Texas AI Summit Sessions

We will be publishing the session list over the next few weeks. Below are the sessions already confirmed.

Interpretability of ML Systems: Can Physical Models Learn from Deep Learning?

Graham Ganssle - Expero Inc.

The development of deterministic physical models is founded on (or verified by) experimentation. When the results of these experiments don’t match up to our theories it’s time to change the theory. By interpreting machine learning models of these same physical systems, we can improve our deterministic models, thus increasing our understanding of physics. We claim this is true across all fields of study in which practitioners are building machine learning models. The hard part (and a current field of intense research) is the interpretation of the latent spaces of these machine learning models to extract information used to correct our models.
Join us as we demonstrate the extraction of latent space information and apply it to a set of physical models to increase the accuracy of the models. We’ll show three open source projects aimed at model interpretability, and demonstrate their use on our physics neural network. We’ll discuss several custom approaches, like factor analysis, to model interpretability and show why they’re powerful. We’ll wrap up with a discussion of where these techniques are applicable and why model interpretability is the future career of most data scientists.
Intended audience: introductory data scientists, managers of data science and engineering teams, product owners interested in applying machine learning.

Using Deep Learning to Measure Objects in 3DImages

Graham Ganssle - Expero Inc.

This talk is an end-to-end discussion of the ideation, development, and deployment of a deep learning system crafted to extract dimensionality and volumetric information from 3D images. The client wanted the ability to extract the dimensionality of packages in real time as they stream past a sensor on a conveyor belt. Our system uses a high speed 3D camera to capture an image, process the data, infer the dimensionality of the package, and deliver a result in real time.
In this presentation we’ll discuss two approaches taken, one, a deterministic approach used as a baseline, and two, the deep learning system used for production. We’ll tear apart the details and the surprising results of our deep learning methodology using texture images, depth-wise point clouds, and a combination of both. We’ll also discuss in detail the training-time and inference-time cloud architecture used so attendees can gauge the simplicity for deploying their own models.

Fighting human trafficking with AI

Mayank Kejriwal - USC Information Sciences Institute

The growth of the web combined with the ease of sharing information it makes possible has led to increased illicit activity both on the Open and Dark Web, an egregious example being human trafficking. The DARPA MEMEX program, which funded research into domain-specific search, has collected hundreds of millions of online sex advertisements, a significant (but unknown) number of which are believed to be sex (and human) trafficking instances. At the same time, such data also provides an opportunity to study, investigate, and ultimately prosecute perpetrators of human trafficking by grouping and extracting patterns from millions of ads using automatic machine learning and natural language processing techniques.
Mayank Kejriwal discusses the development of a knowledge-centric architecture called Domain-specific Insight Graphs (DIG)—built under three years of MEMEX-funded research—that integrates cutting-edge AI techniques in a variety of fields. DIG reads and processes millions of ads from the web and places this information before investigators using a frontend interface. At the time of writing, DIG is being used by over 200 law enforcement agencies in the US for combating human trafficking and has led to actual prosecutions in both San Francisco and New York. DIG has also been extended in promising ways to combat other social problems like securities fraud and counterfeit electronics manufacturing.
Mayank offers an overview of DIG and explains how knowledge-centric architectures can help facilitate AI for social good. Along the way, he shares case studies on its successes and the key lessons learned during its development.

Embeddings all the way down

Alex Korbonits - Textio

Embeddings are an extremely powerful method in representation learning. For decades, but more recently since Mikolov et al. 2013's word2vec paper, embeddings have played a powerful role in recent advances in NLP, NLU, and deep learning. Typically the focus has been on embedding individual words, but now we are seeing more powerful abstractions such as combining hidden states of LSTMs in new ways (ELMo) or embedding whole sentences (BERT). In this talk, we will immerse ourselves in embeddings. Embeddings can be for anything, not just known elements of linguistic import (words, sentences, paragraphs, documents), but more abstract things such as style, music, or cats. Via transfer learning, I will show how embeddings all the way down help power Textio and improve our baselines dramatically.

Character-Level Convolutional Neural Networks for Semantic Classification

Paul Azunre / Numa Dhamani - New Knowledge

We employ character-level convolutional neural networks to semantically classify columns in a tabular dataset, as part of a larger ``automated machine learning" (AutoML) system. Simulated data for base classes is first used to learn an initial set of weights. Transfer learning from these weights is then employed to learn realistic data imperfections, as well as to rapidly expand the set of classes from the base class set with minimal data requirements. Numerical results demonstrate the flexibility of the approach, via applicability to geographical data, age prediction from twitter, and email spam classification. We present our open-source toolkit SIMON, an acronym for Semantic Inference for the Modeling of Ontologies, which implements this approach in a user-friendly scalable/parallelizable fashion.
Intended audience: Natural Language Processing, Neural Networks, Automated Machine Learning
Skills required: Beginner to intermediate knowledge of classification and neural networks

AI-based Autonomous Response: Are Humans Ready?

Chris Thomas - Darktrace

Global ransomware attacks like WannaCry already move too quickly for humans to keep up, and even more advanced attacks are on the horizon. Cyber security is quickly becoming an arms race — machines fighting machines on the battleground of corporate networks. Algorithms against algorithms.
Artificial intelligence-based cyber defense can not only detect threats as they emerge but also autonomously respond to attacks in real time. As the shortage of trained cyber analysts worsens, the future of security seems to be automatic. But are humans ready to accept the actions machines would take to neutralize threats?
Darktrace recently ran tests across enterprises of all sizes in a variety of industries and has subsequently deployed AI-based autonomous response in over one hundred organizations. In this presentation explore lessons learned and hear about several use-cases in which autonomous response technology augmented human security teams.”

In this session learn about:
- AI approaches and algorithms for detecting and responding to threats
- How human teams adopt (or resist) automated defenses
- The concepts of ‘human confirmation’ mode and ‘active defense’
- Success stories across Smart Cities, genomics organizations, and industrial control systems

Reading China: Predicting Policy Change with Machine Learning

Weifeng Zhong - American Enterprise Institute

For the first time in the literature, we develop a quantitative indicator of the Chinese government’s policy priorities over a long period of time, which we call the Policy Change Index (PCI) of China. The PCI is a leading indicator of policy changes that runs from 1951 to the third quarter of 2018, and it can be updated in the future. It is designed with two building blocks: the full text of the People’s Daily — the official newspaper of the Communist Party of China — as input data and a set of machine learning techniques to detect changes in how this newspaper prioritizes policy issues. Due to the unique role of the People’s Daily in China’s propaganda system, detecting changes in this newspaper allows us to predict changes in China’s policies. The construction of the PCI does not require the researcher’s understanding of the Chinese context, which suggests a wide range of applications in other settings, such as predicting changes in other (ex-)Communist regimes’ policies, measuring decentralization in central-local government relations, quantifying media bias in democratic countries, and predicting changes in lawmaker’s voting behavior and in judges’ ideological leaning.