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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?

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?

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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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?

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:

C++**
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.

Python
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.

Solidity
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.

Java
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.

Ruby
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

**Reference:

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?**

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.

Reference:

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

Artificial Intelligence (AI) as a technology is still in its nascent stages of Narrow AI, however, the application-base has expanded from computers to various other fields of human interaction with technology. One of the notable successes AI has achieved is personalization of customer experiences trough its dynamism.
**
Machine Learning (ML)** has proven to be a great way to stay interactive and relevant to its users. Virtual Personal Assistants like Siri, Alexa, Cortana and Echo are living examples of the same.

Moving on from computer applications, machines have been made AI sensitive for greater user experience. Smart cars are an example of it. Transportation has been one of the areas where AI can be seen transforming the way people travel in future. Artificial Intelligence (AI) is revolutionizing driving, making transportation more efficient and safer and as exciting as any other car is.

Marketing is yet another area where AI and ML can be found leaving their imprint by predictive analyses which helps companies identify the typical and general customer behavior. It allows the marketers to up-scale their content marketing strategies for better optimization and eventual ROI.

Healthcare is hugely benefiting from AI. The technology uses software and complex algorithms to analyze human medical data, thus helping in diagnosis, the treatment, see the patient outcomes and provide a well-defined output to the patients. ML algorithms are responsible for this.

There are many other industrial and technological benefits achieved using AI including service industries, manufacturing industries, cognitive computing, robotics, and a lot more and this is just the tip of the ice-berg.

SOURCE:Successful Applications 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.

Reference:
What are the major types of artificial intelligence?
What are the major types of artificial intelligence? Sphinx WorldBiz Limited
What are the types of AI(Artificial Intelligence) ?

Artificial Intelligence (AI) is Awesome. How?

Days are long gone when outsourcing services were valued only to curtail production cost. Today, the term has a glorified meaning, a global phenomenon for industries to gain an upper edge vis. a vis. the end-to-end business strategy.

Be it healthcare, finance, education, banking, advertising or retail customer service sector, AI is fundamentally changing the entire pattern of how industries operate today.

Enterprises are not only focused on the end result, the product, today, they are equally interested and stay involved at every step of the production and want the service providers to be open to the same. Hence the concept of Software Lifecycle Management was born.

Days are long gone when companies would hire skilled work forces from within a limited geographic radius. Thanks to the introduction of internet and smart technologies that companies regardless their size can find wide talent pool from around the world without investing much. According to a 2018 research conducted by Deloitte, over 38% of all IT services are being outsourced while Statista added that Global outsourcing industry earned $88.9 billion last year.

No doubt a company can earn humongous amounts of profit with a glut of resources but only when it is collaborated with the right resource augmentation company. Below are the qualities to look for in a vendor that provides software development outsourcing:

High-Quality Recruitment Process

Recruiting right talent is pivotal to gain more profits with lesser risk. Seek an outsourcing company that always has a bench full of skilled employees that are ever-ready to provide dedicated support service. It should have dedicated R&D and QA centre that analyses and understands dynamic client requirements to bring eligible resource(s)for the project.

Transparent Communication Process

As they say, ‘Good communication is the key to quality development,’ hence it is vital to understand the vendor’s communication process. Many outsourcing software companies have layers of management between them and the clients which sometimes result in distorted end message. Therefore,opt for a managed competency centre which has transparent communication channels and puts you in direct communication with the developing team.

Continuous Training Program

For any outsourcing vendor, it is important to upgrade their employees’ skills by giving optimum training regularly. Information technology world is impulsive as it faces regular complex updates.Therefore, it is smart to work with a vendor that has extensive knowledge of market and trains its resources accordingly before it becomes too late. The services should not only be competent with skilled resources but should also have a lucrative client-centric outsourcing format to benefit both- the customization of solutions and enterprise product engineering.

Also Read: The Surging Demand of Software Development Outsourcing: Top Reasons

Comprehensive Working Model

Every company has its own working model that defines its competencies to offer quality operational support and services to outsource. Choose a company that has scalability as a business offering to redefine the projects with automation and technology. Also ensure that the outsourcing company provides a set of other comprehensive services including onsite, offshore and hybrid services under one roof.

Monitoring Efficiency

One of the biggest fears for companies while settling for a service vendor is that they might not be able to track or monitor their team’s productivity from say,halfway around the world. Your offshore vendor should have transparent work-measuring systems and applications that allow clients to monitor their team’s performance while sitting in any corner of the world.

Flexible Pricing

Startups and small-scale enterprises mostly choose outsourcing services to gain more profits while investing less from their pockets. Large investments are deterrent to kick-start any new project therefore,pick a company which prospers in flexibility in costs with their offshore services without having to worry about compromising the work quality.

From application development to data management, companies globally are outsourcing services to accomplish maturing requirements of the IT world. Resource augmentation has not only helped the companies to improve their efficiency and meet deadlines but also making the software development processes affordable than ever. Hence, a sensible choice in collaborations with vendors offering outsourcing services is essential.
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SOURCE: What to Look for in Vendors Offering Software Development Outsourcing _

85% of customer interactions will be accomplished without a human by 2020, as suggested by a recently conducted Gartner study. AI has incredibly given a new meaning to the relationship between technology and its application by humans. Siri and Alexa have become almost redundant in every piece written on smart voice assistants or AI-driven voice assistants, and that itself is an example of how well versed we are with the open presence of this technology. Artificial Intelligence (AI) has been around for a while but has never been this close to non-experts or regular users, it is safe to say that AI has become a household concept however latent or ignored it may be by common users or non-experts.

Customer experience is at the helm of any new technology being utilized by products or by merely an idea with viable economic prospects. Every investment made for the growth of AI, even though it is still in narrow stage, it is aimed at making the lives of the users simpler. Experts do believe that AI is set to offer re-imagined and more transparent customer experiences in order to transit into smoother trading process which are more personal. But, the big question is what's influencing companies, big or small, to incorporate AI as an integral part of their customer relationship service?

Chatbots and Virtual Assistants- new age customer care
Chatbots and virtual assistants are two of the most integral ways how AI is reforming the customer service process. Where customer care executives fail to determine a customer's long standing history with a product or the brand, AI on the other hand, has progressed enough to make it possible. AI is set to revolutionise the way customers are approached and dealt with- care, accuracy, transparency and no-hassles. Automation plays a huge role in achieving this.
Poncho, Facebook Messenger's chatbot and top ranked on ChatBottle. In a bigger market share, Amazon's rockstar- Alexa, holds over 70% of the current chatbot market.

Automation is the new road to be taken

Productivity and efficiency gets raised manifold with voice process automation. Cognitive computing allows you to enter the customer's mind. AI also helps in brining bots to troubleshoot customer queries for effective customer experience.

Personalising Customer Experience
Understanding customers' buying behaviour allows marketers to drive better engagement with lesser risks involved. The smart prediction of customer behaviour and dynamic market situations help in customizing the plan while amplifying customer experience. AI models, however, utilise online behavioural signals and determine what's best for customers in real-time. Personalisation is not limited however to just chatbots. AI also allows to optimize emails, do more effective advertisements through programmatic ads, broaden customer base with lookalike modelling of the demography likely to convert to new brands and by customising online retail shopping experience. Gartner has predicted that by the year 2020, digital companies will have their profits boosted by as much as 15% using AI to predict customer intent.

Dedicated Customer Support, Really
Customers and users seek 'one-click' services because nobody really has the time for elaborates processes. One-touch isn't always single touch per se but it refers to quick service with less manual work. Accessibility is an essential part of the services. AI promises to make a long-term customer support possible by chatbots and emailers to begin with. They give instant response to queries and are responsive round-the-clock. Humans appreciate those who are always around to have their back. Chatbots give personalised human interactions. The technology is capable of eliminating human errors made during customer service.
Polite conversations are possible without any heated arguments, which is the case sometimes with human interaction.

Also Read: Successful Applications of AI

Good customer experience is essential to retain customers. However, understanding customer behaviour through their history was the challenge and AI-driven solutions make this possible, make this happen.
This is mere introduction to how technology is put to test vis. a vis. its users' experiences and this is just the beginning. AI experts across the world are constantly developing the technology while maintaining the sanity of human intelligence and offer more with proper training. AI is still young and has a foreseeable bright future within the native stage itself. Come to think of how customers can have a great time engaging with AI and avoid clutter-free experience in their busy lives being customers.

Source: https://www.comfortskillz.com/2019/09/ai-and-customer-experience-all-set-to-overtake-human.html

According to a research conducted by the IBM Marketing Cloud in 2017, 90% of the data was generated in last two years. Just imagine how much data has been created since then!

Big Data has become the hot topic among the business world and their tech-experts. It is astonishing to imagine that the advancement of technology has made the data organization a cakewalk. The concept has made it possible to collect, analyze, and utilize the required data from different sources like social media, transactions, and equipment embedded with sensors. Curious to know more about Big Data? Below are some interesting facts you should know about this technology now.

If a recently released report is to be believed, about 80% of USA and European companies trust Indian companies for their outsourcing services. National Association of Software & Service Companies (NASSCOM) discovered that over half of the total Fortune 500 companies prefer India for outsourcing software development. The stats are amazing, there must be something outstanding about Indian outsourcing practice that hooks developed countries to Indian expertise.

?“Development of full Artificial Intelligence could spell the end of the human race,” late scientist Stephen Hawking shared his views with BBC in 2014.

PICTURE COURTESY: Zohar Lazar Remember Jarvis, Iron Man’s AI-backed companion with British accent and an obedient “at-your-service sir” attitude? How astounding it is to witness a tech-world where imaginary characters can become a reality with the help of AI. Facebook

Globalization has led the market competition to another level altogether. To cope up with the dynamic working space at an unprecedented pace,