SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

Bringing Artificial Intelligence in Business

Now a days, companies are investing a huge amount of money in Artificial Intelligence, Automation, Robotics etc in order to be up to date with technology. However, still there are few limitations that each of them faces. Even after adopting various technologies, the reports stated that the company still struggles in defining goals. Below are the five steps that companies must follow in order to improve and to meet their expectation for automation technology:

  1. Recognize that the use of intelligent automation is transformative, and built on the use of new machines and data sources
  2. Formulate a comprehensive approach to automating the service delivery model
  3. Measure value vs. risk
  4. Consider the “operating model” in all forms
  5. Disrupt from within

Bringing innovation in always good but it is also suggested to look for other alternatives for investment as not all technologies proves to be powerful for a company.

Read more about it at https://www.techrepublic.com/article/5-steps-to-help-your-company-benefit-from-ai/

 

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Rules to Follow in Data Analytics

Analytics is one of the major jobs performed in companies these days. Daily operations are carried out involving data that presents us with results which helps an organization to carry out further processes and helps in decision making. Effective business intelligence is the product of data processed. This data is raw and can be either structured or unstructured. 

Firstly, one needs to manage data before processing it. Rules are to be set for the analytics process which can offer better insight and an easy processing. Below are the five rules that can help in managing your data more effectively:

  1. Establish Clear Analytics Goals Before Getting Started
  2. Simplify and Centralize Your Data Streams
  3. Scrub Your Data Before Warehousing
  4. Establish Clear Data Governance Protocols
  5. Create Dynamic Data Structures

The field of data analytics is always evolving and thus it is important to create a proper structure that can help in future. By establishing them we can enhance the quality of data processing.

Read more about it at: https://www.sisense.com/blog/data-management-rules-analytics/

 

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Learning Neural Network and Deep Learning

Artificial Intelligence is a combination of various sub topics. Whether it be Machine Learning, Deep Learning, Neural Network etc, each one of them finds shelter under artificial intelligence. Below are the basics of two important topics – Neural Network and Deep Learning.

Neural Networks: Neural network is modelled in the same way as human brain. It compromises various algorithms and aims to find relationship in data provided by us through processes that mimics like human brains. It is one of the trending technologies and is finding its applications in various fields like trading, medicine, pattern recognition etc.

Deep Learning: Deep Learning is basically a network composed of several layers. It is also known as ‘stacked neural networks’. It is a subfield of machine learning and is inspired by the structure and function of brain. It can be widely used in classification, clustering, predictions etc.

We are walking towards the era where technology will dominate and the main technology will be artificial intelligence. Hence it is important to get an insight of what we will be with in future. For a beginner, these topics might be confusing but they form the crust of artificial intelligence.

Read more about them at: https://www.technotification.com/2018/08/neural-networks-deep-learning.html

 

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Reasoning for Slow Pace of Digital Transformation

Digitalizing is becoming the need of hour in every business. Every organization is trying to in cooperate technology as per there needs. Companies believe that cloud storage, analytics, mobile and social advancement are all the tools they require for digital transformation. However, this is not enough. Digital Transformation is still lagging behind even after great efforts by organizations. One reason behind it is the fact that one could not match the speed at which technology is growing. 

Following are few challenges that organizations faces while trying to keep digital transformation up to date:

  1. Lack of vision and leadership
  2. IT and business don’t see eye to eye
  3. Little to no engagement
  4. Transforming ops is hard
  5. Governance is lagging
  6. Critical functions are being shortchanged
  7. Shying away from the cutting edge
  8. Metrics misalignment
  9. Failure to change culture
  10. Not failing enough

Read more about them at: https://www.cio.com/article/3274447/digital-transformation/why-digital-transformations-are-lagging.html

 

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Finding Data!

Data is very important for various technologies. Whether it be Artificial Intelligence or Machine Learning, Data analysis or Research work, Data is mandatory to implement them. However, the task of finding right data is very tedious and time consuming. One needs to find data that is most appropriate in terms of information available, size and other factors. 

Every day a huge amount is data is generated on internet. To our help there are few open data sources that are free to use. This data can be in raw form which might need further processing. But to start with the process and to get a data set, one could visit below mentioned sites that provides data for free: 

  1. Kaggle
  2. UCI machine learning repository
  3. data.gov

Know more about them at : https://www.technotification.com/2018/04/building-data-science-models.html

 

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Aiming to Become A Data Scientist? Read This!

Data Sciences is a very vast field and in recent times, there is a high demand of professionals in this field. Dealing with data is not easy. Data sets available with companies are very large and to extract meaningful data is a tough job. Thus, the job of data scientist is becoming very important for decision-making and is based on automation and machine learning. The main role of data scientist is to organize and analyse data. Other than this, data can help in predictions, pattern detection analysis etc. All this can be done the help of some software which is specially designed for the task. The responsibilities of data scientist begin with data collection and ends with decision making on the basis of data.

To know more about the key roles of data scientist, requirements and skills visit: https://www.cio.com/article/3217026/data-science/what-is-a-data-scientist-a-key-data-analytics-role-and-a-lucrative-career.html#tk.cio_rs

 

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Stepping into Digital Transformation

As the name suggest, Digital Transformation involves thinking and rethinking about how an organization can use technology in business. It helps in improving ongoing processes and bring changes to business. Digital Transformation vary widely based on specific demands of an organization. However, there are few digital transformation elements that should remain in focus. They are – Workforce Enablement, Operational Agility, Customer Experience, Culture and Leadership, and Digital Technology Integration. Even though many companies are trying to in cooperate digital transformation, 84% of them are failing!

Following are the three keys to success:

  1. Establish Clarity
  2. Prepare from the core
  3. Learn to iterate quickly

Read more about them at https://www.cio.com/article/3292262/digital-transformation/3-fundamental-keys-to-digital-transformation.html

 

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Dealing with Predictive Analytics Challenges

One of the most trending and look for technology, Predictive Analysis is a powerful tool that can help us to forecast and predict what lies ahead us. However, it is usually accompanied by few issues that user encounters while using it. They might not be visible during early stages of development but they can become great concern when they will not be able to deliver results to customer. Prevention is always better than cure and thus it is recommended to study the technology well before use. 

Following are few tips that one should use to avoid and resolve common project challenges:

  1. Create and execute a formal strategy
  2. Ensure data quality
  3. Manage data volume
  4. Respect data privacy and ownership
  5. Maximize usability
  6. Control costs
  7. Choose the right tools

    To read more about them visit: https://www.cio.com/article/3287937/predictive-analytics/7-tips-for-overcoming-predictive-analytics-challenges.html?upd=1532674958240

     

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AI Contributing Towards Medicine

Artificial Intelligence is spreading its wings and is coming into rescue in various fields. One such field which comes into rescue for humans is the health care sector. Combination of these two fields can bring great advancement in health care sector. Artificial Intelligence and Machine learning have already come into action in medicine. Following are the top 4 applications:

    1. Diagnosing Diseases: Not all diseases can easily be rectified. This could be time consuming and expensive. Here, various Deep Learning algorithms prove to be a solution. This focus on automatic diagnosis, making diagnosis much cheaper and accessible. 
    2. Developing Drugs Faster: Drug development is a time taking and a tedious task. It involves analytics and various rounds of testing. AI has already aced in speeding up the process.
    3. Personalizing Treatment: Same medical procedure can not be carried out on every patient. Choosing the course of treatment can be a difficult and a great responsibility. Machine Learning can automate this task. It can help in designing the right treatment plan.
    4. Improving Gene Editing: This is a technique that relies on targeting and editing specific location on the DNA. A careful selection needs to be made. Machine Learning models have successfully been able to predict target and effects successfully.

To read more at https://towardsdatascience.com/artificial-intelligence-in-medicine-1fd2748a9f87

 

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Data utilization is more important than data creation

Today, Edge, IoT, mobile devices etc are everywhere and are creating plenty of data. By 2025, almost 75% of the world, both consumers and businesses will be connected. If seen in a broader view, there is a huge pile of data and it’s still increasing with a rapid rate. There should be more focus on proper and more usage of this data. Get people who can help you out to resolve the issue of better and more efficient utilization of data.

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Cryptocurrency wallets

Cryptocurrency is often seen as an asset. Few months back, there was a hype for purchase and sale of cryptocurrencies, especially, bitcoins. So along with the money, you obviously need a wallet to hold it. And actually there are various kinds of wallets to store this currency. Since Crypto-Currency is just a software, not a hard cash, so it requires a software wallet. There are various types of wallets like Mobile wallet, Desktop Wallet, paper wallet/cold storage, hardware wallet. These wallets have their own traits and the selection of wallet appropriate for you depends on you. 

Have a detailed review about cryptocurrency wallets and their attributes at https://www.analyticsinsight.net/investing-in-cryptocurrencies-you-need-a-cryptocurrency-wallet/

 

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All new type of memory

Scientists from Fudan University, Shanghai, recently published their research revealing an all new type of memory. We have heard about RAM and ROM, and both of these memories have their own limitations like RAM is volatile but Super-fast and ROM can store huge amount of data but it is Very slow. This all new type of memory is claimed to be able to store data as long as we want. This technology works on two dimensional semi-floating gate transistors. Unfortunately, research is still going on and we will require to use our traditional RAM ROM for a little long till this research turns out to be a public product. And also there’s no doubt that this technology can bring revolution in terms of data storage, security, data transfer and many more.

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Computers writing code by itself

Recently a team of researchers developed an AI based system that can write codes for the programmers and it can also predict the solutions for them. The system uses neural sketch learning method of deep learning to recognize patterns in millions of codes written in Java. Also, the system itself is trained by using millions of Java programs. Currently, it can be used to evaluate undocumented APIs that can be quite difficult for any programmer to use. This is the starting and we can visualize the upcoming future of how AI can help programmers in writing codes, moreover solving the problems which are quite difficult to solve by humans or may take a long time. 

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Tips to move from IT professional to IT manager

As the time passes by, it becomes a boring schedule to do the same task over and over again. Everyone wishes for some promotions in his career. There are some people who wish to move from complete IT and coding schedule to some IT management routine. So here are some tips for moving in that direction. 

Keep a check on the company visions and missions. Talk with your managers regarding how the company is performing and what are its future perspectives. Begin with leading your own team. Give your contribution in both technical and managerial rolls .Consult others on how they use the technology to achieve the same business task. Try picking up some managerial skills. All this small efforts will guide you in the path towards an IT Manager.

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Rise of Edge Computing

Evidently, there has been massive adaptation of cloud in the industry. As everything begins to operate on cloud, it also generates massive amount of data. Also, just IoT is not just enough, because it’s no more just a “Thing”; this thing, now, can be an automated car, a drone or anything which makes it a necessity to think beyond Cloud computing and brings us a glimpse of Edge computing. Edge computing can significantly provide better throughput, improved performance, and customised processing of data as per the needs of each user.

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Robots, Now in Space

On 29th June 2018, NASA launched, CIMON, the very first AI-Powered robot into the space. The bot is built with a really interactive AI program which is meant to interact with the people in the space station. Also, it can even assess the human mood and is programmed to answer the voice commands in English. And one more great thing, is it is unlike any mechanical bot/part of space station. It has got its own smiley like face on a space and it can itself move around the space station in zero gravity. It is really interesting to see the pace and direction of technological advancements. 

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How big data can help in customer service domain?

In every enterprise the customer care services are the most essential part because it not satisfies the customers but it also leads to more productivity. To build the customer loyalty and drive your business to heights it’s important to care the customer service domain. Services of new customer care software platform which involves big data has took this domain to a new level.

For more information go to:

https://www.analyticsinsight.net/big-data-in-customer-support-delivering-values/

 

 

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AI based First Forex trading robot

Foreign exchange market is the word which is enough capable to draw attention of many people. Some people treat it as a fast money making technique while some people says it’s a way to enter the gambling world. Foreign exchange market is just a virtual platform which involves trading of different currencies. So what forex trading is? How AI and robotics can be involved in trading? What actually forex robots can do?

Want to know? Visit:

http://bigdata-madesimple.com/ai-driven-forex-trading-robot-first-kind/

 

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How to become a data scientist?

Data science comes with a new era on IT industries. From AI, ML, big datadata analytics and many more data science is proving its importance. With the emerging business plans on big data the requirement and demands of data scientists are also getting higher. Here are the guidelines to the students who want to pursue data science as their career. 

 Education background should relate to computer science.

 Beginning of your career experience and work focus

 Learning opportunities and certification

 Mid-career experience and certification

 Data science expertise and professionalism

For more details, visit:

https://www.analyticsinsight.net/how-to-become-a-data-scientist-the-skills-certifications-and-education-required-for-the-trendy-job/

 

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How AI and ML can contribute towards developing an intelligent cloud?

With reference to the aura spread by artificial intelligence and machine learning every enterprise is making their ideas with the relevance of these two giants. We are aware of the concept of cloud computing that it provides the storage and networking space over the internet which eventually reduces human efforts and cost. This sounds absolutely awesome. But what’s next? Can cloud be developed as “Intelligent Cloud”? Introducing the union of AI, ML and cloud, this will definitely raise the standards of cloud computing in future. The intelligent cloud will have the ability to learn from the enormous amount of data, builds up predictions accordingly and end up analyzing situations. This platform seems to perform tasks with high speed and provides greater efficiency. 

https://www.analyticsinsight.net/developing-intelligent-cloud-with-artificial-intelligence-and-machine-learning/

 

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