Top 10 Artificial Intelligence Applications Example that we see in our daily life

 Today, Artificial Intelligence technology is the most trending technology that is widely talked over in Enterprises. AI itself is a broader concept that everyone needs to understand. AI now become the big tech giant that every industry focused on. AI has been growing for the past few decades and enabling businesses with advanced features by giving machines the power to think and perform better tasks in the computing world. Because of this AI & Machine Learning adoption, organizations have removed most of their repetitive tasks and focused on solving special and important issues. As result, the businesses are able to provide better results and enhancements. Many AI experts argue that AI is the future but if you look around then you will get AI is not the future, AI is the present. We all know that this advanced technology is at an early stage, and a significant number of businesses are investing in machine learning, indicating rapid growth in AI products and applications shortly. Artificial intelligence is not limited to IT technology but is used widely to simplify human tasks in many fields, such as education, medicine, the supply chain, law, business, and manufacturing. Artificial intelligence technology is also used to make science fiction films. 

The AI industry will rise to $190.61 billion by 2025 at a CAGR of 36.62%, according to the Markets report. IDC estimates that by 2022, 75% of commercial business applications will use AI. Through this, you can assume that the rising demand for this technology is completely unpredictable. 

You have to believe that Artificial Intelligence is affecting our choices and lives every day and here we discuss Artificial Intelligence Application examples that we see in our daily life.  

 

Commuting

The artificial intelligence-enabled travel aids provide more than charts. Using AI to track traffic, Google maps and other travel apps send you real-time traffic and weather conditions and recommend ways to avoid gridlock. The car you drive to work could have driver-assist technology, and you may order a self-driving car through Google's sister company, Waymo, to drive you to and from work in locations. Also Uber Machine Learning Head reported Uber's use of Machine Learning on UberEats for estimated meal delivery times, ETA for trips, fraud detection, and optimum pickup position computing.

Smart Cars and Drones 

Most AI technologies are used by manufacturers of smart cars and drones. Only a few years ago, it was a dream to use a full automatic car, but now companies like Tesla and Waymo have made so much progress that we already have a forefront of semi-automatic vehicles on the road. Tesla autopilot cars are a perfect example of how AI, with features such as predictive capabilities, self-driving, and technical advancement, impacts our everyday lives. The Tesla car is getting more smarter day by day.

When it comes to drone delivery programs, Companies like Amazon and Walmart are making heavy investments to make it a reality as soon as possible. If you think that is far-fetched, remember that active drone systems are already being used by military forces all over the world.

Open your phone with Face-ID

Every morning, one of the first things many people do is reach for their smartphones. And when biometrics such as face ID are used to unlock your smartphone, it uses artificial intelligence to allow the feature. FaceID for Apple can be viewed in 3D. It lights your face up and puts on it 30,000 invisible infrared dots and captures an image. Then it uses AI algorithms to compare the face scan what it has stored about your face to determine if the person trying to unlock the phone is you or not. It also function even when users wear accessories such as glasses, scarves, make-up, and facial hair. Other phone manufacturers, using Facial Recognition apps, have followed suit and launched their take on Face Unlock technology.

Travel & Navigation

We could have used mapping systems or others at some stage in our lives to find our way across. For many, it's a part of everyday life. You use AI-enabled services to travel from one location to another, whether it is using maps for navigation or using a taxi-hire service like Uber. AI is used by Google, Apple, and several other navigation-related service providers to interpret the scores of data collected and provide you with information that helps navigate as well as get live traffic alerts, allowing you to drive more efficiently.

Smart Home

In everyday life, a smart home appliance is also the best example of artificial intelligence. AI-based tools can be used by consumers of smart home devices to track their actions so that they can change settings to make the experience as friction-free as possible. 

Smart thermostats devices will be available that adjust the temperature based on your choices, AI applications that save energy by automatically switching on/off the lights based on human presence, smart speakers, applications that change the contrast of the light based on the time of the day & Night etc.

Video Games

One of the first industries to embrace AI technology is the video game industry, and it has greatly experienced the use of artificial intelligence in everyday life. This AI integration is introduced with a simple level that players can play and enjoy, but fortunately, it has achieved a level that can't even be imagined. Players can easily experience this in the form of: 

AI-controlled Non-Playable Characters (NPCs) that react to player actions dynamically, 

Procedural content creation in conjunction with Machine Learning for creating automatic new game environments, Modeling of player experience for dynamically balancing gameplay tasks based on player skills. For Example,  if you play racing games, you will be playing against AI bots.

Security & Surveillance 

When we were debating the advantages of using a wider security and surveillance system, everybody understood that Artificial Intelligence played an important role in it. People don't monitor several monitors with feeds from thousands of cameras at the same time, so using AI-based software will make perfect sense. With software technologies like facial recognition and object recognition AI cameras quickly recognize the unusual behaviours and provide alert signal. Through this we can easily identify the unusual patterns that may harm us. 

Vehicle Recognition System 

Vehicle recognition systems use a camera to capture the licence plates of moving vehicles, rapidly scan different character details based on image processing on the licence plate, and generate data. These systems often produce data other than character information from supplementary information (e.g., vehicle type). Various vehicle management systems use the vehicle data such as parking lot management, vehicle access control at gates, and traffic census. 

E-Payment 

Artificial intelligence has made it possible from the comfort of your home to deposit cheques.  AI is proficient in handwriting deciphering, making it possible to process online cheques. An example of artificial intelligence is also the way that fraud can be identified by analysing the credit card spending habits of users.

Music and Video Streaming

While YouTube suggestions and suggested playlists on music apps have been around for some time, as time goes on, you can see these suggestions becoming more relevant to your likes. This is AI at work, which monitors the videos and music you listen to most and offers recommendations for the same thing. Music services apps use Artificial Intelligence to track your listening habits. Then, they use the details to recommend other songs you would like to hear.

Conclusion 

The few best examples of Artificial Intelligence in everyday life that are omnipresent and simplify the vast majority of our daily lives. This is proof that AI really transforms our lives by making us smarter and more efficient in order to concentrate on the real challenges.

Also Read: Top 12 Best Machine Learning Datasets for Practicing 

So as above you got to know Top Artificial Intelligence Applications Example that we see in our daily life, Apart from this if you want to start your career in Artificial Intelligence and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Artificial Intelligence Training Institute in Bangalore and also the best Machine Learning Training Institute. They provide highly skilled trainers having 10+ years of industry experience in these filed. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies and also provide internship facilities to their students.

 

For more information contact us:

Visit: www.nearlearn.com

Top 12 Best Machine Learning Datasets for Practicing

 In this technological era, human-related tasks are mostly done with the help of machines. All of these things are now possible only through Machine learning and Artificial Intelligence. It is like a blessing for us that we make all our tasks easier and efficient with the help of these technologies. Nowadays it becoming the most demanding technology that everyone wants to implement this technology in his businesses/industries.  According to the report, the global machine learning market valued at $1.59 Billion in 2017 and expected to rise to $20.84 Billion in 2024. Through this report, you can assume how it will bring revolution in coming years.

 The software for machine learning is just as good as the training sets. For testing purposes, machine learning datasets are typically used. Collecting homogeneous data is a dataset. The dataset is used to train and assess the model of machine learning. Building an effective and secure infrastructure plays a vital role. If your dataset is noise-free and standard, better accuracy will be given by your system. However, At present, we're enriched with various datasets. It can be data relevant to companies, or it can be medical data, and many more. However, the actual problem is to figure out the relevant ones according to the specifications of the method.

Here let us take a look at best machine learning datasets for practicing:

 Image Datasets for Computer vision

 ImageNet: ImageNet is one of the strongest datasets of Machine Learning based on Computer Vision out there. It has more than 1,000 object types or individuals with several pictures associated with them. ImageNet's Large-Scale Visual Recognition Challenge (ILSVRC), which created many of the latest state-of-the-art Neural Networks, even faced one of the biggest ML challenges.

Google’s Open Image: 9 million URLs for over 6,000 types of classified public images. Under creative commons, each picture is licensed.

 Natural Langauge Processing

 Amazon Reviews: A set from the last 18 years with more than 35 million ratings. This includes items such as reviews, ratings in plain text, and user details. It also provides full information on the product for reference.

 Wikipedia Links Data: The full authority of Wikipedia including 4 million articles containing 1.9 billion words. Your search choices are diverse and include searches for both words and phrases as well as paragraph sections. 

 Facial Recognition Datasets

 The facial image dataset is based on both male and female facial images. Machine learning and deep learning algorithms can be carried out to detect gender and emotion using the facial image dataset. It has a variety of details, such as context and scale variations and variation of expressions.

UMDFaces Datasets: Both still and video photos are included. The dataset is annotated and contains over 8,000 subjects with about 367,000 faces.

 Public Government Datasets

 Data.gov: This site makes it possible for multiple US government agencies to download data. Data can vary from govt. budgets to school performance scores. However, be warned: much of the knowledge needs additional research.

EU Open Data Portal: In fields as diverse as economics, jobs, research, the environment and education, the EU Open Data Portal offers access to open data released by EU institutions.

US Healthcare Data: In this dataset, the FDA drug database and the USDA Food composition database have collected data on population health, diseases, drugs, and health plans.

Iris Datasets

Another dataset suitable for linear regression, and hence for beginner machine learning projects, is the Iris dataset. This includes details on the scale of various parts of the flowers. All these sizes are numerical, making it easy to get started and no preprocessing is needed. The target is pattern recognition, based on different sizes, classifying flowers.

Finance and Economics Datasets

World Bank Open Data: Datasets include the composition of the population and a wide range of global economic and development indicators.

IMF Data: Open data collection from the International Monetary Fund on topics such as debt rates, prices of goods, international markets and foreign exchange reserves.

Google Trends: Examine and interpret internet search activity information and trending news stories worldwide.

Financial Times Market Data: Up-to-date information, including stock price indices, commodities and foreign exchange, on financial markets from around the world.

Autonomous Driving Datasets:

Comma.ai: It includes information such as the speed, acceleration, steering angle, and coordinates of the GPS of a vehicle.

WPI Datasets: Traffic signs, pedestrian and lane detection datasets.

Oxford’s Robotic Car: More than 100 repetitions of the same road, collected over a span of a year, via Oxford, UK. In addition to long-term changes such as construction and roadworks, the dataset records various combinations of weather, traffic and pedestrians.

MIT AGE Lab: A sample of the 1,000+ hours of data obtained at AgeLab for multi-sensor driving.

Open Datasets Finder

Kaggle: A data science platform that includes a number of interesting datasets that are externally contributed. In its master list, you can find all sorts of niche datasets, from ramen scores to basketball data to even Seattle pet licenses.

Google Datasets Search: Dataset Search lets you locate datasets wherever they are hosted, whether it's a publisher's site, a digital library, or the web page of an author, similar to how Google Scholar works. It's a wonderful dataset finder, and it has over 25 million datasets in it.

UCI Machine Learning Repository: One of the web's oldest dataset sources, and a great first stop in search of interesting datasets. The vast majority are clean, but the data sets are user-contributed and thus have varying levels of cleanliness. You can download info, without registration, directly from the UCI Machine Learning repository.

MNIST Datasets

The MNIST dataset will assist you with creating your model. This dataset for Machine Learning is for picture recognition. It's a well-known and fascinating data collection for machine learning. The interesting fact of this dataset is that it provides both 60000 training and 10000 testing instances.

Boston Housing Datasets:

Based on various variables, such as number of rooms, area, crime rates and many others, the Boston House Price Dataset consists of the house prices in the Boston area. For beginners to ML seeking simple machine learning ventures, it is a great starting point, as you can exercise your linear regression skills to predict what a certain house's price should be. It is also a really common dataset for machine learning, so you can find a lot of helpful resources about it online if you get stuck.

Credit Card Fraud Detection Datasets

The dataset includes credit card purchases which are classified as fraudulent or genuine. This is important in developing a model for detecting fraudulent transactions for businesses that have transaction systems.

Youtube Datasets

This dataset contains a large-scale, labeled dataset with high-quality annotations created by the computer. It helps to use a machine-learning algorithm to create a video classification model. This dataset has enhanced the quality of annotations and machine-generated labels and has 6.1 million URLs, labeled with 3,862 visual entities in the vocabulary. All videos with one or more marks are annotated (an average of 3 labels per video).

Conclusion:

The dataset is an important part of applications for machine learning. It can be made available in various formats, such as .txt, .csv, and many more. The labeled training dataset is used in supervised machine learning, and no label is needed in unsupervised testing.

 

Also Read: Top Prerequisites to learn Machine Learning

 

Apart from this if you want to start your career in Artificial Intelligence and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Machine Learning Training Institute in Bangalore and also the best Artificial Intelligence Training Institute. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

 

For more information contact us:

Visit: www.nearlearn.com

Top Prerequisites to learn Machine Learning

Machine Learning, the one the most demanding and trending course to learn and for making a career in the technical field. While you don't actually need to have prior skills in the field while doing machine learning courses, it eventually comes down to how well you can do and function with programming languages, mathematics, variables, linear equations, histograms, etc. It's important to know the prerequisites for Machine Learning if you're a beginner who's getting started with Machine Learning. This blog help you to get knowledge of prerequisites to learn machine learning:

Prerequisites for Machine Learning

Statistics 

Statistics provide methods that can be used to extract some results from the data. When we talk about statistics, there are two types i.e. One is descriptive statistics, and the another one is inferential statistics.There are descriptive statistics that are used to turn some valuable details into raw data. Inferential statistics may also be used instead of using a full dataset to get useful information from a subset of data.

Machine Learning expert should familiar with:

• Mean

• Median

• Standard deviations

• Outliers

• Histogram

Probability

Probability helps to estimate the probability of events, which allows one to reason why the case may or may not happen again. We can usually research the probability of their occurrence or the probability that they have those characteristics if they have not occurred yet. If they have taken place in the past already, we may use probabilities to show our measure of uncertainty in that situation. The theory of probability is the basis for building models of machine learning that include uncertainties. For Machine Learning we can deal with:

• Notation

• Probability distribution (joint and conditional)

• Different rules of probability such as Bayes theorem, sum rule, and product or chain rule

• Independence

• Continuous random variables

Programming languages

As Machine Learning algorithms are implemented with code, this is good news for you if you have a good foundation in programming. Although as an inexperienced programmer you could get away and concentrate on the mathematics front, it is advisable to pick up at least one programming language as it will really assist you to understand the internal mechanisms. You need to pick up a programming language, however, which will make it easy to implement algorithms for machine learning.

Here some popular programming languages to learn:

Python

In machine learning programming, Python is very popular. Python is one of the first programming languages to use a range of libraries and tools to support machine learning. Python leads all the other languages with more than 60% of machine learning developers using and prioritising it. Python has many great visualisation packages and helpful core libraries such as Numpy, Scipy, Pandas, Matplotlib, Seaborn, Sklearn that make it very easy for you to function and empower the machines to learn.

Numpy: Numpy, is a Python Linear Algebra Library with strong data structures for efficient multi-dimensional array and matrix computation.

Pandas: It is the most common Python library which provides data analysis with highly optimised performance.

Matplotlib: It is a popular library of python plotting used to create fundamental graphs such as line charts, bar charts, histograms, and many more.

Seaborn: Provides a high-level interface for attractive graph development.

Scikit Learn: It is used for data mining and data analysis to implement a broad variety of machine-learning algorithms, including support vector machines, random forests, gradient boosting, k-means, classification, regression and clustering algorithms.

R

Another of the AI and Machine Learning prerequisites used as often as Python is R programming. Nowadays, various machine learning frameworks are implemented by R.

• Regression and classification-based operations at Kernlab and Caret 

• DataExplorer for data exploration

• Rpart and SuperML for Machine Learning

• Mlr3 for workflows in Machine Learning 

• Plotly and ggplot for data visualization

 

C++

In the field of machine learning, the superfast C ++ programming language is also very popular. Most machine-learning systems support this powerful language. If you have some decent working knowledge using C++, then learning machine learning using C++ is a pretty good idea. Compared to most programming languages, C++ is much more powerful. In the C++ programming language, several powerful libraries such as TensorFlow and Torch are implemented, so machine learning and C++ are truly a great combination.

Java

This programming language is the "Jack of all business" and in the ML industry it still continues to dominate. Java offers many good algorithms such as Weka, Knime, RapidMiner, Elka, which use graphical user interfaces to perform machine learning tasks.

Also Read: How Machine Learning Help in Businesses

Conclusion

As above are among the essential prerequisites for machine learning, it is also crucial to know how to work with knowledge. It is also important that you know how to extract, process and analyse data, in addition to having basic programming skills. This is one of the most crucial skills that Machine Learning requires.

Apart from this if you want to start your career in Artificial Intelligence and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Machine Learning Training Institute in Bangalore and also the best Artificial Intelligence Training Institute.They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

 

For more information contact us:

Visit: www.nearlearn.com

Top 10 Artificial Intelligence Software 2021 that you should know

Artificial Intelligence is the most demanding and emerging technologies nowadays. The demand for AI is continuously increasing in every enterprise. AI is like a blessing for us because of its specific features. Every day we able to see new technologies and this thing happen only because of AI. That is the main reason that every industry is adopting AI technologies to enhance the customer experience and bring new and advance technology to customers. Today all industries are moving toward new innovation bring revolution in every state of emerging technology. As per the report, the market value of AI in 2020 is USD 62.4 Billion and expected to grow to USD 733.7 Billion, revenue may forecast by 2027. From July to August 2020, the Indian Artificial Intelligence market is estimated at $6.4 Billion. As reported, this covers revenue from all AI operations originating from India, irrespective of the type of stakeholder or customer, type of company offering AI services. Industries have the largest market size in the IT services sector, led by technology with a market share of 41.1% and 23.3%, respectively. With an average salary of INR 14.7 Lakhs, there are approximately 91,000 Artificial Intelligence workers employed across companies in India. So by these data, you can see the rising demand for AI in India. Developers are working in different AI software.

 So here we discuss top Artificial Intelligence software 2021:

 TensorFlow

For the last decade or so, TensorFlow has been a popular AI platform and will still remain popular even in 2021. It's easy to see the appeal as one of the finest open source libraries around for machine learning and other aspects of artificial intelligence. However, TensorFlow is more than an AI platform. The term TensorFlow, introduced by Google, has basically become synonymous with machine learning. Significantly, TensorFlow is free and open-source, and this open model has made it possible for it to spread to a wide community of developers, enterprises, and science and academia. This same open architecture allows GPUs (graphical processing units, the "super-charged" hardware that powers AI) or CPUs to be used flexibly for computing (central processing unit, the not-quite-so fast hardware). Tensorflow is undoubtedly the top AI platform in the world for the creation and implementation of machine learning models.

 H2O.ai

H2O is another AI platform dedicated to making companies aware by giving them access to these resources of the various advantages of Artificial Intelligence and Machine Learning. H2O drives major industries in the industry with its open-source software, such as healthcare, banking, telecommunications, retail, marketing, pharma, and many others. H2O Q, which allows businesses to make their own AI applications, is probably the most interesting. In order to enable a kind of data storytelling based on artificial intelligence, these AI apps feature a variety of dashboards, modified with real-time data that can be sourced from several connectors. 

 Infosys Nia

Infosys Nia is an Artificial Intelligence platform that collects and aggregates organizational data from individuals, processes, and legacy structures into a knowledge base for self-learning and then automates repetitive business and IT processes, freeing up the human effort to solve higher-value customer problems that involve innovation, enthusiasm, and imagination. Infosys Nia has been used by our customers to harness their organizational expertise, create deep insights, and discover possibilities for optimizing, simplifying, and automating complex business processes.

Google AI Platform

The Google AI Platform is simply deserving of being at the top of the list. In one way or another, most other platforms below refer to it. For its ease of use, its unique models, its amazing cloud technology, its architecture, its beautiful visual interfaces, its library, and more, it is loved by millions of users. An array of tools, including TensorFlow and TPU, or Tensor processing units, which is an AI accelerator built by Google, are provided by the open-source Google AI toolset. This, along with Kubeflow and other main AI and ML tools, allows businesses to create their own on-premise or Google Cloud AI implementations without significant code tweaks for either environment. In essence, to create your own AI, you use the software-hardware ecosystem of Google AI, which is continuously modified.

Azure Machine Learning

Azure ML provides its comprehensive ML platform without upfront costs, and on a "pay only for what you use," basis, actively pushing to gain market share in an increasingly crowded field of machine learning vendors. MLOps, which can be thought of as DevOps for ML, is part of the Azure toolset; the ML workflow is greatly improved. Azure also has a complete collection of features to protect and monitor your data, with a view to avoiding prejudices that distort the results of the ML model. The Azure ML solutions are, of course, entirely interoperable with the Azure cloud, which is a significant benefit of this AI toolset.

Wipro Holmes

Wipro Holmes is the AI and automation platform of Wipro that bridge between the builders of Fundamental AI algorithms and AI applied. In order to monetize heterogeneous AI solutions, the Wipro Holmes Platform manages all the specifications, calculations, and governance. The Holmes offering can create, track, and even handle revenue chores for an AI application that exists in a mixed case environment. Aiding this method are pre-built AI properties. The ultimate objective is to set up a hyper-scale business by using AI to drive processes that are so powerful that they can expand with great speed and agility.

IBM Watson

The IBM Machine Learning Studio makes it easy, through automation and collaboration, to develop, train, and deploy model processes. Therefore, IBM's AI platforms are worth a look in 2021, as they vow to get even better. With a full library of solutions and approaches under one name, the IBM Watson AI solution is comprehensive, all intended to either provide an AI-fueled service or to integrate AI into your systems and applications. This can be as small as the functionality of the chatbot that provides directed reaction for consumer-facing applications, or as all-encompassing AI-based systems to coordinate and analyze vast data repositories in more cost-conscious and effective ways. An AI-powered system that enhances and streamlines IT operations is also included.

Rank Bird

In 2021, Rain Bird, the award-winning program designed to make business operations smarter and more efficient, is one artificial intelligence platform to look out for. Rain Bird is the AI platform of choice for companies looking to automate the knowledge-work process for productivity. It works by allowing smart systems to be developed that are generated from a mixture of business data and existing business knowledge. For its RBLang language, unique analytics, its managed learning algorithms, and use visual user interface, users love it.

Ayasdi

The software framework and application collection of Ayasdi enable businesses to build their own data-driven models for a wide range of use cases, from research to defense, industrial applications to fintech applications. In order to uncover solutions and grasp trend lines, the company's market solution, AyasadiAI, uses geometric and mathematical algorithms, ML, and data analytics. The company's approach basically provides an AI-powered platform to extract more data value. It is possible to deploy the Ayasdi AI software solution on-premise or in the cloud.

SparkNLP

Spark NLP is only three years old, but it is already one of the most popular sites for open-source AI to consider. The focus is on the processing of natural language and it comes with one of the largest libraries for data scientists and different NLP examples to refer to. It is based on Apache Spark and TensorFlow, so it's also impressive in its architecture.

 Also Read: Top Artificial Intelligence technologies your enterprise need Today

So these are the top 10 Artificial Intelligence software in 2021. Apart from this if you want to start your career in Artificial Intelligence and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Artificial Intelligence Training Institute in Bangalore and also the Best Machine Learning Training Institute. They provide highly skilled trainers having 10+ years of industry experience in these filed. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies and also provide internship facilities to their students.

 

For more information contact us:

Visit: www.nearlearn.com 

Top Artificial Intelligence Technologies your Enterprise need Today

Yes, It is true that today every enterprise needs Artificial Intelligence. Because of this it’s now become the most rising and demanded technologies in the global market. Not only this, investment in these technologies now become rising. As per the report, the global Artificial Intelligence market is expected to grow at a CAGR of 42.2% from 2020 to 2027. If we talk about the market size value of AI then it crosses USD 62.4 billion and will generate revenue of USD 733.7 billion. Every sector are now adopting AI technologies and they totally depended on these technologies and bring innovations in industries and this is one of the main AI generating high revenue is the market. 

 The reason behind of rising AI technologies is that we all entered the modern world and we totally depend on technologies and want to do our tasks without any efforts that why we human invent AI technologies to do our works in the most innovative and advanced ways and make our tasks more efficient and effective. 

 Now here we discuss the top Artificial Intelligence technologies your enterprise need today:

 Natural Langauge Generation:

Machines process information can communicate in a different process than the human brain. Natural language generation is a sub-discipline of AI that converts the structured data into the native language. The machines are programmed with algorithms that convert the data to the user's desired format. The computers are programmed with algorithms in order to convert the data to the user's desired format. Natural language is a subset of artificial intelligence that allows creators of content to automate and deliver content in the desired format. In order to reach the target audience, content creators may use automated content to promote it on different social media channels and other media platforms. It is commonly customer services to generate reports and market summaries.  Natural Language Generation is offered by companies including Attivio, Automated Insights, Cambridge Semantics, Digital Logic, Lucidworks, Narrative Research, SAS, and Yseop.

Speech Recognition:

Speech recognition is one of the best and most demanding artificial technologies to recognize and convert contextual types and languages into numerical data. The new artificial intelligence technology developed by Siri is used in neural networks in which mobile apps communicate with the user in voice recognition. Speech recognition services are provided by companies such as NICE, Nuance Communications, OpenText, and Verint Systems.

Machine Learning Platforms:

Machine learning is both a computer science sub-discipline and a significant artificial intelligence branch. Its aim is to develop new methods that allow computers to learn and thus become smarter. Machine learning platforms are becoming more popular with the aid of algorithms, APIs (application programming interface), development, training tools, big data, and applications. For the purposes of categorization and prediction, they are used commonly. Some businesses that offer machine learning platforms are Amazon, Fractal Analytics, Google, H2O AI, Microsoft, SAS, Skytree, and Ad text.

Virtual Agent:

The virtual agent is nothing more than a machine agent or software capable of communicating with humans. Chatbots are the most common example of this type of technology. As their customer support agents, online and mobile apps have chatbots to communicate with humans and answer their queries. Google Assistant helps to plan meetings, and Amazon's Alexa helps make shopping simple for you. A virtual assistant often serves as a language assistant, which selects knowledge based on your option and preference. Amazon, Apple, Artificial Solutions, Assist AI, Innovative Virtual, Google, IBM, IPsoft, Microsoft, and Satisfi are some of the companies that have virtual agents. 

Robotic Process Automation:

The automation of robotic processes uses scripts and methods to help organizational processes that replicate and automate human activities. It is especially useful for cases where it is too costly or impractical to employ people for a particular job or task. 

Again, Adext AI, a network that automates digital advertisement processes using AI, is a clear example of this, preventing companies from devoting hours to mechanical and repetitive tasks. It is a solution that allows you to take advantage of your human talent and transfer workers into more strategic and innovative roles so that their decisions can really influence the growth of the business. Other examples of robotic process automation companies are Advanced Systems Concepts, Automation Anywhere Blue Prism, UiPath, and WorkFusion.

Decision Management:

Decision management systems for data translation and analysis into predictive models are adopted by modern organizations. In order to assist in corporate decision-making, enterprise-level technologies incorporate decision management systems to collect up-to-date information to execute business data analysis. Management of decisions allows us to make fast decisions, to minimize risks, and to simplify the process. In the financial sector, the health care sector, trading, insurance sector, e-commerce, etc., the decision management system is widely applied.

Biometrics:

Biometrics are used to identify the person through fingerprint mapping, facial expressions, voice recognition, and retina scanner. As it operates with touch, image, speech, and body language, it fosters organic interactions between machines and people. It is primarily used for market research purposes. Multiple authentications are required for this best artificial technology to identify anything. VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, and Tahzoo are provided by this technology service.

Cyber Defence:

Cyber Defence is a method for computer defense aimed at detecting, preventing, and minimizing attacks and threats to device data and infrastructure. In order to uncover suspicious user behavior and identify cyber threats, neural networks that are capable of processing sequences of inputs can be used along with machine learning techniques to build learning technologies.

Image Recognition:

Image recognition is an AI technology sub-set. It acts as an identifier for various purposes to fit the company's images such as protection, diagnosis of diseases, and payment methods. Now how hot AI is mostly used in facial lock businesses and in training robots such as self-driving cars to use them for human assistance. Clarifai offers image recognition systems for individuals to identify duplicates and find related uncategorized images. 

Emotion Recognition:

This kind of AI technology makes it possible to read and interpret emotions conveyed by humans using advanced image processing or audio data processing. We can capture "micro-expressions," and vocal intonation that betrays the feelings of an individual, or subtle body language signals. Thus, law enforcers also use technology during interrogation. Beyond Verbal, and Affectiva is several companies that use emotion recognition.

Deep Learning Platforms:

A special form of machine learning with several abstraction layers consisting of artificial neural networks. Used mainly in pattern recognition and classification applications currently assisted by very broad data sets. All of the deep learning options worth exploring are Deep Instinct, Ersatz Laboratories, Fluid AI, MathWorks, Peltarion, Saffron Technology, and Sentient Technologies.

Conclusion

Artificial Intelligence reflects intelligence computing models. Intelligence can be characterized as structures, models, and operational functions that can be programmed to solve problems, inferences, process languages, etc. In several industries, the advantages of the use of artificial intelligence are already being reaped. Slowly every industry will adopt AI technology and it will bring a huge revolution in each and every type of industry.

Also Read: Industry 4.0-How Artificial Intelligence redefining the Manufacturing Process

 So as above you got to know the top Artificial Intelligence technologies your enterprise needs Today, Apart from this if you want to start your career in Artificial Intelligence and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Artificial Intelligence Training Institute in Bangalore and also the best Machine Learning Training Institute. They provide highly skilled trainers having 10+ years of industry experience in these filed. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies and also provide internship facilities to their students.

 

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