paper, we describe a large scale data set of news items from multiple official media sources and their respective social feedback in multiple social media platforms.

The main objective is to establish this data set as a well-known baseline for research efforts pertaining to various fields of research concerning web content and specifically predictive analytics. From the extracted features we tend to analyze which social media network has the highest position and this analysis is done by applying a novel architecture that involves Binary bat algorithm for feature selection and Deep learning network for effective and efficient classification.

The proposed hybrid feature selection algorithm reduces the number of attributes and selects appropriate attribute which plays a vital role in finding the best social media network which gives frequent news information and which is the most frequent news in the social media network. The performance efficiency of the algorithm is evaluated using several metrics and the proposed algorithm shows 99% accuracy in predicting the news popularity.

Pitchumani is having Seventeen years of experience as Associate Professor in the Department of computer Science, Lady Doak College, Madurai.

She Handled many innovative and emerging courses in technology like Internet of Things, Computer Networks, Code Hacking, Big data Analytics and Soft Computing etc. and Developed an Augmented Reality based content delivery for teaching Collision Theory in Chemistry and Web Server concept in Internet of Things.

How to select best process candidates for automation. How to choose the right technology to use: RPA, Machine vision, BPM, ML, ChatBots, Virtual assistant.