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Updated by Aarushi Sharma on Aug 14, 2019
Headline for Sphinx WorldBiz Limited- Offshore Development | Artificial Intelligence | Product Engineering
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Sphinx WorldBiz Limited- Offshore Development | Artificial Intelligence | Product Engineering

Sphinx Worldbiz is an India-based IT Solutions & Services provider which offers customized applications, mobility, e-Commerce portals, ERP solutions, software migration, systems integration and a host of other IT service requirements across verticals to its global clientele.


Working Model - Sphinx WorldBiz Limited

Our working model is quintessentially customer focused- engagement and enterprise centric, aiming to address and enhance our customers’ core competencies, backed by quality technical operations and support. Offering definitive advantage to our customers- they are assured of consistent and predictably high levels of efficiency in their processes.

What exactly is Artificial Intelligence?

Artificial Intelligence *or more commonly called acronym- AI, is an advanced technology wherein machines can perform human-like activities and daily tasks with the help of computerized learning and comprehending abilities. To put it simply, *AI is a computer technology which does tasks for humans in a way that it seems be using human intelligence to carry them out but it’s really a learned and trained technology or system that operates so automatically. It is a field of study which allows the machines to exhibit intelligence.

Artificial Intelligence has been instrumental with its purpose of inception i.e. to eliminate or deplete human physical efforts for various tasks so that humans can focus on other important works and save time alongside.

The roots of this technology date as early as 1950s and a lot of research has gone into enhancing the technology since to be able to bring it to where it is today. The usage has been far and wide in several domains of our lives today which often times go unnoticed. It is because of AI’s smooth entry into our lives, the sheer beauty of this technology in adapting human intelligence can be seen in the fact that we hardly notice it. Some of the daily use AI examples can be seen in voice driven technology like** Alexa, Siri, Hound, humanoid robots like Sophia**, etc.

While experts are evolving this technology rapidly to be used in many other ways to support industries, Sphinx Worldbiz is contributing to make AI more accessible to companies worldwide, thus doing its part in technological advancements.

SOURCE: What exactly is Artificial Intelligence?

What are the types of AI?

Fundamentally AI is a technology which imitates human intelligence to make machine process and respond any form of data while dedicated human efforts are reduced and the task is carried out synthetically by the machine but in a very humanly way. There are several applications of *Artificial Intelligence *and it can be distinguished under two heads- Type 1 & Type 2 based on its primary functions, applications and its learning stages.

Type 1- Functionality

Purely Reactive AI– The most fundamental form of AI where the machines perform based on the presently available data in the current situation using narrowed-down predefined tasks and cannot either form memories and use past experiences, nor assess the future implications. Computer games like Deep Blue, IBM’s chess-playing supercomputer and Google’s AlphaGo are classic and most sophisticated examples of reactive AI.

Limited Memory AI– As the name suggests, machines are capable of doing tasks but with limited memory to assess steps in the current situation as it uses data from its pre-fed history. Often cited example is the self-driving cars and chatbots trained through Machine Learning (ML).

Theory of Mind AI– This is perhaps most challenging and yet in its early development phases. This type of AI should be able to train machines to comprehend human emotions, thoughts, beliefs and expectations to imitate the same in order to become socially interactive.

Self-aware AI– This form of AI will have the machines understand and have consciousness. This is in corollary to theory of mind AI. These machines will be highly self-aware and can take decisions based on that judgement. Today humans may be far from creating such elevated form of AI but AI researchers and developers like us at Sphinx Worldbiz are dedicated to this cause aiming to make this dream a near future reality.

Type 2- Learning Stages

Artificial Narrow Intelligence (ANI)/Narrow AI – Also known as Weak AI, at this stage machine can only perform very narrowed-down specific tasks without any ability to think or comprehend on its own. Most common examples of ANI are Apple’s Siri, Amazon’s Alexa, humanoid Sophia, RankBrain, Alpha Go, etc. This will not be wrong to say that all the AI based inventions made till date are functionally at Narrow learning stage and even then are hugely benefiting businesses and industries.

Artificial General Intelligence (AGI)/General AI– Also known as Strong AI or Deep AI, this allows machines to think as wide, as much as humans can. Although this is futuristic scenario but according to many experts this is absolutely possible considering years of research has been dedicated to it.

Artificial Super Intelligence (ASI)/ Super Intelligence– This is a stage where machines and computers surpass human intelligence and take them over. It is not a reality as of today but a highly speculative one as a lot of experts are divided between positive and negative aspects of the same.

SOURCE: What are the types of AI?

Why do we need Artificial Intelligence?

We are living in the age of rapid population growth leading to demand and supply chain. The technology therefore needs a kick every now and then.** Technology like **Artificial Intelligence (AI), Machine Learning (ML) – a branch of AI prove to be useful in supporting humans and dedicated human efforts through automation and imitate their work for them as if machines have used human intelligence. Thus the need for technological advancement we now call- AI!

*AI *makes it possible for machines to understand and learn to attempt tasks with as much precision as any human by the virtue of human experience and is highly dynamic as it can be adjusted according to human requirements. A lot of tasks are being made possible which use cerebral capacity using Deep Learning and Natural Language Processing.

AI is being widely used today to steer almost all the industries and facets of life in big or small ways. Right from analytics to industrial support, there’s a bit of AI everywhere.

In a nutshell, we need Artificial Intelligence because:

*AI *supports existing products with intelligence and support their performance by taking it several notches higher.
AI redefines automation as it performs critical computerized tasks tirelessly.
AI lets the data do the programming as it works through progressive learning algorithms.
AI efficiently analyzes deeper information using neural networks to unravel data encrypted under several layers.
It works with incredible accuracy which was earlier impossible to achieve.
AI creates avenues for greater competitive advantages as it extracts the most out of simple data.

SOURCE: Why do we need Artificial Intelligence?

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What are the major components of Artificial Intelligence?

Artificial Intelligence aka AI has become such an interesting topic for the IT experts and data engineers that almost everyone is talking about it and the ones still not talking about it, will do so in no time. Although AI as technology is essentially in its Narrow stage which is the first stage and even then, it is making itself heard in all corridors of industrial and technological domains.

The natural question arises- what is it made of? What should it consist?
Since we are talking about synthetically achieved intelligence to replicate human behavior and capabilities and make the machines operate as if operated using human intelligence thus making machines more ‘human like’, it becomes imperative that the AI (Artificial Intelligence) should have the following:

*Cognitive Abilities *– If we want the machine to interact like humans, it will have to think and work like humans.

•The computer-generated intelligent machine will need to have Natural Language Processing (NLP) for successful interactions.
•Automated Reasoning for analysing the database stored in the system.
•Machine Learning (ML)- the machine will be fed in current inputs and after training sessions, the machine will be able to speak learned language owing to the pre-fed data to respond more accurately and less like a bot.
•Rationality and introspection come with more training.

*Database *– The machines can be made more intelligent and introspective rationals only by incorporating and supported them with database, information and feed them in the form of rules to process information and be helpful in finding solutions for humans.

*Hardware *– Graphic Processing Units aka GPUs are an important part of AI hardware to extend from one approach to a physical entity using the technology.

Framework – For better Machine Learning processing a sound framework is required. The most commonly used are Python, R and Azure Machine Learning Studio.

APIs are required to publish AI services. Text classification, image classification, sentiment analysis, etc can be achieved using APIs.

AI Applications are what we see at customer end like Siri and Alexa.

While there is a lot that facilitates AI in creating an interesting and successful outcome, the above mentioned are the prerequisites as AI components. At** Sphinx Worldbiz,** the AI experts are dedicatedly using thre best of class infrastructure towards successful AI results in bringing the technology more close to its end users.

SOURCE: What are the major components of Artificial Intelligence?

Which programming language is best for Blockchain?

Blockchain is a revolutionary digital payment technology still in its nascent stage. To simply put it, blockchain contains digital payment information in block forms that does not allow hackers to steal your data in any way possible.

**Top Programming Languages Best for Blockchain:

It is the most preferred blockchain programming language as it offers advanced multi-threading potentials, access over memory, and semantics. It also bids object-oriented features such as function overloading and runtime polymorphism to perfectly link the data together.

This language is fairly new but hyping the world of programming hastily. The language is viably preferred in the blockchain industry as it can perform many tasks with just single command.

Influenced by PowerShell, JavaScript, and C++, Solidity is a high-level language used to implement smart contracts on multiple blockchain platforms. The language is very popular amongst Ethereum developers.

Due to its highly-potential portability, Java is valued in the blockchain industry. Java and C++ have many similar features as they both are object and procedural-oriented programming languages. It has almost 9 million developers across the world. NEM’s basic blockchain network has been written in Java only.

Although the oldest programming language, Ruby made its comeback with the blockchain technology. It allows users to use a perfect blend on different languages to develop the ideal blockchain. The language gives the access to make amendments if required.

While developing a blockchain, the right programming language is the key to safeguard cyber security. The expert programmers at Sphinx Worldbiz offer ideal solutions to befit your complex requirements


Which Programming Language is the Best for Blockchain? |

What are the best programming languages for blockchain development? Sphinx WorldBiz Limited

What are the best programming languages for blockchain development?**

Emerging Big Data Trends to Watch in 2019: Keeping Up or Falling Behind?

Ever searched for a product or service and later seen an advertisement popping up on your Facebook news feed? If yes, then congratulations, you have been re-targeted. It shouldn’t be a surprise as companies collect data to target their audiences for better customer relations with the help of Big Data.

Big Data (

has gained lot of traction but, are you really clear about the meaning of these two words? How it is impacting our daily lives and why big firms are using it? To put it simply, Big Data is a large set of unorganized data that are computationally analyzed to unfurl the trends and patterns on a certain subject. From small firms to established enterprises, the process has become one of the most promising technologies of the epoch.
It is surprising to note that in every two days we create as much information as we did until 2003 and over 90% of all the data globally is created in just past two years. It is mind-blowing to see how big data is increasingly becoming the backbone of every industrial sector. Meanwhile, people are looking for the answer if big data will grow or fall behind in the coming times? Let’s dig deeper and look at the possible trends for Big Data in the coming years.

Data Silos will Continue to Boom

When Hadoop boomed five years ago, there was a possibility to consolidate all the data onto a single platform regardless its nature- analytical and transactional workloads. However, the prediction panned out in the presence of many challenges. One of the biggest challenges was the different data types that require different storage units. From relational database, HDFS, object stores to time-series databases, all have their own obstacles. Therefore, it will become complex for developers in maximizing strengths while packing all the data into one size. However, cloud data stores like Hadoop and S3 are helping companies to store their data in a cost-effective manner. But that doesn’t mean data silos will decrease especially in the absence of strong centralizing force. So, we might have to get used to it!

Enhanced Data Retention Policies

According to Carlos M. Meléndez, chief operating officer at AI consulting firm Wovenware, it is not essential to store every data forever. Only some needs storage for some time. Coming years will focus on machine learning in a way that will clean and protect the integrity of stored data. Also, it will have automated flush feature to dispose such data which is no longer needed.
Don’t worry, the data will not be lost forever. You can recover the disposed data anytime as algorithms will be scripted in a manner that the backup feature will be provided.

A CIO Showdown

As per James Markarian, CTO of SnapLogic, “The days of forgetting that the ‘I’ in CIO stands for ‘information’ are over.” He added that CIO will not only be limited to infrastructure but will also become a process to manage and create strategies for company’s data. By the end of this year, the process will pick up steam due to digitization and data transformation.
Skills will be Proliferated as the Tech Evolves
As it requires skills and knowledge to manage and run the data in the right stream, thus in coming years, the demand for any individual who can infuse neural network into final production is expected to increase exponentially. There is plenty of scope for folks who have a good knowledge of Matlab, Scala, C, and Java but Python will continue to dominate among all the languages. Meanwhile, data engineers who know Spark, Airflow, and databases will tend to grow. Machine learning engineers will not remain behind in the Big Data world as well.
In a Nutshell
Indeed, converting huge unorganized data collections into an actionable insight is a complex task. But Big Data experts and industry big-wigs surely see keeping up with the technology to leverage information for better customer relations. Experts are analyzing legit, substantial, ethical and technical hurdles in Big Data and AI processing, but its promising benefits are difficult to ignore.


Emerging Big Data Trends to Watch in 2019: Keeping Up or Falling Behind?

What are the current trends in Big Data?

What are The Current Trends in Big Data? Sphinx WorldBiz Limited