Emergingtreands class11 cs
- 1. EMERGING TRENDS IN COMPUTER SCIENCE
- 2. ARTIFICIAL INTELLIGENCE (AI) • Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning. • Today, Artificial Intelligence is a verypopular subject that is widely discussed inthe technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it’s not the future – it is the present. • With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future. • Unlike general perception, artificial intelligence is not limited to just IT or technology industry; instead, it is being extensively used in other areas such as medical, business, education, law, and manufacturing. • Netfix,Tesla,flying drones for delivery of goods are applications based on AI •
- 3. CONCEPT OF AI- • Artificial Intelligence applications simulate the natural intelligence of human beings into machines, thus making them behave intelligently. • An intelligent machine is supposed to imitate some of the cognitive functions of humans like learning, decision making and problem solving. • In order to make machines perform tasks with minimum human intervention, they are programmed to create a knowledge base and make knowledge base and make decision based on it • AI system can also learn from past experiences or outcomes to make new decisions.
- 4. MACHINE LEARNING • MachineLearning is a system that can learn from example through self-improvement and without being explicitlycoded by programmer. The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results. • Machine learning is an applicationor subset of AI that allows machines to learn from data without being programmed explicitly.It comprises algorithms that use data to learn on their own and make predictions. These algorithmscalled models, are first trained and tested using a training data and testing data,respectively. After successive trainings, once these models are able to give results to an acceptablelevel of accuracy, they are used to make predictions aboutnew and unknown data.
- 5. NATURAL LANGUAGE PROCESSING • The predictive typing feature of search engine that helps us by suggesting the next word in the sentence while typing keywords and the spell checking features are examples of NaturalLanguage Processing (NLP). • It deals with the interactionbetween human and computers using humanspoken languages, such as Hindi, English, etc. In fact it is possible to search the web or operate or control our devices using our voice. • All this has been possible by NLP. An NLP system can perform text-to speech and speech-to-text conversion as depicted in Figure 3.1. Machine translation isa rapidlyemerging field where machines are already able to translate texts from one language to another with fair amount of correctness. Another emerging applicationarea is automated customer service where a computersoftware can interact with customers to serve their queries or complaints.
- 6. IMMERSIVE EXPERIENCES Immersive experiences allow us to visualise, feel and react by stimulating our senses. It enhances our interaction and involvement, making them more realistic and engaging. There are two types of immersive experiences, one being when you are actually in a physical environment-AUGMENTED REALITY The other one -where you are shown around a real or imagined environment via desktop, tablet, mobile or via VR ( Virtual Reality) headset.
- 7. • Augmented reality (AR) adds digital elements to a live view often by usingthe camera on a smartphone.Examplesofaugmented realityexperiences include Snapchat lenses and the game Pokemon Go. • Virtual reality (VR) implies a complete immersion experience that shuts out the physical world.Using VR devices such as HTC Vive, Oculus Rift or Google Cardboard,users can be transported into a number of real-world and imagined environmentssuch as the middle of a squawkingpenguin colony or even the back of a dragon. • In a Mixed Reality (MR) experience, which combines elements of both AR and VR, real-world and digital objects interact.Mixed realitytechnologyis just now startingto take off with Microsoft’s HoloLens one of the most notable earlymixed reality apparatuses. • Extended Reality (XR) is an umbrella term that covers all of the various technologies that enhance our senses,whether they’reprovidingadditional information about the actual world or creating totallyunreal,simulated worlds for us to experience. It includes Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies.
- 8. EXAMPLES OF AR AND VR https://youtu.be/SCrkZOx5Q1M
- 10. ROBOTICS • A robot is basicallya machine capableof carrying out one or more tasks automaticallywith accuracy and precision. • Unlike other machines, a robot is programmable by a computer, which means it can follow the instructions given through computer programs. • Robots were initiallyconceptualisedfor doing repetitive industrialtasks that are boring or stressful for humans or were labourintensive.Sensors are one of the prime components of a robot. Robot can be of many types, such as wheeled robots, legged robots, manipulatorsand humanoids. • Robots that resemble humans are known as humanoids.Robots are being used in industries, medical science, bionics, scientific research, military, etc. • Some examples are: • NASA’s Mars Exploration Rover (MER) mission is a robotic space mission to study about the planet
- 11. BIGDATA
- 12. BIG DATA –WHAT CHALLENGES IT PUTS UP? • BIG DATA cannot be processed and analysed using traditionaldata processing toolsas the data is not only voluminous,but also unstructured like our posts, instant messages and chats, photographsthat we share through varioussites, our tweets, blog articles, news items, opinionpollsand their comments, audio/videochats, etc. • Big Data not only represents voluminousdata, it also involves variouschallenges like integration, storage, analysis,searching, processing, transfer, querying and visualisation ofsuch data. Big data sometimes hold rich information and knowledge which is of high business value,and therefore there is a keen effort in developingsoftware and methods to process and analyse big data.
- 13. CHARACTERISTICSOF BIG DATA • Volume The most prominent characteristic of big data is its enormous size. If a particular dataset is of such large size that it is difficult to process it with traditional DBMS tools, it can be termed as big data. • Velocity It represents the rate at which the data under consideration are being generated and stored. Big data has an exponentially higher rate of generation than traditional data sets. • Variety It assertsthat a dataset has varied data, suchas structured,semi-structuredand unstructureddata. Some examples are text, images, videos, web-pages and so on. • Veracity Big data can be sometimes inconsistent,biased, noisy or there can be abnormality in the data or issues with the data collection methods. Veracity refers to the trustworthinessof the databecause processing such incorrect data can give wrong results or mislead the interpretations. • Value Big data is not only just a big pile of data, but also possess to have hidden patterns and useful knowledge which can be of high business value. But as there is cost of investment of resources in processing big data,we should make a preliminary enquiry to see the potential of the big data in terms of value discovery or else our efforts could be in vain.
- 15. On the farm, automatically getting informationaboutthe soil moisture can tell farmers exactly when their crops need to be watered. Instead of watering too much (which can be an expensive over-use of irrigation systems) or watering too little(which can be an expensive loss of crops), the farmer can ensure that crops get exactly the right amount of water. More money for farmers and more food for the world! IOT-INTERNET OF THINGS The Internet of Things (IoT) is a system of interrelated computingdevices, mechanical and digital machines,objects, animals or people that are provided with unique identifiers and the abilityto transfer data over a networkwithout requiring human-to-human or human-to-computer interaction. • In the Internet of Things,all the things that are being connected to the internet can be put into three categories: • Things that collect information andthen send it. • Things that receive informationand then act on it. • Things that do both. • And all three of these have enormous benefits that feed on each other
- 16. CLOUD COMPUTING • Cloud Computingprovides us means of accessing the applicationsas utilities over the Internet. • It allows us to create, configure, andcustomize the applications online. • Applicationssuch as e-mail, web conferencing, customer relationship management (CRM) execute on cloud. •
- 17. • The term Cloud refers to a Network or Internet. In other words, we can say that Cloud is something, which is present at remote location. Cloud can provide services over public and private networks, i.e., WAN, LAN or VPN. WHAT IS CLOUD?
- 18. WHAT IS CLOUD COMPUTING? • CloudComputingrefers to manipulating, configuring, and accessing the hardware andsoftware resources remotely. It offers onlinedata storage, infrastructure, and application. • Cloud computing offers platform independency, as the software is not required to be installedlocallyon the PC. Hence, the Cloud Computingis making our business applicationsmobileand collaborative .
- 19. DEPLOYMENT MODELS • Deployment models define the type of access to the cloud, i.e., how the cloud is located? Cloud can have any of the four types of access: Public, Private, Hybrid, • and Community.
- 20. SERVICE MODELS • Cloud computing is based on service models. These are categorized into three basic service models which are - • Infrastructure-as–a-Service (IaaS) • Platform-as-a-Service (PaaS) • Software-as-a-Service (SaaS)
- 21. IAAS • Infrastructure-as-a-Service (IaaS) • IaaS provides access to fundamental resources such as physical machines, virtual machines, virtualstorage, etc.
- 22. ADVANTAGES OF CLOUD COMPUTING • Platform-as-a-Service (PaaS) • PaaS provides the runtime environment for applications,developmentanddeploymenttools, etc. • Software-as-a-Service (SaaS) • SaaS model allows to use software applicationsas a service to end-users.