The recent issues of Australian and Amazon wildfires have raised a burning question – the technology that has been a major facilitator to human evolution and growth, could it not do anything to predict, manage or control such destruction? Its high time that technologies like AI, data science and 5G connectivity should take charge of climatic advancement as well. The latest development in these technologies has shown some significant traits that can work for the betterment of the environment. Let's see how they can serve nature and climate. As noted by a report, the problem with climate change is that time is not on the side of humans -- mankind has to find and implement some solutions relatively fast.

Forrester Research recently released a report entitled, Shatter the Seven Myths of Machine Learning. In it, the authors warn, "Unfortunately, there is a pandemic of ML misconceptions and literacy among business leaders who must make critical decisions about ML projects." When executives and managers talk about AI and machine learning, they sometimes make factual mistakes that reveal their true level of knowledge. Forrester senior analyst Kjell Carlsson, who is the lead author of the report, said in a recent interview that he's heard audible sighs over the phone when experts hear what lay people have to say. "When the head of product says something like, 'We're using reinforcement learning because we're incorporating user feedback into the trends modeling,' that's probably not a good thing," said Carlsson.

Our SMS search engine, called Text Engine, was originally created in 2013. The idea was to create a utility that would enable users to search the Web without needing to use a web browser and without using data. Text Engine accomplishes this by giving you access to vital, basic web information just by sending and receiving text messages. To keep Text Engine relevant for an ever-changing mobile market, our investors suggested that we think about adding a chatbot experience to Text Engine. So that's what we did.

An updated version of Marc Andreessen's famous quote, "Software is eating the world" probably is "AI is eating software." We tend to think of AI in incremental ways, and we need to urgently change that thought process because our approach to AI will demarcate the difference between linear thinking and transformational thinking. Most organizations want to use AI to cut costs and do the thing they are already doing faster and quicker; this is an incremental approach to AI, whereas we need to focus on the next-level use of AI, that exponentially transforms the way we have been doing things thus far by creating new systems. For example, Amazon Go (Amazon's retail store) isn't using AI to simply remove the role of the cashier, but it is designing a new retail experience that is data and information-driven. Thus, instead of simply putting a layer of AI on top of existing processes, Amazon Go is changing the average grocery-shopping exercise into an experience-driven activity that is all about data, understanding people, behavior and design layout. Similarly, the objective of driverless autonomous vehicles is not merely to eliminate the cost of the driver, but to change the way we travel and redesign the entire transportation industry as well as create ripple effects in the e-commerce and delivery industries.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. One of the most influential technologies of the past decade is artificial neural networks, the fundamental piece of deep learning algorithms, the bleeding edge of artificial intelligence. You can thank neural networks for many of applications you use every day, such as Google's translation service, Apple's Face ID iPhone lock and Amazon's Alexa AI-powered assistant. Neural networks are also behind some of the important artificial intelligence breakthroughs in other fields, such as diagnosing skin and breast cancer, and giving eyes to self-driving cars. The concept and science behind artificial neural networks have existed for many decades.

Our SMS search engine, called Text Engine, was originally created in 2013. The idea was to create a utility that would enable users to search the Web without needing to use a web browser and without using data. Text Engine accomplishes this by giving you access to vital, basic web information just by sending and receiving text messages. To keep Text Engine relevant for an ever-changing mobile market, our investors suggested that we think about adding a chatbot experience to Text Engine. So that's what we did.

Our SMS search engine, called Text Engine, was originally created in 2013. The idea was to create a utility that would enable users to search the Web without needing to use a web browser and without using data. Text Engine accomplishes this by giving you access to vital, basic web information just by sending and receiving text messages. To keep Text Engine relevant for an ever-changing mobile market, our investors suggested that we think about adding a chatbot experience to Text Engine. So that's what we did.