Artificial Intelligence, Machine Learning and Robotics

Robotics and remote technology could transform one of the world’s most labor-intensive and potentially dangerous industries. The mining sector is notoriously hazardous due to the nature of the sector, however the introduction of robots, drones, driverless trucks and pilotless trains could transform mine safety. A technology boom could also allow for the development of mines in regions once thought too precarious or remote to exploit. Rio Tinto is not the only mining powerhouse about to utelise drones and remote technology on site. BHP Billiton and Anglo American are also ramping up efforts to automate, believing that new equipment will cut costs and improve returns, while allowing them to exploit deposits so far considered too complex or too dangerous for humans.

Cyber Security: Threat &Road Ahead:

The global is changing, and with it so is the Cyber Threat. At one hand extra connectivity allows to offer new commercial enterprise and social possibilities however concurrently needs for greater responsibility. The subsequent technology conflict can be fought via way of means of sitting in the front of laptop screens. As the threat is higher, call for certified experts is likewise high. This consultation approximately the Cyber Security, Cyber Threat, its demanding situations and destiny can be beneficial for each player to speak about approximately demanding situations of Cyber Security and what may be the feasible answer for a non-violent planet.

AI in Electric Cars:

Tesla being a tech innovator has evolved a main call with inside the global of electrical vehicles. The utilization of the contemporary-day synthetic intelligence technology likes autopilot in automobiles has created hobby amongst passionate car enthusiasts. Tesla has made use of the consumer datasets for statistics analytics to assume and collect statistics approximately patron necessities and used it to enhance the capabilities of its vehicles. Tesla is the usage of AI Technology in incorporated chips, Autopilot, Design of AI chip.

Machine Learning:

Machine Literacy entails the usage of Artificial Intelligence to permit machines to examine a assignment from revel in without programming them precisely approximately that assignment. (In short, Machines examine mechanically without mortal hand holding!!!) This method begins off evolved with feeding them proper nice records and additionally schooling the machines through erecting colourful gadget literacy fashions the usage of the records and in contrast to algorithms. The preference of algorithms relies upon on what kind of records do we`ve and what type of assignment we are seeking to automate.

Still, normally speaking, Machine Learning Algorithms are divided into three types

Supervised Machine Learning Algorithms

Unsupervised Machine Learning Algorithms

Underpinning Machine Learning Algorithms

Knowledge-Based Systems:

The global artificial intelligence market size was valued at USD 136.55 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. The continuous research and innovation directed by tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing. For instance, in November 2020, Intel Corporation acquired Cnvrg.io, an Israeli company that develops and operates a platform for data scientists to build and run machine learning models, to boost its artificial intelligence business. 

The essential fact accelerating the rate of innovation in AI is accessibility to historical datasets. Since data storage and recovery have become more economical, healthcare institutions and government agencies build unstructured data accessible to the research domain. Researchers are getting access to rich datasets, from historic rain trends to clinical imaging. The next-generation computing architectures, with access to rich datasets, are encouraging information scientists and researchers to innovate faster.

The artificial intelligence industry has been a software player so far, and very few companies manufacture hardware components such as CPU, ASIC, FPGA, and GPU. Recent trends in design tools have made FPGAs compatible with complex software practices and accessible to those who organize and build algorithm models. Hardware-based artificial intelligence solutions are being introduced to combat challenges associated with power consumption, sluggish processing, and lack of efficiency. Furthermore, as the market is maturing, there is an increasing need for creating a new business model through predictive, efficient automation and scalable parallel processing capabilities.

The need for hardware-based artificial intelligence products was recognized as end-use applications demanded lower power and higher performance. Over the past few years, very few companies ventured into the development of these components. However, to gain a competitive advantage, vendors such as IBM Corporation and Intel Corporation have started manufacturing artificial intelligence chipsets to obtain high performance in scaling dynamic processes parallelly.