Adnan Masood, PhD
Adnan Masood, Ph.D. is a software architect, machine learning researcher, author, speaker, and Microsoft MVP for Data Platform. He works as Chief Architect of AI and Machine Learning at UST Global. UST Global is a fast-pace digital company providing Advanced Computing and Digital Innovation Services including but not limited to Advanced Analytics, BI, Information Management, IoT, Mobility, Cloud, Infrastructure Management, Legacy Modernization, and Cybersecurity, Before UST, Dr. Masood worked as a Software Architect at Green Dot Corporation, a leading prepaid financial technology institution. In the past life he served as principal engineer for an e-commerce start-up, and as a solutions architect for a leading British nonprofit organization. A strong believer in the development community, Adnan is an active member of the Open Web Application Security Project (OWASP), an organization dedicated to software security. In the .NET community, he is a cofounder and president of the Pasadena .NET Developers group, co-organizer of Tampa Bay Data Science Group, and Irvine Programmer meetup. A certified ScrumMaster, Dr. Masood also hold certifications in big data, machine learning, and systems architecture from Massachusetts Institute of Technology; Application Security certification from Stanford University, and SOA Smarts certification from Carnegie Mellon University. he is a Microsoft Certified Solutions Developer, and Sun Certified Java Developer. Dr. Masood teaches Data Science course at Park University, and has taught Windows Communication Foundation (WCF) courses at the University of California, San Diego. He is a regular speaker to various academic and technology conferences (, IEEE-HST, IASA, and DevConnections), local code camps, and user groups. He is also a volunteer STEM FLL robotics coach for middle school students.
Democratization of AI with Microsoft Cognitive Services
Cloud (Room 326)
10:00 AM - 10:50 AM
Microsoft Cognitive Services let you build apps with powerful algorithms to see, hear, speak, understand and interpret our needs using natural methods of communication, with just a few lines of code. Easily add intelligent features – such as emotion and sentiment detection, vision and speech recognition, language understanding, knowledge and search – into your app, across devices and platforms such as iOS, Android, and Windows, keep improving, and are easy to set up. In this talk we will review the API around Vision, Speech, Language, Knowledge, and write code to implement Microsoft cognitive services APIs. The session covers how to work with unstructured text and turn unstructured text into meaningful insights into mobile, web and line of business applications. We will see how to use a few lines of code to easily analyze sentiment, extract key phrases, detect topics, and detect language for any kind of text. The session is code driven & will provide samples on how to build smart apps with cognitive services from Microsoft.
A Lap around Algorithmic bias, and AI’s Ethical Imperative
Cloud (Room 326)
01:00 PM - 01:50 PM
Algorithmic bias is shaping up to be a major societal issue as Artificial Intelligence and Machine Learning continue to rapidly transform the industries. Implicit algorithmic bias poses a threat to fairness, diversity, transparency, and neutrality associated with data driven decision making. It is easy to say that the Algorithms Aren’t Biased, we (humans) Are, but is the kind of prejudice and discrimination that already prevails in society inscrutable? GDBR’s right of explanation for all individuals to obtain “meaningful explanations of the logic involved” when automated (algorithmic) individual decision is involved is making leadership across industries think long and hard about upcoming regulations pertaining to black-box automated decision-making systems. In this talk, we will explore the question of why do algorithms discriminate? What is unfair bias, Who is in control of the data, How can outsiders validate algorithms and given these risks, how should we use algorithms? Fairness and Bias in an Algorithmic Age has countless examples from Norman’s Rorschach inkblots to COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), flawed and misrepresentative systems used to rank teachers, gender-biased models for natural language processing, and voice interfaces, chatbots, and other systems are discriminating against certain minority dialects. Algorithms that may conceal hidden biases are already routinely used to make vital financial and legal decisions. Proprietary algorithms are used to decide, for instance, who gets a job interview, who gets granted parole, and who gets a loan. This talk focuses on questions like controlling machine-learning algorithms and their biases, the merit of approximation models as a reasonable way to get insight, right of explanation, and how to apply AI within many domains which requires transparency and responsibility such as health care, finance, surveillance, autonomous vehicles, and government. We will briefly cover concepts around algorithmic discrimination, sources of algorithmic bias, measures of discrimination and finally ACM's guidelines for detecting and preventing algorithmic bias. This is an active area of research and this talk manifests tip of the ice-berg; by exposing spectrum of hard questions around algorithmic bias we need to answer if we expect to benefit from advances in algorithmic technology.
Operationalizing AI - Portable ML Model Sharing across Enterprise
Cloud (Room 326)
03:00 PM - 03:50 PM
The tremendous impact of Artificial Intelligence and Machine learning, and the uncanny effectiveness of deep neural networks are hard to escape in both academia and industry. Albeit, the eco-system of deep learning frameworks is complex, making it difficult to choose the 'right one'. But what if you don't have to limit your choice, and if you could use the most developer-friendly framework for designing a neural network, the most efficient framework for training, and the most efficient one for evaluation and inference on the edge devices? Facebook, Microsoft and Amazon jointly created the ONNX (Open Neural Network Exchange) as an open format to represent deep learning models. ONNX enables interoperability between deep learning frameworks such as Apache MXNet, Caffe2, Microsoft Cognitive Toolkit, and PyTorch. ONNX model zoo enables developers to easily and quickly get started with deep learning using any framework supporting ONNX. In this talk, we would review the role of deep learning frameworks in democratization of Artificial intelligence, and how to empower AI developers to choose the right tools as their project evolves. Furthermore we will explore the ONNX ecosystem which includes converters from and to popular deep learning frameworks (currently Caffe2, Microsoft Cognitive Toolkit, MXNet, and PyTorch) as well as bindings to hardware-optimized libraries like NVidia TensorRT. We will discuss the challenges of transferring models from one framework to another and how ONNX solves for this. Also, in this session you can find out how the ONNX framework can help you take AI from research to reality as quickly as possible.