Have you ever wondered how everything around us is becoming so smart? How the gadgets possess the to make human-like decisions? And how are we able to change our surrounding in just the click of a button? Well, the answer to all these wonders lie in the buzzwords IoT and AI, which are the most powerful technologies today and when blended, lead to the emergence of even more powerful technology that possesses the ability to change the world around itself. Now, before discussing how IoT and AI are help organizations and people and the need of blending the two, I would like to brief you with AI and IoT as two different technologies and their relevance in the present world for better understanding.

The amount of data is increasing rapidly. More and more devices can produce and transmit data. We live in a world that threatens to drown in the flood of data. The merger of 5G, IoT, and AI could help us and be of great importance when it comes to profiting from the rapid increase in available data. Before I go into the fusion of 5G, IoT, and AI, I would like to explain these three terms briefly.

As we are living in a fully connected world, enabling Machine Learning in data from IoT devices is essential as we all know "data driving the world today and insights from the data deriving the path to future." In this article, I am trying to touch upon the necessary building blocks to enable Machine Learning in IoT and how cloud infrastructure helps here if we can use the power of "open source" tools effectively. Message Queuing Telemetry Transport is an open OASIS and ISO standard (ISO/IEC 20922) lightweight, a publish-subscribe network protocol that transports messages between devices. The protocol usually runs over TCP/IP; however, any network protocol that provides ordered, lossless, bi-directional connections can support MQTT. It is designed for connections with remote locations where a "small code footprint" is required or the network bandwidth is limited.

What was the last thing you bought online? Why didn't you go to a store to buy it? Was it for the vast number of items you could scroll through before making a choice or the recommendations that the website or app pulled up for you -- remember your choices and interests from a previous visit, or the ease with which you paid for it using a card already saved on the website? The online shopping experience is leaps and bounds ahead of the traditional experience in terms of using data and technology to provide unique and personalized customer experiences. While brick and mortar stores have their own upsides, the move towards omnichannel retailing today is key.

In this modern era where everything is moving towards digitization, edge computing has left no stone unturned in making its way into businesses and industries. Its popularity is growing due to the huge computing demands of cloud users and massive data explosion through IoT. Business associates are finding it the best way to streamline IoT traffic and facilitate real-time, local data analysis. IIoT has paved way to move data processing closer to its source, which involves actual industrial machines. A survey says that edge computing will reach peak productivity in the very near future. The growing innovations in AI and IoT indicate that edge computing is likely to gain maturity as well.

An inference engine using forward chaining applies a set of rules and facts to deduce conclusions, searching the rules until it finds one where the IF clause is known to be true. The process of matching new or existing facts against rules is called pattern matching, which forward chaining inference engines perform through various algorithms, such as Linear, Rete, Treat, Leaps, etc. When a condition is found to be TRUE, the engine executes the THEN clause, which results in new information being added to its dataset. In other words, the engine starts with a number of facts and applies rules to derive all possible conclusions from those facts. This is where the name "forward chaining" comes from -- the fact that the inference engine starts with the data and reasons its way forward to the answer, as opposed to backward chaining, which works the other way around.

I was alarmed to read this week that the Alabama Senate has passed a bill to outlaw abortion under almost all circumstances, including cases of rape and/or incest. Under the bill, doctors face 10 years in prison for attempting to terminate a pregnancy and 99 years for carrying out the procedure. Fortunately, for decades, women's reproductive rights groups, health providers, technologists, and hackers have been working together internationally to support a women's right to choose. They've created a legacy of work that may be able to help. The worst major city for abortion access is Rapid City, South Dakota, where women must travel 318 miles to get an abortion.

Business rules work very well to represent the logic for certain domains. They work well because they result intuitive and close to the way of thinking of many types of domain experts. The reason for that it is that they permit to decompose a large problem in single components. In this way, the user does not deal with the orchestration of all the single rules: this is the added value provided by the business rule engine. In this article, we will discuss one specific example of an application written by using business rules. We will write the rules to decide, which email to send to the subscribers to a newsletter.

Augmented reality is in the process of succeeding in changing the way we apprehend our whole ecosystem. That includes the way we play, the way we learn, and also the way we work, or even just interact in everyday life. Do you know exactly what augmented reality is? What is the difference between AR and VR? What are some of the use cases for businesses, if there are any?

No one wants to be hurt because they're inadvertently driving next to an unproven self-driving vehicle. However, the costs of validating self-driving vehicles on the roads are extraordinary. To mitigate this, most autonomous developers test their systems in simulation, that is, in virtual environments. Starsky uses limited low-fidelity simulation to gauge the effects of certain system inputs on truck behavior. Simulation helps us to learn the proper force an actuator should exert on a steering mechanism, to achieve a turn of the desired radius.