STUFF WE DO
- Facebook Apps and Pages
- Web Development
- Custom Software Development
- Mobile (IOS,Android,Windows)
- BIG DATA
- BUSINESS INTELLIGENCE
- IMAGE PROCESSING
- PATTERN RECOGNITION
- SPEECH RECOGNITION
- NEURAL NETWORKS
- COMPUTER VISION
- MACHINE LEARNING
- DEEP LEARNING
- CONNECTED PRODUCTS
- CONNECTED ASSESTS
- CONNECTED FLEETS
- CONNECTED INFRASTRUCTURE
- CONNECTED MARKETS
- CONNECTED PEOPLE
- Contractual Services
- Permanent Placements
- Recruiter Process
Inherent Technologies excels in offering customized and professional application designing and developing services over an extensive variety of technical areas, ranging from the client’s server applications to object oriented technologies, from intranet or internet application to the legacy applications.
The list of services offered at Inherent Technologies, consist of:
- Application design,development and implementation.
- Application Enhancement
- Application Maintenance
- Application Migration
- Custom application , Software and Product development
- Date Warehouse/Business Intelligence
- Testing Services
- Feasibility and requirement analysis for business case.
Are you looking for Social Media integration for your current products and websites ?
We can help you achieve the same. Our team can deliver custom facebook apps based on
customers need and help market your products using various other social media platforms.
Feel free to contact us for a free consultation at firstname.lastname@example.org
Each and every day we take journeys.
We embark on customer journeys in both the physical and digital worlds.
Years ago our journeys were limited to store fronts and physical goods.
Today we live in a multimedia world of websites, social media, chat sessions, newsletters, email, and call centers.
These digital journeys have changed the way we interact with companies and products.
We embarace the change and help customers and work and walk with them to acheive there journey of creating
stunning websites with SEO in mind.
Custom software (also known as bespoke software or tailor-made software) is software that is specially developed for some specific organization or other user.
Why is customized software developed?
Custom software development is important because it helps meet unique requirements at a cost competitive with purchasing, maintaining and modifying commercial software. ... Scalability: Custom software can grow as an organization or business grows and changes.
The top advantages of custom software are:
- It's tailor-made to the specific needs of your enterprise. ...
- It's a smart long-term investment. ...
- It Increases productivity. ...
- Your software is maintained as long as you require. ...
- It's more secure against external threats. ...
- Scalability. ...
A Mobile Application, also referred to as a mobile app or simply an app, is a computer program or software application designed to run on a mobile device such as a phone, tablet, or watch. Apps were originally intended for productivity assistance such as email, calendar, and contact databases, but the public demand for apps caused rapid expansion into other areas such as mobile games, factory automation, GPS and location-based services, order-tracking, and ticket purchases, so that there are now millions of apps available. Apps are generally downloaded from application distribution platforms which are operated by the owner of the mobile operating system, such as the App Store (iOS) or Google Play Store. Some apps are free, and others have a price, with the profit being split between the application's creator and the distribution platform. Mobile applications often stand in contrast to desktop applications which are designed to run on desktop computers, and web applications which run in mobile web browsers rather than directly on the mobile device.
Mobile applications may be classified by numerous methods. A common scheme is to distinguish native, hybrid, and web-based apps.
All apps targeted toward a particular mobile platform are known as native apps. Therefore, an app intended for Apple device do not run in Android devices. As a result, most businesses develop apps for multiple platforms.
While developing native apps, professionals incorporate best-in-class user interface modules. This accounts for better performance, consistency and good user experience. Users also benefit from wider access to application programming interfaces and make limitless use of all apps from the particular device. Further, they also switch over from one app to another effortlessly.
The main purpose for creating such apps is to ensure best performance for a specific mobile operating system.
The concept of the hybrid app is a mix of native and web-based apps. Apps developed using Xamarin, React Native, Sencha Touch and other similar technology fall into this category.
These are made to support web and native technologies across multiple platforms. Moreover, these apps are easier and faster to develop. It involves use of single code base which works in multiple mobile operating systems.
Despite such advantages, hybrid apps exhibit lower performance. Often, apps fail to bear the same look-and-feel in different mobile operating systems.
These apps may capture minimum memory space in user devices compared to native and hybrid apps. Since all the personal databases are saved on the Internet servers, users can fetch their desired data from any device through the Internet.
The three biggest app stores are Google Play for Android, App Store for iOS, and Microsoft Store for Windows 10, Windows 10 Mobile, and Xbox One.
Google Play (formerly known as the Android Market) is an international online software store developed by Google for Android devices. It opened in October 2008. In July 2013, the number of apps downloaded via the Google Play Store surpassed 50 billion, of the over 1 million apps available. As of September 2016, according to Statista the number of apps available exceeded 2.4 million. Over 80% of apps in the Google Play Store are free to download. The store generated a revenue of 6 billion U.S. dollars in 2015.
Apple's App Store for iOS was not the first app distribution service, but it ignited the mobile revolution and was opened on July 10, 2008, and as of September 2016, reported over 140 billion downloads. The original AppStore was first demonstrated to Steve Jobs in 1993 by Jesse Tayler at NeXTWorld Expo As of June 6, 2011, there were 425,000 apps available, which had been downloaded by 200 million iOS users. During Apple's 2012 Worldwide Developers Conference, CEO Tim Cook announced that the App Store has 650,000 available apps to download as well as 30 billion apps downloaded from the app store until that date. From an alternative perspective, figures seen in July 2013 by the BBC from tracking service Adeven indicate over two-thirds of apps in the store are "zombies", barely ever installed by consumers.
Microsoft Store (formerly known as the Windows Store) was introduced by Microsoft in 2012 for its Windows 8 and Windows RT platforms. While it can also carry listings for traditional desktop programs certified for compatibility with Windows 8, it is primarily used to distribute "Windows Store apps"—which are primarily built for use on tablets and other touch-based devices (but can still be used with a keyboard and mouse, and on desktop computers and laptops).
- Amazon Appstore is an alternative application store for the Android operating system. It was opened in March 2011 and as of June 2015, the app store has nearly 334,000 apps. The Amazon Appstore's Android Apps can also be installed and run on BlackBerry 10 devices.
- BlackBerry World is the application store for BlackBerry 10 and BlackBerry OS devices. It opened in April 2009 as BlackBerry App World.
- Ovi (Nokia) for Nokia phones was launched internationally in May 2009. In May 2011, Nokia announced plans to rebrand its Ovi product line under the Nokia brand and Ovi Store was renamed Nokia Store in October 2011. Nokia Store will no longer allow developers to publish new apps or app updates for its legacy Symbian and MeeGo operating systems from January 2014.
- Windows Phone Store was introduced by Microsoft for its Windows Phone platform, which was launched in October 2010. As of October 2012, it has over 120,000 apps available.
- Samsung Apps was introduced in September 2009. As of October 2011, Samsung Apps reached 10 million downloads. The store is available in 125 countries and it offers apps for Windows Mobile, Android and Bada platforms.
- The Electronic AppWrapper was the first electronic distribution service to collectively provide encryption and purchasing electronically
- F-Droid — Free and open Source Android app repository.
- Opera Mobile Store is a platform independent app store for iOS, Java, BlackBerry OS, Symbian, iOS, and Windows Mobile, and Android based mobile phones. It was launched internationally in March, 2011.
- There are numerous other independent app stores for Android devices.
So we have All Data , Big Data and Data is all over ..It is estimated that we will have 20 peta bytes of data which will be generated accoss devices world wide. As we are moving to the 22nd century data accessiblity now has no bounderies and cloud has made it possible.. Inherent Techologies not only have all products and processess build on cloud but strongly feel that cloud is the future for data to be available instantly from every where all the time.
What is BigData?
Big Data is also data but with a huge size. Big Data is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently.
Types of Big Data
BigData could be found in three forms:
Any data that can be stored, accessed and processed in the form of fixed format is termed as a 'structured' data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. However, nowadays, we are foreseeing issues when a size of such data grows to a huge extent, typical sizes are being in the rage of multiple zettabytes.
Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Now day organizations have wealth of data available with them but unfortunately, they don't know how to derive value out of it since this data is in its raw form or unstructured format.
Semi-structured data can contain both the forms of data. We can see semi-structured data as a structured in form but it is actually not defined with e.g. a table definition in relational DBMS. Example of semi-structured data is a data represented in an XML file.
Benefits of Big Data Processing
Ability to process Big Data brings in multiple benefits, such as-
- Businesses can utilize outside intelligence while taking decisions
Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies.
- Improved customer service
Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
- Better operational efficiency
- Early identification of risk to the product/services, if any
Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data.
- Big Data is defined as data that is huge in size. Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time.
- Examples of Big Data generation includes stock exchanges, social media sites, jet engines, etc.
- Big Data could be 1) Structured, 2) Unstructured, 3) Semi-structured
- Volume, Variety, Velocity, and Variability are few Characteristics of Bigdata
- Improved customer service, better operational efficiency, Better Decision Making are few advantages of Bigdata
What is Business Intelligence?
BI(Business Intelligence) is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions.It is a suite of software and services to transform data into actionable intelligence and knowledge.
BI has a direct impact on organization's strategic, tactical and operational business decisions. BI supports fact-based decision making using historical data rather than assumptions and gut feeling.
BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and charts to provide users with detailed intelligence about the nature of the business.
Why is BI important?
- Measurement: creating KPI (Key Performance Indicators) based on historic data
- Identify and set benchmarks for varied processes.
- With BI systems organizations can identify market trends and spot business problems that need to be addressed.
- BI helps on data visualization that enhances the data quality and thereby the quality of decision making.
- BI systems can be used not just by enterprises but SME (Small and Medium Enterprises)
How Business Intelligence systems are implemented?
Here are the steps:
Step 1) Raw Data from corporate databases is extracted. The data could be spread across multiple systems heterogeneous systems.
Step 2) The data is cleaned and transformed into the data warehouse. The table can be linked, and data cubes are formed.
Step 3) Using BI system the user can ask quires, request ad-hoc reports or conduct any other analysis.
Four types of BI users
Following given are the four key players who are used Business Intelligence System:
The Professional Data Analyst:
The data analyst is a statistician who always needs to drill deep down into data. BI system helps them to get fresh insights to develop unique business strategies.
The IT users:
The IT user also plays a dominant role in maintaining the BI infrastructure.
The head of the company:
CEO or CXO can increase the profit of their business by improving operational efficiency in their business.
The Business Users"
Business intelligence users can be found from across the organization. There are mainly two types of business users
- Casual business intelligence user
- The power user.
The difference between both of them is that a power user has the capability of working with complex data sets, while the casual user need will make him use dashboards to evaluate predefined sets of data.
Trends in Business Intelligence
The following are some business intelligence and analytics trends that you should be aware of.
Artificial Intelligence: Gartner' report indicates that AI and machine learning now take on complex tasks done by human intelligence. This capability is being leveraged to come up with real-time data analysis and dashboard reporting.
Collaborative BI: BI software combined with collaboration tools, including social media, and other latest technologies enhance the working and sharing by teams for collaborative decision making.
Embedded BI: Embedded BI allows the integration of BI software or some of its features into another business application for enhancing and extending it's reporting functionality.
Cloud Analytics: BI applications will be soon offered in the cloud, and more businesses will be shifting to this technology. As per their predictions within a couple of years, the spending on cloud-based analytics will grow 4.5 times faster.
What is Cloud Computing?
Cloud computing is a term referred to storing and accessing data over the internet. It doesn't store any data on the hard disk of your personal computer. In cloud computing, you can access data from a remote server.
What is AWS?
Amazon web service is a platform that offers flexible, reliable, scalable, easy-to-use and cost-effective cloud computing solutions.
AWS is a comprehensive, easy to use computing platform offered Amazon. The platform is developed with a combination of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS) offerings.
AWS Compute Services
Here, are Cloud Compute Services offered by Amazon:
- EC2 (Elastic Compute Cloud) - EC2 is a virtual machine in the cloud on which you have OS level control. You can run this cloud server whenever you want.
- LightSail -This cloud computing tool automatically deploys and manages the computer, storage, and networking capabilities required to run your applications.
- Elastic Beanstalk — The tool offers automated deployment and provisioning of resources like a highly scalable production website.
- EKS (Elastic Container Service for Kubernetes) — The tool allows you to Kubernetes on Amazon cloud environment without installation.
- AWS Lambda — This AWS service allows you to run functions in the cloud. The tool is a big cost saver for you as you to pay only when your functions execute.
Types of Clouds
There are three types of clouds − Public, Private, and Hybrid cloud.
In public cloud, the third-party service providers make resources and services available to their customers via Internet. Customer’s data and related security is with the service providers’ owned infrastructure.
A private cloud also provides almost similar features as public cloud, but the data and services are managed by the organization or by the third party only for the customer’s organization. In this type of cloud, major control is over the infrastructure so security related issues are minimized.
A hybrid cloud is the combination of both private and public cloud. The decision to run on private or public cloud usually depends on various parameters like sensitivity of data and applications, industry certifications and required standards, regulations, etc.
Cloud Service Models
There are three types of service models in cloud − IaaS, PaaS, and SaaS.
Applications of AWS services
Amazon Web services are widely used for various computing purposes like:
- Web site hosting
- Application hosting/SaaS hosting
- Media Sharing (Image/ Video)
- Mobile and Social Applications
- Content delivery and Media Distribution
- Storage, backup, and disaster recovery
- Development and test environments
- Academic Computing
- Search Engines
- Social Networking
Advantages of AWS
Following are the pros of using AWS services:
- AWS allows organizations to use the already familiar programming models, operating systems, databases, and architectures.
- It is a cost-effective service that allows you to pay only for what you use, without any up-front or long-term commitments.
- You will not require to spend money on running and maintaining data centers.
- Offers fast deployments
- You can easily add or remove capacity.
- You are allowed cloud access quickly with limitless capacity.
- Total Cost of Ownership is very low compared to any private/dedicated servers.
- Offers Centralized Billing and management
- Offers Hybrid Capabilities
- Allows you to deploy your application in multiple regions around the world with just a few clicks
Disadvantages of AWS
- If you need more immediate or intensive assistance, you'll have to opt for paid support packages.
- Amazon Web Services may have some common cloud computing issues when you move to a cloud. For example, downtime, limited control, and backup protection.
- AWS sets default limits on resources which differ from region to region. These resources consist of images, volumes, and snapshots.
- Hardware-level changes happen to your application which may not offer the best performance and usage of your applications.
Microsoft Azure (formerly Windows Azure) is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. It provides software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS) and supports many different programming languages, tools and frameworks, including both Microsoft-specific and third-party software and systems.
Azure was announced in October 2008, started with codename "Project Red Dog" and released on February 1, 2010, as "Windows Azure" before being renamed "Microsoft Azure" on March 25, 2014
Azure as PaaS (Platform as a Service)
As the name suggests, a platform is provided to clients to develop and deploy software. The clients can focus on the application development rather than having to worry about hardware and infrastructure. It also takes care of most of the operating systems, servers and networking issues.
- The overall cost is low as the resources are allocated on demand and servers are automatically updated.
- It is less vulnerable as servers are automatically updated and being checked for all known security issues. The whole process is not visible to developer and thus does not pose a risk of data breach.
- Since new versions of development tools are tested by the Azure team, it becomes easy for developers to move on to new tools. This also helps the developers to meet the customer’s demand by quickly adapting to new versions.
- There are portability issues with using PaaS. There can be a different environment at Azure, thus the application might have to be adapted accordingly.
Azure as IaaS (Infrastructure as a Service)
It is a managed compute service that gives complete control of the operating systems and the application platform stack to the application developers. It lets the user to access, manage and monitor the data centers by themselves.
- This is ideal for the application where complete control is required. The virtual machine can be completely adapted to the requirements of the organization or business.
- IaaS facilitates very efficient design time portability. This means application can be migrated to Windows Azure without rework. All the application dependencies such as database can also be migrated to Azure.
- IaaS allows quick transition of services to clouds, which helps the vendors to offer services to their clients easily. This also helps the vendors to expand their business by selling the existing software or services in new markets.
- Since users are given complete control they are tempted to stick to a particular version for the dependencies of applications. It might become difficult for them to migrate the application to future versions.
- There are many factors which increases the cost of its operation. For example, higher server maintenance for patching and upgrading software.
- There are lots of security risks from unpatched servers. Some companies have welldefined processes for testing and updating on-premise servers for security vulnerabilities. These processes need to be extended to the cloud-hosted IaaS VMs to mitigate hacking risks.
- The unpatched servers pose a great security risk. Unlike PaaS, there is no provision of automatic server patching in IaaS. An unpatched server with sensitive information can be very vulnerable affecting the entire business of an organization.
- It is difficult to maintain legacy apps in Iaas. It can be stuck with the older version of the operating systems and application stacks. Thus, resulting in applications that are difficult to maintain and add new functionality over the period of time.
It becomes necessary to understand the pros and cons of both services in order to choose the right one according your requirements. In conclusion it can be said that, PaaS has definite economic advantages for operations over IaaS for commodity applications. In PaaS, the cost of operations breaks the business model. Whereas, IaaS gives complete control of the OS and application platform stack.
Azure Management Portal
Azure Management Portal is an interface to manage the services and infrastructure launched in 2012. All the services and applications are displayed in it and it lets the user manage them.
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.
What Is Intelligence?
All but the simplest human behavior is ascribed to intelligence, while even the most complicated insect behavior is never taken as an indication of intelligence. What is the difference? Consider the behavior of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behavior is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of Sphex—must include the ability to adapt to new circumstances.
Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
- Speech recognition
- Problem solving
There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and procedures—known as rote learning—is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the “add ed” rule and so form the past tense of jump based on experience with similar verbs.
To reason is to draw inferences appropriate to the situation. Inferences are classified as either deductive or inductive. An example of the former is, “Fred must be in either the museum or the café. He is not in the café; therefore he is in the museum,” and of the latter, “Previous accidents of this sort were caused by instrument failure; therefore this accident was caused by instrument failure.” The most significant difference between these forms of reasoning is that in the deductive case the truth of the premises guarantees the truth of the conclusion, whereas in the inductive case the truth of the premise lends support to the conclusion without giving absolute assurance. Inductive reasoning is common in science, where data are collected and tentative models are developed to describe and predict future behaviour—until the appearance of anomalous data forces the model to be revised. Deductive reasoning is common in mathematics and logic, where elaborate structures of irrefutable theorems are built up from a small set of basic axioms and rules.
There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, true reasoning involves more than just drawing inferences; it involves drawing inferences relevant to the solution of the particular task or situation. This is one of the hardest problems confronting AI.
Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis—a step-by-step, or incremental, reduction of the difference between the current state and the final goal. The program selects actions from a list of means—in the case of a simple robot this might consist of PICKUP, PUTDOWN, MOVEFORWARD, MOVEBACK, MOVELEFT, and MOVERIGHT—until the goal is reached.
In perception the environment is scanned by means of various sensory organs, real or artificial, and the scene is decomposed into separate objects in various spatial relationships. Analysis is complicated by the fact that an object may appear different depending on the angle from which it is viewed, the direction and intensity of illumination in the scene, and how much the object contrasts with the surrounding field.
A language is a system of signs having meaning by convention. In this sense, language need not be confined to the spoken word. Traffic signs, for example, form a minilanguage, it being a matter of convention that ⚠ means “hazard ahead” in some countries. It is distinctive of languages that linguistic units possess meaning by convention, and linguistic meaning is very different from what is called natural meaning, exemplified in statements such as “Those clouds mean rain” and “The fall in pressure means the valve is malfunctioning.”
Methods and Goals in AI
Symbolic vs. connectionist approaches
AI research follows two distinct, and to some extent competing, methods, the symbolic (or “top-down”) approach, and the connectionist (or “bottom-up”) approach. The top-down approach seeks to replicate intelligence by analyzing cognition independent of the biological structure of the brain, in terms of the processing of symbols—whence the symbolic label. The bottom-up approach, on the other hand, involves creating artificial neural networks in imitation of the brain’s structure—whence the connectionist label.
Natural Language Processing is the technology used to aid computers to understand the human’s natural language.
It’s not an easy task teaching machines to understand how we communicate.
Leand Romaf, an experienced software engineer who is passionate at teaching people how artificial intelligence systems work, says that “in recent years, there have been significant breakthroughs in empowering computers to understand language just as we do.”
This article will give a simple introduction to Natural Language Processing and how it can be achieved.
What is Natural Language Processing?
Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.
Most NLP techniques rely on machine learning to derive meaning from human languages.
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In fact, a typical interaction between humans and machines using Natural Language Processing could go as follows:
1. A human talks to the machine
2. The machine captures the audio
3. Audio to text conversion takes place
4. Processing of the text’s data
5. Data to audio conversion takes place
6. The machine responds to the human by playing the audio file
What is NLP used for?
Natural Language Processing is the driving force behind the following common applications:
- Language translation applications such as Google Translate
- Word Processors such as Microsoft Word and Grammarly that employ NLP to check grammatical accuracy of texts.
- Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests.
- Personal assistant applications such as OK Google, Siri, Cortana, and Alexa.
Why is NLP difficult?
- Natural Language processing is considered a difficult problem in computer science. It’s the nature of the human language that makes NLP difficult.
- The rules that dictate the passing of information using natural languages are not easy for computers to understand.
- Some of these rules can be high-leveled and abstract; for example, when someone uses a sarcastic remark to pass information.
- On the other hand, some of these rules can be low-levelled; for example, using the character “s” to signify the plurality of items.
- Comprehensively understanding the human language requires understanding both the words and how the concepts are connected to deliver the intended message.
- While humans can easily master a language, the ambiguity and imprecise characteristics of the natural languages are what make NLP difficult for machines to implement.
How does Natural Language Processing Works?
- NLP entails applying algorithms to identify and extract the natural language rules such that the unstructured language data is converted into a form that computers can understand.
- When the text has been provided, the computer will utilize algorithms to extract meaning associated with every sentence and collect the essential data from them.
- Sometimes, the computer may fail to understand the meaning of a sentence well, leading to obscure results.
- For example, a humorous incident occurred in the 1950s during the translation of some words between the English and the Russian languages.
- Here is the biblical sentence that required translation:
- “The spirit is willing, but the flesh is weak.”
- Here is the result when the sentence was translated to Russian and back to English:
- “The vodka is good, but the meat is rotten.”
What are the techniques used in NLP?
Syntactic analysis and semantic analysis are the main techniques used to complete Natural Language Processing tasks.
Here is a description on how they can be used.
Natural Language Processing plays a critical role in supporting machine-human interactions.
As more research is being carried in this field, we expect to see more breakthroughs that will make machines smarter at recognizing and understanding the human language.
Have you used any NLP technique in enhancing the functionality of your application?
Or, do you have any question or comment?
Please share below.
Digital Image Processing Basics
Digital Image Processing means processing digital image by means of a digital computer. We can also say that it is a use of computer algorithms, in order to get enhanced image either to extract some useful information.
Image processing mainly include the following steps:
1. Importing the image via image acquisition tools;
2. Analyzing and manipulating the image;
3. Output in which result can be altered image or a report which is based on analyzing that image.
What is an image?
An image is defined as a two-dimensional function F(x, y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x, y) is called the intensity of that image at that point. When x, y and amplitude values of F are finite, we call it a digital image.
In other words, an image can be defined by a two-dimensional array specifically arranged in rows and columns.
Digital Image is composed of a finite number of elements, each of which elements have a particular value at a particular location. These elements are referred to as picture elements, image elements and pixels. A Pixel is most widely used to denote the elements of a Digital Image.
Types of an image
1. BINARY IMAGE– The binary image as its name suggests, contain only two pixel elements i.e 0 & 1,where 0 refers to black and 1 refers to white. This image is also known as Monochrome.
2. BLACK AND WHITE IMAGE– The image which consists of only black and white color is called BLACK AND WHITE IMAGE.
3. 8 bit COLOR FORMAT– It is the most famous image format. It has 256 different shades of colors in it and commonly known as Grayscale Image. In this format, 0 stands for Black, and 255 stands for white, and 127 stands for gray.
4. 16 bit COLOR FORMAT– It is a color image format. It has 65,536 different colors in it. It is also known as High Color Format. In this format the distribution of color is not as same as Grayscale image.
A 16 bit format is actually divided into three further formats which are Red, Green and Blue. That famous RGB format.
PHASES OF IMAGE PROCESSING:
1. ACQUISITION– It could be as simple as being given an image which is in digital form. The main work involves:
b) Color conversion(RGB to Gray or vice-versa)
2. IMAGE ENHANCEMENT– It is amongst the simplest and most appealing in areas of Image Processing it is also used to extract some hidden details from an image and is subjective.
3. IMAGE RESTORATION– It also deals with appealing of an image but it is objective (Restoration is based on mathematical or probabilistic model or image degradation).
4. COLOR IMAGE PROCESSING– It deals with pseudocolor and full color image processing color models are applicable to digital image processing.
5. WAVELETS AND MULTI-RESOLUTION PROCESSING– It is foundation of representing images in various degrees.
6. IMAGE COMPRESSION-It involves in developing some functions to perform this operation. It mainly deals with image size or resolution.
7. MORPHOLOGICAL PROCESSING-It deals with tools for extracting image components that are useful in the representation & description of shape.
8. SEGMENTATION PROCEDURE-It includes partitioning an image into its constituent parts or objects. Autonomous segmentation is the most difficult task in Image Processing.
9. REPRESENTATION & DESCRIPTION-It follows output of segmentation stage, choosing a representation is only the part of solution for transforming raw data into processed data.
10. OBJECT DETECTION AND RECOGNITION-It is a process that assigns a label to an object based on its descriptor.
Pattern Recognition | Introduction
Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms.
Example: The colors on the clothes, speech pattern etc. In computer science, a pattern is represented using vector features values.
What is Pattern Recognition ?
Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. One of the important aspects of the pattern recognition is its application potential.
Examples: Speech recognition, speaker identification, multimedia document recognition (MDR), automatic medical diagnosis.
In a typical pattern recognition application, the raw data is processed and converted into a form that is amenable for a machine to use. Pattern recognition involves classification and cluster of patterns.
·In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning.
·Clustering generated a partition of the data which helps decision making, the specific decision making activity of interest to us. Clustering is used in an unsupervised learning.
Features may be represented as continuous, discrete or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object.
Example: consider our face then eyes, ears, nose etc are features of the face.
A set of features that are taken together, forms the features vector.
Example: In the above example of face, if all the features (eyes, ears, nose etc) taken together then the sequence is feature vector([eyes, ears, nose]). Feature vector is the sequence of a features represented as a d-dimensional column vector. In case of speech, MFCC (Melfrequency Cepstral Coefficent) is the spectral features of the speech. Sequence of first 13 features forms a feature vector.
Pattern recognition possesses the following features:
- Pattern recognition system should recognize familiar pattern quickly and accurate
- Recognize and classify unfamiliar objects
- Accurately recognize shapes and objects from different angles
- Identify patterns and objects even when partly hidden
- Recognize patterns quickly with ease, and with automaticity.
Basic concepts of speech recognition
- Structure of speech
- Recognition process
- Other used concepts
- What is optimized
Speech is a complex phenomenon. People rarely understand how is it produced and perceived. The naive perception is often that speech is built with words and each word consists of phones. The reality is unfortunately very different. Speech is a dynamic process without clearly distinguished parts. It’s always useful to get a sound editor and look into the recording of the speech and listen to it. Here is for example the speech recording in an audio editor.
All modern descriptions of speech are to some degree probabilistic. That means that there are no certain boundaries between units, or between words. Speech to text translation and other applications of speech are never 100% correct. That idea is rather unusual for software developers, who usually work with deterministic systems. And it creates a lot of issues specific only to speech technology.
Structure of speech
In current practice, speech structure is understood as follows:
Speech is a continuous audio stream where rather stable states mix with dynamically changed states. In this sequence of states, one can define more or less similar classes of sounds, or phones. Words are understood to be built of phones, but this is certainly not true. The acoustic properties of a waveform corresponding to a phone can vary greatly depending on many factors - phone context, speaker, style of speech and so on. The so-called coarticulation makes phones sound very different from their “canonical” representation. Next, since transitions between words are more informative than stable regions, developers often talk about diphones - parts of phones between two consecutive phones. Sometimes developers talk about subphonetic units - different substates of a phone. Often three or more regions of a different nature can be found.
The number three can easily be explained: The first part of the phone depends on its preceding phone, the middle part is stable and the next part depends on the subsequent phone. That’s why there are often three states in a phone selected for speech recognition.
Sometimes phones are considered in context. Such phones in context are called triphones or even quinphones. For example “u” with left phone “b” and right phone “d” in the word “bad” sounds a bit different than the same phone “u” with left phone “b” and right phone “n” in word “ban”. Please note that unlike diphones, they are matched with the same range in waveform as just phones. They just differ by name because they describe slightly different sounds.
For computational purpose it is helpful to detect parts of triphones instead of triphones as a whole, for example if you want to create a detector for the beginning of a triphone and share it across many triphones. The whole variety of sound detectors can be represented by a small amount of distinct short sound detectors. Usually we use 4000 distinct short sound detectors to compose detectors for triphones. We call those detectors senones. A senone’s dependence on context can be more complex than just the left and right context. It can be a rather complex function defined by a decision tree, or in some other ways.
Next, phones build subword units, like syllables. Sometimes, syllables are defined as “reduction-stable entities”. For instance, when speech becomes fast, phones often change, but syllables remain the same. Also, syllables are related to an intonational contour. There are other ways to build subwords - morphologically-based (in morphology-rich languages) or phonetically-based. Subwords are often used in open vocabulary speech recognition.
Subwords form words. Words are important in speech recognition because they restrict combinations of phones significantly. If there are 40 phones and an average word has 7 phones, there must be 40^7 words. Luckily, even people with a rich vocabulary rarely use more then 20k words in practice, which makes recognition way more feasible.
Words and other non-linguistic sounds, which we call fillers (breath, um, uh, cough), form utterances. They are separate chunks of audio between pauses. They don’t necessary match sentences, which are more semantic concepts.
On the top of this, there are dialog acts like turns, but they go beyond the purpose of this document.
The common way to recognize speech is the following: we take a waveform, split it at utterances by silences and then try to recognize what’s being said in each utterance. To do that, we want to take all possible combinations of words and try to match them with the audio. We choose the best matching combination.
There are some important concepts in this matching process. First of all it’s the concept of features. Since the number of parameters is large, we are trying to optimize it. Numbers that are calculated from speech usually by dividing the speech into frames. Then for each frame, typically of 10 milliseconds length, we extract 39 numbers that represent the speech. That’s called a feature vector. The way to generate the number of parameters is a subject of active investigation, but in a simple case it’s a derivative from the spectrum.
Second, it’s the concept of the model. A model describes some mathematical object that gathers common attributes of the spoken word. In practice, for an audio model of senone it is the gaussian mixture of it’s three states - to put it simple, it’s the most probable feature vector. From the concept of the model the following issues raise:
- how well does the model describe reality,
- can the model be made better of it’s internal model problems and
- how adaptive is the model if conditions change
The model of speech is called Hidden Markov Model or HMM. It’s a generic model that describes a black-box communication channel. In this model process is described as a sequence of states which change each other with a certain probability. This model is intended to describe any sequential process like speech. HMMs have been proven to be really practical for speech decoding.
Third, it’s a matching process itself. Since it would take longer than universe existed to compare all feature vectors with all models, the search is often optimized by applying many tricks. At any points we maintain the best matching variants and extend them as time goes on, producing the best matching variants for the next frame.
What is a Neural Network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.
Basics of Neural Networks
Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives.
A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.
In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be “buy,” “hold” or “sell.”
Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.
- Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.
- They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.
- Use of neural networks for stock market price prediction varies.
Application of Neural Networks
Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection and risk assessment.
A neural network evaluates price data and unearths opportunities for making trade decisions based on the data analysis. The networks can distinguish subtle nonlinear interdependencies and patterns other methods of technical analysis cannot. According to research, the accuracy of neural networks in making price predictions for stocks differs. Some models predict the correct stock prices 50 to 60 percent of the time while others are accurate in 70 percent of all instances. Some have posited that a 10 percent improvement in efficiency is all an investor can ask for from a neural network.
There will always be data sets and task classes that a better analyzed by using previously developed algorithms. It is not so much the algorithm that matters; it is the well-prepared input data on the targeted indicator that ultimately determines the level of success of a neural network.
What is Computer Vision?
Computer vision has been around for more than 50 years, but recently, we see a major resurgence of interest in how machines ‘see’ and how computer vision can be used to build products for consumers and businesses. Few examples of such applications are- Amazon Go, Google Lens, Autonomous Vehicles, Face Recognition.
The key driving factor behind all these is Computer Vision. In the simplest terms, Computer Vision is the discipline under a broad area of Artificial Intelligence which teaches machines to see. Its goal is to extract meaning from pixels.
From the biological science point of view, its aims are to come up with computational models of the human visual system. From the engineering point of view, computer vision aims to build autonomous systems which could perform some of the tasks which the human visual system can perform (and even surpass it in many cases).
A brief history
The summer of the year 1966, Seymour Papert and Marvin Minsky at MIT Artificial Intelligence group started a project titled Summer Vision Project. The aim of the project was to build a system that can analyze a scene and identify objects in the scene. So the vast, puzzling area of computer vision that researchers and tech giants are still trying to decode was first thought to be simple enough for an undergraduate summer project by the very people who pioneered artificial intelligence.
In the 70s, taking ideas from studies of the cerebellum, hippocampus and cortex for human perception, David Marr, a neuroscientist at MIT, set up the building blocks for the modern Computer Vision and thus is known as the father of the modern Computer Vision. Majority of his thoughts are culminated in the major book simply titled VISION.
Deep Learning has taken off since 2012. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Powering recommender systems, identify and tags friends in photos, translate your voice to text, translate text into different languages, Deep Learning has transformed Computer vision leading towards superior performance.
Image classification error rate over time, drastic drop after the introduction of deep learning. Source.
These Deep Learning based computer vision algorithms such as Convolutional Neural Networks have started giving promising results with superior accuracies even surpassing human level accuracy on some tasks.
Smartphones: QR codes, computational photography (Android Lens Blur, iPhone Portrait Mode), panorama construction (Google Photo Spheres), face detection, expression detection (smile), Snapchat filters (face tracking), Google Lens, Night Sight (Pixel)
Web: Image search, Google photos (face recognition, object recognition, scene recognition, geolocalization from vision), Facebook (image captioning), Google maps aerial imaging (image stitching), YouTube (content categorization)
VR/AR: Outside-in tracking (HTC VIVE), inside out tracking (simultaneous localization and mapping, HoloLens), object occlusion (dense depth estimation)
Medical imaging: CAT / MRI reconstruction, assisted diagnosis, automatic pathology, connectomics, AI-guided surgery
What is Machine Learning? A definition
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of
Computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. 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. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
- 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. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures 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. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiringunlabeled data generally doesn’t require additional resources.
- 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. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
Types of machine learning algorithms
Just as there are nearly limitless uses of machine learning, there is no shortage of machine learning algorithms. They range from the fairly simple to the highly complex. Here are a few of the most commonly used models:
- This class of machine learning algorithm involves identifying a correlation -- generally between two variables -- and using that correlation to make predictions about future data points.
- Decision trees. These models use observations about certain actions and identify an optimal path for arriving at a desired outcome.
- K-means clustering. This model groups a specified number of data points into a specific number of groupings based on like characteristics.
- Neural networks. These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.
·Reinforcement learning. This area of deep learning involves models iterating over many attempts to complete a process. Steps that produce favorable outcomes are rewarded and steps that produce undesired outcomes are penalized until the algorithm learns the optimal process.
The future of machine learning
While machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence (AI) has grown in prominence. Deep learning models in particular power today's most advanced AI applications.
Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, model building, training and application deployment. As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.
Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and able to apply context learned from one task to future, different tasks.
What is Deep Learning?
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990s and early 2000s.
The leaders and experts in the field have ideas of what deep learning is and these specific and nuanced perspectives shed a lot of light on what deep learning is all about.
In this post, you will discover exactly what deep learning is by hearing from a range of experts and leaders in the field.
Discover how to develop deep learning models for a range of predictive modeling problems with just a few lines of code in my new book, with 18 step-by-step tutorials and 9 projects.
Let’s dive in.
Deep Learning is Large Neural Networks
Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.
He has spoken and written a lot about what deep learning is and is a good place to start.
In early talks on deep learning, Andrew described deep learning in the context of traditional artificial neural networks. In the 2013 talk titled “Deep Learning, Self-Taught Learning and Unsupervised Feature Learning” he described the idea of deep learning as:
Why Call it “Deep Learning“?
Why Not Just “Artificial Neural Networks“?
Geoffrey Hinton is a pioneer in the field of artificial neural networks and co-published the first paper on the backpropagation algorithm for training multilayer perceptron networks.
He may have started the introduction of the phrasing “deep” to describe the development of large artificial neural networks.
He co-authored a paper in 2006 titled “A Fast Learning Algorithm for Deep Belief Nets” in which they describe an approach to training “deep” (as in a many layered network) of restricted Boltzmann machines.
Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
This paper and the related paper Geoff co-authored titled “Deep Boltzmann Machines” on an undirected deep network were well received by the community (now cited many hundreds of times) because they were successful examples of greedy layer-wise training of networks, allowing many more layers in feedforward networks.
In a co-authored article in Science titled “Reducing the Dimensionality of Data with Neural Networks” they stuck with the same description of “deep” to describe their approach to developing networks with many more layers than was previously typical.
We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.
In the same article, they make an interesting comment that meshes with Andrew Ng’s comment about the recent increase in compute power and access to large datasets that has unleashed the untapped capability of neural networks when used at larger scale.
It has been obvious since the 1980s that backpropagation through deep autoencoders would be very effective for nonlinear dimensionality reduction, provided that computers were fast enough, data sets were big enough, and the initial weights were close enough to a good solution. All three conditions are now satisfied.
In a talk to the Royal Society in 2016 titled “Deep Learning“, Geoff commented that Deep Belief Networks were the start of deep learning in 2006 and that the first successful application of this new wave of deep learning was to speech recognition in 2009 titled “Acoustic Modeling using Deep Belief Networks“, achieving state of the art results.
It was the results that made the speech recognition and the neural network communities take notice, the use “deep” as a differentiator on previous neural network techniques that probably resulted in the name change.
The descriptions of deep learning in the Royal Society talk are very backpropagation centric as you would expect. Interesting, he gives 4 reasons why backpropagation (read “deep learning”) did not take off last time around in the 1990s. The first two points match comments by Andrew Ng above about datasets being too small and computers being too slow.
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Inherent Technologies endeavors to provide its clients with a wide breadth of services across the Information Technology spectrum. This includes software design, development and implementation; improving business through custom business solutions. Inherent Technologies believes that teamwork is the key to success. Together Everyone Achieves More. We have a motivated and well qualified team with relevant experience to handle and provide solutions to a vast variety of applications. The Premier Technology Execution Company As the premier technology execution company, we promise you the right expertise and an unrelenting commitment to service. Our ability to deploy superior technology expertise is rivaled only by our deep commitment to service and reliability. This commitment isn't just something we talk about; it's part of who we are, and it shows in everything we do.
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