There has been a lot of concerns about the black-box nature of AI. People have been asking for a sensible AI guideline with the weight of Law behind it. In April 2019, the EU released it's Ethics guidelines for trustworthy AI. Before that during the Obama administration, the National Science and Technology Council came up with its own set of broad guidelines called "Preparing for the Future of Artificial Intelligence."

Most of these cover an impressive amount of ground in several major categories:

• Transparency: Any time an AI system makes decisions on a user's behalf, that person should be aware of it. The reasoning behind decisions should be easily explainable.
• Safety: AI systems should be designed to withstand attempted hijacking and other attacks performed by hackers.
• Fairness: Decisions made by AI systems should not be influenced by gender, race or other personal identifiers. They should be as impartial as possible and not reflect human biases.
• Environmental stewardship: Not all the stakeholders in AI development are human. The development of these platforms and the implications of their decision-making and sustainability should take into account the needs of the larger environment and other forms of life.
• And so on...

At this conference, we would like to bring our experts together to hear their views/concerns on this topic.

We will train the audience how to develop advanced image segmentation with FCN/DeepLab algorithms which can help visualize the driving scenarios accurately, so as to allow the autonomous driving system to take appropriate action considering the obstacle views.

It is too tedious to keep on asking questions, seek explanations or set thresholds for trends or anomalies. Why not find problems before they happen, find explanations for the glitches and suggest shortest paths to fixing them? Businesses are always changing along with their competitive environment and processes. No static model can handle that. Using dynamic models that find time-delayed interactions between multiple time series, we need to make proactive forecasts of anomalous trends of risks and opportunities in operations, sales, revenue and personnel, based on multiple factors influencing each other over time. We need to know how to set what is “normal” and determine when the business processes from six months ago do not apply any more, or only applies to 35% of the cases today, while explaining the causes of risk and sources of opportunity, their relative directions and magnitude, in the context of the decision-making and transactional applications, using state-of-the-art techniques.

Real world processes and businesses keeps changing, with one moving part changing another over time. Can we capture these changing relationships? Can we use multiple variables to find risks on key interesting ones? We will take a fun journey culminating in the most recent developments in the field. What methods work well and which break? What can we use in practice?

For instance, we can show a CEO that they would miss their revenue target by over 6% for the quarter, and tell us why i.e. in what ways has their business changed over the last year. Then we provide the prioritized ordered lists of quickest, cheapest and least risky paths to help turn them over the tide, with estimates of relative costs and expected probability of success.

Lung cancer is the leading cause of cancer death among both men and women in the U.S., with more than a hundred thousand deaths every year. The five-year survival rate is only 17%; however, early detection of malignant lung nodules significantly improves the chances of survival and prognosis.

This study aims to show that 3D Convolutional Neural Networks (CNNs) which use the full 3D nature of the input data perform better in classifying lung nodules compared to previously used 2D CNNs. It also demonstrates an approach to develop an optimized 3D CNN that performs with state of art classification accuracies. CNNs, like other deep neural networks, have been black boxes giving users no understanding of why they predict what they predict. This study, for the first time, demonstrates that Gradient-weighted Class Activation Mapping (Grad-CAM) techniques can provide visual explanations for model decisions in lung nodule classification by highlighting discriminative regions. Several CNN architectures using Keras and TensorFlow were implemented as part of this study. The publicly available LUNA16 dataset, comprising 888 CT scans with candidate nodules manually annotated by radiologists, was used to train and test the models. The models were optimized by varying the hyperparameters, to reach accuracies exceeding 90%. Grad-CAM techniques were applied to the optimized 3D CNN to generate images that provide quality visual insights into the model decision making. The results demonstrate the promise of 3D CNNs as highly accurate and trustworthy classifiers for early lung cancer detection, leading to improved chances of survival and prognosis.

What do product name generation and neural machine translation have in common?

Both involve sequence analysis which can be implemented via recurrent neural networks (RNN) with LSTM layers.

LSTM Neural Networks are the state of the art technique for sequence analysis. In this presentation, we find out what LSTM layers are, learn about the difference between many-to-one, many-to-many, and one-to many-structures, and train many-to-many LSTM networks for both use cases.

In recent years, a wealth of tools has appeared that automate the machine learning cycle inside a black box. We take a different stance. Automation should not result in black boxes, hiding the interesting pieces from everyone. Modern data science should allow automation and interaction to be combined flexibly into a more transparent solution.

In some specific cases, if the analysis scenario is well defined, then full automation might make sense. However, more often than not, these scenarios are not that well defined and not that easy to control. In these cases, a certain amount of interaction with the user is highly desirable.

By mixing and matching interaction with automation, we can use Guided Analytics to develop predictive models on the fly. More interestingly, by leveraging automated machine learning and interactive dashboard components, custom Guided Analytics Applications, tailored to your business needs, can be created in a few minutes.

We'll build an application for automated machine learning using KNIME Software. It will have an input user interface to control the settings for data preparation, model training (e.g. using deep learning, random forest, etc.), hyperparameter optimization, and feature engineering. We'll also create an interactive dashboard to visualize the results with model interpretability techniques. At the conclusion of the workshop, the application will be deployed and run from a web browser.

Ever thought about having a mini interactive visualization tool that caters to your specific requirements. That is the product I created when I started independent consulting. 2 years since, and I have now decided to make it public – even the source code.

This session will give you an overview about creating a custom, personalized version of a visualization platform built on R and Shiny. We will focus on a mix of structure and flexibility to address the varying requirements. We will look at the code itself and the various components involved while exploring the customization options available to ensure that the outcome is truly a personal product.

Won't it be great to have ML models that can update their “learning” as and when they make mistake and correction is provided in real time? In this talk we look at a concrete business use case which warrants such a system. We will take a deep dive to understand the use case and how we went about building a continuously learning system for text classification. The approaches we took, the results we got.

For most machine learning systems, “train once, just predict thereafter” paradigm works well. However, there are scenarios when this paradigm does not suffice. The model needs to be updated often enough. Two of the most common cases are:

1. When the distribution is non-stationary i.e. the distribution of the data changes. This implies that with time the test data will have very different distribution from the training data.
2. The model needs to learn from its mistakes.

While (1) is often addressed by retraining the model, (2) is often addressed using batch update. Batch updation requires collecting a sizeable number of feedback points. What if you have much fewer feedback points? You need model that can learn continuously - as and when model makes a mistake and feedback is provided. To best of our knowledge there is a very limited literature on this.

Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is pretty cumbersome involving a series of sequential and interconnected decisions along the way that is pretty time-consuming. What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated Machine Learning does exactly that!

With the goal of accelerating AI for data scientists by improving their productivity and democratizing AI for other data personas who want to get into machine learning, Automated ML comes in many different flavors and experiences. Automated ML is one of the top 5 AI trends this year. This session will cover concepts of Automated ML, how it works, different variations of it and how you can use it for your scenarios.

With the big boom in Data Science and Analytics Industry in India, a lot of data scientists are keen on learning a variety of learning algorithms and data manipulation techniques. At the same time, there is this growing interest among data scientists to give back to the society, harness their acquired skills and help fix some of the major burning problems in the nation. But how does one go about finding meaningful datasets connecting to societal problems and plan data-for-good projects? This session will summarize our experience of working in Data-for-Good sector in last 5 years, sharing few interesting datasets and associated use-cases of employing machine learning and artificial intelligence in social sector. Indian social sector is replete with good volume of open data on attributes like annotated images, geospatial information, time-series, Indic languages, Satellite Imagery, etc. We will dive into understanding journey of a Data-for-Good project, getting essential open datasets and understand insights from certain data projects in development sector. Lastly, we will explore how we can work with various communities and scale our algorithmic experiments in meaningful contributions.

Machine learning and deep learning have been rapidly adopted in various spheres of medicine such as discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating biomedical data into improved human healthcare. Machine learning/deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis.

We have successfully developed three deep learning based healthcare applications and are currently working on two more healthcare related projects. In this workshop, we will discuss one healthcare application titled "Deep Learning based Craniofacial Distance Measurement for Facial Reconstructive Surgery" which is developed by us using TensorFlow. Craniofacial distances play important role in providing information related to facial structure. They include measurements of head and face which are to be measured from image. They are used in facial reconstructive surgeries such as cephalometry, treatment planning of various malocclusions, craniofacial anomalies, facial contouring, facial rejuvenation and different forehead surgeries in which reliable and accurate data are very important and cannot be compromised.

Our discussion on healthcare application will include precise problem statement, the major steps involved in the solution (deep learning based face detection & facial landmarking and craniofacial distance measurement), data set, experimental analysis and challenges faced & overcame to achieve this success. Subsequently, we will provide hands-on exposure to implement this healthcare solution using TensorFlow. Finally, we will briefly discuss the possible extensions of our work and the future scope of research in healthcare sector.

The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.

Data is the new oil and unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language based unstructured data - text, speech and so on.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this bootcamp.

In our journey in this field, we have struggled with various problems, faced many challenges, and learned various lessons over time. This workshop is our way of giving back a major chunk of the knowledge we’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. You might have had questions like ‘What is the right technique to solve a problem?’, ‘How does text summarization really work?’ and ‘Which are the best frameworks to solve multi-class text categorization?’ among many other questions! Based on our prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid some of the pressing issues in NLP and learn effective strategies to master NLP.

The intent of this workshop is to make you a hero in NLP so that you can start applying NLP to solve real-world problems. We start from zero and follow a comprehensive and structured approach to make you learn all the essentials in NLP. We will be covering the following aspects during the course of this workshop with hands-on examples and projects!

• Basics of Natural Language and Python for NLP tasks
• Text Processing and Wrangling
• Text Understanding - POS, NER, Parsing
• Text Representation - BOW, Embeddings, Contextual Embeddings
• Text Similarity and Content Recommenders
• Text Clustering
• Topic Modeling
• Text Summarization
• Sentiment Analysis - Unsupervised & Supervised
• Text Classification with Machine Learning and Deep Learning
• Multi-class & Multi-Label Text Classification
• Deep Transfer Learning and it's promise
• Applying Deep Transfer Learning - Universal Sentence Encoders, ELMo and BERT for NLP tasks
• Generative Deep Learning for NLP
• Next Steps

With over 10 hands-on projects, the bootcamp will be packed with plenty of hands-on examples for you to go through, try out and practice and we will try to keep theory to a minimum considering the limited time we have and the amount of ground we want to cover. We hope at the end of this workshop you can takeaway some useful methodologies to apply for solving NLP problems in the future. We will be using Python to showcase all our examples.

Machine Learning (ML) offers innovation for every business and with the advancement in the ML technology we are solving ambitious problems using Machine Learning. In this session we will learn how Amazon Sagemaker helped Zoomcar in their ML journey by providing a scalable platform for doing exploratory analysis on the vast amount of data they have and running multiple ML model before finalizing on the best fit model for solving business-critical problem of car damage analysis assessment

In recent times, with the advent of advanced machine learning techniques especially neural networks, decision trees, etc., the hunger for data has increased dramatically. Several thousand, if not millions, of observations, are required to make a satisfactory model using these algorithms. But due to several reasons like operational challenges, cost considerations, time paucity, etc. we may not have enough number of observations. In such cases, we are either forced to use other statistical models or are forced to collect more data (which usually is time infeasible and expensive). Coming to one’s rescue, Generative Adversarial Networks (GAN-a class of neural networks) provide a method of creating synthetic data by learning the distribution of the smaller data you already have.

GANs have been very popular in creating synthetic images and other unstructured data. But little success has been seen in working with structured datasets. At American Express, we gather multiple data points about our customers in a structured format – used widely for assessing the credit risk of the customer. One of the key issues we face is lack of data – in quantity and quality - about our newly acquired customers as they are low tenured, and we can’t know their historical behavior to assess their risk better. GAN offers an interesting solution to this problem – Can we use GAN to create synthetic customers who look like our newly acquired portfolio and use this to augment our datasets and build superior and stable credit risk models?

We have seen interesting and promising results in this application and in this talk, we will share our story of how to work with GAN on structured data in Financial services domain – data pipelines, architectures, key changes needed, etc. as well as delve into the application of applying GAN for risk model advancement.

Building a successful career in the field of data science needs a lot more than just a thorough understanding of the various machine learning models. One has to also undergo a paradigm shift with regards to how s/he would typically approach any technical problems. In particular, patterns and insights unearthed from the data analysis have to be the guiding North Star for the next best action rather than the path of action implied by the data scientist's or his/her superior's intuition alone. One of the things that makes this shift tricker, in reality, is the 'confirmation bias': Confirmation bias is defined as a cognitive bias to interpret information in such a way that it further’s our pre-existing notions.

In this session, we will discuss how the seemingly disjoint components of the digital ecosystem are working in tandem to make data-driven decisioning central to every functional aspect of every business vertical. This centrality accorded to the data makes it imperative that

• (a) the data integrity is maintained across the lifetime of the data,
• (b) the insights generated from the data are interpreted in the holistic context of the sources of the data and the data processing techniques, and
• (c) human experts are systematically given an opportunity to overwrite any purely-data-driven-decisions, especially when such decisions may have far-reaching consequences.

We will discuss these aspects using three case studies from three different business verticals (financial sector, logistics sector and the third one selected by popular vote). For each of these three case studies, the "traditional" way of solving the problem will be contrasted with the data-driven approach of solving. The participants will be split into three groups and each group will be asked to present the best data-driven approaches to solve one of the case studies. The other two groups can critique the presentation/approach. The winning group will be picked based on the presentation and the proposed approach.

At the end of the session, the attendees should be able to work through any new case study to

• (a) translate a business problem into an appropriate data-driven problem,
• (b) formulate strategies to capture and access relevant data,
• (c) shortlist relevant data modelling techniques to unearth the hidden patterns, and
• (d) tie back the value of the findings to the business problem.

Videos account for about 75% of Internet traffic today. Enterprises are creating more and more videos and using them for various informational purposes, including marketing, training of customers, partners & employees and internal communications. However, videos are considered as the blackholes of the Internet because it is very hard to see what’s inside them. The opaque nature of videos equally impacts end users who spend a lot of time navigating to their point of interest, leading to severe underutilization of videos as a powerful medium of information.

In this talk, we will describe visual processing pipeline of VideoKen platform which includes

1. Graph-based algorithm along with deep scene text detection to identify key visual frames in the video,
2. FCN-based algorithm for semantic segmentation of screen content in visual frames,
3. Transfer-learning based visual classifier to categorize screen content into different categories such as slides, code walkthrough, demo, handwritten, etc. and
4. Algorithm to detect visual coherency and select indices from the video.

We will discuss challenges and experiences in implementing/iterating on these algorithms using our experience with processing 100K+ video hours of content.

In a network of connected devices, there are two critical aspects of the system to succeed:

1. Security – with a number of internet-connected devices, securing the network from cyber threats is very important.
2. Privacy - The devices capture business sensitive data that the Organisation has to safeguard to maintain their differentiation.

I've used Federated learning to build anomaly detection models that monitor data quality and cybersecurity – while preserving data privacy.

Federated learning enables Edge devices to collaboratively learn deep learning models but keeping all of the data on the device itself. Instead of moving data to the cloud, the models are trained on the device and only the updates of the model are shared across the network.

Using federated learning gave me the following advantages:

• Ability to build more accurate models faster
• Low latency during inference
• Privacy-preserving
• Improved energy efficiency of the devices

I built deep learning models using tensorflow and deployed using uTensor. uTensor is a light-weight ML inference framework built on Mbed and Tensorflow.

In this talk, I will discuss in detail on how I built federated learning models on the edge devices.

"In God we trust; all others must bring data." - W. E. Deming, Author & Professor

This philosophy is imbibed in the very core of American Express being a data-driven company makes all strategic decisions based on numbers. But who ensures that numbers are correct? That is the work of Data Quality and Governance. Given the dependency on Data, ensuring Data quality is one of our prime responsibilities

At American Express, we have Data getting generated and stored across multiple platforms. For example, in a market like the US, we process more than ~200 transactions every second and make an authorization decision. Given this speed and scale of data generation, ensuring Data quality becomes imperative and a unique challenge in itself. There are hundreds of models running in production platforms within AMEX having thousands of variables. Many variables are created/populated originally in legacy systems (or have components derived from there) which are then passed onto downstream systems for manipulation and creating new attributes. A tech glitch or a logic issue could impact any variable at any point of this process resulting in disastrous consequences in model outputs which can get transformed into real-world customer impact leading to financial and reputational risk for the bank. So how do we catch these anomalies before they adversely impact processes?

Traditional approaches to anomaly detection have relied on measuring the deviation from the mean of the variable. The more fancy ones employ time series based forecasting. But both these approaches are fraught with high levels of false positives. When every alert generated has to be analyzed by the business which has a cost, high levels of accuracy is desired. In this talk, we will discuss how AMEX has approached and solved this problem.