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

Advertisement is one of the major ways through which businesses can attract customers. A lot of money and time is invested in order to create ads. However, these days a helping hand has come for rescue and is successfully able to attract customers by presenting customized ads. Machine Learning Algorithms, Artificial Intelligence and Deep Learning have come into play. With the help of these technologies, customized ads can be created based on the current searches done by customer. For example, you recently searched for “affordable mobile phones”. These learning algorithms tracks it down and soon starts displaying mobile phones ads presented by various companies. Other than that, Data Mining also plays an important role in this. Among various data that is available on world wide web, data mining algorithms browser and stores valuable data. 

Read more about this at https://www.techworm.net/2018/06/how-machine-learning-algorithms-help-businesses-target-their-ads.html

Innovations and Inventions do not stop for anyone. They are always surprising us with something new. Due to them, the world is moving towards future at a tremendous pace. Artificial Intelligence, Machine Learning and more are taking over various jobs and easing our life. 

Following are the most astonishing tech inventions of 2018:

1. Crypto Anchors: They are digital footprints that helps to check product’s authenticity. They can either be codes or small computers embedded in a product and linked with blockchain.
2. Omron Robots: Omron Robots are robots that are gradually taking place of factory workers. They not only lift heavy objects but also move at a rapid speed and can freely navigate the environment. 
3. Myo Armband: This can be used both in video games and during surgeries. They hold the sensors in place.
4. AI Microscopes: They found their application in studying underwater life. They can monitor sea creatures 24x7 providing us important data.
5. Earprint: It is a new biometric identification device what send sounds to one’s ear. This sound is echoed back and this echo is different for every person.
6. Online Supermarket: Online market is the major attraction for everyone. Sit at one place and order from anywhere makes our lives easy. Though this is not new but it has gain more attention.

Read more at https://www.techworm.net/2018/07/most-astonishing-tech-inventions-2018.html

Data is the utmost important thing for any company or individual. Data Breach is an incident that happens when sensitive, confidential or otherwise protected data is been accessed or disclosed in an unauthorized manner. Many companies intentionally or unintentionally expose and leak consumers and commercial data. According to the Breach Level Index (BLI), many organizations fails to safeguard their databases. 

For any person or company, any such incident can be confusing. In such situation, there is a need of a standard policy which must be followed. Following are few steps one should consider in order to save guard the data in case you are at risk:

1. Isolate – Isolate the machine from rest of the network if a particular hardware is on risk.
2. Document – It is important to keep detailed records of everything you do from the moment data breach is discovered.
3. Photograph – Photographs can help in solving digital data breach.
4. Interview – Any person directly or indirectly involved with the systems that were breached should be interviewed.
5. Use Your Knowledge – Learn and take steps.

To know more about it visit:  https://www.techworm.net/2018/04/first-5-steps-when-faced-with-a-data-breach.html

Data is the most valuable asset for any company and any person dealing with this data needs to be cautious. Modern businesses rely on data. They store, process and access data for information gathering and use it for decision making. According to reports of 2017, a single mistake in handling this data can result into loss of nearly $3.6 million. 

However, data can be loss due to various. Few of them are:

1. Human Error

2. Hardware Failure

3. Theft

4. Online Crime

5. Natural Disaster

The best way to deal with this is to take prevention and keep an up-to-date recovery plan and a 3-2-1 backup strategy, i.e. there should be three copies of data, kept in two different mediums, and at least one of the backups should be off site.

Read about it at: https://www.bigdatanews.datasciencecentral.com/profiles/blogs/five-ways-your-business-is-at-risk-of-data-loss

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/

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/

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

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

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

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

    

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

Data is very valuable for any business. It can help in decision making, planning, and more. Here, Business Intelligence comes into action. BI uses various softwares, applications, tools and services that enables access to and analysis of data. This improves and optimizes decisions and performances. Using Business Intelligence can help you to get more value by improving customer service, employee productivity, and more.

Following are the few ways one could get more value from Business Intelligence:

1. Build real-time BI into your customer-facing services
2. Improve employee performance through BI
3. Improve Customer Service
4. Predict new revenue streams
5. Automate budgeting and forecasting
6. Shift the emphasis to analysis
7. Embed BI into other platforms
8. Cut time wasted on data gruntwork
9. Bring unstructured data on board

Read more about them at: https://www.cio.com/article/3254646/business-intelligence/9-ways-to-get-more-value-from-business-intelligence.html

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

Slowly and steadily we are moving towards digital transformation and its has become one of the important tasks for businesses. With the evolving technology, businesses are also growing and evolving. Here are the top 12 tech trends that we need to look for in year 2018 :

1. Computer Vision
2. Deep Learning
3. Natural Language Generation
4. Businesswide Networking Fabric
5. Distributed Ledger Technology
6. Edge Computing
7. Quantum Computing
8. Serverless Computing
9. Augmented, Virtual and Mixed Reality
10. Digital Twin
11. Additive Manufacturing
12. Nanotechnology

It is believed that to stay in the competition of business, one should welcome new forms of technology and implement them helping business to grow. This will bring the needed digital transformation and help in decision making and planning strategies. 

Read more at https://www.techrepublic.com/article/here-are-the-12-tech-trends-that-will-dominate-business-in-2018/

After Artificial Intelligence and Machine Learning, the next most emerging field in todays world is the field of Data Science. It is said to be the cousins of AI and ML and mainly deals with data. It intakes data, uses processes, algorithms and scientific methods to extract knowledge and valuable data from large data sets. This field is need of each and every type of organization. Whether it be business or an IT firm, every organization needs data for improvement. Thus, outcomes from the processing of data are further used for decision making and for improving current functioning.

People often gets confused between Data Science, Data Analytics and Big Data. The key difference between them is that Data Analytics and Big Data are components of Data Science. Data Science extract values from the output of Data Analytics and Big Data to solve problems.
The goal of Data Science is to extract business-focused insights from business. This could help organizations in many ways.

Read more about this topic at: https://www.cio.com/article/3285108/data-science/what-is-data-science-a-method-for-turning-data-into-value.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