Genomic data is outpacing traditional Big Data disciplines, producing more information than Astronomy, twitter, and YouTube combined. As such, Genomic research has leapfrogged to the forefront of Big Data and Cloud solutions using artificial intelligence and machine learning to generate insights from these unprecedented volumes of data. This talk hence showcases how we find the disease genes responsible for ALS using VariantSpark, which is a custom random forest implementation built on top of Spark to deal with the 80 million columns in genomic data. This talk also outlines how we use a serverless architecture to translate these insights onto the clinical practice by provide a decision support framework for clinicians to find actionable genomic insights and process medical records at a speed fit for point-of-care application. Furthermore, the talk also touches on how to evolve serverless architecture more efficiently through an hypothesis-driven approach to DevOps and how we keep data and functions secure in a serverless environment.

In the last decade, deep neural networks have created a major paradigm shift in speech recognition. This has resulted in dramatic and previously unseen reductions in word error rate across a range of tasks. These improvements have fueled products such as voice search and voice assistants like Amazon Alexa and Google Home.

The main components of a speech recognition system are the acoustic model, lexicon, and language model. In recent years, the acoustic model has evolved from using Gaussian mixture models to deep neural networks, resulting in significant reductions in word error rate. Recurrent neural network language models have also given improvements over the traditional statistical n-gram language models. More recently sequence to sequence recurrent neural network models have subsumed the acoustic model, lexicon, and language model into one system, resulting in a far simpler model that gives comparable accuracy to the traditional systems. This talk will outline this evolution of speech recognition technology, and close with some key challenges and interesting new areas to apply this technology.

Through the conference, we would have heard different speaker's perspective and experience with Data Science and AI. In this closing panel, we want to step back and look at any unanswered questions that the audience would have.

This talk will help you understand the vision behind ODSC Conference and how it has grown over the years.

Applying analytics to determine an optimal answer to business decision problems is relatively easy when the future can be predicted accurately. When the business environment is very complex and the future cannot be predicted, the business problem can become intractable using traditional modeling and problem-solving techniques. How do we solve such complex and intractable business problems to find globally optimal answers in highly uncertain business environments? The talk will discuss modeling and solution techniques that allow us to find optimal solutions in highly uncertain business environments without ignoring or underestimating uncertainty for revenue management and dynamic price optimization problems that arise in the airline and hospitality industry.

Various Chinese achievements in the field of Artificial Intelligence (AI) has widely been reported in the media recently. Be it a remotely controlled Tank to a demonstration of Swarms of Autonomous UAVs during an Air Show, China is building them fast and possibly in numbers while preparing for 'Intelligized Warfare'. These technology demonstrations are not very far from field deployment and are a cause for worry for several countries. Chinese State Council also released a detailed plan for the development of AI as a tool for the development of the nation, including military application. It is an ambitious plan but China has a strong foundation in its Academia, Public Sector Industry and Startups to make it possible and enable it to become a Global Leader in AI. It is therefore important to examine this foundation of Industry, Startup Ecosystem, Academia and their mutual cooperation to truly understand the potential of China and be able to predict their military deployment of AI.

Over the last two decades, trading has seen a remarkable evolution from open-outcry in the Wall Street pits to screen trading all the way to current automation and high-frequency trading (HFT). The success of machine learning and artificial intelligence (AI) seems like natural progression for the evolution of trading. However, unlike other fields of AI, trading has some domain specific problems that project the dream of set-it-and-forget-it money making machines still some way in the future. This talk will describe the current challenges for intelligent autonomous trading systems and provides some practical examples where machine learning is already being used in financial applications.

Apache Spark is an amazing framework for distributing computations in a cluster in an easy and declarative way. Is becoming a standard across industries so it would be great to add the amazing advances of Deep Learning to it. There are parts of Deep Learning that are computationally heavy, very heavy! Distributing these processes may be the solution to this problem, and Apache Spark is the easiest way I could think to distribute them. Here I will talk about Deep Learning Pipelines an open source library created by Databricks that provides high-level APIs for scalable deep learning in Python with Apache Spark and how to distribute your DL workflows with Spark.

While architecting a data-based solution, one needs to approach the problem differently depending on the specific strategy being adopted. In traditional machine learning, the focus is mostly on feature engineering. In DL, the emphasis is shifting to tagging larger volumes of data with less focus on feature development. Similarly, synthetic data is a lot more useful in DL than ML. So, the data strategies can be significantly different. Both approaches require very similar approaches to the analysis of errors. But, in most development processes, those approaches are not followed leading to substantial delay in production times. Hyper parameter tuning for performance improvement requires different strategies between ML and DL solutions due to the longer training times of DL systems. Transfer learning is a very important aspect to evaluate in building any state of the art system whether ML or DL. The last but not the least is understanding the biases that the system is learning. Deeply non-linear models require special attention in this aspect as they can learn highly undesirable features.

In our presentation, we will focus on all the above aspects with suitable examples and provide a framework for practitioners for building ML/DL applications.

In this course we cover the major steps in a data science project, from data access, data pre-processing, and data visualization to machine learning, model optimization, and deployment using the KNIME Analytics Platform.

Hi! My name is Emil and I am a Teacher Bot. I was built to answer your initial questions about using KNIME Analytics Platform. Well, actually, I was built to point you to the right training materials to answer your questions about KNIME.

Puzzling together all the pieces to implement me wasn't that difficult. All you need are:

• A user interface - web or speech based - for you to ask questions
• A text parser for me to understand
• A brain to find the right training materials to answer your question
• A user interface to send you the answer
• A feedback option - nice to have but not a must - on whether my answer was of any help

The most complex part was, of course, my brain. Building my brain required: a clear definition of the problem, a labeled data set, a class ontology, and finally the training of a machine learning model. The labeled data set in particular was lacking. So, we relied on active learning to incrementally make my brain smarter over time. Some parts of the final architecture, such as understanding and resource searching, were deployed as microservices.

In evolutionary history, the evolution of sensory organs and brain plays very important role for species to survive and prosper. Extending human’s abilities to achieve a better life, efficient and sustainable world is a goal of artificial intelligence. Although recent advances in machine learning enable machines to perform as good as, or even better than human in many intelligent tasks including automatic speech recognition, there are still many aspects to be addressed to bridge the semantic gap and achieve seamless interaction with machines. Auditory intelligence is a key technology to enable natural man machine interaction and expanding human’s auditory ability. In this talk, I am going to address three aspects of it:

(1) non-speech audio recognition,

(2) video highlight detection,

(3) one technology to surpassing human’s auditory ability, namely source separation.

Do you want to know what your customers, users, contacts, or relatives really think? Find out by building your own sentiment analysis application.

In this workshop you will build a sentiment analysis application, step by step, using KNIME Analytics Platform. After an introduction to the most common techniques used for sentiment analysis and text mining, we will work in three groups, each one focusing on a different technique.

• Deep Learning: This group will work with the visual Keras deep learning integration available in KNIME (completely code free)
• Machine Learning: This group will use other machine learning techniques, based on native KNIME nodes
• Lexicon Based: This group will focus on a lexicon based approach for sentiment analysis

This introductory level workshop will give you the ability to navigate the world of quantitative finance. It will focus on core principles of rigorous statistical research, and try to teach overall intuitions so you are comfortable learning more on your own. It will discuss the workflow of designing trading strategies and executing them in the market with practical examples based on the Quantopian platform.

Conversational Agents (Chatbots) are machine learning programs that are designed to have conversation with a human to help them fulfill a particular task. In recent years people have been using chatbots to communicate with business, help get daily tasks done and many more.

With the emergence of open source softwares and online platforms building a basic conversational agent has become easier but making them work across multiple domains and handle millions of requests is still a challenge.

In this talk I am going to talk about the different algorithms used to build good chatbots and the challenges faced to run them at scale in production.

Cleaning, Preparing , Transforming and Exploring Data is the most time-consuming and least enjoyable data science task, but one of the most important ones. With Optimus we’ve solve this problem for small or huge datasets, also improving a whole workflow for data science, making it easier for everyone. You will learn how the combination of Apache Spark and Optimus with the Python ecosystem can form a whole framework for Agile Data Science allowing people and companies to go further, and beyond their common sense and intuition to solve complex business problems.

Amazon Alexa, the cloud-based voice service that powers Amazon Echo, provides access to thousands of skills that enable customers to voice control their world - whether it’s listening to music, controlling smart home devices, listening to the news or even ordering a pizza. Alexa developers use advanced natural language understanding that to use capabilities like built-in slot & intent training, entity resolution, and dialog management. This natural language understanding is powered by advanced machine learning algorithms that will be the focus of this talk.

This session will tell you about the rise of voice user interfaces and will give an in-depth look into how Alexa works. The talk will delve into the natural language understanding and how utterance data is processed by our systems, and what a developer can do to improve accuracy of their skill. Also, the talk will discuss how Alexa hears and understands you and how error handling works.

Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The chal- lenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative fac- tors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year’s worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.

Productionizing data science applications is non trivial. Non optimal practices, the people-heavy way of the traditional approaches, the developers love for complex solutions for the sake of using cool technologies makes the situation even worse.

There are two key ingredients required to streamline this: “the cloud” and “the right level of devops abstractions”.

In this talk, I’ll share the experiences of building a cloud-based platform for streamlining data science and how such solutions can greatly simplify building and deploying data science and machine learning applications.

About District Treasuries: District Treasuries are the nodal offices for all financial transactions of the Government within the district, managing both payment and receipts. They also monitor the activities of various sub-treasuries which work as an extension of the Treasuries at the Tehsil/Taluka level. Each district has various Drawing & Disbursing Officers who are authorized to draw money can present their claims in the Treasury which are then accounted for by concerned authorities. Various states in India have developed Integrated Financial Management System which publishes detailed information on daily transactions happening at district treasuries within a state.

About Time Series Analysis & Inferences: The detailed information of daily transactions at district treasury can help us perform near real-time tracking of flow and utilization of funds. This can be used to track expenditure on various schemes and social sectors, anomalies in fund disbursement, understanding near real-time alerts and predicting timely utilization of budgets. In this talk, we will explore how we can harness time-series modeling and analysis to better understand the functioning of various district treasuries in India.