Python is an interpreted high-level and dynamic programming language for general-purpose programming. The syntax in Python allows programmers to code in fewer steps as compared to C++ or Java. Founded by the developer Guido Van Rossum in 1991, the language makes programming easy and fun.
The Python language has varied application in the software development firms such as in web frameworks and applications, gaming, language development, graphic design applications, prototyping etc. This provides the programming language a higher plethora over other languages used in the software industry. Some of its benefits or advantages are extensive support libraries, integration feature, improved programmer’s productivity and productivity.
The software development companies use Python language because of its fewer programming codes and versatile features. Nearly 14 percent of the programmers use it on the operating systems such as UNIX, Mac OS, Linux, and Windows. The programmers of leading companies use it as it has created a mark for itself in the industry with characteristic features like interactive, interpreted, dynamic, modular, object-oriented, high level, portable and extensible in C++ & C.
Despite having varied advantageous features, Python has still not made its place in some computing arenas such Enterprise Development Shops. This language, therefore, may not solve some of the enterprise solutions. Limitations of Python include difficulty in using other languages, weak in mobile computing, gets slow in speed, run-time errors, and underdeveloped database access layers.
Recently, some computer scientists from the University of Bonn developed a program that can look into the future. First, the program learns the typical order of activities, such as cooking, from audiovisual sequences. With this knowledge, it then correctly predicts in new conditions what the cook will do at which point in time.
The perfect butler has a special capability: He senses the wishes of his employer before they have even been uttered. Researchers want to teach computers something similar. They want a computer to predict the duration and timing of actions – minutes before they happen.
A robot in the kitchen, for example, could pass the ingredients immediately they are required, pre-heat the oven in time and even warn the cook if he is about to forget or overlook a preparation step. Meanwhile, the automatic vacuum cleaner knows that it is not needed in the kitchen, and therefore takes care of the living room.
Humans are very good at anticipating at anticipating other people’s actions. However, for computer this ability is still in its infancy. Having developed self-learning program that can estimate the duration and timing of future activities , computer scientists at the Institute of Computer Science at the University of Bonn are about to announce their success.
Many people consider themselves as either a night person or a morning person because they are aware through experience that they carry out certain jobs better at certain times of the day.
The human brain, however, has certain diurnal patterns of which people are unaware of and which still call for research. Researchers are still learning how best to optimize human efforts and time and it seems the time of day a person carries out certain types of tasks can affect performance.
According to a research carried out in 2016, students perform better in mathematics classes held in the morning than those held in the afternoon. Published in the Review of Economics and Statistics, Nolan Pope’s paper found a notable difference in math marks for students who took the subject in the morning as compared to students who studied it toward the end of the day.
The study involved about 2 million students in the Los Angeles Unified School District who went to schools with 6 class periods. Pope studied the students’ grades, class schedules, and exam scores from 2003 to 2009.
Another study by Ms. Velichka Dimitrova, a research student from the Royal Holloway University of London, examined academic achievement, absence rates, and class schedules at a Bulgarian high school for more than nine years. The study found that students who took mathematics s classes in the morning scored on average 7 percent more compared to students who attended mathematics classes in the afternoon.
Machine learning tools promises faster deliverance and improved accuracy of medical diagnostics. A team of researchers from Faculty of Applied Science and Engineering at University of Toronto has designed a new and effective training program for AI specifically created for diagnostics purposes,
By using large data set of X-ray images which shows medical conditions, the team trained the machine learning’s neural network to distinguish the ailments on other X-ray images. But producing massive amount of data about rare medical conditions is impossible. To solve this challenge, they generated artificial X-rays through deep convolutional generative adversarial network, also known as DCGAN. The combined organic and synthetic X-rays images promote better and deeper AI understanding of medical conditions.
The training resulted to raise of 20% analysis accuracy on common diseases and 40% analysis accuracy on rare conditions; another medical discovery through technology aiming to improve humanity’s health.