Top 10 Emerging Technologies in Computer Science You Must Know..
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5. Google Opens Up VR180 Standard for Virtual Reality Photos and Videos
Virtual reality can be a fun experience, and more phones than ever support VR capabilities. That makes mobile VR easy for people to start using. While VR is cool, the main issue is a lack of content. Google hopes to change that with its VR180 video format. It introduced VR180 last year, and the first consumer devices were announced in January 2018. Now, Google has published additional details so developers and hardware makers can gear up to make new VR180 products. Google’s VR180 video is based on the Spherical Video Metadata V2 standard, but there are a few additions to make it suitable for mobile VR. VR180 includes a so-called Camera Motion Metadata Track.
6. GPS maker Trimble’s AR foray for construction industry
Speaking to ET, Rajan Aiyer, Managing Director, Trimble, SAARC region said that engineers and supervisors at one of the largest construction companies in India used mixed reality tools to better interpret and interact with physical and digital information and their spatial relationships in near real-time.
Back in 2015, Trimble entered into a collaboration with Microsoft to bring Microsoft HoloLens wearable holographic technology to the Architecture, Engineering and Construction (AEC) industry.
7. Machine Learning
Machine learning is the science of getting computers to act without being explicitly programmed. In 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. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Some machine learning methods
Machine learning algorithms are often categorized as supervised or unsupervised.
- Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.
- In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
- Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.
- Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.
If you’re unsure about what Cloud Computing is, you are probably among the 95% of people that are already using cloud services, like online banking and social networks, but don’t realize it.
The “cloud” is a set of different types of hardware and software that work collectively to deliver many aspects of computing to the end-user as an online service.
Cloud Computing is the use of hardware and software to deliver a service over a network (typically the Internet). With cloud computing, users can access files and use applications from any device that can access the Internet.
An example of a Cloud Computing provider is Google’s Gmail. Gmail users can access files and applications hosted by Google via the internet from any device.
Unlike traditional computing where data is stored on your PC’s local hard drive, the data in the cloud is stored on many physical and/or virtual servers that are hosted by a third-party service provider. An example of a cloud computing file storage provider is Dropbox. Dropbox files can be accessed from any device via the Internet.
The Cloud Computing market continues to grow year after year because companies are becoming more aware of the cost saving benefits of adopting the cloud.
With Hardware services, companies are able to use the cloud service provider’s equipment (storage, hardware, servers, and networking components) instead of spending large amounts of capital on equipment.
9. Cyber Security
Cyber security or information technology security are the techniques of protecting computers, networks, programs and data from unauthorized access or attacks that are aimed for exploitation.
Major areas covered in cyber security are:
- Application security encompasses measures or counter-measures that are taken during the development life-cycle to protect applications from threats that can come through flaws in the application design, development, deployment, upgrade or maintenance. Some basic techniques used for application security are: a) Input parameter validation, b)User/Role Authentication & Authorization, c) Session management, parameter manipulation & exception management, and d) Auditing and logging.
- Information security protects information from unauthorized access to avoid identity theft and to protect privacy. Major techniques used to cover this are: a) Identification, authentication & authorization of user, b) Cryptography.
- Disaster recovery planning is a process that includes performing risk assessment, establishing priorities, developing recovery strategies in case of a disaster. Any business should have a concrete plan for disaster recovery to resume normal business operations as quickly as possible after a disaster.
- Network security includes activities to protect the usability, reliability, integrity and safety of the network. Effective network security targets a variety of threats and stops them from entering or spreading on the network. Network security components include: a) Anti-virus and anti-spyware, b)Firewall, to block unauthorized access to your network, c)Intrusion prevention systems (IPS), to identify fast-spreading threats, such as zero-day or zero-hour attacks, and d) Virtual Private Networks (VPNs), to provide secure remote access.
Analytics is a category tool for visualizing and navigating data and statistics.d Most analytics tools resemble a series of reports that can be customized and explored in a fluid user interface. The following are illustrative examples of analytics.
Tools that make predictions about the future from patterns in past data. Although no one can predict the future with certainty, such tools are useful when assumptions need to be made. For example, an airline that is considering investing in a new route may attempt to predict future travel demand for a destination
Analytics are often used to model customer behavior. For example, modeling the events that lead to a customer becoming brand loyal.
Analytics can be applied to any area of a business including strategy, operations and sales. For example, operations analytics might look at product cost, quality control and the throughput of resources such asproduction lines
Predictive analytics is often used to model business risks such as the credit risk associated with a particular customer.
Tools that look at current as opposed to historical data. For example, a bank might monitor credit card transactions in real time using an analytics tool.
Tools that report web traffic with the ability to explore data by factors such as cohort, demographics, client technology, geography, traffic source, page, campaign and conversions.