This talk was given to an internal audience of non-engineers at Freshworks primarily product managers, marketers & sales folks to educate on machine learning basics & future.
PPT used during my speech during NASSCOM's BRAINS Event in Hyderabad, Sep 2019. This covers the emerging trends in Inferencing for Artificial Intelligence. PPT discusses about Edge Computing, VPUs, TPUs, GPUs etc.
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
This short presentation provides context for the field of AI today and makes some predictions about the advancements of the field in the enterprise in the next 5-10 years
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
This talk was given to an internal audience of non-engineers at Freshworks primarily product managers, marketers & sales folks to educate on machine learning basics & future.
PPT used during my speech during NASSCOM's BRAINS Event in Hyderabad, Sep 2019. This covers the emerging trends in Inferencing for Artificial Intelligence. PPT discusses about Edge Computing, VPUs, TPUs, GPUs etc.
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
This short presentation provides context for the field of AI today and makes some predictions about the advancements of the field in the enterprise in the next 5-10 years
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
International Journal of Artificial Intelligence and Soft Computing (IJAISC)MiajackB
International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.
A very high level introduction to the field of Data Science, Artificial Intelligence. Covers an introduction to Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks. Given as part of Industry Lectures event at GVP College of Engineering
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
4th International Conference On Recent Advances in Mathematical Sciences and Applications (RAMSA - 21) organized by GVP College of Engineering. This deck is an overview of the trends in ML Engineering which is evolving as a discipline and how Mathematics, Machine Learning and ML Engineering are related to one another.
Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Un...AI Frontiers
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.
My talk on AI for Human Resource Management at the Faculty Development Programme conducted by Department of Management Studies MVGR College of Engineering
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
GreenBiz 17 Tutorial Slides: "How Corporates are Aligning with the Sustainabl...GreenBiz Group
The Sustainable Development Goals define global priorities and aspirations for 2030. Where does your company strategy align with these global goals? Learn how the SDGs affect your business, and gain the tools and knowledge needed to maximize your company's contribution to the success of the SDGs.
GreenBiz 17 In-Depth Tutorials are intensive half-day sessions held prior to the start of the conference. These are designed to offer participants an opportunity to dive deeper into a topic of interest and develop tangible knowledge and skills. In addition, attendees will have a greater opportunity to network with their peers in these interactive sessions. Concurrent tutorials will be held the morning of Tuesday, February 14, and are available only to those who purchase an All Access Pass.
FIWARE: Managing Context Information at large scaleFermin Galan
This presentation describes how context management is implemented in FIWARE platform, base don Orion Context Broker GEri. Both basic usage of Context Broker and advanced topics are included.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
Read the full story on the Drift blog here: http://blog.drift.com/sales-team-tips
International Journal of Artificial Intelligence and Soft Computing (IJAISC)MiajackB
International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.
A very high level introduction to the field of Data Science, Artificial Intelligence. Covers an introduction to Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks. Given as part of Industry Lectures event at GVP College of Engineering
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
4th International Conference On Recent Advances in Mathematical Sciences and Applications (RAMSA - 21) organized by GVP College of Engineering. This deck is an overview of the trends in ML Engineering which is evolving as a discipline and how Mathematics, Machine Learning and ML Engineering are related to one another.
Training at AI Frontiers 2018 - Ni Lao: Weakly Supervised Natural Language Un...AI Frontiers
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.
My talk on AI for Human Resource Management at the Faculty Development Programme conducted by Department of Management Studies MVGR College of Engineering
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
GreenBiz 17 Tutorial Slides: "How Corporates are Aligning with the Sustainabl...GreenBiz Group
The Sustainable Development Goals define global priorities and aspirations for 2030. Where does your company strategy align with these global goals? Learn how the SDGs affect your business, and gain the tools and knowledge needed to maximize your company's contribution to the success of the SDGs.
GreenBiz 17 In-Depth Tutorials are intensive half-day sessions held prior to the start of the conference. These are designed to offer participants an opportunity to dive deeper into a topic of interest and develop tangible knowledge and skills. In addition, attendees will have a greater opportunity to network with their peers in these interactive sessions. Concurrent tutorials will be held the morning of Tuesday, February 14, and are available only to those who purchase an All Access Pass.
FIWARE: Managing Context Information at large scaleFermin Galan
This presentation describes how context management is implemented in FIWARE platform, base don Orion Context Broker GEri. Both basic usage of Context Broker and advanced topics are included.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
Read the full story on the Drift blog here: http://blog.drift.com/sales-team-tips
Tiedonhallinnan ongelmat ja semanttisen teknologian keinotHeimo Hänninen
Suomenkielinen esitys Talentumin sisällönhallinta seminaarista 2013. (Sorry, in Finnish only). Mitkä on kolme suurinta ongelmaa nyt ja mihin semanttinen teknologia voi tuoda apuja. Kolmas (ja kenties pahin) ongelma on mainittu mutta siihen ei teknologia tepsi - kenties aika parantaa tai putoava meteoriitti...
Smart Data Webinar: A Roadmap for Deploying Modern AI in BusinessDATAVERSITY
Adopting elements of modern AI and cognitive computing - including advanced natural language processing, natural interface technologies such as gesture and emotion-recognition, and machine learning - is rapidly becoming a necessity for new applications. As people in all industries are exposed to better, more personalized and responsive experiences with software, they will begin to demand more from every system they use. For product strategists and developers, the issue is not whether to consider modern AI, the issue is how to do so most effectively.
Webinar participants will learn:
•How to classify and map application attributes to AI technologies and tools; including data attributes, end-user attributes, and context attributes such as weather and location
•How to prioritize applications in an existing portfolio for AI-enhancements, and
•How to assess organizational readiness for leveraging AI
GLOA:A New Job Scheduling Algorithm for Grid ComputingLINE+
The paper review presentation of 'GLOA:A New Job Scheduling Algorithm for Grid Computing' published in International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 1.
How to build Open Hardware self-navigating car robotTomáš Jukin
Slides from my lecture at mDevCamp Prague 2016 about How to build a Open Hardware self-navigating car robot #Probee using RaspberryPi and Arduino, Multi-Agent Systems and actor programming in Ruby.
The presentation includes small overview of few AI techniques and a short introduction to Behavior Trees.
Source codes of the #Probee robot can be found at https://github.com/Juicymo/probee
"Continuous Digital Biomarkers from Wearable Devices" - Brandon Ballinger (Co...Hyper Wellbeing
"Continuous Digital Biomarkers from Wearable Devices" - Brandon Ballinger (Co-Founder, Cardiogram)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
Real-world applications of AI - Daniel Hulme @ PAPIs ConnectPAPIs.io
This talk will offer answers to the following questions: What is data-driven decision making? What is AI? What is Business Intelligence? Why are these concepts important? What are the biggest challenges and opportunities?
Daniel is the CEO of Satalia that provides AI inspired solutions to solve industries hardest problems. He’s the co-founder of the ASI that transitions scientists into data scientists. Daniel has a MSci and EngD in AI from UCL, and is Director of UCL’s Business Analytics MSc; applying AI to solve business/social problems. Daniel has many Advisory and Executive positions, holds an international Kauffman Global Entrepreneur Scholarship and actively promotes innovation across the globe.
Individual-In-The-Loop (for Ethically Aligned Artificial Intelligence)John C. Havens
This presentation was created as a speech for the launch of the Privacy & Sustainable Computing Lab at WU Vienna (http://www.privacylab.at/events/launch/).
Using Algorithmia to leverage AI and Machine Learning APIsRakuten Group, Inc.
We are entering a new era of software development. Companies are realizing that AI and machine learning are critical to success in business, both to save cost on repetitive tasks, and to enable to new features and products that would be impossible without machine intelligence. Algorithmia makes these tools available through web APIs that makes tools like computer vision and natural language processing available to companies everywhere. Kenny will talk about how sharing of intelligent APIs can improve your applications.
https://rakutentechnologyconference2016.sched.org/event/8aS5/using-algorithmia-to-leverage-ai-and-machine-learning-apis
Rakuten Technology Conference 2016
http://tech.rakuten.co.jp/
Igniting Next Level Productivity with AI-Infused Data Integration Workflows Safe Software
Learn where FME meets AI in this upcoming webinar to offer you incredible time savings. This webinar is tailored to ignite imaginations and offer solutions to your data integration challenges. As the new digital era sets sail on the winds of AI, the tangibility of its integration in our daily schema is unfolding.
Segment 1, titled “AI: The Good, the Bad and the FME” by Darren Fergus of Locus, navigates through the realms of AI, scrutinizing its pervasive impact while underscoring the symbiotic potential of FME and AI. Join in an engaging demonstration as FME and ChatGPT collaboratively orchestrate a PowerPoint narrative, epitomizing the alliance of AI with human ingenuity.
In Segment 2, “Integrating GeoAI Models in FME” by Dennis Wilhelm and Dr. Christopher Britsch of con terra GmbH, the spotlight veers towards operationalizing AI in our daily tasks through FME. A practical approach to embedding GeoAI Models into FME Workspaces is unveiled, showcasing the ease of incorporating AI-driven methodologies into your FME workflows, skyrocketing productivity levels.
To follow, Segment 3, "Unleash generative AI on your terms!" by Oliver Morris of Avineon-Tensing. While the prospects of Generative AI are thrilling, security and IT reservations, especially with 'phone home' tools, are genuine concerns. However, with open-source tools, you can locally harness large language models. In this demo, we'll unravel the magic of local AI deployment and its seamless integration into an FME workspace.
Bonus! Dmitri will join us for a fourth segment to tie us off, showcasing what he has been up to this week, including using OpenAI API for texturing in FME, amoung other projects.
Join us to explore the synergy of FME and AI: opening portals to a realm of revolutionized productivity and enriched user experiences.
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
Start Getting Your Feet Wet in Open Source Machine and Deep Learning Ian Gomez
At H2O.ai we see a world where all software will incorporate AI, and we’re focused on bringing AI to business through software. H2O.ai is the maker behind H2O, the leading open source machine and deep learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
In this webinar, you will learn about the scalable H2O core platform and the distributed algorithms it supports. H2O integrates seamlessly with the R and the Python environments. We will show you how to leverage the power of H2O algorithms in R, Python and H2O Flow interface. Come with an open mind and some high level knowledge of machine learning, and you will take away a stream of knowledge for your next ML/DL project.
Amy Wang is a math hacker at H2O, as well as the Sales Engineering Lead. She graduated from Hunter College in NYC with a Masters in Applied Mathematics and Statistics with a heavy concentration on numerical analysis and financial mathematics.
Her interest in applicable math eventually lead her to big data and finding the appropriate mediums for data analysis.
Desmond is a Senior Director of Marketing at H2O.ai. In his 15+ years of career in Enterprise Software, Desmond worked in Distributed Systems, Storage, Virtualization, MPP databases, Streaming Analytics Platform, and most recently Machine Learning. He obtained his Master’s degree in Computer Science from Stanford University and MBA degree from UC Berkeley, Haas School of Business.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
Automate your Data Science pipeline with Ansible, Python and Kubernetes - ODSC Talk
What is Data Science and the Data Science Landscape
Process and Flow
Understanding Data
The Data Science Toolkit
The Big Data Challenge
Cloud Computing Solutions
The rise of DevOps in Data Science
Automate your data pipeline with Ansible
Artificial Intelligence in practice - Gerbert Kaandorp - Codemotion Amsterdam...Codemotion
In this talk Gerbert will give an overview of Artificial Intelligence, outline the current state of the art in research and explain what it takes to actually do an AI project. Using practical cases and tools he will give you insight in the phases of an AI project and explain some of the problems you might encounter along the way and how you might be able to solve them.
NLU-MAP. IBM Watson NLU with Mind Mapping automationJosé M. Guerrero
NLU-MAP . An application for the visualization of the results of the semantic analysis of text using IBM Watson Natural Language Understanding through mind mapping automation.
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
Learn where FME meets AI in this upcoming webinar to offer you incredible time savings. This webinar is tailored to ignite imaginations and offer solutions to your data integration challenges. As the new digital era sets sail on the winds of AI, the tangibility of its integration in our daily schema is unfolding.
Segment 1, titled “AI: The Good, the Bad and the FME” by Darren Fergus of Locus, navigates through the realms of AI, scrutinizing its pervasive impact while underscoring the symbiotic potential of FME and AI. Join in an engaging demonstration as FME and ChatGPT collaboratively orchestrate a PowerPoint narrative, epitomizing the alliance of AI with human ingenuity.
In Segment 2, “Integrating GeoAI Models in FME” by Dennis Wilhelm and Dr. Christopher Britsch of con terra GmbH, the spotlight veers towards operationalizing AI in our daily tasks through FME. A practical approach to embedding GeoAI Models into FME Workspaces is unveiled, showcasing the ease of incorporating AI-driven methodologies into your FME workflows, skyrocketing productivity levels.
To follow, Segment 3, "Unleash generative AI on your terms!" by Oliver Morris of Avineon-Tensing. While the prospects of Generative AI are thrilling, security and IT reservations, especially with 'phone home' tools, are genuine concerns. However, with open-source tools, you can locally harness large language models. In this demo, we'll unravel the magic of local AI deployment and its seamless integration into an FME workspace.
Bonus! Dmitri will join us for a fourth segment to tie us off, showcasing what he has been up to this week, including using OpenAI API for texturing in FME, amoung other projects.
Join us to explore the synergy of FME and AI: opening portals to a realm of revolutionized productivity and enriched user experiences.
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
This is the slidedeck used for the presentation of the Amsterdam Pipeline of Data Science, held in December 2016. TensorFlow in the open source library from Google to implement deep learning, neural networks. This is an introduction to Tensorflow.
Note: Videos are not included (which were shown during the presentation)
Similar to How Will AI Change the Role of the Data Scientist? (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
How Will AI Change the Role of the Data Scientist?
1. How Will AI Change the Role of
the Data Scientist?
Hugo Gävert
@hgavert
Helsinki Data Science meet-up 2017-02-16
2. Who am I?
Currently:
Chief Data Scientist @ Sanoma
Past:
• HUT Infolab
• Xtract
• Nokia
Hugo Gävert, 2017-02-16
3. Artificial Intelligence
World Goals Use cases Examples
Special
purpose AI
Restricted, clear
inputs
Well defined,
finite
- Recommendation
engines,
- Credit scoring,
- Insurance claim
handling
- Image recognition
- Playing games;
chess, go, ping
pong, …
- Driving car
- GOFAI,
- ML,
- ANN / Deep
Learning
- Expert systems
- Supervised
- Unsupervised
- Reinforcement
General AI
Open, chaotic,
messy inputs
Poorly defined,
unconstrained
Requirements:
- Reasoning,
- communication,
- learning new
things
- ability to apply
skills to new
problems
- Design better AI
- Whole brain
simulation?
- Robotic form?
- Sensing?
- Manipulating the
world?
Super human intelligence?
Hugo Gävert, 2017-02-16
4. Artificial Super-Intelligence
Human
Intelligence
Artificial
Intelligence
Intelligence/Performance
Time
Games Expert tasks Mundane tasks
- Checkers, 1952 / 1994
- Backgammon, 1979
- Othello, Chess, 1997
- Jeopardy, 2010
- Go, 2016
- Poker, 2017
- Theorem proving, eq solving
- Credit scoring / probability
to default, insurance claim
fraud
- Medical diagnosis
- Speech to text, translation…
- Image recognition
- Natural language /
understanding text
- Walking
- Object manipulation
- Driving cars
Lieutenant Commander Data, year 2338?
Human Level
Machine Intelligence:
10%: 2020
50%: 2040-2050
90%: 2080-2100
Hugo Gävert, 2017-02-16
5. • Original ideas inspired by brains, but nowadays it’s more engineering for machine
learning tasks.
• Artificial Neural Network ≈ Layers of connected simple neurons
• Multiple different architectures for different uses
Neural Networks?
A cartoon drawing of a biological neuron (left) and its mathematical model (right).
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
Hugo Gävert, 2017-02-16
7. Why Deep Learning?
• Rebranded artificial neural networks, so what is different now?
Big Data
- Text, images, video
- Large annotated data
sources, like images
155k words, 117k senses
14M images, 1M BBoxes, 22k synsets
Computational power
Some new algorithms;
ReLU, dropouts,
initializations, ConvNets
-4 -3 -2 -1 0 1 2 3 4
-1
1
-4 -3 -2 -1 0 1 2 3 4
-1
1
-4 -3 -2 -1 0 1 2 3 4
-1
1
Hugo Gävert, 2017-02-16
8. Deep Belief Networks
• 2006, Geoff Hinton: A Fast Learning Algorithm for Deep Belief Networks
• First major results in 2009 in Acoustic Model using Deep Belief Networks
—> Speech recognition
• What is it?
• Multilayer feedforward network with
• Input layer
• Many hidden layers
• Output layer
• Training…
Train as RBM
Train as RBM
Train with
backpropagation
Hugo Gävert, 2017-02-16
9. From feature engineering to feature learning
Input Output
Hand
designed
program
Rule-based AI
Trained
classifier
Input Output
Hand
designed
features
Classic ML
Features
Trained
classifier
Input Output
Representation
Learning
Simple
features
Mid level
abstract
features
Trained
classifier
Input Output
High level
abstract
features
Deep
Learning
Hugo Gävert, 2017-02-16
10. • Deep Belief Networks have largely been replaced by convolutional networks for image recognition
• Architecture, layers:
• Input (width, height, depth = RGB)
• Convolutional layer
• Neuron calculates convolution of the weights over the local image area
• N filters with size (width, height, N)
• Relu activation layer
• Pooling layer
• Downsampling along the spatial width and height dimension
• Fully connected layer (output: 1 x 1 x num of classes)
• The conv + relu + pooling layers are repeated.
• Of course, other architectures also…
Convolutional networks?
Hugo Gävert, 2017-02-16
11. Example, 17 layers, 7000 params.
http://cs231n.stanford.edu/Hugo Gävert, 2017-02-16
13. Convolutional networks - What is deep?
• AlexNet, 2012
• ImageNet challenge, top 5 error rate 16% (previous 26%)
• 5 conv, max-pooling, drop-out layers, 3 fully connected
• ZF Net, 2013
• Top 5 error rate 11.2%
• Similar architecture, only 10% of training data
• DeConvNet - visualisation of the layers
• VGG Net, 2014
• Top 5 error rate 7.3%
• 19 layers, but simple 3x3 convolution and 2x2 max pooling
• CNNs need to be deep, but otherwise simple
• GoogLeNet, 2015
• Top 5 error rate 6.7%
• 22 layers, but has inception-modules that do work in parallel
• Microsoft ResNet, 2015
• Top 5 error rate 3.6% (better than human)
• 152 layers, ultra deep
Hugo Gävert, 2017-02-16
14. Speech Recognition at Google
Brandon Ballinger: Deep Learning and the Dream of AI, Strata 2013
Jaitly et al (2012), Application of pretrained deep neural networks to LVSRHugo Gävert, 2017-02-16
15. Chatbots and AI
• Speech recognition ok
• Natural language
understanding needs work
• Logic
• If … then…
• No memory in session
• Behavior / approach
• Reactive, just answers
questions
• Proactive would be helpful…
Hugo Gävert, 2017-02-16
16. Products you should test / use
• Google APIs
• Machine learning platform (Deep
Learning: TensorFlow)
• Natural Language API
• Speech API
• Translation API
• Vision API
• IBM Watson analytics…
• Also, some of the famous image
ConvNets are downloadable in pre-
trained format
• MS Azure ML (Cortana analytics,
cognitive services)
• Deep Learning: CNTK
• Vision: Face API, Emotion API,
Computer Vision API, Content
Moderation API
• Recommendations API, Academic
knowledge API, Entity linking API,
Anomaly Detection
• Language: Text Analysis, Web
Language Model, spell checking,
translation
• Speech: Speech to text, speaker
identification, translation
Hugo Gävert, 2017-02-16
17. So is AI going to take the job of Data Scientists?
• Yes, absolutely
• Why?
• We, the data scientists, are building the
AI - we’re lazy, we build AI to do our
job…
• Harder to build the robots (or cars,
trucks, flying machines) than to just run
the AI inside computer. The early use
cases will be confined in the computers.
• When?
• Not very soon…
Hugo Gävert, 2017-02-16
18. What does typical data science project look like?
Business
understanding
Data understanding
and quality
Data pre-processing
Feature engineering
Modeling
Evaluation
Production
deployment
Hugo Gävert, 2017-02-16
19. What does typical data science project look like?
Business
understanding
Data understanding
and quality
Data pre-processing
Feature engineering
Modeling
Evaluation
Production
deployment
Data collection
design
Monitoring, control
Feature learning
Deep Learning
architecture
Communications,
internal consulting
How do we get
representative data for
the network?
Ok, images easy - how
about others?
Does it work?
Still expected results?
Fraudulent use?
What is this Black Box?
APIs
Hugo Gävert, 2017-02-16
20. Recommendations for
Data Scientists
• Keep on doing what you do
• Evolve with the world
• You still need
• Math; stats, probabilities, linear algebra…
• Algorithms and data structures
• You also need now
• Deep Learning (hype!)
• More communications skills
• Software writing & engineering skills (APIs)
• Google and Stack Overflow helps…
Hugo Gävert, 2017-02-16
21. Recommendations for companies
• Data
• Create data strategy; collect, store and make data available
• Data is key business asset in building AI capability. Deep
Learning needs data in training. Software can be replicated,
but data cannot - if a business has data, then it’s already in
better position than competitors.
• Hire talent
• AI models need to be customized for the business need,
application and context.
• Downloading open source software is not enough.
Applying it is far from trivial. The APIs solve only specific
problems and are too much black boxes.
• You need to be able to explain the models to customers -
specially in the legal, finance, insurance, health etc.
business.
“The best ideas
come from the guys
closest to the data.”
Todd Holloway
Head of Data Science at Trulia.
Hugo Gävert, 2017-02-16