What’s new on Information and Communication Technology (ICT)

What’s new on ICT

This blog shows the new techniques on ICT also declares some terms according to many sites declarations and my personal view. It takes three main fields which are Artificial Intelligence , Android and Big Data Science.

Although for decades several technologies have been charting the landscape of higher education and are in the continuous process of development, some latest technologies are changing the way education would be practiced in the future. In this section we would recommend for these institutes to invest in the following emerging technologies that will impact education.

What is ICT ?

Stands for information and communication technologies , defined as diverse set of technological tools and resources used to communicate, and to create, disseminate, store, and manage information.

These technologies include computers, the Internet, broadcasting technologies (radio and television), and telephony.

What’s new on Artificial Intelligence

By building computer systems that begin to approximate these capacities, the researchers believe they can help answer questions about what information-processing resources human beings use at what stages of development. Along the way, the researchers might also generate some insights useful for robotic vision systems.

“The common theme here is really learning to perceive physics,” Tenenbaum says. “That starts with seeing the full 3-D shapes of objects, and multiple objects in a scene, along with their physical properties, like mass and friction, then reasoning about how these objects will move over time. Jiajun’s four papers address this whole space. Taken together, we’re starting to be able to build machines that capture more and more of people’s basic understanding of the physical world.”

Three of the papers deal with inferring information about the physical structure of objects, from both visual and aural data. The fourth deals with predicting how objects will behave on the basis of that data.

Two-way street:

Something else that unites all four papers is their unusual approach to machine learning, a technique in which computers learn to perform computational tasks by analyzing huge sets of training data. In a typical machine-learning system, the training data are labeled: Human analysts will have, say, identified the objects in a visual scene or transcribed the words of a spoken sentence. The system attempts to learn what features of the data correlate with what labels, and it’s judged on how well it labels previously unseen data.

In Wu and Tenenbaum’s new papers, the system is trained to infer a physical model of the world — the 3-D shapes of objects that are mostly hidden from view, for instance. But then it works backward, using the model to resynthesize the input data, and its performance is judged on how well the reconstructed data matches the original data.

For instance, using visual images to build a 3-D model of an object in a scene requires stripping away any occluding objects; filtering out confounding visual textures, reflections, and shadows; and inferring the shape of unseen surfaces. Once Wu and Tenenbaum’s system has built such a model, however, it rotates it in space and adds visual textures back in until it can approximate the input data.[4]

Big Data Analytics:

Data is one of the most valuable assets a company has at its disposal. Valuable insights can be gained from data to drive better business decisions, and the technology that enables the collection and measurement of this detailed data is making it easier than ever to leverage these insights for the development of more intelligent products, services and manufacturing processes.

The prospect of integrating big data-fueled insights into products and workflows is enticing, and it can be straightforward if domain experts – scientists and engineers – are provided with the appropriate tools. Software analysis and modeling tools, such as MATLAB, enable domain experts to accomplish tasks previously exclusive to data scientists, including accessing and combining multiple datasets, creating predictive models and ultimately bringing previously hidden insights to their organizations’ decision makers.

Take Big Data Down to Size:

Engineers and scientists need scalable tools that provide access to a wide variety of systems and formats to efficiently capture and incorporate the benefits of big data (Figure 1). This is especially important because companies often use more than one type of system or format to store and manage data. For example, sensor or image data stored in files on a shared drive may need to be combined with metadata stored in a database, or in certain instances, data of many different formats must be aggregated to develop a predictive model.

Understand "What Is in the Data?"

To understand the behavior of a system before developing predictive models, domain experts need scalable tools to access and explore big data before employing machine learning techniques.

Software analysis and modeling tools can simplify the process of observing, cleaning and effectively working with big data. These tools also can help domain experts decipher which algorithms should be used across large datasets when creating a model using machine learning techniques. Before domain experts create a model or theory, it’s important to first understand what is in the dataset, as that may have a major impact on the final result.

Often software can help decipher the data and identify:

Slow-moving trends or infrequencies spread across the data
Bad or missing data that needs to be cleaned before a valid model or theory can be established
Data that is most relevant for a theory or model

Additionally, big data tools are adept at finding additional information that may be derived for use in later analysis and model creation.[3]

Android & Kotlin:

Kotlin for Android is production-ready. From startups to Fortune 500 companies, developers are already using Kotlin to build their apps. Developers from Pinterest, to Expedia, to Basecamp -- and many others -- are finding their use of Kotlin is increasing productivity and their overall developer happiness levels.

Update on Kotlin for Android

02 November 2017

Posted by James Lau, Product Manager (twitter.com/jmslau)

Today is the beginning of KotlinConf. It's been almost 6 months since we announced Kotlin as a first-class language for Android at Google I/O. During this period, the number of apps on Google Play using Kotlin has more than doubled. More than 17% of the projects in Android Studio 3.0 are now using Kotlin. We are really excited about the strong momentum, and we are thrilled that Android developers all over the world are discovering the joy of Kotlin programming.

With the recent release of Android Studio 3.0, there is now a stable version of our IDE that has Kotlin support built-in. With Support Library 27, we have started adding nullability annotations to make the APIs friendlier to use in Kotlin. We recently published the Android Kotlin Guides on GitHub to provide some guidance for Android Kotlin style and interop. We have also been porting some of our Android samples to Kotlin, and we are adding Kotlin to our official documentation.

Android Studio 3.0:

Last week, we released Android Studio 3.0 on the stable channel. This is the first stable release of Android Studio that has Kotlin support built-in. Building on the strength of IntelliJ's Kotlin support, many critical IDE features like code completion and syntax highlighting work well for Kotlin. You can choose to convert Java code to Kotlin by using Code → Convert Java File to Kotlin File, or you can convert snippets of code just by pasting Java code into a Kotlin file.

Project and code templates have also been updated with Kotlin support. When you create a new project or add a new code file, you can choose Kotlin as one of the language options.

The tooling experience with Kotlin is by no means perfect yet. We are aware of several known issues, and we will continue to improve the IDE support for Kotlin in future releases.

Android Kotlin Guides:

There are two separate Android Kotlin Guides:

Style guide - details a set of rules and coding standards that Google recommends when writing Kotlin for Android. The guide addresses naming conventions, formatting, structure of the source contents, and much more.
Interop guide - provides a set of rules for creating APIs in the Java and Kotlin programming languages, so that the consuming code in the other language will feel idiomatic.

We intend these guides to be living documents and will evolve them over time. They are hosted on GitHub and we welcome your contributions.

Nullability Annotations:

Null-safety is an important feature of the Kotlin language. It helps developers avoid NullPointerExceptions and improves the quality of their apps. Null-safety is a bit more complicated when using Java code from Kotlin. Since any reference in Java may be null, Kotlin's requirement for strict null-safety becomes impractical for Java objects. Types declared in Java that do not contain nullability annotations are called platform types - this means the Kotlin compiler does not know whether it is nullable or not. When calling methods with variables of platform types, the Kotlin compiler relaxes null-safety checks. That means the overall null-safety of your app is weakened.

To let developers take more advantage of Kotlin's strict null-safety, we have started adding nullability annotations in Support Library 27. The Support Library contains a huge API surface area, and we will continue to expand the nullability annotation coverage in the next several releases. In addition, we will also be adding nullability annotations to other Android APIs over time.

While the Kotlin adoption growth is fantastic, our commitment to the Java and C++ programming languages remains unchanged. We've added Java 8 language features support in Android Studio 3.0, and we've added more Java 8 language APIs in Android Oreo. We are also continuing to improve our support for C++17 in the NDK. So even if you are not using Kotlin, your language support will continue to improve. [1]

References :

Key Words :

Ict.
Artificial intelligence.
Data analytics.
Kotlin.
Nullability.

Written By:
Tasneem Mohamed

Created on 13 March 2018 Tagged ICT - Big Data - Artificial Intelligence