The Growth of Solar Power In India

In Past few years India is moving toward the renewable energy. Recently Isro has launched a Solar Calculator application, that allows for accurate calculation of the benefit of installing solar panels at any location in the country. The application allows for the calculation of solar energy potential, which is an important preliminary step for selecting appropriate locations to set up solar photovoltaic thermal power plants. The application has been developed by the Space Application Center facility of Isro, in Ahmedabad, at the request of the Ministry of New and Renewable Energy.The Solar Calculator is very useful for setting up photovoltaic solar panels. A number of locations are available in the drop down menu, it is possible to directly click on a location in a map, or navigate using precise geographic co-ordinates. The solar energy potential of that location is shown in kWh/m2 or mJ/m2. If application can also acquire the location using GPS. The solar potential is calculated using data processed from Indian satellites, including the Kalpana-1, the Insat-3D and the Insat-3DR.

The information of the solar energy potential is accompanied by other useful information for setting up solar panels. An internet connection is needed to make the necessary calculations. There are visualisations and tables included for the duration of the days over the course of a year. If there are obstruction to sunlight because of terrain, that is calculated as well, using a digital elevation model. There is a suggestion for the optimum angle to set up the solar panels as well.The application is available On the web and for the Android Platform. The Android application is a small file, less than 2 MB in size, but we were not able to get it to work. The application is not yet available on the Play Store or any other store. The solar calculator app provides similar functionality to Google’s Project Sunroof.

After few day of launching Solar Calculator Android App, ISRO successfully demonstrated solar electric hybrid car. The effort was a initiative that combined expertise from the fields of automotive, electrical, mechanical & chemical engineering. A solar panel to fit on the roof of the car was developed, along with an integral gearbox, control electronics for the battery & solar panel & a conversion kit for fitting an electric motor to a vehicle with an internal combustion engine.

Energy was supplied to the car through Lithium ion batteries, with a high power super-capacitor to meet the power demand during conditions that required peak torque. The high capacity solar panel mounted on the roof of  the vehicle continuously geared up the battery,as long as sunlight was available. The solar hybrid vehicle was successfully demonstrated in a test drive, including an uphill drive.In these efforts ISRO promotes the use of renewable energy in various sectors which will going to act as an important resource in future.

Artificial Intelligence Will Going to Play an Important Role in Future

The booming growth of machine learning and artificial intelligence (AI), like most transformational technologies, is both exciting and scary. It’s exciting to consider all the ways our lives may improve, from managing our calendars to making  medical diagnoses, but it’s scary to consider the social and personal implications — and particularly the implications for our careers. As machine learning continues to grow, we all need to develop new skills in order to differentiate ourselves. But which ones?

It’s long been known that AI and automation/robotics will change markets and workforces. Self-driving cars will force over three thousand truck drivers to seek new forms of employment, and robotic production lines like Tesla's will continue to eat away at manufacturing jobs, which are currently at 12 million and falling. But this is just the beginning of the disruption. As AI improves, which is happening quickly a much broader set of “thinking” rather than “doing” jobs will be affected. We’re talking about jobs, that, until the last few years, we couldn’t imagine being done without the participation of an actual, trained human being.  Jobs like teacher, doctor, financial advisor, stockbroker, marketer, and business consultant.

We can look at any number of occupations to see that this holds true.  Doctors perform tests, analyze the results, interpret the results to make a diagnosis, plan a course of treatment, and then work with the patient to make this treatment plan a reality.Financial advisers gather and analyze data about their clients and potential investment vehicles, interpret the implications given a variety of factors such as risk tolerance, recommend an investment strategy, and help their clients carry this strategy out over time.

Some people may say that we will never trust machines with important decisions such as the management of our health and money, but this is twentieth century thinking.  But a new generation is engaging with smart machines that they trust, and often prefer.  Further, it’s hard for anyone to argue with results.  IBM’s Watson is already cracking medical cases that stump doctors.It takes a person, however, to sit with a patient, understand their life situation (finances, family, quality of life, etc.), and help determine what treatment plan is optimal.

Don’t fight the progress of technology. Machine learning and AI have the ability to improve outcomes and lower cost — so don’t fight the robots. Welcome the change in our industry and work to make it fruitful and complementary.What we have to offer — what we can do better than any smart machine — is relate to the people around us.If we can be an outstanding motivator, manager, or listener, then we will still have a part to play as technology changes our industry.

Machine Learning

Nowadays, Machine Learning is the most popular growing technology. Machine Learning is a type of Artificial Intelligence that provides computers with the ability to learn without being explicitly programmed. Machine Learning focused on the development of computer programs that can change when exposed to new data.

The process of Machine Learning is similar to that of data mining. Both system search through data to look for patterns. However extracting data for humans comprehension as in the case in data mining application machine learning uses that data to detect pattern in data & adjust accordingly. Machine Learning algorithms are often categorized as being Supervised or Unsupervised. Supervised algorithms can apply what has been learned in the past to new data while Unsupervised algorithm can draw inference from data sheet.

                          

 How Machine Learning is different from Artificial Intelligence?

AI is a branch of computer science attempting to build machines capable of intelligent behavior, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need  machine learning experts to make them truly intelligent. Google currently working on developing this machine learning; desperately pushing computers to learn the way a human would in order to progress what many are calling the next revolution in technology – machines that 'think' like human.

Over the past decade, machine learning has given us self driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. But how does it work?

Let’s take a very simplified example. When you make a typo, for instance, while searching in Google, it gives you the message: "Did you mean..."? This is the result of one of Google's machine learning algorithms; a system that detects what searches you make a couple seconds after making a certain search.

                          

For example, suppose you were searching for 'WIRED' on Google but accidentally typed 'Wored'. After the search, you'd probably realize you typed it wrong and you'd go back and search for 'WIRED' a couple of seconds later. Google’s algorithm recognizes that you searched for something a couple of seconds after searching something else, and it keeps this in mind for future users who make a similar typing mistake. As a result, Google 'learns' to correct it for you. While this is a very basic example, data scientists, developers, and researchers are using much more complex methods of machine learning to gain insights previously out of reach. 

AI is the science and machine learning is the algorithms that make the machines smarter. "AI is going to bring major shifts in society through developments in self-driving cars, medical image analysis, better medical diagnosis, and personalized medicine. And it will also be the backbone of many of the most innovative apps and services of tomorrow". But in order for AI to progress, machine learning must make big jumps in terms of performance, and this is rarely possible in the traditional high-performance computing world, where problems are well-defined and optimization work has already been happening for many years.

                             

Machine learning algorithms still have room for improvement, and that’s why a lot of the large technology companies are making it a central focus to their strategy, and working tirelessly to make it more intelligent, in order to push forward and create the next innovation, such as completely autonomous and 100 per cent safe self-driving cars.