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Fostering the communities of robotics and AI in Vietnam and in International

Siloed development, wasted labor, and high start-up cost are limiting the pace of robotics innovation. With Kambria, our mission is to accelerate this process – enabling faster, cheaper, and easier robotics development and adoption by everyone. In this talk we will discuss the unique game-theoretical design of Kambria based on blockchain and crypto-economics to align the incentives of all key stakeholders in the robotics community. Ultimately, the Kambria platform will foster an ecosystem where collaborators, top developers, and companies, who share our passion for robotics technology will deliver affordable and impactful robots to end users. We will then discuss how Kambria could help build a strong AI and robotics community in Vietnam to capture the upcoming opportunities. We will also give a quick demo of our first robot and an arm prototype manufactured based on 3D-printing technology. Our mission is to equip Vietnam with the engineering talents and the manufacturing capabilities to become a significant AI & Robotics hub in the world.

Dr. Vu Duy Thuc – Ohmilabs (USA)

Graduated from Carnegie Mellon with a BS and Stanford with a PhD both in Computer Science, Ohmnilabs’ CEO and co-founder Thuc Vu is an expert in Artificial Intelligence and Algorithms. His ambition is to bring robots into every home to create a positive impact in people’s life. And Kambria – a decentralized AI and Robotics platform – is his next move to bring this goal one step closer to reality. He previously founded Katango and Tappy which were quickly acquired by Google and Weeby.co, respectively. He also invests in early stage startups and helps grow the ecosystem in Vietnam. Apart from being an entrepreneur, Thuc is also a Research Scientist and Assistant Professor at John Von Neumann Institute of Vietnam National University. As a way to pay it forward, Thuc co-founded VietSeeds in 2011, a nonprofit organization empowering hundreds of underprivileged students with excellent academic record in Vietnam. In 2017, he was named as one of the “40 Under 40” of Silicon Valley by the Silicon Valley Business Journal for his tireless effort to bring about a significantly positive impact in the business world.

AI and data in cancer research

Artificial intelligence is playing an increasingly important role in cancer research. Advancement in acquisition technologies has led to largescale data production of various molecular readouts. Coupled with smart learning algorithms, this brings exciting challenges and opportunities for early detection, diagnostic, treatment selection and monitoring of cancers. Is it possible to identify cancers early in a non-invasive manner? Can cross-cancer analysis offer guidance to treatment of patients whose tumors reside in a different organ and yet sharing driver gene mutations with other tumors that have previously been successfully managed?
In this presentation, I will survey current methods and data in cancer genomics and proteomics with a view towards real-life applications. I will also discuss sustainable software tools and computing infrastructures to maintain the momentum of AI in the field of cancer research.

Dr. Pham Viet Thang – VU University Medical Center (Netherland)

Pham Viet Thang received a BSc degree in computer science from RMIT University, Australia in 1998. After a brief period working as teaching assistant at Vietnam National University in 1999, he joined the Intelligent Systems Lab Amsterdam, the Netherland where he earned a doctoral degree on the topic of machine learning and computer vision. During this period, he devised new methods for Bayesian network classification, support vector classification, boosting algorithms, and sparse representation of images. Since 2006, he has been working first as research scientist and now as assistant professor at the Cancer Center Amsterdam, VU University Medical Center, the Netherlands. His current interest is to advance computer algorithms to explore the vast amount of data in cancer research. A highlight of his work is the development of statistical methods for significance analysis of mass spectrometry-based proteomics data.

Application of artificial intelligence techniques in building economic-financial forecast models on high dimensional data sets.

Forecasting in the economic-financial field plays a very important role for the direction and regulation of the government, the development and implementation of business-production strategies of enterprises, investment and consumption of people. So far, the econometric approach based on a combination of mathematics, economic theory and statistical prediction with regression techniques, remains the most popular and important one to make forecasts in the field of economics and finance.
With the increasing economic globalization and the rapid development of science and technology, each event in the field of economics – finance is affected by many other economic-financial and social factors in the country as well as abroad. The current econometric approach to build economic-financial forecasting models is not applicable to input data sets with high dimensions and it is a very big challenge for the implementation of economic-financial forecasts.
The purpose of this report is to present some approachs of application of artificial intelligence techniques in building economic-financial forecasting models on huge input data sets. Artificial intelligence techniques are very useful for dimensionality reduction of input data sets and to improve the quality of forecasting models built under the econometric approach. Some forecasting models in the economic – financial field built arcoding to the mentioned approachs on the real data sets of the economy show that the forecast accuracy by the models is not only increased but also can use these models for evaluation of the impact of economic-financial shocks as well as of many other external factors. Some new researchs by author and some open issues will also be presented and discussed in this report.

Assoc. Prof. Do Van Thanh – Nguyen Tat Thanh University (Vietnam)

Assoc. Prof. PhD. Do Van Thanh was a senior researcher, deputy director of the National Center for Socio-Economic Information and Forecasts, Ministry of Planning and Investment, and a part – time lecturer of the Information Technology Department, University of Technology, Hanoi National University. Now he is a full time lecturer, the Information Technology Department, Nguyen Tat Thanh University. He received PhD’s degree in Information Technology in 1996 from the Vietnamese Academy of Science and Technology. His research interests include Data Analysis and Data Mining, Knowledge representation and automatic reasoning, Economic – Financial Analysis and Forecast. He is the author and co-authors of more than 70 peer – reviewed publications concerning the all fields above. He has strong experience in knowledge discovery in databases and building economic-financial forecast models.

AI in 5G networks

The telecommunications industry have pursued efforts to realize the idea of 5G networks. Besides increasing speeds and more efficient utilization of spectrum, a main goal is to establish the foundation and the common framework of new innovative network services.
5G operators may create new revenue streams from hosting 3rd party applications in their infrastructure in addition to the provisioning of their own services.
To achieve the aims of 5G, the development of scalable self-managed software and cloud platforms that supports the rapid deployment of services are needed. European Telecommunications Standards Institute (ETSI) has specified the Network Functions Virtualisation concept where Network Services are constructed by an appropriate chaining of
Network Functions (either physical network functions or virtualized network functions — VNF). The NFV initiative transformed the way telecom network operators architect their networks. It embraced virtualization techniques widely used in the IT industry and introduced the Infrastructure-as-a-Service cloud computing model into the Telco world.Some use cases of 5G applications have strict requirements (i.e., high availability and low latency for VR, AR, V2X, etc.) that can be achieved by the careful engineering, the new operating rules of cloud platforms and the optimal placement of application components. In the talk, the application possibilities of AI will be outlined for the efficient operation and management of 5G networks.

Prof. Do Van Tien – Budapest University Of Technology And Economics (Hungary)

Tien Van Do received the M.Sc. and Ph.D. degrees in telecommunications engineering from the Technical University of Budapest, Hungary, in 1991 and 1996, respectively. He is a professor in the Department of Telecommunications of the Budapest University of Technology and Economics, and a leader of Communications Network Technology and Internetworking Laboratory. He habilitated at BME, and received the DSc from the Hungarian Academy of Sciences in 2011. He has participated and lead work packages in the COPERNICUS-ATMIN 1463, the FP4 ACTS AC310 ELISA, FP5 HELINET, FP6 CAPANINA projects funded by EC (where he acted as a work package leader). He led various projects on network planning and software implementations that results are directly used for industry such ATM & IP network planning software for Hungarian Telekom, GGSN tester for Nokia, performance testing program for the performance testing of the NOKIA’s IMS product, automatic software testing framework for Nokia Siemens Networks. His research interests are queuing theory, telecommunication networks, cloud computing, performance evaluation and planning of ICT Systems. He is also a board member of Discrete Dynamics in Nature and Society, Hindawi.

General Game Playing: a Challenge for AI

Games represent an exciting challenge for Artificial Intelligence. The ability of computers to confront human beings in a convincing manner, or even to defeat them, fascinate most people. Besides, games are a good framework to test algorithms developed for more general problems. Thus games are a good area to test out AI techniques and to develop new approaches. Recently, stronger results were provided and, for many games, computer skills are far away from human abilities.
We focus in this talk on the challenge of General Game Playing. The topic of General Game Playing (GGP) is to develop artificial agents able to play any game, without human intervention. The rules of each game are described in a declarative representation language, called Game Description Language (GDL). These rules are given to the agent only a few minutes before playing, which makes it difficult to apply current techniques. We discuss in this talk the difficulty of this challenge, some recent results and some possible applications in real life.

Assoc. Prof. Sylvain Lagrue – University of Artois (France)

LAGRUE Sylvain is an Associate Professor of Computer Science at Université d’Artois/CRIL CNRS UMR 8188 (France). His research includes Artificial Intelligence, Knowledge Representation, Uncertainty in AI and Games. He is currently invited at the VNU in the framework of the EU Project Aniage.