[TALK]@Telematika

ai

Where is my Code Vulnerable: Matching CVEs and Source Code

February 7, 2020

Surprisingly, often it's hard to find the precise correspondence between a known software vulnerability (in CVE) and the exact origin of the software (Maven coordinates or github repo/tag). Ideally, t …

Data science + design thinking: A perfect blend to achieve the best user experience

February 6, 2020

Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.

Data science + design thinking: A perfect blend to achieve the best user experience

February 6, 2020

Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs

February 6, 2020

Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.

Delivering AI vision ecosystem offers with Intel AI: In Production

February 6, 2020

Join Lindsay Hiebert and Vikrant Viniak as they explore challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem to accelerate prototype to production at the edge with Intel and partner offerings.

Design thinking for AI

February 6, 2020

Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Design thinking for AI

February 6, 2020

Developing AI responsibly

February 5, 2020

Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Sarah Bird outlines her perspective on some of the major challenges in responsible AI development and examines promising new tools and technologies to help enable it in practice.

Executive Briefing: Rigorous application of domain insights in AI projects

February 5, 2020

Domain insights are crucial for successful AI/ML initiatives. This talk discusses three areas of concerns: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.

Executive Briefing: Rigorous application of domain insights in AI projects

February 5, 2020

Executive Briefing: Unpacking AutoML

February 5, 2020

Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoMLtranslating from the hype into business concerns and practices in a vendor-neutral way.

From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies)

February 5, 2020

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs.

Getting from A to AI

February 5, 2020

Businesses recognize the transformational potential for advanced analytics, machine, and deep learning but often get lost on their path to AI. Eric Gardner spends his days advising customers about AI and shares a four-step journey that organizations of every kind can use to evaluate their unique path from data to insight.

Getting the AI you want on the infrastructure you know: 2 deep-dive case studies of AI on CPU

February 5, 2020

Kushal Datta specializes in optimizing AI applications on CPUs; hear two of his latest customer success stories and get the details behind the technical collaboration that led to incredible performance for AI on CPU.

Machine Learning Models and Datasets Versioning Practices and Tools

February 5, 2020

The rise of AI and ML changes development workflow and requires new development tools: data versioning, ML pipeline versioning, experiments metrics tracking and others that have not been formalized an …

Lightning Talk: A Perfect Match: AI4EU and Acumos for Europe

February 4, 2020

This presentation will make the link between Linux Foundations' Acumos project and the AI4EU project, which has started at the beginning of 2019 and will run for 3 years. At the beginning of 2020 we w …

Mozart in the box: Interacting with AI tools for music creation

February 4, 2020

Alessandro Palladini explores the role of experts and creatives in a world dominated by intelligent machines by bridging the gap between the research on complex systems and tools for creativity, examining what he believes to be the key design principles and perspectives on making intelligent tools for creativity and for experts in the loop.

Open-endedness: A new grand challenge for AI

February 4, 2020

We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continuallyeven endlessly.

Safe and smarter driving, powered by AI (sponsored by Amazon Web Services)

February 3, 2020

Lei Pan examines how Nauto uses Amazon SageMaker and other AWS services, including Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) to continually evolve smarter data for driver behavior.

Scaling AI at Cerebras

February 3, 2020

Long training times are the single biggest factor slowing down innovation in deep learning. Today's common approach of scaling large workloads out over many small processors is inefficient and requires extensive model tuning. Urs Kster explains why with increasing model and dataset sizes, new ideas are needed to reduce training times.

TFX: Production ML pipelines with TensorFlow

February 2, 2020

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.

The moral responsibility of AI builders (sponsored by Dataiku)

February 2, 2020

With the adoption of AI in the enterprise accelerating, its impactsboth positive and negativeare rapidly increasing. Triveni Gandhi explores why the builders of these new AI capabilities all bear some moral responsibility for ensuring that their products create maximum benefit and minimal harm.

Ubers deep learning applications in NLP and conversational AI

February 2, 2020

Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems.

Unlocking the value of your data (sponsored by IBM Watson)

February 2, 2020

Dinesh Nirmal examines how, with a unified, prescriptive information architecture, organizations can successfully unlock the value of their data for AI as well as trust and control the business impact and risks of AI while coexisting in a multicloud world.

AI pipelines powered by Jupyter notebooks

February 1, 2020

The Jupyter Notebook has become the de facto platform for data scientists and AI engineers to build interactive applications and develop AI/ML models. Luciano Resende details how to schedule related notebooks that correspond to different phases of the model lifecycle into notebook-based AI pipelines and walks you through scenarios that demonstrate how to reuse notebooks via parameterization.

AI pipelines powered by Jupyter notebooks

February 1, 2020

The Twilight Zone: The AI / Privacy Governance Dilemma

February 1, 2020

Artificial Intelligence (AI) is becoming an important component of most business models. As a result of this technologys rise and the data collection and use AI requires, many companies find themselv …

Are Data Struggles Holding Back your AI Projects? Are you Ready for Open Metadata and the CDLA?

January 30, 2020

Data challenges are halting AI projects for multiple reasons, and open source developers are looking for solutions. Do you know how to share data sets properly? Just like software, you don't want to p …