You are in an era where you can see the fast evolution of technology from being reactive to proactive. You are witness to the time when computers are slowly taking up space in every aspect of your life. You will have some form of electronic intelligence around you. Why is the song you hear on your favourite music app driven by algorithms that use artificial intelligence? What is this artificial intelligence, and in what other ways does this technology help?

We've been hearing the term Artificial Intelligence a lot in the last decade, some of us still suffer from the lack of a proper definition of AI. People usually define AI on the basis of different activities that they witness in their day to day lives, e.g, the computers playing chess or automated systems that drive a car, but on the other hand, we also use terms like human intelligence and universal intelligence. So the question arises, what is the differentiating factor between artificial general intelligence and artificial intelligence? Here's an exclusive conversation between neuroscience researcher and AI veteran Dileep George (Founder & CTO, Vicarious.ai) If one were to ask, what was the original goal of AI, the answer would be that the original goal of AI was to create software algorithms that are equivalent to or even smarter than human beings.

Artificial Intelligence has been around for a while now and we have witnessed some of the breakthrough advancements in this technology. Now, researchers are making efforts to evolve artificial intelligence to act like natural intelligence. It would be a long way to go before that happens but efforts are being made now. Here are two examples of initiatives to improve AI. A new paper from the University of Waterloo, Ontario suggests that AI models should be able to learn in "less than one" shot, or LO-shot.

Artificial Intelligence has made an impact on the global financial services ecosystem and has reshaped the building blocks of success. The finance sector has been the pioneer to understand the importance of AI and gainfully use it to enhance numerous capabilities, improving recommendations, customer experiences, and efficiencies through automation. From handling risk management to credits & trading decisions, AI has accelerated the growth of leading financial verticals. The demand for to an extent that an artificial intelligence course is high in demand in the current fintech market. A recent Power Ahead Series webinar on "Trends in AI for Financial Services" shares insights on the latest AI trends and how the Finance industry is at the forefront of scaling the adoption of AI.

Machine learning becomes engaging when we face various challenges and thus finding suitable datasets relevant to the use case is essential. Flexibility refers to the number of tasks that it supports. For example, Microsoft's COCO( Common Objects in Context) is used for object classification, detection, and segmentation. Add a bunch of captions for the same, and we can use it as a dataset for an image caption generator as well. Well, when we are just starting, we shall be working with some of the small and standard machine learning datasets like the CIFAR-10, MNIS, Iris, etc.

Artificial Intelligence has multiple applications in the healthcare domain. We have also seen many AI solutions being developed to identify symptoms of COVID-19 among patients. Here's one such case of AI-based screening of visitors at Mumbai railway stations to identify COVID-19 symptoms. Also, the application of Artificial Intelligence in adjusting insulin dose to control glucose levels among Type I Diabetes patients. Body-screening facility "FebriEye thermal cameras" have been set up at Chhatrapati Shivaji Maharaj Terminus and Lokmanya Tilak Terminus in Mumbai to scan passengers for COVID-19 symptoms.

Perfection is achieved only by making mistakes. Same holds true when you work with machine learning algorithms to build models. Most of the time, it is not obvious how to proceed and navigate at the beginning and professionals are bound to make mistakes, especially those who are a novice in the domain. Here is a list of most common mistakes that are committed while working with machine learning algorithms. Hopefully, you will learn and draw valuable insights from this article that you could apply in your work.

While working on different Machine Learning techniques for Data Analysis, we deal with hundreds or thousands of variables. Most of the variables are correlated with each other. Principal Component Analysis and Factor Analysis techniques are used to deal with such scenarios. Principal Component Analysis (PCA) is an unsupervised statistical technique algorithm. PCA is a "dimensionality reduction" method.

While each country is working its way around curbing the outbreak of the Coronavirus through quarantines, lock-downs, social distancing and more, technologies like Artificial Intelligence are becoming beyond useful to monitor and respond to the crisis. Artificial Intelligence has assisted in various ways to fight COVID-19, including diagnosis of the disease, accelerated research, development of drugs, and vaccinations out of the many. However, considering that social distancing has been recognised as a fundamental measure to slow down the spread of the pandemic, an Indian tech company has developed a thermal-vision AI-powered camera that not only measures forehead temperatures but also detects facemask and social distancing violations. Developed by Vehant Technologies -- a pioneer in AIML based physical security, surveillance and traffic monitoring solutions, FebriEye deals with a thermal screening system specifically created with analytics that monitor facemask and social distancing. This AI-powered camera will prove to be useful at entrances to public events, airports, metro stations, manufacturing plants, buildings, hotels, commercial complexes, shopping malls, as well as gated societies.

There are diverse topics in the field of Artificial Intelligence and Machine learning. Research is required to find optimal solutions in this field. In Deep learning, various neural networks are used but optimization has been a very important step to find out the best solution for a good model. In the field of AI, many complex algorithms have been used. It is also important to find out an optimal solution.