AI DevWorld -- Main Stage
Wednesday, October 28, 2020
In our software-driven world, business success depends on engineering organizations. But manual data gathering, meeting cycles and ad hoc reports weigh down engineering teams’ ability to build collaborative solutions, and disjointed and overlapping data silos prevent engineering teams from productive collaboration.
This session will outline the problems engineering teams face in creating streamlined workflows, and the AI and machine learning solutions that enable engineering organizations to produce accurate insights on team performance and potential. Jeff will address how machine learning and applied AI leverage data science to transform engineering operations from the bottom up, allowing engineers to focus on what they do best: building software.
Strategically using AI in business operations comes with an inherent ethical responsibility. This means a multifaceted approach is needed to address it. Ashutosh can explain how using career data from a large enough data set, equal parity algorithms, and audit and monitoring processes creates a transparent system that is independent from bias due to race, gender, ethnicity, age, and other characteristics. Breaking down each of these steps, Ashu can share how combining all of these levers allows candidates to move through the hiring process efficiently, accurately and while significantly reducing the potential for bias. Lastly, Ashu will share how an AI-based hiring process can help enterprises hire for potential, increase diversity, and even contribute to flattening the unemployment curve at scale today and in the future.
Edge computing and AI might suffer from trust and interoperability issues. Edge computing often creates environments more difficult to control. So trust depends on the rightfulness of data used to feed or leverage an AI model. Trust is also the result of being able to validate that an AI model has not been tampered while used in the edge, the same for the result received after computation. Hybride and heterogeneous infrastructures are common in Edge environments. It is key to have platforms managing interoperability in order to allow already existing infrastructures to interact together while managing security and privacy compliances. Blockchain technologies will be presented, through the iExec’ stack, as solutions to address the issues of trust and interoperability AI and Edge computing. Use cases integrating Nvidia GPU for smart sensors will be showcased in order to illustrate concrete implementations.
Building a tech stack in today’s world means constantly making decisions about whether to automate or abstract challenges, but the goals are always the same – simplicity, security and speed. As organizations embrace myriad technologies, such as Kubernetes, to abstract away DevOps challenges, they also increase the need for automation to help them manage increasingly complex processes across platforms. In this session, Kong’s VP of Product Reza Shafii will explore how organizations can use automation to reduce friction in adopting new platforms, eliminate repetitive, error-prone tasks and increase the overall effectiveness of their development teams.
Machine and deep learning become essential for a lot of companies for internal and external use. One of the main issues with its deployment is finding the right way to train and operationalize the model within the company. Serverless approach for deep learning provides simple, scalable, affordable and reliable architecture for it. My presentation will show how to do so within AWS infrastructure.
Serverless architecture changes the rules of the game - instead of thinking about cluster management, scalability, and queue processing, you can now focus entirely on training the model. The downside within this approach is that you have to keep in mind certain limitations and how to organize training and deployment of your model in a right fashion.
I will show how to deploy train and inference pipelines for Tensorflow models on serverless AWS infrastructure.
My talk will be beneficial for machine learning engineers and data scientists.
Applying AI to healthcare is a great opportunity — better predictions on who is more likely to develop diabetes, back pain, and other chronic diseases, better predictions on which patients will require hospital re-admissions — not only in saving money but also improving patient health. In this talk, we will discuss our technology solution and our challenges in building AI/ML solutions in this domain:
* We built a data ingestion and extraction process using Apache Beam and Google Cloud DataFlow. We will describe our obstacles around joining and normalizing disparate patient datasets and our heuristics to solve this problem. We will also talk about performance and scalability obstacles and our solutions.
* We built model training and serving pipelines using Kubeflow (TensorFlow on Kubernetes and Istio). We will talk about how we built a HIPAA/SOC2 compliant infrastructure with these technologies and our experience using Katib for model tuning.
Amplify.ai, the world leader in Conversational AI, is the first and only enterprise-class AI-driven omni-channel messaging platform, with over 500M users and over 9B interactions across all three pillars of digital marketing: web, social media and search.
When the COVID-19 pandemic hit, our team turned our attention to helping governments, health agencies and NGOs deliver critical information to millions of people around the world, and to engage them in automated conversations to measure the most pressing issues. We also partnered with key players like Facebook, Google and Zendesk to deliver these services at hyperscale. A great example of this work can be seen at MyGov.in, where in collaboration with the Government of India and the Ministry of Health we deployed AI powered digital assistance on their website, Facebook, Facebook Messenger, and Google Search and Maps. These systems that have engaged more than 10 million people, provide real-time, critical information about COVID-19 and connect citizens with more than 11 thousand food banks and shelters locations across India.
Additionally, we’ve helped commercial customers automate customer care interactions, dramatically reducing the burden on human agents by as much as 60%. This has been especially helpful in enabling social distancing in call centers that are typically house agents in tight quarters.
The chatbots have evolved a long way in the last few years, from the remote process automation, server orchestrations, account provisioning, customer agents to managing your schedule. One key area, where the chatbots are slowly penetrating and will be the key components, is enterprise. There're various challenges when it comes to building an enterprise chatbot and in this talk, the speaker would share a journey of enterprise chatbot, along with how to build a one that actually works.
In this session let's discuss some important concepts of Azure AI in Customer Interaction services. Let's look deeply about Azure Bot services that includes Azure QnA maker and LUIS Platform and how to implement solutions using the Azure AI Service.A deep dive of complete chat bot Services with integration with SDK and other social media networks.
Thursday, October 29, 2020
As a C-Level executive, you understand that a hypothesis-driven market strategy is key to the success of your business. Increasingly, however, the IT Department is taking over, using data analysis to furnish “answers” and creating an environment where hypotheses ending in questions are “defeats.” You know that data explains the past while your domain knowledge and experience informs the future. If this success/failure climate takes hold and persists at your company, your business will fail. IT is analyzing the past, but they are mistaking it for the future. For the holistic health and future of your business, you must reclaim your hypothesis-driven methodology. You must use your experience to reconnoiter analyst perspective, remind all of company credo, and recalibrate to encourage hypothesis-driven analysis.
Breakthroughs in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have helped customers and call agents alike, to get more done in less time. It draws on multiple data sources to anticipate customer and company needs, handles interactions on its own where possible, and provides in-call support where needed.
The future of AI in the contact center is one where software tools make humans more efficient and allow the customers to have natural conversations with a bot via voice, webchat, social messaging app or other channels, handling requests, retrieving information and delivering answers to frequently asked questions. In short, creating the ultimate customer experience.
During this session, Tony Hung, senior software engineer at Vonage will discuss how enterprises with limited machine learning expertise can leverage communications APIs to unlock simple, secure and flexible solutions to deploy AI in their contact centers, elevating issues to experienced agents when needed to ensure personalized, emotive CX. He will draw on his experience to explain how enterprises can automate their agent-based live chats and streamline their support channels and operations, while offering a personalized human-like interaction. Most importantly, he will discuss how to find the right balance between seamless, intelligent self-service and efficient human intervention using integrated AI-driven communications - applications, APIs and the best of both.
OPEN TALK (AI): Abusing Your CI/CD: Running Abstract Machine Learning Frameworks Inside Github Actions
We all love the conventional uses of CI/CD platforms, from automating unit tests to multi-cloud service deployment. But most CI/CD tools are abstract code execution engines, meaning that we can also leverage them to do non-deployment-related tasks. In this session, we'll explore how GitHub Actions can be used to train a machine learning model, then run predictions in response to file commits, enabling an untrained end-user to predict the value of their home by simply editing a text file. As a bonus, we'll leverage Apple's CoreML framework, which normally only runs in an OSX or iOS environment, without ever requiring the developer to lay their hands on an Apple device.
Personalization is a game changing goal for an organization. It can bring a boost to revenue as well as increase customer satisfaction. In this talk, I’ll show you the real life technology use cases of AI for implementing Personalization. I’ll share the tips and tricks of effectively using AI in the context of personalization. Join this session to learn the core AI models that can help in building awesome Personalized experiences for your customer.
Determining the best and most suitable Machine Learning model for a given
data science problem isn't an easy task and it can be rather challenging at times.
It is like benchmarking sports cars created by different racing teams!
This presentation will show an easily extensible framework
that implements several Machine Learning models for supervised,
unsupervised and semi supervised learning to execute and/or compare models. Additionally, the talk will introduce the open source python scikit learn toolkit through several Machine Learning Models and the open source python Hydra package from Facebook and how they have been used in the framework.
The framework is extensible, generic, portable and easy to use.
In this talk, we will discuss Machine Learning practices in Software Testing stages in detail with a case study. This is an important study since nowadays, researches are looking for adaptation of Machine Learning algorithms to testing processes to reduce the manual effort and improve quality.
We start with a quick view of the machine learning types. Then, we list AI applications in testing these perspectives: test definition, implementation, execution, maintenance and grouping, and bug handling. What’s more, we do not only present existing AI applications but also what can be done in the future. Finally, we summarize the application areas with algorithms and discuss the advantages and potential risks of AI applications in software testing.
NLP is a key component in many data science systems that must understand or reason about text. This hands-on tutorial uses the open-source Spark NLP library to explore advanced NLP in Python. Spark NLP provides state-of-the-art accuracy, speed, and scalability for language understanding by delivering production-grade implementations of some of the most recent research in applied deep learning. It's the most widely used NLP library in the enterprise today. You'll edit and extend a set of executable Python notebooks by implementing these common NLP tasks: named entity recognition, sentiment analysis, spell checking and correction, document classification, and multilingual and multi domain support. The discussion of each NLP task includes the latest advances in deep learning used to tackle it, including the prebuilt use of BERT embeddings within Spark NLP, using tuned embeddings, and 'post-BERT' research results like XLNet, ALBERT, and roBERTa. Spark NLP builds on the Apache Spark and TensorFlow ecosystems, and as such it's the only open-source NLP library that can natively scale to use any Spark cluster, as well as take advantage of the latest processors from Intel and Nvidia. You'll run the notebooks locally on your laptop, but we'll explain and show a complete case study and benchmarks on how to scale an NLP pipeline for both training and inference.
Customer servicing is evolving from reactive to proactive approach to retain your customer and build loyalty. I would like to share how to build AI driven Customer Service Platform with my experience at PayPal.
I will share a framework to assess your current landscape and provide a step by step journey to reach highest level of transformational impact for your customers at scale using AI.