4:00PM – 4:45PM–Registration, Food, Drink, Networking
4:45 – 5:15PM — IEEE CS chapter presentation
5:15PM – 6:00PM — Machine Intelligence in Design Automation 6:00PM – 6:45PM — Dinner and Networking
6:45PM – 7:30PM– AI and Machine Learning
7:30PM – 8:15PM –Cognitive Architecture in Models for Natural and Artificial Intelligence
8:15PM – 8:30PM — Q&A and Adjourn
Speaker #1: Abstract:
Motivation behind this talk is to throw some light on use of machine intelligence in design automation; a topic that is largely absent from the media and academia. Machine Intelligence is advancing at a rapid pace and claim to this fame is that it is bound to enable an unprecedent degree of automation in every walk of life. Design automation, a field that has been automating semiconductor design for decades, continues to struggle successful applications of Machine Learning
Rohit Sharma is an engineer, author and entrepreneur. He has published many papers in
international conferences and journals. He has contributed to electronic design automation
domain for over 20 years learning, improvising and designing solutions. He is passionate about many technical topics including machine learning, deep learning, VLSI characterization, analysis, and modeling. It led him to design and architect several design automation products. He wrote a book on Machine Intelligence in Design Automation in 2018. He has been disseminating information on the use of machine intelligence in design automation since 2017 with his book, blogs, opens source code, contests, webinar, symposium, and other collateral. He currently works for AI Technology and Systems (www.ai-techsystems.com).
2) Speaker #2: Abstract:
Most of us don’t realize how pervasive AI is in our everyday lives. Facebook recently reported that 200 trillion AI predictions run on their platforms every day. In this talk, we will start by explaining what the various AI terms are, demonstrate what is possible with modern AI methods that was not possible before, and then talk about AI use cases that range from sports, to gaming, to industrial & enterprise.
Sumit Gupta is VP, AI, Machine Learning, and HPC in the IBM Cognitive Systems business. Sumit leads the business strategy and software and hardware products for machine learning, deep learning, & HPC, including Watson ML Accelerator (formerly PowerAI) and Spectrum Compute. Prior to IBM, Sumit was the general manager of the AI & GPU accelerated data center business at NVIDIA and was central in building that business from the ground-up to what is now a multi-billion dollars business for NVIDIA. Sumit has a Ph.D. in CS from UC, Irvine, and a BS in EE from IIT Delhi
3) Speaker #3: Abstract:
*Cognitive Architecture* is a key organizing framework for AI and Machine Learning as well as for understanding natural intelligence. Narrow applied AI and ML can afford to neglect the concept; AGI (Artificial General Intelligence) cannot. This talk will relay major motivations, concepts, and challenges for Cognitive Architectures as exemplified by SOAR and Copycat. At base is the perception/action loop. For conversational agents, the architectural elements of this loop display progress and limitations that explain why we are simultaneously delighted and disappointed by their capabilities.
Eric Saund is a research scientist, developer, and consultant whose work spans multiple fields of intelligent systems, including conversational agents, document analytics, perceptually supported user interfaces, machine learning, and computational vision. Eric received a B.S. from Caltech and a Ph.D. in Cognitive Science from MIT. He served in research and research management roles at the Xerox Palo Alto Research Center, he has published widely, his prototypes have served thousands of users, and he has been awarded over 50 patents to date.
Open to all to attend
(Online registration is needed. If you did not register, seating is not guaranteed.)
Call for volunteers for this event:
If you are interested in volunteering at our IEEE CS chapter Open House email us Here.
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Location: Qualcomm Inc. Building B – Café, 3165 Kifer Rd, Santa Clara, CA
5:00 pm – 6:00 pm Registration, Networking, Hors D’oeuvre and Demos
6:00 pm – 7:45 pm Welcome and Tech Talks
7:45 pm – 8:00 pm Closing and Raffle
For addition information follow the link.
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August 16, 9:00 AM – August 17, 6:00 PM PDT, @ Z-Park Silicon Valley, 4500 Great America Pkwy, Santa Clara, CA 95054, USA.
IEEE CS SCV is listing this event here as a relevant event for our members.
Join this 2-day event as part of series of regularly planned events to learn about the state-of-the-art advances in artificial intelligence (AI) and machine learning (ML) technology. Get IEEE PDH Certificate from IEEE Continuing Education.
Go to ValleyML.ai and hit register to go to Eventbrite page and use discount code VALLEYML_CS25 to get 25% off.
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DATE & TIME: Wednesday, May 22nd, 2019. 1 PM – 5 PM
PROGRAM: 1:00 – 1:30 PM Check-in / Networking & Refreshments 1:30 – 2:15 PM Prof. Kurt Keutzer (UC Berkeley) 2:15 – 3:00 PM Prof. Yung-Hsiang Lu (Purdue University) 3:30 – 4:15 PM Dr. Pradeep Dubey (Intel) 4:15 – 5:00 PM TBD 5:00 PM Adjourn
LOCATION: International Technological University, Main Auditorium 2711 N 1st St, San Jose, CA 95134 (between Montague & Trimble along N. 1st Street) VT Light Rail access from downtown San Jose and Mountain View. In person attendance requested. Maximum capacity: 280. Please register to guaranty seating.
AGENDA: 1:30 – 2:15 PM Prof. Kurt Keutzer (UC Berkeley) TITLE: Co-Design of Deep Neural Nets and Neural Net Accelerators for Embedded Vision Applications ABSTRACT: Deep Learning is arguably the most rapidly evolving research area in recent years. As a result it is not surprising that the design of state-of-the-art deep neural net models proceeds without much consideration of the latest hardware targets, and the design of neural net accelerators proceeds without much consideration of the characteristics of the latest deep neural net models. Nevertheless, we show that there are significant improvements available if deep neural net models and neural net accelerators are co-designed. 2:15 – 3:00 PM Prof. Yung-Hsiang Lu (Purdue University) TITLE: Low-Power Computer Vision: Status, Challenges, and Opportunities ABSTRACT: Energy efficiency plays a crucial role in making computer vision successful in battery-powered systems, including drones, mobile phones and autonomous robots. Since 2015, IEEE has been organizing annual competition on low-power computer vision to identify the most energy-efficient technologies for detecting objects in images. The scores are the ratio of accuracy and energy consumption. Over the four years, the winning solutions have improved the scores by a factor of 24. The speaker will describe this competition and summarize the winning solutions, including quantization and accuracy-energy tradeoffs. Based on technology trends, the speaker will identify the challenges and opportunities in enabling energy-efficient computer vision. 3:30 – 4:15 PM Dr. Pradeep Dubey (Intel) TITLE: AI: What Makes it Hard and Fun! ABSTRACT: The confluence of massive data with massive compute is unprecedented. This coupled with recent algorithmic breakthroughs, we are now at the cusp of a major transformation. This transformation has the potential to disrupt a long-held balance between humans and machine where all forms of number crunching is left to computers, and most forms of decision-making is left to us humans. This transformation is spurring a virtuous cycle of compute which will impact not just how we do computing, but what computing can do for us. In this talk, I will discuss some of the application-level opportunities and system-level challenges that lie at the heart of this intersection of traditional high-performance computing with emerging data-intensive computing.
4:15 – 5:00 PM TBD 000000000
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Abstract: The use of machine learning to generate models from data is replacing traditional software development for many applications. This fundamental shift in how we develop software, known as Software 2.0, has provided dramatic improvements in the quality and ease of deployment for these applications. The continued success and expansion of the Software 2.0 approach must be powered by the availability of powerful, efficient and flexible chips that are tailored for machine learning applications. This talk will describe a design approach that optimizes computer systems to match the requirements of machine learning applications. The full-stack design approach integrates machine learning algorithms that are optimized for the characteristics of applications and the strengths of modern hardware, domain-specific languages and advanced compilation technology designed for programmability and performance, and a reconfigurable dataflow architecture called Plasticine that achieve both high flexibility and high energy efficiency.
Plasticine is a new spatially reconfigurable architecture designed to efficiently execute applications composed of parallel patterns. I will describe the Plasticine architecture: the compute pipeline that exploits nested parallelism, the configurable memory system that captures data locality and sustains compute throughput with multiple banking modes, and the on-chip interconnect that supports communication at multiple levels of granularity.
Bio: Kunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is well known as a pioneer in multicore processor design and the leader of the Stanford Hydra chip multipocessor (CMP) research project. Olukotun founded Afara Websystems to develop high-throughput, low-power multicore processors for server systems. The Afara multicore processor, called Niagara, was acquired by Sun Microsystems. Niagara derived processors now power all Oracle SPARC-based servers. Olukotun currently directs the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate the use of heterogeneous parallelism in all application areas using Domain Specific Languages (DSLs). Olukotun is a member of the Data Analytics for What’s Next (DAWN) Lab which is developing infrastructure for usable machine learning. Olukotun is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design and is the recipient of the 2018 IEEE Harry H. Goode Memorial Award. Olukotun received his Ph.D. in Computer Engineering from The University of Michigan.
Admission Fee: Open to all to attend(Online registration is needed. If you did not register, seating is not guaranteed.) Non-members – $5 (Register at Door $15) IEEE SSCS/CAS/CS/SPS/CIS society members – FREE Students – FREE IEEE (non-society) members – FREE (Register at Door $10) You do not need to be an IEEE member to attend!
Related Events for AI/ML community (ValleyML.ai event):
State of AI and ML-Spring 2019
Intel, SC12 Auditorium, 3600 Juliette Ln, Santa Clara, CA 95054
In this event (State of AI and ML-Spring 2019), as part of series of regularly planned events, we plan to cover the state-of-the art advances in AI technology. For this event, we focus on AI Accelerators, Self-Driving and Face Processing. We feature five thought leaders from Computing, Autonomous systems and Computer Vision.
Early bird tickets till March 22nd IEEE CS/CAS/CIS/ITSoC/RAS/SSCS Members -$65 IEEE/ACM Members -$70 Others- $90
Regular Pricing from March 23rd till March 29th IEEE CS/CAS/CIS/ITSoC/RAS/SSCS Members -$85 IEEE/ACM Members -$100 Others- $125 Free tickets for IEEE/ACM student members and members in transition. Free tickets for ACM and IEEE CS/CAS/CIS/ITSoC/RAS/SSCS officers.
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IEEE CS Chapter of Silicon Valley is organizing this free workshop technical event for all its IEEE CS chapter members in collaboration with: · https://ieee-region6.org/scv-cis/ IEEE CIS (Santa Clara Valley Chapter of IEEE Computational Intelligence Society) · Oracle providing this free workshop for IEEE CS members.
Valley Machine Learning and Artificial Intelligence (ValleyML.ai) is the most active and important community of ML & AI Companies and Start-ups, Data Practitioners, Executives and Researchers in the Silicon Valley. ValleyML.ai bring together business leaders and applied machine learning and deep learning practitioners. We collaborate with other non-profit professional organizations such as ACM, IEEE Silicon Valley AI/CS/CAS/ITSoC/RAS/SSCS Chapters and other AI related organizations bringing a series of regularly planned events, seminars, conferences, and workshops. We plan to cover the state-of-the-art advancements in AI technology. We have free tickets for IEEE/ACM students, members in transition and discounted tickets for IEEE/ACM members. There are minimal fees to break even for the event expenses. Attendees for most of the workshops get IEEE Certificates for Professional Development Hours.
Keynote: 1) Kris Bhanushali, Senior Principal Product Manager for Oracle’s Database Cloud Service
Abstract: How is Data Management different today than it used to be? Well, to begin with, there is a gazillion times more data today than there used to be just a decade ago. Clearly, traditional tools of data management are not going to work. Let’s take a look at ways to manage enterprise data at scale without getting a PhD in database administration.
We all wear multiple hats in this cloud era so whether you are a developer, DBA, IT Ops or DevOps, this session is for you. We’ll also look at terraform based orchestration, ways to deploy microservices on a Kubernetes cluster and whip up some python/java apps quickly in the Oracle cloud.
WORKSHOP CREW: 1) Kiran Makarla, Principal Solution Marketing Director. 2) Vishal Atreja, Vishal is a cloud technology evangelist and a member of the Oracle Digital North America Technology Division Solution Engineering 3) Santosh Kumar Ramarathnam, Senior Technical Consultant at Oracle and is an OCI certified Associate.
The lab flow: • Lab 1: Provisioning an Autonomous Database. • Lab 2: Managing and Scaling Data using Autonomous Features. • Lab 3: Configuring a Node.js app (as an example) to work with the Autonomous Database. • Lab 4: Understanding and Working with REST APIs. • Lab 5: Configuring Infrastructure • Lab 6: Modern App Dev on Oracle’s Cloud Native Framework with Oracle Autonomous as the Backend, Database Layer.
IMPORTANT Please fill out this https://forms.gle/W1mcm8kxdPtuw1zX9 to expedite the registration process and for us to provision your FREE Cloud Platform Trial account so that you can run this workshop smoothly. Please provide the email address that you have never used for Oracle Cloud Platform Trial account before. You will not be required to input your credit card for creating this account. Prerequisites: bring Laptop- Either Windows/Mac and pre-install: 1. SQL Developer: https://www.oracle.com/technetwork/developer-tools/sql-developer/downloads/index.html On Windows: Select the OS for your computer. (This page also has instructions on how to install SQL Developer on Windows, Mac OSX and Linux.) If you already have SQL Developer installed on your computer please check the minimum version required to connect to an Oracle ADW Cloud is SQL Developer 17.4. Mac: https://www.oracle.com/technetwork/developer-tools/sql-developer/downloads/sqldev-install-mac-1969675.html 2. Data Visualization Desktop: Oracle Data Visualization Desktop makes it easy to visualize your data so you can focus on exploring interesting data patterns. Choose from a variety of visualizations to look at data in a specific way. Data Visualization Desktop comes with Oracle ADW. To download and install Data Visualization Desktop please follow: https://www.oracle.com/technetwork/middleware/oracle-data-visualization/downloads/oracle-data-visualization-desktop-2938957.html Select the OS for your computer. This page also has instructions on how to install DVD on Windows and Mac OSX. If you already have Data Visualization Desktop installed on your computer then please check the version. The minimum version that is required to connect to an Oracle ADW Cloud is 12c 126.96.36.199.0. Here is the Link to the learning Library you will need during the workshop. bit.ly/atpLabs
Open to all to attend (Online registration is needed. If you did not register, seating is not guaranteed.)
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Abstract: Why did Silicon Valley come into being? The storygoes back to local Hams (amateur radio operators) trying to break RCA’s tube patents, “angel” investors, the sinking of the Titanic, Fred Terman and Stanford University, local invention of high-power tubes, WW II and radar, William Shockley’s mother living in Palo Alto, and the SF Bay Area infrastructure that developed – these factors pretty much determined that the semiconductor and IC industries would be located in the Santa Clara Valley, and that the Valley would remain the world’s innovation center as new technologies emerged – computers, then software, mobile, biotech, Big Data, VR, and now autonomous vehicles – and it would become the model for innovation worldwide.
Bio: Paul Wesling
Paul Wesling, an IEEE Life Fellow and Distinguished Lecturer and ACM member, has observed the Valley for decades as an engineer, executive, resident, and educator, and has presented this talk world‐wide. In this non-technical presentation, he gives an exciting and colorful history of device technology development and innovation that began in Palo Alto, then spread across the Santa Clara Valley during and following World War II. You’ll meet some of the colorful characters – Leonard Fuller, Lee de Forest, Bill Eitel, Charles Litton, Fred Terman, David Packard, Bill Hewlett, Russ Varian and others – who came to define the worldwide electronics industries through their inventions and process development. You’ll understand some of the novel management approaches that have become the hallmark of tech startups and high-tech firms, and the kinds of engineers/developers who thrive in this work environment. You’ll handle an original Audi on tube, invented by Lee de Forest and improved by him in Palo Alto. Paul will end by telling us about some current local organizations that keep alive the spirit of the Hams, the Homebrew Computer Club, and the other entrepreneurial groups where geeks gather to invent the future.
Open to all to attend Online registration is needed. If you did not register, seating is not guaranteed.)
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SFBay ACM is conducting a full-day workshop on “Distributed Tensorflow with Kubernetes – AI Workshop” by leading data scientists & practitioners. Early registration discounts available till April 15, 2019. For details and IEEE members registration click (Here)
IEEE CS Chapter SCV is sponsoring this event and our member can use code “SFBayMeetup15” for a $15 discount.
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Time & Location:
From 10am to 11.30am on May 11th 2019 at SJSU.
This event is concurrent with the Spring 2019 SJSU Technology Showcase and it has important logistical information for the event.
The event will be held on the SJSU Campus in the Student Union on Saturday, May 11, 2019.
The event schedule is below:
9:30 AM -10:30 AM Beverage Service
10:00 AM -11:30 AM IEEE Chapter Meetings
11:30 AM – 1:30 PM Box Lunch pick-up
11:00 AM – 3:00 PM Student Technology Showcase
A box lunch, either vegetarian or non-vegetarian, and free parking will be provided if you sign up using the Registration Google Form at: https://forms.gle/N7JwFC1dWkusL2CL8
This form will close for entries at midnight, Sunday, May 5, 2019.
We look forward to your participation at our IEEE CS chapter technical event and the Technology Showcase.
1) Talk title1: The Evolution of Artificial Intelligence: From the Past to the Future
In this talk, I will trace the development of AI through the key inventions in AI, covering the development of machine learning, speech recognition, image understanding, deep learning and reinforcement learning. You will get a behind-the-scene view of Amazon Alexa, Apple Siri, and DeepMind’s AlphaGo. This talk will give you an understanding of core AI technologies today and the historical context behind them. Finally, I will share my view on the next big things, where jobs go, and where new products can be built.
Dr. Junling Hu
Dr. Junling Hu is the author of a new book titled The Evolution of Artificial Intelligence.. Dr. Hu has been an AI researcher, technology leader, and educator for the last 20 years. She is a recipient of National Science Foundation Career Award. She has worked as Director of data mining at Samsung, and managed AI teams at PayPal, eBay and Bosch. She has also worked as an Assistant Professor at University of Rochester. Currently she is the CEO of AIPro Camp LLC dba AIPro.io, an AI education company devoted to training and education about AI. Dr. Hu received a PhD in Computer Science from University of Michigan at Ann Arbor and her research was focused on reinforcement learning. Her dissertation title was “Learning in Dynamic Non-cooperative Multiagent Systems” and led to many well-cited papers.
2) Talk Title2: Machine Learning for Veracity of Big Data
Machine Learning is increasingly proving itself to be the mortar of modernization. The talk will examine how Machine Learning can be applied to the problem of veracity of Big Data, particularly, Web information. We are overwhelmingly depending on data for crucial tasks like driving as in self-driving cars, delivery as in autonomous drones and even in electing Presidents of countries, owing to the role of Online Social Networks like Twitter in these elections. Trusting technology has become inevitable. Compromising the quality of data in these circumstances can be hazardously risky. Ideally, data should be entirely truthful and accurate. However, there have been a number of instances where data was manipulated or posted fraudulently for ulterior motives, causing serious damage. Misinformation Containment is indeed a difficult task and computationally, has been proven to be NP-hard.
The Web is an important enabler of the evolving world economy and has the potential to bring more and more people into the mainstream. The Web, being humanity’s largest source of information and interaction, can serve as a conduit of humanitarian services and presents a huge opportunity to enhance the quality of life further. Unfortunately, a significant percent of the information posted on the Web is not entirely true, which substantially limits its ability to serve the needs of the humanity. this talk will go deeper into the ideas from Machine Learning to see how we can help make the World Wide Web, particularly the Social Media part of it, entirely truthful, which should ideally be an important milestone to achieve in the near future. The talk will draw from the speaker’s recent book “Big Data: Machine Learning and Other Approaches to Verifying Truthfulness”.
Dr. Vishnu S. Pendyala
Dr. Vishnu S. Pendyala is the author of a new book titled Veracity of Big Data. Dr. Pendyala leads the DevOps activities at Cisco for some significant products. He is a seasoned Technical Leader with over two decades of development, porting, and DevOps experience with industry leaders like Cisco, Synopsys, Informix (now IBM), and Electronics Corporation of India Limited. He received his PhD in Computer Engineering from Santa Clara University. His dissertation title was “Evolving a Truthful Humanitarian World Wide Web”. Earlier, he received MS (Computer Engineering) degree from San Jose State University. He also received BE (Computer Science), MBA(Finance) degrees both from Osmania University, Hyderabad, India.
Open to all to attend
(Online registration is needed. If you did not register, seating is not guaranteed.)
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Locatelli Student Activity Center, Santa Clara University
The 2018 NFIC Conference will address the innovation in edge cloud and the increased automation of associated technologies that are driving urban agglomeration to meet our lifestyle demands. The conference will explore how these technologies are being used in 1) Mobile Edge Computing with Distributed Cloud, 2) Smart Devices and Gateways, and 3) Location-Based Applications.
About IEEE Silicon Valley CS
The IEEE Computer Society of Silicon Valley (also known as IEEE Computer Society Santa Clara Valley) emphasizes all aspects of computing to our local members and we welcome visitors. Please select the above link for more information about our chapter and consider volunteering for enhanced professional networking opportunities. Please select this Join Our Email Distribution link to join our email distribution list.