With the emergence of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence – helping software make sense of the messy and unpredictable real world. Machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.

Robots are becoming an important feature in our day-to-day life and also becoming a revolutionary based on innovative technologies. It is a platform in which the students are engaged in ground- breaking work in mechanism design, sensors, computer vision. Robotics is the engineering branch that deals with the conception, design, construction, operation, application, and usage of robots. Digging a little deeper, we see that robots are defined as an automatically operated machine that carries out a series of actions independently and does the work usually accomplished by a human. Artificial Intelligence (AI) increases human-robot interaction, collaboration opportunities, and quality. The industrial sector already has co-bots, which are robots that work alongside humans to perform testing and assembly. Advances in AI help robots mimic human behaviour more closely, which is why they were created in the first place. Robots that act and think more like people can integrate better into the workforce and bring a level of efficiency unmatched by human employees.

Deep learning is a kind of machine learning that uses artificial neural networks to solve complex problems. The term "deep" in deep learning refers to the use of numerous layers in neural networks, which allows models to gain hierarchical data representations and effectively capture complex patterns and features from large datasets. Deep learning systems are modeled after the neural networks in the neo cortex of the human brain, where higher-level cognition occurs. In the brain, a neuron is a cell that transmits electrical or chemical information. When connected with other neurons, it forms a neural network. In machines, the neurons are virtual—basically bits of code running statistical regressions. String enough of these virtual neurons together and you get a virtual neural network. The most advanced deep learning networks today are made up of millions of simulated neurons, with billions of connections between them, and can be trained through unsupervised learning.

Data Science is an interdisciplinary profession that draws on expertise from a wide range of disciplines, including statistics, mathematics, computer science, and subject-specific knowledge. It analyses organized and unstructured data using scientific methodologies, algorithms, and procedures to get valuable insights and knowledge. Data science is a field that analyzes data to extract meaningful insights. The data from which these insights are extracted can come from various sources, including databases, business transactions, sensors, and more. It is a broad term that encompasses many disciplines and skill sets, including statistics, machine learning, artificial intelligence (AI), computer programming, and more. Data science is about analyzing large amounts of unstructured information (such as text or images) to find patterns that will enable businesses to improve their operations and increase profits. Data science can get used in any industry, but it is more profound in certain fields like healthcare, retail, and finance, where large volumes of raw data are generated daily. It has become an essential part of businesses today.

AR is a technology that combines digital content with the physical environment to improve the user's perception and experience. It superimposes computer-generated images, videos, or information on top of the user's vision of reality. VR is a computer-generated, immersive simulation that recreates an artificial environment. Users can interact and interact with this virtual world as if they were physically there in it. VR is typically accomplished with the use of specialized headgear that display a 3D virtual environment and may include hand controllers for interaction. When comparing augmented reality vs. virtual reality, VR creates a completely simulated world while AR adds digital elements to the existing world. With augmented reality (AR), computer-generated images are superimposed over a user's real-world view. Virtual reality (VR), on the other hand, is a completely simulated environment.

Computer vision is the process of using artificial intelligence to enable computers to obtain meaningful data from visual inputs. Computer vision helps medical professionals improve patient outcomes and diagnostic accuracy. It can be used to create visualizations of organs and tissues to aid in diagnosis, and for early disease detection and patient monitoring. If AI enables computers to think, computer vision enables them to see, observe and understand. Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving or something is wrong with an image. Computer vision trains machines to perform these functions, but it must do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex.

Artificial Intelligence (AI), is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities. Generative AI techniques, which have advanced rapidly over the past few years, can create realistic text, images, music and other media. Advances in AI techniques have not only helped fuel an explosion in efficiency, but also opened the door to entirely new business opportunities for some larger enterprises. As a field of computer science, artificial intelligence encompasses machine learning and deep learning. Artificial intelligence has gone through many cycles of hype, but even to sceptics, the release of ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing (NLP). Today, generative AI can learn and synthesize not just human language but other data types including images, video, software code, and even molecular structures.