IoT and artificial intelligence are two of the buzzwords recently. According to IDC, spending of IoT is expected to exceed $ 1.4 trillion by 2021. And more the IoT means more the data and as the amount of data increases it becomes difficult to manage and analyze the data. Here, AI acts as a reliever. AI can help find patterns and analyze those huge stacks of data. So, overall, AI and IoT are considered common friends that have enough potential to build a bright smart future of upcoming technological world.

Read more at : https://www.informationweek.com/big-data/ai-machine-learning/ai-and-iot-techs-new-bffs/a/d-id/1332108

Great businesses are powered by great people. The term human capital signifies that the value of an employee can be measured and enhanced through investments in training, benefits, compensation and so on. And, of course, as the value of the workers has been growing in an organization, the software to manage HR functions and human capital has also evolved as well. Human resource information systems (HRIS) or human capital management (HCM), they arguably provide similar functions that are either part of, or integrated with, ERP software and go well beyond handling hiring, firings, payroll and benefits.

Read more at: https://www.cio.com/article/3269807/software/best-hcm-software-comparing-7-human-capital-management-platforms.html

Enterprise level organizations should try to incorporate blockchain into their system. At the same time they should not just get blockchain just for the trend, but it should be done with a strong sight towards companies’ scope. Blockchain can benefit these enterprises by simplifying the third party transactions and overseas value transfers. Blockchain can also provide incredible advancement to analytics systems which will inflate the data-input quality and insight outputs. 

Earlier the commodity trading advisor is supposed to trade commodities and futures for a managed futures fund. But now selection of investment products is more complex and varied which calls for the need of acute understanding of CTA, of these products. Role of today’s CTA is related to derivative analysis also and hence not only limited role to trading. Analysis is now, the catalyst for the inclusion of value added service to retain customers which includes structured products, risk management and OTC derivatives.

Read more at: http://www.articlesfactory.com/articles/finance/the-role-of-a-cta-commodity-trading-advisor.html

The companies that manage the human capital in the most effective and efficient manner are considered to be the most successful companies. Gary S. Becker, a Noble Prize-winning economist, coined the term “human capital”, saying that “the basic resource of a company is the people”.  With time, corporations are recognizing the importance of human capital more than ever. Companies are now beginning to understand that to stay on top of the global economy; they need to put more and more emphasis on developing and retaining their people. Cloud Computing infrastructure is the new salient trend in the IT industry which helps not only to expand business globally also provides a sophisticated and successful strategy to make business more reliable and flexible. Organizations need cloud-based HR system in upcoming days.

Read more at: https://www.hr-jump.com/blog/2017/10/31/why-is-human-capital-management-important-in-organizations

With many companies still stuck to take advantage of data, analytics has to be number one question because this is a key stage to implement AI successfully. There is a sequence of evolution in analytics, starting from descriptive to prescriptive. 

Nowadays, organizations tend to skip traditional analytics and shift into AI. Many enterprises use the descriptive analytics, applying BI techniques: combine all your data to get a quick review on what’s going on in the company. 

Without the insight that analytics brings, it will be hard to assess the outcome of any artificial intelligence system. Analytics keeps AI transparent, responsible and may help increase the productivity of AI systems. Unproductive information control leads to the unnecessary operational costs. AI analytics helps to find out cost savings and prepares a report with the help of main ROI metrics to keep up productive decision making.

From food to clothing, to real estate, and to everything; now AI and machine learning is everywhere. They can recognize foods, value of real estates, things in your images and many more. They are really good at whatever it does. There have been varieties in application of AI and it will be interesting to integrate all those stuffs into one general intelligence.

CES 2018 showed the present scenario of the robot industry. The best of the companies presented the best of their futuristic gadgets, mostly smart home integration. The current progress is ready steady and mesmerizing but we are still about a decade behind having a real human like butler, or in better words a real human like robot friend sitting next to us. 

Read more at: https://www.popsci.com/personal-robot-butlers-ces-2018

Classification of data is very important in many organizations. They can be used to make decisions. But the task of classification can be very tedious. Now imagine a machine doing this job. Classification using machine learning is with the help of supervised learning approach and algorithms. Machine learns from the data input given to it and with the help of this learning, it classifies new observation.

For example, we want to check number of male and female members in an organization. Here we can train our machine to do this classification. 

Classification using machine learning is one of the trending technologies being used in various fields. It has many applications in many domains other than IT.

Various algorithms can be used to implement classification. There are two types of learners in classification – 
Lazy Learners - which simply store the training data and wait until a testing data appears. They classify the data based on most related data.
Eager Learners – that construct a classification model based on given training data.

Different classification algorithms are – Decision Tree, Naive Bayes, Artificial Neural Networks, K-nearest neighbor.

Read more about them and various evolution methods at https://towardsdatascience.com/machine-learning-classifiers-a5cc4e1b0623

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at: https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382

Internet is a vast place where one could get and post information globally. Many search engines help you to find what you want using different search algorithms. Ever since the first search algorithm was discovered, many new searching algorithms are being invented and used to make searching process easier. However, there are times when text-based searching becomes really exhausting. Take an example of flower. You are very fascinated by a flower you saw in wilds and is very curious to know about it. You start searching about it using it properties like colour of petals, number of petals, description of leaves etc. This would be very tedious and still there is no surety whether you will get results or not. 

Now imagine for searching with the help of picture. You just click a picture and rest will be done for you. This is known as Visual Searching and to achieve that Machine Learning is used. This type of searching can be extensively used in various domains. Initially a large amount of dataset will be required to train your machine. However, by using the concept of neural networks, this could be achieved and used. Read more at: 

Read more about it at https://medium.com/gsi-technology/ml-in-visual-search-part-i-d54cf4f2b509

First question that must be popping in your mind is what is SLAM? SLAM stands for simultaneous localization and mapping. Wherein, a device or robot can create a map in real-time as per its surrounding and position itself within the map. SLAM is a technology which is developing under virtual and augmented reality. SLAM technology requires number of algorithms which can simultaneously localize and the objects.

Why do we need SLAM? What are the applications of using SLAM technology? These questions will be answered here:

https://www.analyticsinsight.net/future-ar-slam-technology-slam/

Under the implementation of data analytics, AI and robotics the concept of “smart airbase” is being discovered by RASF in a treaty with defence science and technology agency (DSTA) for sustaining power for armed forces. This amazing concept includes the drones which are capable of detecting damaged aircrafts. There are such more examples of advances in defence section with the adoption of AI, robotics and data analytics. 

Want to know more about it, visit:

https://www.analyticsinsight.net/singapore-air-force-and-navy-embraces-data-analytics-ai-and-robotics-to-improve-operations/

Digital twin technology is actually the virtual replica of physical devices. This technology can help us in manufacturing, energy, transportation and construction. The complex items like aircraft engines, offshore platforms, trains and turbines can also be constructed and established digitally before being physically developed. These digital twins could also be used to help with maintenance works. Let’s say, technicians can use digital twins for testing. So, how it works? How it gets started? What are its applications?  Why it is important to us?

For more information go to:

https://www.analyticsinsight.net/digital-twin-technology-important-big-data-age/

How the Amazon is able to raise itself as one of the titanic disruptors in retail space? With its warehouses, full of robots is actually enhancing the speed and efficiency of logistics operations. FedEx is also tracing the footsteps of Amazon. The deployment of mobile robots inside FedEx is showing up how customer’s E-commerce habits are improving the retail and logistic sector. Commonly companies are making use of robots as store-service robots, inventory robots and delivery robots.

Robots are actually complimenting the human colleagues’. But the main objective of retail industry is to make profit margins and increase the productivity using robotic automation technology. 

For more information visit:

https://www.analyticsinsight.net/how-worlds-biggest-companies-use-robotics-in-supply-chain/