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IoT Product Development: 6 Essential Considerations

Category : Blog

As per Gartner, there will be at least 20.8 billion connected devices by 2020; other research anticipates as many as 100 billion. And the largest beneficiaries of IoT devices will be consumers – people who want to control everything from their refrigerators and home security systems to their utility costs, cars, and beyond more than that. As per the data published by SYK Cleaning:

a. 61% of older generations want smart technology for its cost savings

b. 52% of Generations Y have priorities for home security

c. 39% of millennial just think smart devices are trendy and cool. And 72% of them would pay up to $3000 more for a home that has smart technology.

d. Generation Z, just entering the consumer marketplace will consider IoT a given part of their lifestyles.

But this entire boom in manufacturing and supply of IoT technology does not come without its challenges. And manufacturers have much work to do in some key areas in order to truly ensure that IoT will and can become fully accustomed. Here are the six of them.

1. Compatibility

The IoT ecosystem at its current state comes with a lack of cohesive standards in the fields of data exchange and connectivity. What that means for manufacturers is that they must take off their competitive “gloves” and collaborate on standards for the good of everyone. In doing so, they will all harvest the benefits of greater consumer adoption and market demand.

2. Security

This is an ongoing issue for both businesses and individual consumers. When an entire ecosystem can be threatened through hacking into just one individual device, the concern is real.
Just recently, we learned that a couple of our power grids were compromised, and researchers at the University of Oklahoma demonstrated how easy it was by hacking into a wind farm through a single unit.

Security testing of all devices must include identifying any potential vulnerability, processes for validating user access, and data encryption, etc. Fortunately, there are some pilot programs investigating the use of blockchain technology, and this may indeed hold some effective solutions.

3. User Experience

For IoT devices to go thoroughly standard, users have to be comfortable with them, and they have to see them as more valuable than traditional devices. This means ease of understanding and use.

Manufacturers must conduct a lot of testing of devices before putting them on the market, including the following:

a. Compatibility of device hardware, operating systems, software versions, and communication protocols

b. Reliability of all components in a variety of environments and conditions.

c. User friendliness of application as well as usability in a variety of network connections, so that everything operates seamlessly regardless of platform.

4. Platforms:

This will be the key to success of any manufacturer of IoT devices. Devices and connectivity will certainly become less expensive, making them more attractive, but applications that allow devices to connect and share information with other devices, aka platforms, are numerous and growing That manufacturer who will be able to bundle multiple platforms into a single product will meet a challenge that will give him a huge competitive edge.

5. Ecosystems

These will grow in the coming years, and there will definitely be “battles” among them. Ultimately, however, a few will emerge victoriously and will dominate entire sectors – smart homes, smart cities, healthcare, etc. This is just another reason why manufacturers need to find ways to collaborate to achieve standardization.

6. The Need for Real-Time Data Streams and Scaling

While a refrigerator will not necessarily need to provide real-time data to its owner (other than alerts if there is a malfunction), the need for real-time data will become critical in some IoT device use and management, for instance – smart cities. For manufacturers of devices that require real-time data streams, there will be a need for continual updating as newer technologies and apps are developed.

Blockchain, again, has been named among the possible solutions to more efficient, near-real-time data exchanges. Yet, this technology currently lacks proper scaling mechanism, making it a questionable choice for larger ecosystems i.e. those created for smart cities.

Image Courtesy- IBM

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Approaches in Natural Language Processing

Natural Language Processing involves machines or robots to understand and execute the language that human speak. It is a connection between computers and human language. Natural Language is the computer understanding, execution, twiddle and creation of natural language. Natural language processing (NLP) is a way to translate between a computer and human languages.

The creation of NLP is daring because computers conventionally require humans to speak with them in a coded language that is brief, unambiguous and organised. Human speech is not always brief it is often ambiguous and semantic structure which can depend on many variables including slang, regional dialects and social context etc. In other words, NLP automates the translation process between computers and humans.

Natural Language refers to speech analysis in both audible speeches as well as of a text language. NLP system grabs meaning from an input of words (sentence, paragraphs, pages etc.) in the form of formal and organised results. Natural Language is a basic part of Artificial Intelligence. It is far more than just speech interpretation. There are various approaches for human language which includes:


The symbolic approach to natural language processing is based on human-developed regulations and lexicons. In other words, the foundation behind this approach is in generally approved regulations of speech within a specific language which is materialised and recorded by experts.


The statistical approach to natural language processing is based on observable and persistent examples of semantic phenomena. Models which are based on statistics identify persistent themes through mathematical interpretation of the large text. By recognising trends in huge samples of text the computer system can develop its own semantic rules that it will use to interpret future input variables and the development of language output.


The connectionist approach to natural language processing is a mixture of the symbolic and statistical approaches. This approach starts with generally approved rules of language and converts them to specific applications from input procured from statistical inference.

How Systems Interpret Language:

Morphological Level:

Morphemes are the smallest units of meaning within words and this level deals with morphemes in their role as the parts that makeup word.

Lexical Level:

This level of speech analysis examines how the parts of words (morphemes) combine to make words and how slight differences can dramatically change the meaning of the final word.

Syntactic Level:

This level aims at text at the sentence level. Syntax rotates around the plan that in most languages the sense of a sentence is dependent on word order and dependency.

Semantic Level:

Semantics focuses on how the context of words within a sentence helps determine the meaning of words on an individual level.

Discourse Level:

How sentences relate to one another. Sentence order and arrangement can affect the meaning of the sentences.

Pragmatic Level:

Bases meaning of words or sentences on situational awareness and world knowledge. Basically, what meaning is most likely and would make the most sense.

Ultimate Aim

The ultimate aim of natural language processing is for computers to achieve human-like comprehension of texts/languages. When this is attained, computer systems will be able to interpret, summarise, translate and generate accurate and natural human text and language.

Image Courtesy- Expert Systems

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IBM Watson: Application of Intellectual computing in Life Sciences Study

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Category : Blog

Life Sciences Researchers are under pressure to innovate faster than ever. Big data offer the promise of unlocking unique insights. Today data is of utmost importance in every field ranging from Internet data to data collected from customers. Nowadays we can see that in every shopping malls, cabs drivers, home delivery services, fast food chain stores take feedback from their customers because they wanted to gather data in order to expand their business with the help of data which permanently give them a huge customer database. Although more data are available than ever, only a portion of it is being executed, understand and analyzed.

New technologies like cognitive computing proposed promise for inscribing this dare because cognitive solutions are exclusively designed to integrate and analyze big data sets. Cognitive solutions are designed to understand various datasets in a structured database. Cognitive solutions are structured to understand technical, industry-specific content, advanced reasoning and predictive modeling.

Watson, a cognitive computing technology, has been configured to support life science research. The Watson edition encompasses medical literature, patents, chemical and pharmaceutical data that researchers would commonly use in their work.

Many people know Watson as the IBM-developed cognitive supercomputer that won the Jeopardy game show in 2011. In truth, Watson is not actually a computer but a set of algorithms and APIs. IBM has used in every industry from finance to healthcare.

Recently IBM has announced several new partnerships which focus on taking things further and level extent. Also, it puts its cognitive capabilities to solve a new set of technologies around the world.

Meaning of Cognitive computing as per the Vice President of IBM Watson Steve Gold, what started with his own company’s development of tabulation computing, to process US census data at the inception of the 20th century developed into programmatic computing in the middle of the century, with the arrival of transistors, relational databases, magnetic storage and microprocessors.
We can see the tremendous growth in unstructured data we have faced in recent past years, the artificial methods that have been developed to help us make sense and learn from this data have given rise to cognitive computing. These cognitive computers don’t need to be programmed- they can learn from themselves.

The concept works like traditionally, computers have done what we tell them to do. We give them command and they need to follow that command as computers were already programmed to follow commands. We give them code containing instructions whatever we want them to be done and they follow the orders and carry out the result in the same fashion.
There is a limitation that they can only do whatever we want them to do things and what we teach them to do. We can’t simply order computers to develop a remedy to cure AIDS and expects the results for the same. But considering the fact that the computers can process the data and information far faster than a human can do. It will be much efficient to give them all the data available and let them work out the best solution for it.

Image Courtesy: Getty Images

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In Vogue Blockchain Trends in 2018

Category : Blog

Many digital marketers, SEO and other digital world experts believe that we people are living in fourth digital generation. Technology is getting really advanced now a day. In digital technology domain blockchain has developed as one of the tremendous inventions. Everyone is aware of the fact that blockchain retains the way to endless technological possibilities. Today, everyone starting from government, entrepreneurs, companies and new start-ups are doing growth with the help of blockchain. The crowd funding domain has already distorted in 2017 by Initial Coin Offerings. Traditional crowd funding models were overtaken by ICOs.

Blockchain application is in various domains of human effort, expectations are set to be up for this year. This blog scrutinizes blockchain technology recent trends to look out in 2018.

1. Blockchain and the internet of things:

Since 2014, it was known that blockchain technology can be used only for financial applications. The Ethereum blockchain and its initiation of the smart contract framework have opened up new possibilities now. Many technical experts in this field have trust that blockchain technology retains the solution to some of the troubles disturbing the successful execution of IoT.

What is an Internet of Things (IoT):

The (IOT) is an augmentation of internet communication limits to involve every possible tangible thing on the planet. It is a network of devices, animals, people, vehicles etc. which are capable of communicating and transmit data. With the help of using an array of sensors, actuators, RFID chips and other network connectivity devices, everything on the planet get linked and connected. Once it gets successfully implemented then without human interaction machine to machine communication can be possible.

Many stakeholders in the IoT development domain are searching the potential usage of blockchain in the IoT model.

Blockchain – IoT implementation:

Blockchain has the prospect to provide a secured framework for the IoT. The Blockchain is descriptively tamper-proof. As blockchain have one financial advantage that the secured blockchain is also cost-effective and provides faster transaction speed? Blockchain has already manifested to be the vigorous decentralized model that resists malicious war through a single point of failure. With data integrity being an even greater concern to the fidelity of the IoT, it is beyond doubt that blockchain will play an important role in its development in 2018.

2. Blockchain and Artificial Intelligence:

Since computers had been invented, scientists have been watching for various ways to develop thinking machines. AI is nothing but an algorithm that enables machines to manifest functions they weren’t programmed for. In order to operate to its fullest capacity, machines which have the potential of learning how to operate “big data”. Exchange of big data is not financially viable, but with the support of blockchain, this all could change. Blockchain can provide a safe atmosphere for big data owners to link with AI developers. By doing so, complex machine learning algorithms can be developed to help smart devices take benefits of the big data available to them in order to achieve artificial intelligence.


At previous decades, machines like computers technologies like internet were developed and they all are meant to be one of the greatest inventions invented in the welfare of mankind which is helping in all-round development of human. Blockchain technology has the potential to assimilate and propound feasible panacea to many of these technology advancements. As the procedures become more strengthen and data points become more scattered, network security will become a major issue. This is a platform where blockchain technology with its descriptive immutability and vigorous security become key to the successful implementation of these procedures.

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All about Natural Language Processing

The domain of research that point out the interactions between human languages and computers is called Natural Language Processing or NLP. It stands at the junction of computer science, artificial intelligence and computational linguistics.

NLP is a path for computers to inspect, understand and derive meaning from human language in a smart and useful manner. Through NLP, developers of it can collect and establish ideas to perform tasks such as automatic summarization, translation, entity recognition, sentiment/emotion analysis and speech recognition. It also has advanced features like correcting grammar, converting speech to text and automatically translates between two languages. NLP is generally used for text mining, machine translation and automated question answering.

A 2017 report on the natural language processing (NLP) market estimated that the total NLP software, hardware and services market share to be around $22.3 billion by 2025. The report also says that NLP software solutions influencing Artificial Intelligence will see a market growth from $136 million in 2016 to $5.4 billion by 2025.

We can see in the below mentioned figuretrends

Example Natural Language Processing Use Cases

NLP is based on machine learning algorithms. Rather than using hand coding commands, NLP can rely on machine learning to automatically learn these set of rules by interpreting a set of examples. Social media analysis is an example of NLP use.

Current Applications of Natural Language Processing

Customer Service

Some of the current virtual assistance solutions using NLP serve as intelligence enhancement. In such applications, a customer’s first request is checked by the artificial intelligence such as apps like Nina. E.g. a banking customer service system uses the AI to answer some basic transactional difficulties such as opening an account or to figure out the best loyal customer of the bank.


It also offers automotive virtual assistants connected to flagship cars OEM like BMW, JAGUAR, AUDI and others. One press release on Nuance’s partnership with BMW mentioned about Dragon Drive AI which enables drivers to access apps and services through voice commands, navigation, music, message, calendar, weather and social media.

It is possible to give a command to Artificial Intelligence to send a text message right from the car like, text Bella, “I will reach 10 minutes late at home” or “Get me directions to Dominos Pizza in Indore” etc.


NLP also provides solutions in healthcare domain. It includes clinical document improvement solutions. CDI is a process of improvising healthcare record of the patients to ensure good health of a patient, data quality etc. In this field AI allows physicians to write progress notes of patients, history of present illness and also plans or strategies need to be adopted for further actions. NLP provides real-time intelligence to physicians by automatically prompting them with clarifying questions while they are documenting.

There are many AI & NLP applications in the market. It is very important to choose the correct application that can resolve business problems with the help of technology and provide value.

Finally, businesses must have enough relevant data for learning algorithms for accurate outputs.

Future possibilities with NLP

  • Researchers are working on making AI more human-like, which is really a tough task. (E.g. making a conversational AI)
  • Expansion of existing AI technologies (E.g. extending automatic picture captioning to healthcare and other applications for clarification of image)