About the event
AI, Machine Learning Forum 2018 – The Intelligent Technology
AI, Machine Learning Forum 2018 – The Intelligent Technology have spring boarded from pages of science fiction and our imagination, into the real world. These technological advancements have game-changing implications for business of all sizes.
As organizations strive to grow and evolve – there is an inherent need to embrace technological advancements where data becomes a key ingredient for these organizations to develop an edge over competitors and determine business success.
Machine learning offers a new solution – the ability of algorithms to learn without the need to be programmed.
Artificial intelligence brings with it a unique combination of man-to-machine interaction – where machines develop the required intelligence to process requests, connect data points, and draw conclusions.
AI is a new level of cognition that has truly grown useful in certain aspects of our daily lives in today’s world.
From automatically offering weather reports and travel alerts of your destination cities- to simple functions like setting reminders- to sending invitations to picking up a birthday cake to- AI assisting you instinctively segmenting your customer groups for targeted messaging and increased response rates- to self driving cars.
Our conference aims at how all these topics – AI, machine learning, deep learning and cognitive sciences relate by exploring basic components of AI and how various technologies have come together to help machines become smarter and more intelligent.
We bring to you leaders in the field to discuss at length the application of AI, machine learning, deep learning in finding a solution to business problems in shaping our daily lives.
“Come be part of our forum – help us help you increase your knowledge base”.
08:45 am – 09:00 am
09:00 am – 09:15 am
09:15 am – 10:15 am
To apply AI is to understand AI
Speaker:Mirco Milletari, Data and Machine Learning Scientist at Microsoft.
Topic of Abstract: Neural Networks based Machine learning algorithms are responsible for much of the advancement in the field of AI in the past few years. Although people have been studying these systems as far back as the 60s, their applications outside of academia are relatively recent. The reason for this success is not only due to increased computational power, but also the device of new algorithms and methods coming from a better understanding of AI. The best example is the Convolutional Neural Network, the paved the way to state of the art image/video recognition. Despite this, the theoretical foundations of Deep Neural Networks as still lacking. In this talk, I will discuss some new insights we have recently obtained in the theory of Neural Networks, how they solve some of the problems afflicting the field and how they lead to better performing algorithms. From the applicational side, this means faster, more stable Machine Learning and the possibility to devise new applications in a targeted way instead of the “trial and error” procedure currently widespread in the industry.
Speaker Bio: Mirco holds a PhD in theoretical physics jointly from the Max Planck Institute (Stuttgart) and the university of Leipzig, Germany. He has worked in the field of random and strongly correlated quantum systems in Italy, Germany and Singapore. These works have been published in leading, international journals. He was the leading AI scientist at Bambu Robot Advisory before moving to Microsoft as a data and machine learning scientist.
Interpretable Forecasting of Financial Time Series with Deep Learning
Speaker: Ilija Ilievski, PhD candidate at National University of Singapore
Topic of Abstract: Interpretable Forecasting of Financial Time Series with Deep Learning
In this talk, I will present a deep learning approach to forecasting financial multivariate time series which indicate the market sentiment towards a financial asset. The interpretable deep neural network reveals the essential dependence between the time series’ variables, and in contrast to the widely used vector autoregressive model, the deep learning model dynamically adapts the dependence coefficients to the ever-changing market conditions. Thus, the proposed method permits the study of the inter-variable relationships which yields a better understanding of the asset’s future price movements and consequently increases the profitability of the asset’s trading activities. I will conclude the talk with dependence analysis and forecasting performance for financial assets from different sectors and with vastly different market capitalization.
Speaker Bio: Ilija is a machine learning researcher building holistic models of unstructured data from multiple modalities. His diverse, seven-year experience as a machine learning researcher includes projects on combining satellite images and census data for complex city models, utilizing movie metadata and watch statistics for recommender systems, and fusing image and text data representations for visual question answering. Currently, Ilija is working on developing an interpretable deep learning model of financial data coming from multiple sources.
11:00am – 11:30am
Networking Tea / Coffee
11:30 am – 12:15 pm
Does AI need a sound data strategy?
Speaker: Janet UY, Lead Data Scientist – Big Data & Advanced Analytics Consulting (ASEAN) at Oracle
Topic of Abstract: Does AI Need A Sound Data Strategy? Artificial Intelligence is rapidly gaining ground in almost all industries, promising to revolutionise business models and transform decision-making throughout the enterprise. The key, of course, is high quality data and lots of it. Unfortunately, many organisations lack a comprehensive data strategy which seeks to acquire, curate, combine and commercialise it. This talk aims to present the importance of having sound data architecture and technologies to unleash the power of artificial intelligence. New data-driven capabilities based on this modern architecture will not only yield radical improvements in operational effectiveness, but also new sources of competitive advantage for organisations.
Speaker Bio: Janet Uy is Oracle’s Solution Engineering Lead for Big Data & Analytics in ASEAN. She is a Data Scientist and has been actively driving education and evangelization of data science internally and externally, particularly around Big Data, Deep Learning, Machine Learning, and Artificial Intelligence. Prior to joining Oracle, Janet was the Head of Data Science at lloopp, a Singapore based startup company, which started the Deep Learning wave in the country. Together with the founders, she had been instrumental in building the company, its culture, and its products around Deep Learning and Artificial Intelligence from scratch. Throughout her career, she has handled projects in various industries such as telecommunications, retail, smart cities, fintech, insurance, oil & gas, and manufacturing in Asia Pacific, Central America, and Africa.
DevOps meets Machine Learning
Speaker: Aki Ranin, Co-founder and Chief Operating Officer at Bambu B2B Robo-Advisory
Topic of Abstract: The way software is created is about to change permanently through Artificial Intelligence. Rather than code strict and static rules, Machine Learning enables us to teach software desired behaviours based on data.
Speaker Bio: Aki has started two startups using A.I. as part of their software: Bambu in Fintech, and Mission-ready in Health & Fitness. Aki also writes frequently on Medium about startups and technology.
01:00 pm – 02:00 pm
02:00 pm – 02:45 pm
The need for human – centered design in AI
Speaker: Damien Kopp, Co-Founder and Chief Technology Officer at Envolve Data
Topic of Abstract:For the past decades, we have been told that data is the future, data is a goldmine, data is valuable. However for most of us, data has been an ever-growing background noise that hardly makes sense. The truth is our human brain is unable to process the 2.5 quintillion bytes of data created each day: we need help. Technology has always been a tool to help augment our human capabilities where our they are insufficient or inefficient: that’s Augmented Human Intelligence.
The challenge is not only about how to deal with the massive amount of data but how to practically transform an organization to deploy Augmented Human Intelligence and capture its full value at scale.
Speaker Bio: Damien is a Technology Executive with 15+ years of global experience in digital innovation, product strategy and technology consulting across Europe, North America and South East Asia.
He is the co-founder and Chief Technology & Data Officer at Envolve Data, an AI platform for Retail; Managing Director at ReBootup, a boutique consulting firm focused on Lean Innovation Management, an Advisory Board Member at the Live With AI foundation, and a Council Advisory Member for FinTech Opportunities in India at YES BANK.
Before that, he was Chief Technology & Innovation Officer at GoSwiff, a mobile payment FinTech based in Singapore; and he spent 11 years at Accenture in technology consulting.
His areas of expertise include Big Data, Machine Learning, Natural Language Processing, Robotic- process automation, Internet of Things and Cloud architectures.
Electrical Engineer by training Damien holds a dual Executive MBA degree from Kellogg School of Management and Hong Kong University of Science and Technology.
03:30 pm – 04:00 pm
Networking Tea / Coffee
04:00 pm – 04:45 pm
Customer Centric AI
04:45 pm – 05:00 pm
Conference End Note
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