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Introduction & FAQs for Artificial Intelligence and Machine Learning


Below is a list of materials that introduces the reader to the field of Artificial Intelligence. These materials are targeted at the novice AI practitioner. Students who are just learning the field may also find this material useful. Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior.

Robotics Automation Journals Peer Reviewed


Robotics and Automation deals with manufacture and applications of robots and computer systems for their control, sensory feedback, and information technology to reduce the need for human work. The journal provides an Open Access platform to publish the latest contributions in the field of robotics, automation technologies, robotic surgery, intelligent robotics, mechatronics, and biomimetics novel and biologically-inspired robotics, modelling, identification and control of robotic systems, biomedical, rehabilitation and surgical robotics, exoskeletons, prosthetics and artificial organs, AI, neural networks and fuzzy logic in robotics etc. This top best scholarly journal is using Editorial Manager System for online manuscript submission, review and tracking. Editorial board members of the Robotics & Automation or outside experts review manuscripts; at least two independent reviewer's approval followed by the editor is required for the acceptance of any citable manuscript. The journal includes a wide range of fields in its discipline to create a platform for the authors to make their contribution towards the journal and the editorial office promises a peer review process for the submitted manuscripts for the quality of publishing.

Neuromorphic engineering - Wikipedia


Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[4] in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.[5] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories,[7] threshold switches, and transistors.[8] A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[9]

Brain like a computer: bad at math, good at everything else.


We all remember the painful arithmetic exercises at school. It takes at least a minute to multiply numbers like 3,752 and 6,901 with pencil and paper. Of course, today, when we have phones at hand, we can quickly check that the result of our exercise should be 25 892 552. Processors of modern phones can perform more than 100 billion of such operations per second. Moreover, these chips consume only a few watts, which makes them much more efficient than our slower brains, which consume 20 watts and require much more time to achieve the same result. Of course, the brain has not evolved to do arithmetic.

AI, Automation & 5G ensuring the future of Industry 4.0


Industry 4.0 involves interconnecting all parts of a company and giving rise to effective automation resulting in a more intelligent organization. It is considered the fourth industrial revolution to the digitization phase of the manufacturing sector that was possible due to 4.0 technology. This process is driven by a surprising increase in data volume, the power of computer systems and connectivity. These changes will allow the different sectors to adapt and evolve, and create synergies with which to become stronger and more competitive. As a proud partner of Ericsson, I had the opportunity to get a preview on this topic, the upcoming Ringside webinar and Ericsson research on this subject.