Stories of AI Failure and How to Avoid Similar AI Fails AND SUCCEED AGAIN

Starting a new AI project in 2019? Don’t fail prey to the hype. The new year will see a separation of the wheat from the chaff, and an AI fail at this stage could spell disaster for your project, team, and company. The author and speaker in this paper will discuss following failures

Fail: Microsoft’s AI Chatbot Corrupted by Twitter Trolls, Apple’s Face ID Defeated by a 3D Mask

Facebook had to shut down their “Bob” and “Alice” chatbots after the computers started talking to each other in their own language.

Artificial intelligence technologies cannot be built in isolation from the social circumstances that make them necessary,

This is a fantastic point. In the rush to stay ahead of the technology curve, companies often fail to consider the impact of their inherent biases. This is particularly dangerous for companies working in data analytics for healthcare, biotechnology, financial services and law.

Just look at Watson for Oncology: data bias and lack of social context doomed that AI project to failure and sent $65 million down the drain.

How to Get Real Value from Artificial Intelligence in 2019 Focus on solving a real business problem.

With the right business case and the right data, AI can deliver powerful time and cost savings, as well as valuable insights you can use to improve your business.

Dr. Shankar Ramamoorthy is currently working as Chief Customer Success Officer at Harman Connected Services , helping the Software Enabled Business of Harman. He was earlier Quality Business Partner –at Amdocs. - where he evangelized and implemented 40 patented Test Management Framework machine using BI/Analytics.

Dr Shankar is a TQM ( Total Quality Management) professional, who has spent much of his 26-years career managing change and improving software Quality Engineering efficiency through Data and Metrics driven Quality Management Systems.

He believes in evangelizing , coaching and mentoring test teams through inspirational leadership.

Shankar has a vast experience in setting up Test COE( Centre of Excellence),and has successfully carried out 6 rollouts on TPI/TMMI/Test Maturity frameworks for several companies.

He has handled the entire gamut of testing landscape right from Pre- Sales, Sales, Delivery and instituted a process temper in his earlier assignments.

I am passionate about how technology can transform business and I help conceive, plan and execute outcome focused technology initiatives. Specialties: Program Management, consulting, digital transformation

In this session, I will give an overview of AI architecture and the components of it which includes deep learning, machine learning & natural language processing. A brief about different algorithms available in deep learning & machine learning which can aid NLP. The session will touch upon the importance & rise of NLP and the tasks associated with natural language processing like text classification, text summarisation, topic modelling, chat bots, sentiment analysis etc and how can NLP transform the AI solution by using ML. Preview & share the url with the audience which includes step by step documentation to implement some of the concepts associated with NLP. The session will cover how NLP & ML can help each other to create powerful combination which are used in different domains to solve difficult problems. After the session, the audience will be able to clearly distinguish between different methodologies & tasks associated with NLP and how it can add value to build an end to end solution using AI.

R K Sharath Kumar is a Data Scientist at IBM India software labs. He specialises in Data Mining, Machine Learning, Artificial Intelligence, Deep Learning, NLP & Cloud Computing. He has designed & built cognitive solutions for different domains. Sharath also specialises in leading and facilitating joint design workshops, architecture reviews, discovery workshops and business value assessment exercises primarily focusing on Cognitive Computing, Cloud and Smarter Infrastructure & Advanced Analytics.

Vishesh is having more than 17 years of industry experience in institutionalising and delivering technical solution in analytics, bigdata, Robotics and conversational artificial intelligence.

Currently working in Concentrix as GM - Global Strategic Assets Area– Intelligent Process Automation and Conversational AI. Prior to that had worked in HP-Analytics, GENPECT, Bharti and ISOFT delivering solutions for Intelligent Process Automation, Analytics, Shared Services, Insurance/Reinsurance, Risk Assessment and Data Modelling, Telecom and Manufacturing etc.

We live in a world where consumers are often presented with a large number of choices across various platforms. Consumers are much more dynamic and hence understanding their ever-changing preferences is of utmost importance. Information needs for businesses are only increasing day by day to understand consumers and identify opportunities. There is a need to employ smarter tools to aid the job of marketer and consumer insight professional. Organizing large volumes of data from heterogeneous sources and building a semantic knowledge system can enable the discovery of competitive intelligence using machine learning. Explore how AI, Bigdata and domain knowledge can come together to build such one-stop knowledge shops.

Sai is the Co-Founder & CTO of Spoonshot. A computer science graduate from IIT (Madras). He has more than 6 years of experience in solving the problems of search and discovery. He worked at Zomato, India's largest food ordering and restaurant discovery platform, as a Software Engineer in search, and re-built its search relevance and ranking models. Later he worked at Myntra, India's biggest online fashion store, as Senior Software Engineer, leading search and style services, working on various aspects, including scaling up its search infrastructure for high revenue days, developing personalized filters based on users fashion preferences, and also conceptualizing a generic search platform.

This talk would cover about the following

• Intro to AD and Levels of AD (Autonomous Driving)
• Building blocks of AD ( ADAS Sensors)
• AI Stack (End to End deep learning)
• Challenges of AI algorithm training & testing

18+ years of Rich experience in SW Development for Automotive and smart Phone products 9+ years of Leadership experience Building and scaling High Performing teams Championing the Innovation Programs

Reinforcement Learning(RL) is one of the most popular research topics under Artificial Intelligence currently and it’s gaining momentum day by day. The session would go deeper into this goal-oriented learning which has immense potential in building large variety of learning algorithms for various applications.

We will compare RL with other Machine learning methodologies and take a look at recent RL advancements. In addition to it the session would also cover the applicability of RL in various industries where the proposed solution of some industry problems using RL would be highlighted

Akshay is working as Product Manager in SAP, driving the backlogs of various integrations with SAP S/4HANA and has also worked to build couple of ML use cases. He is a technology enthusiast and have been working on various PoCs showcasing ML applicability in ERP. He is an active blogger and has spoken in various Internal & External events on several Functional as well as Technical topics including Machine Learning, Extensibility, Big Data and Integrations

There is a lot of unstructured text content that is generated in any domain – software development lifecycle, finance, healthcare, social media, etc. A number of valuable insights are missed when unstructured data is not analyzed. The unstructured data generated has increased exponentially in the digital world over the past decade. In order to have a competitive advantage with respect to decision-making, it is essential to extract all the important insights from unstructured data. The insights can be generated by analyzing unstructured text content and correlating the information across various document sources. This session will talk about a complete analytics solution that generates insights for informed decision-making using Watson Natural Language Understanding, Python Natural Language Toolkit, OrientDB, Node-RED, and IBM Watson Studio.

Shikha is a Senior Software Engineer at IBM with 12+ years of experience. She focuses on delivering applications that showcase IBM's AI and Blockchain portfolio. She has worked in different domains and products in IBM. Currently working for IBM Digital Technology Labs as an AI and Blockchain SME. Her areas of expertise includes IBM Cloud, Blockchain (Hyperledger Fabric), NodeJS, Javascript and Unix Shell Script. She has authored a number of developer works articles and actively participates in local meet ups including Women Who Code, Women in Technology, Developer Connect. She has presented IBM in a number of conferences like AI Dev Days, ISTC etc.

As we know success rate of AI project are very low, many usecases reach till successful PoC phase by creating ML model and desirable outcome based on sample data BUT moving them to production with real data starts coming from various sources ex, S3, ERP , Social media etc, And then challenges starts with change in model during training, accuracy in Inference goes down, versioning of dataset, model explain ability becomes important. This session discuss systematic approach to be taking for scaling AI use cases.

Rahul Lodhe is the Senior Director for SAP Machine Learning / Analytics. In his current role, he is responsible for building Machine learning data intelligence platform for realizing / scaling AI and ML usecases. In the past he was responsible for conceptualizing and technical delivery of Leonardo accelerators working across IoT, Machine Learning, Analytics cloud and S4 applications. He was also responsible for development of TCO application for BOE and heading the Global Test center for the BI hybrid organization.

He has 16 years of experience in SAP and has worked across various departments in SLI and in multiple roles. His area of expertise  is the SAP Analytics portfolio, SAP ERP  and SRM (procurement)  areas.  Before joining SAP he has worked extensively in the Database Administration area. He has over 18 years of software industry experience in various companies like TCS, Indiamarkets and now SAP.

Rahul is passionate about driving innovation in products and quality. Having couple of patents on improvement product quality applications.