Recommended Global EEE & Engineering Webinars & Conferences
Neural Networks 2020
Track 1: Artificial Intelligence
Artificial intelligence is a performance based-system idea in a robot. Artificial Intelligence brings creative way to the robot to be able to render assistance to humans in changeable and dynamic environments, such as homes, hospitals, the workplace, and all around us. It is a process of making a a computer operated robot or a software think logically, in a comparable manner the creative humans imagine. Artificial intelligence is performed by analysing how human brain conceives and how humans learn, decide, and work while seeking to solve a query, and then using the results of this study as a basis of progressing intelligent software and systems. In the actual world, knowledge has some unwelcomed assets.
AI algorithms can tackle learning, perception, problem-solving, language-understanding and/or logical reasoning. In modern world AI can be used in many ways even when it is to control robots. Sensors, actuators and non-AI programming are parts of larger robotic system.
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 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: Machine Learning
Machine learning is a part of artificial intelligence based on the idea that systems can learn from data, make decisions and identify designs with insignificant human intervention. Machine learning is a method for making a personal computer, a PC controlled robot, or a product think smartly, and within the comparative way the perceptive people think. They are normally grouped by either learning style or by comparison in method or function. It simplifies the continuous advancement of scheming through introduction to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in succeeding situations. ML gives possible arrangements in every one of these areas, and is set to be a support of our future progress.
Track 4: Artificial Neural Networks and Deep Learning
Artificial Neural Networks is a computational model based on the structure and functions of biological neural networks. ANNs are considered nonlinear statistical data modelling tools where the complex relationships between inputs and outputs are modelled or patterns are found.
Modern Digital Computers outperform humans in the domain of numeric computation and related symbol manipulation. However, humans can effortlessly solve complex perpetual problems (like recognizing a man in a crowd from a mere glimpse of his face) at such a high speed and extend as to dwarf the world’s fastest computer. The biological neural system architecture is completely different. This difference significantly affects the type of functions each computational model can best perform
Numerous efforts to develop intelligent programs based on von Neumann’s centralized architecture have not resulted in general-purpose intelligent programs. Inspired by biological neural networks, ANNs are massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections. ANN models attempt to use some organizational principles believed to be used in the human
Track 5: 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 behaviours. It can be diverse, such as homes, meeting rooms, offices, hospitals, schools, control centres, 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.
Track 6: Perceptrons
Perceptron is a machine learning algorithm that helps to provide classified outcomes for computing. It is a kind of a single-layer artificial network with only one neuron and a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Multilayer Perceptron is a class of feed forward artificial neural networks. And the layered feed forward networks are trained by using the static back-propagation training algorithm. For designing and training an MLP perceptron several issues are involved:
- Number of hidden layers is selected to use in the neural network
- A solution that avoids local minima is globally searched
- Neural networks are validated to test for overfitting
Track 7: Robotics and Mechatronics
Mechatronics is combination or junction of Electrical, Mechanical, and Computer Science Engineering. Mechatronics is the closest to robotics with the slight and main difference in mechatronics systems inputs are provided whereas in robotics systems it acquires the inputs by their own.
A Mechanical Engineer is mostly responsible for the Mechanical body parts, Electrical/Computer Engineer for the Electrical aspect and Computer Engineer for Programming. And the Mechatronics Engineer must be pretty much qualified for every partition we discussed above.
Robotics is a very broad term; we must go through different application to understand it better.
Track 8: Natural Language Processing
Natural Language Processing (NLP) is a subset of Artificial Intelligence. Its ability is to interpret and understand human language the way it’s spoken or written and to make the machines/computer as intelligent as human beings in understanding language.
Natural language generation (NLG) and Natural language understanding (NLU) are the two main components of NLP. NLU understanding involves mapping the given input in natural language into useful representations and NLG is the process of producing meaningful phrases and sentences in the form of language.
Track 9: Cloud Computing
Cloud computing is branch of information technology which grants universal access to shared pools of virtualised computer resources. A cloud can host different workloads, allows workloads to be scaled/deployed-out on-demand by rapid provisioning of physical or virtual machines, self-recovering, supports redundant, and highly-scalable programming models and allows workloads to recover from hardware/software rebalance and failures allocations.
Artificial Intelligence technology plays a very important role in Making resources available, Distribution transparency and Openness Scalability especially for Cloud Computing Application. Artificial intelligence and cloud computing will have an important impact on the development of information technology by mutually collaborating.
Track 10: Autonomous Robots
Autonomous robots are the intelligently capable machines which can perform the task under the control of a computer program. They are independent of any human controller and can act on their own. The basic idea is to program the robot to respond a certain way to outside stimuli. The combined study of neuroscience, robotics, and artificial intelligence is called neurorobotics.
Track 11: Parallel Processing
Parallel Processing reduces processing time by simultaneously breaking up and running program tasks on multiple microprocessors. There are more engines (CPUs) running, which makes the program run faster. It is particularly useful when running programs that perform complex computations, and it provides a viable option to the quest for cheaper computing alternatives. Supercomputers commonly have hundreds of thousands of microprocessors for this purpose.
Parallel programming is an evolution of serial computing where the jobs are broken into discrete parts that can be executed concurrently. It is further broken down to a series of instructions and the instructions from each part execute simultaneously on different CPUs.
Track 12: Ethics in AI
The ethics of artificial intelligence is the part of the ethics of technology specific to robots and other artificially intelligent beings. It is typically divided into robo ethics, a concern with the moral behaviour of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behaviour of artificial moral agents (AMAs).
The term "robot ethics" (robo ethics) refers to the morality of how humans design, construct, use and treat robots and other artificially intelligent beings. It considers both how artificially intelligent beings may be used to harm humans and how they may be used to benefit humans.
Track 13: Bioinformatics
Bioinformatics is a multidisciplinary research field that combines computer science, biology, science, statistics and mathematics in to a broad-based field that will have profound impacts on all fields of biology. It is the application of computer technology to the management of biological information.
Biocomputing is the computing which designs and constructs the computer containing biological components
Track 14: Ubiquitous Computing
Ubiquitous computing is a branch of computing in computer science and software engineering where computing is made easier so that they can appear anytime and everywhere. It can occur using any device, in any location, and in any format.
Key features include:
- Use of Inexpensive processors which reduces the storage and memory requirements.
- Totally connected and constantly available computing devices and capturing of real time attributes.
- Focus on many-to-many relationships, instead of one-to-one, many-to-one or one-to-many in the environment, along with the idea of technology, which is constantly present.
- Relies on wireless technology, converging Internet and advanced electronics.
The global artificial intelligence (AI) market size was USD 20.67 Billion in 2018 is projected to reach USD 202.57 Billion by 2026, exhibiting a CAGR of 33.1% during the forecast period.
Artificial Intelligence is the result of a perfect blend of several technologies leading to the creation of intelligent hardware or software, capable of replicating human behaviours, namely learning and problem-solving. The version of artificial intelligence technology available at present allows machines to complete various human tasks, such as driving automobiles and reacting to their environment, providing virtual assistance and even playing games. Forms of AL in use today include digital assistants, chat bots and machine learning among others.
AI will facilitate more seamless integration of supply chain data, enabling anticipatory production and efficient delivery of products to customers. As per the current artificial intelligence market trend, three areas with the biggest AI potential are autonomous trucking and delivery, enhanced security, traffic control and reduced congestion. AI is likely to trigger the shift to a customer oriented economy and move up the value chain into a more sophisticated and high tech-driven level for the manufacturing and commerce sectors.
Artificial Intelligence market growth is driven by the increasing adoption of clod-based applications and services, the rise in the connected device market, considerable investments in 5G technology and an increase in demand for intelligent virtual assistants. The number of AI technology experts is limited at present.
Software market for artificial intelligence expected to hold the largest market share
The software market is expected to hold the largest share for artificial intelligence market. The software market is growing due to increasing conversation AI platforms. Also, The general purpose AI platforms and software being used to develop interface and application ranging from conversational interfaces to predictive and prescriptive applications that offer advice and recommendations. The AI software platform is majorly focused on tools and API framework for application and technologies based on AI and ML using both structured and unstructured data to drive these application.
North America expected to hold the largest market share, APAC to witness the highest growth rate
North America is expected to hold the largest share and dominate the artificial intelligence market between 2018 and 2025. Rapid developments in infrastructure and the high adoption of digital technologies are the 2 major drivers of the AI market in the region. Additionally, the region, a well-established economy, has also seen large-scale investments in AI, as a result of which, both start-ups and well-established companies are concentrating more on developing innovative AI-enabled solutions to cater to the various industry verticals. Moreover, the smart city initiatives are increasing in the North American region, due to which, there is an increase in the raw data as well. North America, being technologically advanced in terms of analytical tools, machine learning and natural language processing technologies is expected to maintain its dominant position. Moreover, awareness about the advantages of artificial intelligence tools is widespread in the region. Europe is also expected to hold the second position in this market by witnessing moderate growth during the latter half of the forecast period.
The artificial intelligence market in Asia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period. In security, with increasing incidents of cyberattacks and a growing cyber-war in the region, organizations and governments are focusing on robust defence infrastructure. APAC, especially China, Japan, and South Korea, is considered the largest market for industrial robots. Industrial robots generate a huge volume of data, which is used for training robots. This would act as one of the major drivers for the AI market in APAC.
As per the artificial intelligence market forecast, the software segment accounts for a maximum revenue share of the market. In case of AI-based services, the initial investment is comparatively low while there are high returns. By incorporating AI in cloud services and enterprise software, companies can implement/deploy cognitive technology with low initial cost and minimal risk.
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