Sanjeev Satheesj, Research Scientist, Baidu at The AI Conference 2017

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Sanjeev Satheesh, leads the Deep Speech team at Baidu’s Silicon valley AI lab. Baidu SVAIL is focused on developing hard AI technologies to impact hundreds of millions of people. The Story of End to End Models in Deep Learning The past few years have seen the explosive entrance of end to end deep learning models - in computer vision, speech recognition, machine translation, text to speech and others. In this talk, we look at this trend to identify what has worked well, and try to make some predictions for the future based on the next set of unsolved problems.

Silicon Valley AI Lab
Story of end-to-end covfefe
Sanjeev Satheesh
June 2, 2017

Silicon Valley AI Lab
Story of end-to-end models
Sanjeev Satheesh
June 2, 2017

What are End-to-End Models?
Gaussian Mixture
model
over
Spectrograms
Hidden Markov
Model
over
Phonemes
Lexicon +
Language
Model
of
text
CAT

What are End-to-End Models?
English

End-to-end models
Object
Recognition
Speech
Recognition
Image
Captioning
Language
Translation

Why End-to-end models?
Accuracy
Data + Model Size
Deep End-to-End
model
ML workflow-2
ML workflow-1

Traditional machine learning
pipelines are fairly complicated and
typically need a lot of domain knowledge
to build.
Why End-to-end models?

Why End-to-end models?
Easier to obtain a large
amount of data
Easier on practitioners

Why End-to-end models?
Idea
CodeResults

Why End-to-end models?
We built deep speech with no superior knowledge of speech recognition or
Mandarin language

Challenges
Need large amount of
data

Challenges
Need large amount of
data
Lots of compute to
explore architectures

Challenges
Idea
CodeResults

Challenges
Need large amount of
data
Lots of compute to
explore architectures
Lots of compute needed
for deployment.

Batch Dispatch for Efficiency
Time

What’s coming next (immediately)
Speech
Recognition
Speech
Synthesis
Semantic
Understanding
More natural interfaces

What’s coming next (likely)
Composition of E2E models
Super personalization
Tasks we are not solving because
there’s not enough compute

What’s probably NOT coming (immediately)
Autonomous driving
General Dialog systems

Thank You!

Sanjeev Satheesh
sanjeevsatheesh@baidu.com
http://research.baidu.com
Silicon Valley AI Lab

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Virginia Smith, Researcher, UC Berkeley at MLconf SF 2016

Virginia Smith, Researcher, UC Berkeley at MLconf SF 2016

A Friendly Introduction To Causality: Causality has been studied under several frameworks in statistics and artificial intelligence. We will briefly survey Pearl’s Structural Equation model and explain how interventions can be used to discover causality. We will also present a novel information theoretic framework for discovering causal directions from observational data when interventions are not possible. The starting point is conditional independence in joint probability distributions and no prior knowledge on causal inference is required.

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineeri...

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineeri...

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineeri...

Interpreting Black-Box Models with Applications to Healthcare: Complex and highly interactive models such as Random Forests, Gradient Boosting, and Deep Neural Networks demonstrate superior predictive power compared to their high-bias counterparts, Linear and Logistic Regression. However, these more complex and sophisticated methods lack the interpretability of the simpler alternatives. In some areas of application, such as healthcare, model interpretability is crucial both to build confidence in the model predictions as well as to explain the results on individual cases. This talk will discuss recent approaches to explaining “black-box” models and demonstrate some recently developed tools that aid this effort.

Brian Lucena, Senior Data Scientist, Metis at MLconf SF 2016

Brian Lucena, Senior Data Scientist, Metis at MLconf SF 2016

Brian Lucena, Senior Data Scientist, Metis at MLconf SF 2016

Alex Smola is the Manager of the Cloud Machine Learning Platform at Amazon. Prior to his role at Amazon, Smola was a Professor in the Machine Learning Department of Carnegie Mellon University and cofounder and CEO of Marianas Labs. Prior to that he worked at Google Strategic Technologies, Yahoo Research, and National ICT Australia. Prior to joining CMU, he was professor at UC Berkeley and the Australian National University. Alex obtained his PhD at TU Berlin in 1998. He has published over 200 papers and written or coauthored 5 books. Abstract summary Personalization and Scalable Deep Learning with MXNET: User return times and movie preferences are inherently time dependent. In this talk I will show how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, I will show how to train large scale distributed parallel models using MXNet efficiently. This includes a brief overview of key components of defining networks, of optimization, and a walkthrough of the steps required to allocate machines, and to train a model.

Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016

Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016

Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016

Why Machine Learning Algorithms Fall Short (And What You Can Do About It): Many think that machine learning is all about the algorithms. Want a self-learning system? Get your data, start coding or hire a PhD that will build you a model that will stand the test of time. Of course we know that this is not enough. Models degrade over time, algorithms that work great on yesterday’s data may not be the best option, new data sources and types are made available. In short, your self-learning system may not be learning anything at all. In this session, we will examine how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time. We will show how to apply mathematical optimization algorithms that often prove superior to local optimization methods favored by typical machine learning applications and discuss why these methods can crate better results. We will also examine the role of smart automation in the context of machine learning and how smart automation can create self-learning systems that are built to last.

Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...

Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...

Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...

Caroline Sinders is a machine learning designer/user researcher, artist. For the past few years, she has been focusing on the intersections of natural language processing, artificial intelligence, abuse, online harassment and politics in digital, conversational spaces.Caroline is a designer and researcher at Wikimedia, and a BuzzFeed/Eyebeam Open Lab Fellow. She holds a masters from New York University’s Interactive Telecommunications Program from New York University Emotional Trauma and Machine Learning How do we create, code and make emotional data inside of systems? And how do we create the necessary context t in larger systems that use data. Is it possible to use machine learning to solve very hard problems around conversation? For the past two years, I’ve been studying internet culture, online conversations, memes, and online harassment. I also worked as a user researcher at IBM Watson helping design and layout systems for chat bot software. As a designer and researcher interested in all of the nuances of human conversations and emotions, from humor to sadness, to memes and harassment, I wonder is it possible to code in emotions for machine learning systems? And what are the ethical implications of that? Can we design systems to mitigate harassment, to elevate humor? And can these systems promote human agency, and allow for participation from users to help decide and structure the system the talk in- can design and user participation help set what is harassment and what is not? With machine learning, often the creators of the system are deciding what norms of the system and the users are left out of the collaboration. How do we create systems that are transparent for users, that also facilitate user participation? With online communities, communication, and culture, users make, users, do, users are the community.

Caroline Sinders, Online Harassment Researcher, Wikimedia at The AI Conferenc...

Caroline Sinders, Online Harassment Researcher, Wikimedia at The AI Conferenc...

Caroline Sinders, Online Harassment Researcher, Wikimedia at The AI Conferenc...

Destacado (20)

Scott Clark, CEO, SigOpt, at The AI Conference 2017

Scott Clark, CEO, SigOpt, at The AI Conference 2017

Scott Clark, CEO, SigOpt, at The AI Conference 2017

Daniel Shank, Data Scientist, Talla at MLconf SF 2016

Daniel Shank, Data Scientist, Talla at MLconf SF 2016

Daniel Shank, Data Scientist, Talla at MLconf SF 2016

Luna Dong, Principal Scientist, Amazon at MLconf Seattle 2017

Luna Dong, Principal Scientist, Amazon at MLconf Seattle 2017

Luna Dong, Principal Scientist, Amazon at MLconf Seattle 2017

Nikhil Garg, Engineering Manager, Quora at MLconf SF 2016

Nikhil Garg, Engineering Manager, Quora at MLconf SF 2016

Nikhil Garg, Engineering Manager, Quora at MLconf SF 2016

Andrew Musselman, Committer and PMC Member, Apache Mahout, at MLconf Seattle ...

Andrew Musselman, Committer and PMC Member, Apache Mahout, at MLconf Seattle ...

Andrew Musselman, Committer and PMC Member, Apache Mahout, at MLconf Seattle ...

Stephanie deWet, Software Engineer, Pinterest at MLconf SF 2016

Stephanie deWet, Software Engineer, Pinterest at MLconf SF 2016

Stephanie deWet, Software Engineer, Pinterest at MLconf SF 2016

Ben Lau, Quantitative Researcher, Hobbyist, at MLconf NYC 2017

Ben Lau, Quantitative Researcher, Hobbyist, at MLconf NYC 2017

Ben Lau, Quantitative Researcher, Hobbyist, at MLconf NYC 2017

Layla El Asri, Research Scientist, Maluuba

Layla El Asri, Research Scientist, Maluuba

Layla El Asri, Research Scientist, Maluuba

Mayur Thakur, Managing Director, Goldman Sachs, at MLconf NYC 2017

Mayur Thakur, Managing Director, Goldman Sachs, at MLconf NYC 2017

Mayur Thakur, Managing Director, Goldman Sachs, at MLconf NYC 2017

Scott Clark, CEO, SigOpt, at MLconf Seattle 2017

Scott Clark, CEO, SigOpt, at MLconf Seattle 2017

Scott Clark, CEO, SigOpt, at MLconf Seattle 2017

Jeff Bradshaw, Founder, Adaptris

Jeff Bradshaw, Founder, Adaptris

Jeff Bradshaw, Founder, Adaptris

Corinna Cortes, Head of Research, Google, at MLconf NYC 2017

Corinna Cortes, Head of Research, Google, at MLconf NYC 2017

Corinna Cortes, Head of Research, Google, at MLconf NYC 2017

Anjuli Kannan, Software Engineer, Google at MLconf SF 2016

Anjuli Kannan, Software Engineer, Google at MLconf SF 2016

Anjuli Kannan, Software Engineer, Google at MLconf SF 2016

Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

Virginia Smith, Researcher, UC Berkeley at MLconf SF 2016

Virginia Smith, Researcher, UC Berkeley at MLconf SF 2016

Virginia Smith, Researcher, UC Berkeley at MLconf SF 2016

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineeri...

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineeri...

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineeri...

Brian Lucena, Senior Data Scientist, Metis at MLconf SF 2016

Brian Lucena, Senior Data Scientist, Metis at MLconf SF 2016

Brian Lucena, Senior Data Scientist, Metis at MLconf SF 2016

Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016

Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016

Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016

Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...

Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...

Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...

Caroline Sinders, Online Harassment Researcher, Wikimedia at The AI Conferenc...

Caroline Sinders, Online Harassment Researcher, Wikimedia at The AI Conferenc...

Caroline Sinders, Online Harassment Researcher, Wikimedia at The AI Conferenc...

Similar a Sanjeev Satheesj, Research Scientist, Baidu at The AI Conference 2017

There is an increasing demand for embedding intelligence in software systems as part of its core set of features both in the front-end (e.g. conversational user interfaces) and back-end (e.g. prediction services). This combination is usually referred to as AI-enhanced software or, simply, smart software. The development of smart software poses new engineering challenges, as now we need to deal with the engineering of the “traditional” components, the engineering of the “AI” ones but also of the interaction between both types that need to co-exist and collaborate. In this talk we'll see how modeling can help tame the complexity of engineering smart software by enabling software engineers specify and generate smart software systems starting from higher-level and platform-independent modeling primitives. But, unavoidably, these models will be more diverse and complex than our usual ones. Don't despair, we'll also see how some of these same AI techniques that are making our modeling life challenging can be turned into allies and be transformed into modeling assistants to tackle the engineering of smart software with a new breed of smart modeling tools.

Smart modeling of smart software

Smart modeling of smart software

Smart modeling of smart software

Richard Tibbetts, CTO, StreamBase Systems. StreamSQL EventFlow is one of the most popular languages for Complex Event Processing (CEP), a data management paradigm for real-time applications. Based on a stream-relational data model common to other CEP languages, EventFlow is unique in that it is a visual language. This talk will focus on the design of visual representations for key features including event dispatch, modularity, data parallelism, polymorphism, and dependency injection, and on the co-development of an Eclipse-based IDE along with a new programming language. StreamSQL EventFlow is the primary programming language for the StreamBase Event Processing Platform. Complex Event Processing platforms are used to process large volumes of event-oriented data in real-time, often in latency-critical applications such as securities trading. Combining clustering, messaging, queuing, data storage, and application logic into one system minimizes latency and gives the programmer control over all aspects of the application. StreamSQL EventFlow is an executable visual language for building CEP applications, unlike visual environments designed for non-developers, or architecture-focused modeling tools. The talk will cover experiences overcoming prejudice against visual programming languages, and how critical development tools are to that process. We will also discuss some details of the implementation including the compiler, a visual debugger, and diff/merge functionality.

Stream SQL eventflow visual programming for real programmers presentation

Stream SQL eventflow visual programming for real programmers presentation

Stream SQL eventflow visual programming for real programmers presentation

The State of ML for iOS: On the Advent of WWDC 2018 🕯

The State of ML for iOS: On the Advent of WWDC 2018 🕯

The State of ML for iOS: On the Advent of WWDC 2018 🕯

mbeddr meets IncQuer - Combining the Best Features of Two Modeling Worlds

mbeddr meets IncQuer - Combining the Best Features of Two Modeling Worlds

mbeddr meets IncQuer - Combining the Best Features of Two Modeling Worlds

Over the last couple of years, I have used MPS successfully to build interesting (modeling and programming) languages in a wide variety of domains, targeting both business users and engineers. I’ve used MPS because it is currently the most powerful language workbench, lots of things are good about iz, in particular, its support for a multitude of notations and language modularity. But it is also obvious that MPS is not going to be viable for the medium to long term future; the most obvious reason for this statement is that it is not web/cloud-based. In this keynote, I will quickly recap why and how we have been successful with MPS, and point out how language workbenches could look like in the future; I will outline challenges, opportunities and research problems. I hope to spawn discussions for the remainder of the workshop.

Envisioning the Future of Language Workbenches

Envisioning the Future of Language Workbenches

Envisioning the Future of Language Workbenches

Deep Learning in NLP (BERT, ERNIE and REFORMER)

Deep Learning in NLP (BERT, ERNIE and REFORMER)

Deep Learning in NLP (BERT, ERNIE and REFORMER)

Biswajit Biswas

Are you responsible for developing satellite on-board software? Are you the Dutch government and you have to efficiently implement the public benefits law? Are you a healthcare startup, developing companion apps that help patients through a treatment? Are you an insurance company struggling to create new, and evolve existing products quickly to keep up with the market? These are all examples of organisations who have built their own domain-specific programming language to streamline the development of applications that have a non-trivial algorithmic core. All have built their languages with Jetbrains MPS, an open source language development tool optimized for ecosystems of collaborating languages with mixed graphical, textual, tabular and mathematical notations. This talk has four parts. I start by motivating the need for DSLs based on real-world examples, including the ones above. I will then present a few high-level design practices that guide our language development work. Third, I will develop a simple language extension to give you a feel for how MPS works. And finally, I will point you to things you can read to get you started with your own language development practice.

Build your own Language - Why and How?

Build your own Language - Why and How?

Build your own Language - Why and How?

In this talk, we will discuss how recent advancements in artificial intelligence are transforming traditional search technologies. Deep learning-based image analysis and natural language processing open exciting new horizons for search and recommendation applications across the industries. We’ll talk about how deep learning models can help conventional search engines to achieve better relevance. We will share our experience implementing innovative solutions for online retailers, finance and high tech customers.

Deep learning applications in e-commerce search: Dynamic talks Chicago 3/14/2019

Deep learning applications in e-commerce search: Dynamic talks Chicago 3/14/2019

Deep learning applications in e-commerce search: Dynamic talks Chicago 3/14/2019

Software Modeling and Artificial Intelligence: friends or foes?

Software Modeling and Artificial Intelligence: friends or foes?

Software Modeling and Artificial Intelligence: friends or foes?

Recurrent Neural Network : Multi-Class & Multi Label Text Classification

Recurrent Neural Network : Multi-Class & Multi Label Text Classification

Recurrent Neural Network : Multi-Class & Multi Label Text Classification

Presented by indico co-founder Madison May at ODSC East. Abstract: Transfer learning, the practice of applying knowledge gained on one machine learning task to aid the solution of a second task, has seen historic success in the field of computer vision. The output representations of generic image classification models trained on ImageNet have been leveraged to build models that detect the presence of custom objects in natural images. Image classification tasks that would typically require hundreds of thousands of images can be tackled with mere dozens of training examples per class thanks to the use of these pretrained reprsentations. The field of natural language processing, however, has seen more limited gains from transfer learning, with most approaches limited to the use of pretrained word representations. In this talk, we explore parameter and data efficient mechanisms for transfer learning on text, and show practical improvements on real-world tasks. In addition, we demo the use of Enso, a newly open-sourced library designed to simplify benchmarking of transfer learning methods on a variety of target tasks. Enso provides tools for the fair comparison of varied feature representations and target task models as the amount of training data made available to the target model is incrementally increased.

ODSC East: Effective Transfer Learning for NLP

ODSC East: Effective Transfer Learning for NLP

ODSC East: Effective Transfer Learning for NLP

Large Language Models Bootcamp

Large Language Models Bootcamp

Large Language Models Bootcamp

Data Science Dojo

Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential. In this presentation you’ll learn about: - Why on-device AI is key - Full-stack AI optimizations to make on-device AI possible and efficient - Advanced techniques like quantization, distillation, and speculative decoding - How generative AI models can be run on device and examples of some running now - Qualcomm Technologies’ role in scaling on-device generative AI

Generative AI at the edge.pdf

Generative AI at the edge.pdf

Generative AI at the edge.pdf

Qualcomm Research

Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information. By carefully engineering prompts, practitioners can harness the capabilities of LLMs to achieve different goals.

A comprehensive guide to prompt engineering.pdf

A comprehensive guide to prompt engineering.pdf

A comprehensive guide to prompt engineering.pdf

Bridging the gap between AI and UI - DSI Vienna - full version

Bridging the gap between AI and UI - DSI Vienna - full version

Bridging the gap between AI and UI - DSI Vienna - full version

In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated. Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner. Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.

Vertex AI: Pipelines for your MLOps workflows

Vertex AI: Pipelines for your MLOps workflows

Vertex AI: Pipelines for your MLOps workflows

Large Language Models, Data & APIs - Integrating Generative AI Power into you...

Large Language Models, Data & APIs - Integrating Generative AI Power into you...

Large Language Models, Data & APIs - Integrating Generative AI Power into you...

NETUserGroupBern

Google Cloud Platform - Cloud-Native Roadshow Stuttgart

Google Cloud Platform - Cloud-Native Roadshow Stuttgart

Google Cloud Platform - Cloud-Native Roadshow Stuttgart

Ashutosh's resume (3)

Ashutosh's resume (3)

Ashutosh's resume (3)

Ashutosh Vishnoi

Ai/ML services

Ecosmob Technologies

Similar a Sanjeev Satheesj, Research Scientist, Baidu at The AI Conference 2017 (20)

Smart modeling of smart software

Smart modeling of smart software

Smart modeling of smart software

Stream SQL eventflow visual programming for real programmers presentation

Stream SQL eventflow visual programming for real programmers presentation

Stream SQL eventflow visual programming for real programmers presentation

The State of ML for iOS: On the Advent of WWDC 2018 🕯

The State of ML for iOS: On the Advent of WWDC 2018 🕯

The State of ML for iOS: On the Advent of WWDC 2018 🕯

mbeddr meets IncQuer - Combining the Best Features of Two Modeling Worlds

mbeddr meets IncQuer - Combining the Best Features of Two Modeling Worlds

mbeddr meets IncQuer - Combining the Best Features of Two Modeling Worlds

Envisioning the Future of Language Workbenches

Envisioning the Future of Language Workbenches

Envisioning the Future of Language Workbenches

Deep Learning in NLP (BERT, ERNIE and REFORMER)

Deep Learning in NLP (BERT, ERNIE and REFORMER)

Deep Learning in NLP (BERT, ERNIE and REFORMER)

Build your own Language - Why and How?

Build your own Language - Why and How?

Build your own Language - Why and How?

Deep learning applications in e-commerce search: Dynamic talks Chicago 3/14/2019

Deep learning applications in e-commerce search: Dynamic talks Chicago 3/14/2019

Deep learning applications in e-commerce search: Dynamic talks Chicago 3/14/2019

Software Modeling and Artificial Intelligence: friends or foes?

Software Modeling and Artificial Intelligence: friends or foes?

Software Modeling and Artificial Intelligence: friends or foes?

Recurrent Neural Network : Multi-Class & Multi Label Text Classification

Recurrent Neural Network : Multi-Class & Multi Label Text Classification

Recurrent Neural Network : Multi-Class & Multi Label Text Classification

ODSC East: Effective Transfer Learning for NLP

ODSC East: Effective Transfer Learning for NLP

ODSC East: Effective Transfer Learning for NLP

Large Language Models Bootcamp

Large Language Models Bootcamp

Large Language Models Bootcamp

Generative AI at the edge.pdf

Generative AI at the edge.pdf

Generative AI at the edge.pdf

A comprehensive guide to prompt engineering.pdf

A comprehensive guide to prompt engineering.pdf

A comprehensive guide to prompt engineering.pdf

Bridging the gap between AI and UI - DSI Vienna - full version

Bridging the gap between AI and UI - DSI Vienna - full version

Bridging the gap between AI and UI - DSI Vienna - full version

Vertex AI: Pipelines for your MLOps workflows

Vertex AI: Pipelines for your MLOps workflows

Vertex AI: Pipelines for your MLOps workflows

Large Language Models, Data & APIs - Integrating Generative AI Power into you...

Large Language Models, Data & APIs - Integrating Generative AI Power into you...

Large Language Models, Data & APIs - Integrating Generative AI Power into you...

Google Cloud Platform - Cloud-Native Roadshow Stuttgart

Google Cloud Platform - Cloud-Native Roadshow Stuttgart

Google Cloud Platform - Cloud-Native Roadshow Stuttgart

Ashutosh's resume (3)

Ashutosh's resume (3)

Ashutosh's resume (3)

Ai/ML services

Más de MLconf

Understanding Human Impact: Social and Equity Assessments for AI Technologies Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies. In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.

Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

The Brain’s Guide to Dealing with Context in Language Understanding Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity. In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

Applying Computer Vision to Reduce Contamination in the Recycling Stream With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt. Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology. Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean. Our convolutional neural network flags potential instances of contamination inside a dumpster, enabling garbage haulers to know which containers have the wrong type of material inside. This allows them to provide targeted, timely education, and when appropriate, assess fines, to improve recycling compliance at the businesses and residences they serve, helping keep recycling services financially viable. In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.

Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...

Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...

Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...

Quantum Computing: a Treasure Hunt, not a Gold Rush Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.

Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush

Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush

Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush

Data Labeling as Religious Experience One of the most common places to deploy a production machine learning systems is as a replacement for a legacy rules-based system that is having a hard time keeping up with new edge cases and requirements. I'll be walking through the process and tooling we used to help us design, train, and deploy a model to replace a set of static rules we had for handling invite spam at Slack, talk about what we learned, and discuss some problems to solve in order to make these migrations easier for everyone.

Josh Wills - Data Labeling as Religious Experience

Josh Wills - Data Labeling as Religious Experience

Josh Wills - Data Labeling as Religious Experience

Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has enough idiosyncratic clues that it can be harnessed as a passive biometric. While there were many siloed attempts made by the machine learning community to model Bipedal Gait sensor data, these were done with small datasets oft collected in restricted academic environs. In this talk, we will introduce the ImageNet moment for human gait analysis by presenting 'Project GaitNet', the largest ever planet-sized motion sensor based human bipedal gait dataset ever curated. We’ll also present the associated state-of-the-art results in classifying humans harnessing novel deep neural architectures and the related success stories we have enjoyed in transfer-learning into disparate domains of human kinematics analysis.

Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...

Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...

Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...

Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language Alzheimer's disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer's, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer's disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer's detection performance the most, due to their impact on the features of syntactic and lexical complexity.

Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...

Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...

Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...

Optimized Image Classification on the Cheap In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of the dataset through image augmentation to boost the classifier’s performance. We will use Bayesian optimization to learn the hyperparameters associated with image transformations using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also focus on the features of these augmented images and the downstream implications for our image classifier. To both maximize model performance on a budget and explore the impact of optimization on these methods, we apply a particularly efficient implementation of Bayesian optimization to each of these architectures in this comparison. Our goal is to draw on a rigorous set of experimental results that can help us answer the question: how can resource-constrained teams make trade-offs between efficiency and effectiveness using pre-trained models?

Meghana Ravikumar - Optimized Image Classification on the Cheap

Meghana Ravikumar - Optimized Image Classification on the Cheap

Meghana Ravikumar - Optimized Image Classification on the Cheap

The Importance of Modeling Data Collection Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have. In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set. My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.

Noam Finkelstein - The Importance of Modeling Data Collection

Noam Finkelstein - The Importance of Modeling Data Collection

Noam Finkelstein - The Importance of Modeling Data Collection

The Uncanny Valley of ML Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.

June Andrews - The Uncanny Valley of ML

June Andrews - The Uncanny Valley of ML

June Andrews - The Uncanny Valley of ML

Deep Learning Architectures for Semantic Relation Detection Tasks Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability. We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

Vito Ostuni - The Voice: New Challenges in a Zero UI World The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query. We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.

Vito Ostuni - The Voice: New Challenges in a Zero UI World

Vito Ostuni - The Voice: New Challenges in a Zero UI World

Vito Ostuni - The Voice: New Challenges in a Zero UI World

Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...

Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...

Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...

Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescrip...

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Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescrip...

Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection o...

Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection o...

Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection o...

Rishabh Mehrotra - Recommendations in a Marketplace: Personalizing Explainabl...

Rishabh Mehrotra - Recommendations in a Marketplace: Personalizing Explainabl...

Rishabh Mehrotra - Recommendations in a Marketplace: Personalizing Explainabl...

Soumith Chintala - Increasing the Impact of AI Through Better Software

Soumith Chintala - Increasing the Impact of AI Through Better Software

Soumith Chintala - Increasing the Impact of AI Through Better Software

Roy Lowrance - Predicting Bond Prices: Regime Changes

Roy Lowrance - Predicting Bond Prices: Regime Changes

Roy Lowrance - Predicting Bond Prices: Regime Changes

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Madalina Fiterau - Hybrid Machine Learning Methods for the Interpretation and...

Madalina Fiterau - Hybrid Machine Learning Methods for the Interpretation and...

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Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...

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Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding

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Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush

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Meghana Ravikumar - Optimized Image Classification on the Cheap

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Meghana Ravikumar - Optimized Image Classification on the Cheap

Noam Finkelstein - The Importance of Modeling Data Collection

Noam Finkelstein - The Importance of Modeling Data Collection

Noam Finkelstein - The Importance of Modeling Data Collection

June Andrews - The Uncanny Valley of ML

June Andrews - The Uncanny Valley of ML

June Andrews - The Uncanny Valley of ML

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

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Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

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Soumith Chintala - Increasing the Impact of AI Through Better Software

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Roy Lowrance - Predicting Bond Prices: Regime Changes

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Madalina Fiterau - Hybrid Machine Learning Methods for the Interpretation and...

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Último

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

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"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

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Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Manulife - Insurer Transformation Award 2024

Manulife - Insurer Transformation Award 2024

Manulife - Insurer Transformation Award 2024

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Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Rustici Software

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

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The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

Andrey Devyatkin

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Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

When you’re building (micro)services, you have lots of framework options. Spring Boot is no doubt a popular choice. But there’s more! Take Quarkus, a framework that’s considered the rising star for Kubernetes-native Java. It always depends on what's best for your situation, but how to choose the best solution if you're comparing 2 frameworks? Both Spring Boot and Quarkus have their positives and negatives. Let us compare the two by live coding a couple of common use cases in Spring Boot and Quarkus. After this talk, you’ll be ready to get started with Quarkus yourself, and know when to select Quarkus or Spring Boot.

Spring Boot vs Quarkus the ultimate battle - DevoxxUK

Spring Boot vs Quarkus the ultimate battle - DevoxxUK

Spring Boot vs Quarkus the ultimate battle - DevoxxUK

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Strategies for Landing an Oracle DBA Job as a Fresher

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

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Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

Manulife - Insurer Transformation Award 2024

Manulife - Insurer Transformation Award 2024

Manulife - Insurer Transformation Award 2024

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

FWD Group - Insurer Innovation Award 2024

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

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Sanjeev Satheesj, Research Scientist, Baidu at The AI Conference 2017

1. Silicon Valley AI Lab Story of end-to-end covfefe Sanjeev Satheesh June 2, 2017

2. Silicon Valley AI Lab Story of end-to-end models Sanjeev Satheesh June 2, 2017

3. What are End-to-End Models? Gaussian Mixture model over Spectrograms Hidden Markov Model over Phonemes Lexicon + Language Model of text CAT

4. What are End-to-End Models? English

5. End-to-end models Object Recognition Speech Recognition Image Captioning Language Translation

6. Why End-to-end models? Accuracy Data + Model Size Deep End-to-End model ML workflow-2 ML workflow-1

7. Traditional machine learning pipelines are fairly complicated and typically need a lot of domain knowledge to build. Why End-to-end models?

8. Why End-to-end models? Easier to obtain a large amount of data Easier on practitioners

9. Why End-to-end models? Idea CodeResults

10. Why End-to-end models? We built deep speech with no superior knowledge of speech recognition or Mandarin language

11. Challenges Need large amount of data

12. Challenges Need large amount of data Lots of compute to explore architectures

13. Challenges Idea CodeResults

14. Challenges Need large amount of data Lots of compute to explore architectures Lots of compute needed for deployment.

15. Batch Dispatch for Efficiency Time

16. What’s coming next (immediately) Speech Recognition Speech Synthesis Semantic Understanding More natural interfaces

17. What’s coming next (likely) Composition of E2E models Super personalization Tasks we are not solving because there’s not enough compute

18. What’s probably NOT coming (immediately) Autonomous driving General Dialog systems

20. Sanjeev Satheesh sanjeevsatheesh@baidu.com http://research.baidu.com Silicon Valley AI Lab