Track 2: Cognitive Computing

Cognitive Computing refers to the hardware and/or software that helps to improve human decision-making and mimics the functioning of the human brain. It refers to systems that can learn at scale, reason with purpose and interact with humans naturally. It comprises of software libraries and machine learning algorithms for extracting information and knowledge from unstructured data sources. The main motive is to accurate models of how the human brain/mind senses, reasons, and responds to stimulus. High-performance computing infrastructure is powered by processors like multicore CPUs, GPUs, TPUs, and neuromorphic chips. They interact easily with users, mobile computing, and cloud computing services so that those users can define their needs comfortably.

Neural Informatics for Cognitive Computing

Neural Information theory is a multidisciplinary enquiry of the physiological and biological representation of knowledge and information in the brain at the neuron level.

Track 3: Self-Organizing Neural Networks

Pattern recognition is a part of machine learning that focuses on the recognition of patterns and regularities in data by using supervised learning algorithms that create classifiers based on training data from different object classes. Optical character recognition (OCR) faces detection, face recognition, object detection, and object classification uses supervised pattern recognition. And the unsupervised learning works by finding hidden structures in clustering techniques.

Feature selection or variable selection is the process of selecting a subset of relevant features for use in model construction. They are also used to simplify the models to make them easier to interpret, shorter training times and enhanced generalization by reducing overfitting (reduction of variables). The data contains many features that are either redundant or irrelevant and can be removed without incurring much loss of information.

Track 4:  Backpropagation

Artificial neural networks uses backpropagation method to calculate the error contribution of each neuron after a batch of data (in image recognition) is processed. It is a special case of an older and more general technique called automatic differentiation. It is commonly used by the gradient descent optimization algorithm to adjust the weight of neurons by calculating the gradient of the loss function. This technique is also sometimes called backward propagation of errors because the error is calculated at the output and distributed back through the neural network layers.

Track 5: Computational Creativity

Machine learning is the field of computer science which teaches machines to detect different patterns and to adapt to new circumstances. Machine Learning can be both experienced and explanation-based learning. In the field of robotics machine learning plays a vital role, it helps in taking an optimized decision for the machine which eventually increases the efficiency of the machine and more organized way of performing a particular task. It is employed in a range of computing tasks where designing and programming with good performance is difficult or infeasible for example email filtering, network detection or malicious insiders working towards a data breach optical character recognition (OCR) and computer vision.

Data mining is an extraction process of useful patterns and information from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis. It is a logical process that is used to search through a large amount of data in order to find useful data. The goal of this technique is to find patterns that were previously unknown.

Track 6: Artificial Neural Networks

Artificial Neural Networks are the simulations which perform specific tasks like pattern recognition, clustering etc. on the computer.

Artificial neural networks are mathematical models inspired by the organization and functioning of biological neurons.  They are similar to the human brains, acquire knowledge through learning and their knowledge is stored within interneuron connections strengths known as synaptic weights.

Architecture of ANN

A large number of artificial neurons called units arranged in a series of layers are contained by neural networks. Different layers are Input layer, Output layer, and Hidden layer.

• Input layer contains those units which receive input from outside world on which network will learn and recognize the process.

• Output layer contains units that respond to the information about how it’s learned any task.

• Hidden layers are in between input and output layers. It transforms the input into something that output unit can use in some ways.

Track 7: Deep Learning

Track 8: Ambient Intelligence

Ambient intelligence (AmI) deals with the computing devices, where physical environments interact intelligently and conservatively with people. These environments should be aware of people's needs, customizing requirements and forecasting behaviors. It can be diverse, such as homes, meeting rooms, offices, hospitals, schools, control centers, vehicles, etc. Artificial Intelligence research aims to include more intelligence in AmI environments, allowing better support for humans and access to the essential knowledge for making better decisions when interacting with these environments.

Importance and Scope:

Artificial intelligence technology is changing the face of medical science by creating virtual personal healthcare assistant and health bots to give customer support. AI is mostly used in the production unit of manufacturing company where robots are being used give a different shape to an object, which displaces object from one place to another. The conference aims to discover advances in Artificial Intelligence technologies, educational fields, commercial and private sectors. It will facilitate a series of discussions about building knowledge and sharing new ideas amongst the professionals, industrialists, researchers, and students from research area of Artificial Intelligence.

Why Finland?

In recent time Finland has emerged high in the technology sector because of its high educational standards, it has more internet connections per capita than any other country. Finland has shown interest in developing AI sector which will offer cybersecurity cooperation, unmanned aircrafts, satellite cooperation, and maritime safety issues, such as dissemination of situation awareness information.

Finland is known for its scenic beauty of natural aura as it is surrounded by the land of forests, fells, countless lakes and collection of islands, it is one of the most extensive and unspoiled natural environments in Europe. It is also a country of contrasts, the Midnight Sun in the summer, the Polar Night and the Northern Lights in the winter.

Benefits:

With members from around the world focused on sharing new ideas and learning about Artificial Intelligence and Neural Networks, this is the best opportunity to reach the largest assemblage of participants from the research centers, universities, community, etc. This will conduct demonstrations, distribute information, meet with current and potential researchers and receive name recognition at this 2-day event. World-renowned speakers, the most recent techniques, tactics, and the newest updates in this technical field are hallmarks of this conference. The conference will facilitate a series of discussions about state-of-the-art processes, organizational transitions, analytics, and technology innovation within the industry and academia.

Top Societies and Association around the World

• Association for the Advancement of Artificial Intelligence

• Association for Computing Machinery(ACM)

• Computing Research Association

• Florida Artificial Intelligence Research Society

• IEEE Computer Society

• The IEEE Computational Intelligence Society

• The International Neural Network Society

• Singularity Institute of Artificial Intelligence

• The Society for the Study of Artificial Intelligence and Simulation of Behaviour

• British Computer Society, Specialist Group on Artificial Intelligence

• The European Coordinating Committee on AI (ECCAI)

Target Audience

Researchers, Scientists, Professors, Engineers, Students, Smart innovators, Robotic technologist, Defence Research Professionals, Automation industry leaders, Managers & Business Intelligence Experts, Inventors.

Major Artificial Intelligence Universities in USA

• Carnegie Mellon University (Pittsburgh, PA)

• Stanford University (Stanford, CA)

• Massachusetts Institute of Technology “MIT” (Cambridge, MA)

• The University of California, Berkeley (Berkeley, CA)

• Harvard University (Cambridge, MA)

• Yale University (New Haven, CT)

• Cornell University (Ithaca, NY)

• University of Maryland (College Park, MD)

• Columbia University (New York City, NY)

• University of Texas (Austin, TX)

• The University of California, Los Angeles (Los Angeles, CA)

• University of Michigan (Ann Arbor, MI)

• Georgia Institute of Technology (Atlanta, GA)

Major Artificial Intelligence Universities in Europe

• West University of Timisoara (WUT)

• KU Leuven – Belgium

• Polytechnic University of Catalonia

• Radboud University

• University of Amsterdam

• University of Edinburgh

• University of Georgia

• University of Groningen

• University of Sheffield

• University of Southampton

Major Artificial Intelligence Universities in Middle East

• IZMIR University

• Canadian University Dubai

• Rafik Hariri University

• Yasar University

• TED University

Major Artificial Intelligence Universities in Asia

• K L University

• National University of Singapore

• James Cook University

• Georgian Technical University

• The Hong Kong University of Science and Technology

• KOC University- Istanbul Turkey

Global Artificial Intelligence Market Analysis & Trends

The Artificial Intelligence Market is poised to grow globally at a CAGR of around 44.3% over the next decade to reach approximately $23.4 billion by 2025. This industry report analyzes the global markets for Artificial Intelligence across all the given segments on global as well as regional levels presented in the research scope. It represents market data for 2013, 2014 revenue estimations are presented for 2015 and forecasts from 2016 till 2025.

The Artificial Intelligence market in Western Europe education sector to grow at a CAGR of 38.37% during the period 2017-2021, the education system in Europe is gradually adopting e-learning and other related tools such as social learning. The institutions are integrating social media into another platform to facilitate learning, information sharing, and collaboration among students and teachers. These kinds of social networking platform are offered by software solutions such as next-gen LMS.

The market of autonomous robots is growing rapidly be it for industrial, military, commercial or consumer applications. In 2005 spending on robotic systems were US $11 billion but by 2025 it is expected to rise to $67 billion. Decades ago industrial robots began transforming manufacturing. With their mechanical, programmable arms, they performed tasks such as painting, welding and placing objects. Today’s autonomous robots are more efficient and time-saving. The BCG estimates that more than $67 billion will be spent globally in the robotics sector by 2025.Companies are investing an enormous amount of time, capital, and manpower to produce software solutions with embedded AI applications and other AI-backed products such as an intelligent tutor. In order to improve AI capabilities of present solutions, extensive research and R&D investments are still being conducted.

Artificialintelligence 2017

Thanks to all of our wonderful speakers, conference attendees, Automation and Robotics-2017 Conference were the best!

The 3rd International Conference on Artificial Intelligence and Robotics, hosted by the ME Conferences were held during June 28-29, 2017 at Hilton San Diego Mission Valley Hotel, San Diego, USA with the theme “Future Trends in the Field of Industrial Automation and Robotics". Benevolent response and active participation were received from the Editorial Board Members of ME Conference Group Journals as well as from the scientists, engineers, researchers, students, and leaders from the fields of Automation and Robotics, who made this event successful.

The meeting was carried out through various sessions, in which the discussions were held on the following major scientific tracks:

• New Approaches in Automation and Robotics
• Machine Learning
• Quest for Artificial Intelligence
• Remote and Telerobotics
• Automation Control
• Prototypical Applications
• Humanoid Robots: New Developments
• Computational creativity
• Affective computing
• Robot Localization and Map Building
• Automation Control
• Robot Manipulators: Trends and Development

New Approaches to Automation and Robotics The conference was initiated with a series of lectures delivered by both Honorable Guests and members of the Keynote forum. The list included:

Ashitey Trebi-Ollennu, NASA Jet Propulsion Laboratory, USA

Lin Zhou, IBM, USA

Timothy Sands, Naval Post Graduate School, USA

Bogdan Gabrys, Bournemouth University, UK

Mikhail Moshkov, King Abdullah University of Science and Technology (KAUST), Saudi Arabia

Jose B. Cruz Jr, National Academy of Science and Technology, Philippines

Fuchiang (Rich) Tsui, University of Pittsburgh School of Medicine, USA

Ryspek Usubamatov, Kyrgyz Technical University, Kyrgyzstan

ME Conferences offers its heartfelt appreciation to the Organizing Committee Members, adepts of the field, various outside experts, company representatives and other eminent personalities who supported the conference by facilitating the discussion forums. ME Conferences also took the privilege to felicitate the Organizing Committee Members and Editorial Board Members who supported this event.

For More details visit: https://neuralnetworks.conferenceseries.com/