AI Saturdays | SGInnovate
January 13
2018

Location

SGInnovate, 32 Carpenter Street, Singapore 059911

AI Saturdays

Organised by SGInnovate

Join AI researchers, engineers, data scientists and self-learners going in-depth into materials like actual recorded lecture videos and research paper readings from top universities like Stanford and UC Berkeley – covering practical deep learning, in-depth deep learning theory from multiple perspectives, Reinforcement Learning, computer vision and natural language processing. Free to attend!

Nurture.AI is partnering with SGInnovate to organise AI Saturdays in Singapore, which will be held at its premises at 32 Carpenter Street, and BASH at 79 Ayer Rajah Crescent (starting from March 2018). 

 

In order to cater to a diverse audience, there will be 3 structured sessions every Saturday – you can attend all, some or none, it’s totally up to you! If you don’t want to attend some of the sessions, throughout the day there will be open hacking on creating open-source code implementations of the top research paper pre-prints that week. Or use that time to catch-up on lectures and readings (sessions 2 and 3 have many hardcore readings by the way!) while discussing with peers.

Session 1: 10am - 12pm – Practical Deep Learning (Beginner-Intermediate)
12-1pm – Lunch (occasional brown bag lunch talk from an expert)
Session 2: 1pm - 3pm – Deep Learning Theory (Intermediate-Advanced)
Session 3a: 3pm - 6pm – Reinforcement Learning (Intermediate-Advanced)
Session 3b: 3pm - 6pm – Convolutional Neural Networks for Visual Recognition (Intermediate-Advanced)
Session 3c: 3pm - 6pm – Natural Language Processing With Deep Learning (Intermediate-Advanced)

Community Rules and Philosophy
The course materials we choose are widely known to be clear in explanations, and the presenters are leading expert authorities in the field, so we watch the original lectures directly instead of recreating our own in the meetup sessions
The value of coming together as a study group lies in clarifying doubts, deeper discussions into the material, and accountability on course completion – we are clear about that in the way we structure the activities that happen in the study group. This is a judgement-free, safe zone, and people who have promised to prepare are expected to do so in order to ensure the session is fruitful for everyone.
We constantly co-create and iterate on improving the learning experience together! Because all these sessions are free, we rely heavily on the community’s participation and support to make this work.

What do we do in the Sessions
Session 1: Practical Deep Learning 
Using the free and proven Fast.ai materials, this is perfect for beginners in deep learning and machine learning, with some prior Python programming experience and high school math knowledge – and it’d get you to a stage where you can implement cutting-edge deep learning models, in just 14 weeks! No worries if you have no Python programming experience, feel free to reach out and we’d be happy to advise on what you can use in the weeks leading up to the start date to prepare – you can certainly get up to speed if you work hard in these few weeks, but time is running short so get started now!

We will watch the lectures as a group, stop the video for discussion at any point if anyone has a question, and also breakout into small groups for the in-lecture exercises – removing any obstacles along the way, making sure that you can progress through the course confidently if you stick with us – that’s our commitment to you for your time investment!

Session 2: Deep Learning Theory
We start off with materials from the Stanford STAT385 course on Theories of Deep Learning. For this particular session, the true value of the physical meetup lies in discussing the theoretically-dense research paper readings. 

Participants are expected to have viewed the lecture video for the week beforehand, and each participant will take charge of being the expert authority on one of the readings for the week in the discussion by having thoroughly read and researched it, to make the best use of everyone’s time. You can help each other prepare better by posting your questions on specific parts of the papers using the Nurture.AI platform’s highlight-commenting function.

Session 3a: Reinforcement Learning
We start off with materials from David Silver’s UCL/DeepMind Reinforcement Learning course, before continuing with UC Berkeley CS294 Deep Reinforcement Learning. We will view the lecture as a group, stop the video for discussion at any point if anyone has a question, and breakout into small groups to discuss papers mentioned. We will then end off with code practice on implementing the techniques covered in the session, which can then be completed over the week. 

Session 3b: Convolutional Neural Networks for Visual Recognition
Covering the material in Stanford’s CS231n Spring 2017 course headed by Prof Fei-Fei Li (Chief AI Scientist of Google Cloud, Director of Stanford AI Lab), we will take the first 1.5 hours to view and discuss the lecture together. We will take another half an hour to discuss the specifics of the paper readings for the week and clarify questions. The last hour will be dedicated to kicking off the participant’s implementation of the models discussed, which can be completed over the following week. This session will cover topics like image classification, object detection, image caption, visual question answering, feature visualisation and adversarial training.

For ease of the facilitator in-charge’s preparation for any particular week, participants are encouraged to read the papers beforehand and post questions on specific parts of the papers using the Nurture.ai platform’s highlight-commenting function.

Session 3c: Natural Language Processing with Deep Learning
Covering the material in Stanford’s CS224n Winter 2017 course taught by Professor Christopher Manning and Richard Socher (Chief Scientist of Salesforce), we will take the first 1.5 hours to view and discuss the lecture together. We will take another half an hour to discuss the specifics of the paper readings for the week and clarify questions. The last hour will be dedicated to kicking off the participant’s implementation of the models discussed, which can be completed over the following week. This session will cover topics like word vector representations, dependency parsing, recurrent neural networks and language models, machine translation, attention models, tree recursive neural networks, and speech processing.

For ease of the facilitator in-charge’s preparation for any particular week, participants are encouraged to read the papers beforehand and post questions on specific parts of the papers using the Nurture.ai platform’s highlight-commenting function.

 

Program for 13/01/18
Session 1: Fast.ai Lesson 3 – Why Deep Learning; Intro to Convolutions
Session 2: Stat385 Lecture 3 Readings – Harmonic Analysis of CNNs
Session 3a: UCL/Deep Mind Reinforcement Learning Lecture 2 – Markov Decision Processes
Session 3b: Loss Functions and Optimization
Session 3c: Advanced Word Vector Representations

 

Upcoming Events

  • AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? Together, SGInnovate and Qicstart brings to you this talk which will discuss the brief history of the current interesting technologies and their development to society and mankind.

  • In today's digitally networked world, there is a race to capture opportunities that create new value, transform industries, optimize business ecosystems and reduce risk – all using business networks created with blockchain technology.

  • In today's digitally networked world, there is a race to capture opportunities that create new value, transform industries, optimize business ecosystems and reduce risk – all using business networks created with blockchain technology. Let us discuss the transformational power of blockchain – a technology that is here and real today. In this community meet session, let us discuss: How blockchain can radically transform industries What are some of the use-cases across the globe and across industries Concept of blockchain and considerations for applying the technology in regulated industries Review real life examples where blockchain has been applied. Speaker: Dr. Ernie Teo, IBM Center for Blockchain Innovation Date: 23 January 2018 (Tuesday) Time: 6:30pm - 8:30pm Venue: 32 Carpenter Street, Singapore 059911 Programme (subject to revision): 6:30pm - 7:00pm: Registration 7:00pm - 8:00pm: Blockchain Evening with IBM 8:00pm - 8:30pm: Q&A and Networking Speaker: Dr. Ernie Teo, IBM Center for Blockchain Innovation Economist by training and applying social sciences to technology. Dr. Ernie Teo is an economist by training and specializes in the areas of game theory and applied microeconomics with an application to high tech industries. He is currently a research scientist at IBM Center for Blockchain Innovation where his main research area is in blockchain and distributed ledgers. Ernie received his PhD in Economics from the University of New South Wales, Australia. Prior to IBM, he was a research fellow at the Sim Kee Boon Institute (SKBI) for Financial Economics at the Singapore Management University. Ernie was also Assistant Professor at Nanyang Technological University, Singapore from 2008-2014. He also has an interest in building an inclusive socio-economic system through financial technology.

  • At this event, we host thought-leaders from the Singapore data community to discuss the possibilities of career paths in the Data Science world. They’ll cover how today’s wealth of data drives business and product decisions across industries ranging from journalism to programming, and share their visions for the future and how you can jump into this exciting area.

  • The recent explosion in Artificial Intelligence capabilities has revolutionized the way companies do business and enabled unprecedented opportunities. However, AI services (and the data models that dive them) remain for the most part siloed off in large corporations such as Google, Microsoft and Amazon. This presents two major obstacles: For companies that wish to take advantage of AI in their business, they have little choice other than to purchase services from one of the big providers, which often requires awkward subscription plans that can be prohibitively expensive, especially for small companies. For startups (or individuals) who are creating exciting new AI algorithms, there is no easy way for them to publicize and monetize their services. Their only option is to go the typical route of: try to get VC funding -> build a company to the “exit point” -> hope to sell out to one of the big corporations. SingularityNET – the brainchild of renowned Artificial Intelligence expert, Dr. Ben Goertzel, and famed robotics pioneer, Dr. David Hanson – is a revolutionary new, decentralized, open marketplace for exchanging AI services. In this talk, we will describe how SingularityNET works, its benefits and objectives, and how it will address the two problems above (along with many others). We will conclude with a high-level demo showing how any developer anywhere in the world can easily and quickly publish and monetize their AI code. Date: 25 January 2018 Time: 6:30pm - 8:30pm Venue: 32 Carpenter Street, Singapore 059911 Programme (subject to revision): 6:30pm - 7:00pm: Registration 7:00pm - 8:00pm: A Global AI Economy for All 8:00pm - 8:30pm: Q&A and Networking Speaker: Scott Jones, Managing Director of Six Kin Development and Partner of SingularityNETScott is a Silicon Valley veteran who began his career writing code and managing software development teams for several early Internet startup companies, including Netscape, where he helped build the legendary Netscape Navigator web browser. Mr. Jones also has more than a decade of experience in academia, serving as Senior Lecturer at Nanyang Polytechnic in Singapore, and as a researcher at Hong Kong Polytechnic University, where he worked alongside Dr. Ben Goertzel, co-founder & CEO of SingularityNET.

  • Join SGInnovate and NVIDIA Chief Scientist, Bill Dally in an exclusive fireside chat on the power of Artificial Intelligence, as we discuss: What is causing the current resurgence of AI: AI is nothing new, where algorithms used have been around since the 1980s.  Systems based on deep learning now exceed human capability in areas such as speech recognition and object classification. Is AI "taking over the world, with millions of jobs being lost to machines”? We agree that AI has the capability to bring great impact to some of the world’s biggest challenges. But for AI to reach its full potential, it needs massive volume of data to learn and get better in the process. When people think about human-centric, date-driven technologies, many are worried about the issue of data privacy (in the healthcare sector, we could be talking about healthcare records and even biological data). Data and privacy – can there ever be a win-win?  Date: 31 January 2018 Venue: SGInnovate, 32 Carpenter Street, Singapore 059911 Programme: 6:30pm: Registration 7:00pm: Fireside chat with Bill Dally, NVIDIA Chief Scientist 7:45pm: Q&A and Networking 8:30pm: End Bill Dally, NVIDIA Chief Scientist Bill Dally joined NVIDIA in January 2009 as chief scientist, after spending 12 years at Stanford University, where he was chairman of the computer science department. Dally and his Stanford team developed the system architecture, network architecture, signaling, routing and synchronization technology that is found in most large parallel computers today. Dally was previously at the Massachusetts Institute of Technology from 1986 to 1997, where he and his team built the J-Machine and the M-Machine, experimental parallel computer systems that pioneered the separation of mechanism from programming models and demonstrated very low overhead synchronization and communication mechanisms. From 1983 to 1986, he was at California Institute of Technology (CalTech), where he designed the MOSSIM Simulation Engine and the Torus Routing chip, which pioneered “wormhole” routing and virtual-channel flow control. He is a member of the National Academy of Engineering, a Fellow of the American Academy of Arts & Sciences, a Fellow of the IEEE and the ACM, and has received the ACM Eckert-Mauchly Award, the IEEE Seymour Cray Award, and the ACM Maurice Wilkes award. He has published over 250 papers, holds over 120 issued patents, and is an author of four textbooks. Dally received a bachelor's degree in Electrical Engineering from Virginia Tech, a master’s in Electrical Engineering from Stanford University and a Ph.D. in Computer Science from CalTech. He was a cofounder of Velio Communications and Stream Processors.

  • This is a networking session which involves the Class of 2018 Innovators Under 35 Asia Pacific, whose inventions and research were found to be most ground-breaking and exciting by MIT Technology Review.

    Hosted by SGInnovate for the second year, the 10 young innovators are selected from a pool of nominees coming from countries such as Australia, Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, New Zealand, Philippines, Singapore, Taiwan, Thailand and Vietnam. 

  • This event is most suitable for professionals in Data Analytics or Data Science. Please register your interest to attend the talks with us. In view of the technical nature of the event, we will then send you an email to confirm your attendance should the event suit your profile.  Date: 7 February 2018 Time: 6:30pm - 8:30pm Venue: 32 Carpenter Street, Singapore 059911 Programme: 6:30pm - 7:00pm: Registration 7:00pm - 7:45pm: Talk 1 - A Big Data Exploration with BigQuery and Github 7:45pm - 8:30pm: Talk 2 - Introduction to Serverless Big Data processing on GCP 8:30pm: End Talk 1: A Big Data Exploration with BigQuery and GithubSpeaker: Felipe Hoffa, Developer Advocate from Google Synopsis: What can we learn from 1.1 billion GitHub events and 42 TB of code? Anyone can easily analyze the more than five years of GitHub metadata and 42+ terabytes of open source code. We’ll leverage this data to understand the community and code related to any language or project. Relevant for data analysts and data scientists. Talk 2: Introduction to Serverless Big Data processing on GCPSpeaker: Allen Day, Developer Advocate from GoogleSynopsis: In this presentation we’ll do a high-level review of some of the managed services available on Google Cloud Platform for deriving insights from petabyte-scale datasets. Who should attend: Data engineers, Data scientists, and Data analysts.

  • This seminar is intended for SMEs and entrepreneurs who are looking to use technology to enable the elderly who choose to "age in place" safely and comfortably. We believe that this will be through identifying Solutions and Models through the use of Artificial intelligence, Robotics and other Technologies which are quickly coming on stream.