Rock Stars of
Machine Learning and Deep Learning
September 12, 2017 | Mountain View, CA
Machine Learning Deep Learning Badge
Innovation in Machine Learning is Staggering – Can you Keep Up?
Amazon, Bank of America, Merrill Lynch, the leaders who are out in front of the latest AI and Machine Learning developments, share their insights at Rock Stars of Machine Learning.
What is most important in AI/Machine Learning today?
Hear in-depth about:
- Deep learning
- AI optimized hardware
- Natural language generation
- Virtual agents
Get insights into new analytics, algorithms, and data structures that produce reliable, repeatable decisions and results.
How will Machine Learning and Deep Learning control your business?
Hear and interact with C-Suite and upper management executives at Fortune 100 companies and others on Machine Learning and Deep Learning opportunities for 2017. Also, learn the strategies these industry leaders are utilizing to optimize their business.
Get insights into these critical technologies.
- Machine Learning
Machine Learning is becoming a key technology that influences C-Suite decisions. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction. Does your organization employ the proper analytical models that allow your researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results?
- Deep Learning
What are the promises of deep learning on business? The science of Deep Learning develops efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction from large-scale unlabeled data. Is this the technology solution that your businesses can use to analyze the massive amounts of data becoming available?
After participating in this event, you will have
- Actionable technology insights
- Critical strategies you can apply to your specific business
This is also a great networking opportunity. Attendees will have many opportunities throughout the day to:
- Interact with C-Level executives in a Q&A and interactive panel format to get answers to specific questions.
- Interact with speakers and colleagues during expanded break sessions, lunch, and the reception.
Morning Session: 9:00 a.m. – 12:30 p.m.
Director of AI
Amazon Web Services
Human Language Technology and Machine Learning
Hassan will discuss his expertise opening up new markets and opportunities with smart application of Artificial Intelligence and application-driven research in AI, in particular in language technology, speech processing, computer vision and computational reasoning.
Fellow/Chief Data Scientist, Big Data Platform/Data Science/Cognitive Computing
“Big Metamorphosis”: Spinning up well- Architected Stacks for Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence requires a different treatment of data than the more traditional methods. The topology and different dimensions of data need to be easily accessible. Distributed platforms and dynamic data structures lend itself well to scale the models near real time. This talk will discuss best practices in spinning up well- architected stacks for machines learning and Artificial Intelligence for scale in an industrial/enterprise setting with practical uses cases, tradeoffs, challenges and storage layers required.
Head of Data Science Engineering
Search and Ranking at Bloomberg
Parth Vasa, the Head of Data Science for Bloomberg Engineering, discusses the challenge of providing effective search for financial markets, balancing the need for accuracy and speed, the diversity of the data, and the difficulty of gaining an accurate picture of markets from moment to moment. He will describe the ways in which machine learning is helping Bloomberg to address this challenge.
Data Scientist, ML & DL
Bank of America; Merrill Lynch
Program Machines to Deep Learn and Solve Big Data Investment Problems
The discussion will cover all aspects of machine programming with the exploration of Data Science, deep learning, computational intelligence, and multi-criteria decision optimization.
Lunch: 12:30 p.m. - 2:00 p.m.
Afternoon Session: 2:00 p.m. – 5:00 p.m.
Joshua Greenbaum (moderator)
Principal, Enterprise Applications Consulting
Tensorflow at Google Brain
Trends and Developments in Deep Learning
Deep Learning has come of age over the last few years. Neural networks have been around for decades - what changed over the last few years to make them successful? This talk will describe what got deep learning over the hump, go over some of the recent successes in this field and discuss their implications. The pace of innovation in deep learning is staggering - is it going to continue? The presentation will provide a perspective on research directions that have the potential to keep the innovation going and even accelerate it further.
Understanding How Biologically Inspired Computing May Help us Build Machines that Perceive the World as we do
Lewis will discuss his work in evolutionary and biomorphic robotics, formation control of robotic systems, and investigations into the basis of movement control in humans and robots. Lewis and Colleagues demonstrated a robot that claimed to be the most biologically accurate model of human locomotion to date. This robotic uses a muscle architecture much like a human being, a simplified neural circuit meant to mimic neurons in the spinal cord, and sensory feedback mimicking the primary sensory pathways found in human.
Cocktail Reception 5:00 p.m. – 6:30 p.m.
The Computer History Museum
Attend the Rock Stars of Machine Learning/Deep Learning
The Rock Stars of Machine Learning/Deep Learning will be held on September 12, 2017, at The Computer History Museum.
The Computer History Museum
1401 N Shoreline Blvd
Mountain View, CA 94043, USA
Phone: (650) 810-1010
Fax: (650) 810-1055
Directions from San Jose via US-101 North
Take US-101 North toward San Francisco. Take Shoreline Blvd Exit. Turn right onto Shoreline Blvd. Cross through the intersection. The museum is on your right.
Distance from San Jose: 20 miles
Drive time: 15 minutes