In today’s world, where technology is playing an important role, most of us fear about future. It has been noticed how robots have already stated taking place of humans resulting in humans to lose their jobs. Not only in factories but robots have also taken over sports, medicine and more. However, we humans do have some skills that is tough for robots to beat us in. Below are few of them:

1. Creativity: A piece of art, music, a new recipe and more reflects our creativity. Thus, even though robots can replicate what already exist, it still fails to create something new.
2. Physical Skills: There are some routine jobs which needs to be done quickly and involve out flexible physical skills. Though robots are developing yet they haven’t reached the stage where they could help us in doing our routines.
3. Empathy: Now a days, robots can successfully analyse our emotions but a small change of ton still confuses the program. That empathy, caring nature is still missing.
4. Flexibility: Few decisions are needed to be made from our gut feeling. However, robots miss that. They are only able to make decisions from the data that they have read.
5. Technical Maintenance: Robots are still machines. They require planning, designing, implementation and management. These skills can only be provided by humans. Maintenance can only be done by humans. 

In the end, even though robots can take over few of our jobs, still they will always work under us rather than against us. 

Read more at https://www.techworm.net/2018/08/jobs-robots-will-take-from-humans.html

Improvement is a non ending necessity for any business to survive in this competitive industry. It is well known that bringing right technological change in business cuts the cost of production specially when the human labor costs too high and are replaceable with technology. In various surveys, it has been found that HR spends most of the time in transactional jobs such as data entry and minor mathematical calculations. These tasks can be automated by bots and this human resource can be provided proper certifications and they can be utilized for better tasks. There was a survey done in an organization. They implemented technology which improved the onboarding time of people from 120 minutes to 3 minutes. And the man power can be used for more important tasks such as decision making.

Read more at https://www.informationweek.com/big-data/ai-machine-learning/how-ai-and-intelligent-automation-impact-hr-practices/a/d-id/1332484?

Packing and moving your living space is quite a headache. Especially in India, most of the people try to find someone providing the cheap shifting services and hence get trapped as they do not provide safety to your good, thus resulting in an expensive net cost. Thankfully, technology is again helping humans to get a fair deal in terms of safety of good and proper quotation of charges. Some of the reallocating companies have started to use AI based softwares that can quote exact amount to be charged from customers based of the quantity and nature of goods, destination, path to destination, floor of new location, whether lift is present or not and many more relevant factors. These softwares also helps to find the price for the goods so that they can have an insurance of that amount. Customers can also use the GPS technology to track their goods while they are being shifted to a place too far.

Read more at https://www.analyticsinsight.net/relocation-industry-working-algorithms-comprehend-consumer-needs/

The pace at which technology is moving is unmatchable. Every day some advancement in technology comes into lime light. Among the various fields in technology, one of the major and trending technology is AI. It is growing each day and proving to be a solution in many applications. Below are the 10 areas where Artificial Intelligence can be noticed:

1. Robots predicting the future
2. Robot Soldier
3. Survival Robots
4. Police using AI algorithms to predict crimes
5. AI-based medical treatment
6. Autonomous drones and weapons
7. Supercomputers with imaginations
8. AI communicating with AI
9. AI hackers
10. AI in court

Every thing in this world have a positive side and a negative side. Similarly, a few cases have been there where Artificial Intelligence have gone wrong. However, chances of improvements are always there. 

To know more about these 10 areas and other uses visit: https://www.techrepublic.com/pictures/10-terrifying-uses-of-artificial-intelligence/

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/

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

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

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

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

    

Business and IT are two completely different fields. Yet there is always a need to mix them for the betterment of both fields. However, there is a huge gap between these two and to bridge them a Business Analysts is comes into role. A business analyst is responsible to take IT and Business together by using data analytics to analyse the ongoing processes and methods, determine plans and requirements and recommend future plans on the basis of current studies. Now days, data is very important for a business. It can help in planning, decision making etc and thus business analysts are a need for an organization. It is important for a business analyst to be good in both hard skills and soft skills. He must be good at sharing the information he was able to figure out with the team. Similarly, he must have a strong IT background.

To know more about a Business Analysts visit: https://www.cio.com/article/2436638/careers-staffing/project-management-what-do-business-analysts-actually-do-for-software-implementation-projects.html

Every day a new problem statements emerges in the field of technology and machine learning proves to be a solution in most cases. These days, we tend to find smart solutions for our problems and machine learning is the backbone for the same. Thus, we can correctly state that Machine Learning has already invaded in our lives in some way or another.

However, with the emergence of machine learning, misunderstanding and misconceptions associated with it enters the field. There are few common myths about what and what not machine learning can do. Few of them are mentioned below:

1. Machine Learning is AI
2. All data is useful
3. Anyone can build machine learning system
4. Reinforcement learning is ready to use
5. Machine learning will replace people

One could achieve better results if he avoids these common myths.

To read more about this, visit: https://www.cio.com/article/3263776/artificial-intelligence/machine-learning-myths.html?upd=1531678835984

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

1. Assuming your data is ready to use and all you need
2. Not exploring your data set before starting work
3. Not using control group to test your new data model in action
4. Starting with targets rather than hypotheses
5. Automating without monitoring the final outcome

To study mistakes like these read https://www.cio.com/article/3271127/data-science/12-data-science-mistakes-to-avoid.html?nsdr=true

One of the most interesting thing about the field of technology is that it never stops growing. There are changes that helps in evolution. The rate at which technology is growing is unmatchable and the only way to match that pace is by polishing our skills and keep them up to date.  The following are the top 3 tech skills that are need for tomorrow: 

1. Blockchain Technology – Blockchain is the structure of data that records transactions. It is digitally signed and thus ensure its authenticity. It is a good way to manage cryptocurrency.
2. Artificial Intelligence – Artificial Intelligence is an ongoing technology which is helping humans by making machines intelligent and capable of working the way humans do. Though they are many applications based on AI which we are already using but still there are many unexplored technologies.
3. Augmented and Virtual Reality- Augmented Reality and Virtual Reality have already shown remarkable progress in the field of gaming and there are many more applications which can bring tremendous changes.

To know more visit: https://www.technotification.com/2018/04/top-3-tech-skills.html

Machine Learning is an ongoing trend in the field of technology. However, there are only few machine learning programmers available right now. For beginners who are eager to learn and work on machine learning must work on algorithms. With machine learning algorithms, there is no need of human intervention.  There are different algorithms which will work for you. 

There are basically three types of algorithms:

1. Supervised Algorithms: which uses labelled datasets for training algorithms
2. Unsupervised Algorithms: which uses unstructured datasets for results
3. Reinforcement Learning: it uses feedbacks in order to reinforce a behavior

There are top 10 algorithms of machine learning that are must known for machine learning programmers:

1. Linear regression
2. Logistic regression
3. Classification and regression tree
4. Naïve bayes
5. KNN
6. Apriori
7. K-means
8. Principle Component Analysis
9. Random Forest
10. AdaBoost

Know more about them at https://www.technotification.com/2018/05/top-10-ml-algorithms.html 

Artificial Intelligence is making our lives better each day. It has also spread its wing in the field of Academics and made it more convenient. With computers and other smart devices, technology is making education more accessible to students. Artificial Intelligence is not only helping students but also automating and speeding up administrative tasks helping organizations by saving time. It is believed that soon AI in education industry will grow by 50%. Below are the four ways in which AI is helping education industry to grow:

1. The automation of administrative work
2. The addition of smart content
3. Smart tutors and personalization
4. Virtual lecturers and learning environment

Read more about them at https://towardsdatascience.com/4-ways-ai-is-changing-the-education-industry-b473c5d2c706

Artificial Intelligence is the trend and need of this hour. It has already found its applications in many fields. This technology is changing and improving the world at a tremendous speed and for our betterment. There is no doubt that AI is future. However not many of us knows its basic application in Business. Business needs time to time changes to meet the requirements. AI can help and change business in many ways.

Top five way in how Artificial Intelligence can help and upgrade your business are:

1. Cheaper Analytics
2. Hiring
3. Customization
4. Anticipation 
5. Security

Know more about it at:  https://www.techrepublic.com/article/top-5-ways-ai-will-change-business/

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

To know more visit: https://www-cio-com.cdn.ampproject.org/c/s/www.cio.com/article/3273114/predictive-analytics/what-is-predictive-analytics-transforming-data-into-future-insights.amp.html

Data Science is a new field of interest and used in every sector. Whether it is a business, production line or a tech company, each of them wants someone to analyse their data. This would further help them to make decisions. Even though there is so much need of data scientist, still the number of data scientist is low. There are many characteristics that could define a good data scientist. 

Few of them starting with C are: Curious, Careful, Clever, Confident, Creative, Capable, Communicative, Considerate, Candid and Collaborative. 

To know further about these words visit: https://medium.com/@tableaucoach/characteristics-of-a-data-scientist-ten-cs-4e3b185cc7cd