The term itself originated in the 1950s. Arthur Samuel from IBM coined the term based on his research on computer checkers. In a game between a computer and a Connecticut checkers master, the computer won. This outcome opened up a world of possibilities. Today, machine learning has expanded far beyond simple games of checkers.

AI (Artificial Intelligence) was created by writing numerous lines of code, now AI has the capability to code with ease. Sounds unreal, but it's true. Nowadays, coders and even many companies are using AI to help humans in the software development process. Now, software developers can not only use AI to write and review codes but also test software, find bugs and optimize development projects. AI not only will help the new generation of developers learn to code easily, but also help companies to deploy software and apps efficiently.

The technological revolution of recent decades has made a deep impression on all kinds of businesses. Affecting everything from contact centre productivity to the delivery of healthcare, there's scarcely a sector of the economy that hasn't been radically transformed. Now comes the next phase of this technological revolution – artificial intelligence (AI). Having been a staple of science fiction since the 1950s, AI is now a real-life phenomenon. As a result, we stand on the precipice of a far-reaching digital transformation. One area where AI is already having seismic effects is that of the retention and use of data.

The popularity of JavaScript doesn't need any special introduction. And with digitalization progressing at an increasingly fast pace, businesses are found adopting machine learning and artificial intelligence to conduct operations daily. As technology advances with the time passing by and so do we – a variety of machine learning frameworks came into limelight such as JavaScript. The following post is quite influenced by the book – Hands-on Machine Learning with JavaScript by Burak Kanber. The book mainly acts as a short guide on creating intelligent web applications with the best of machine learning and JavaScript.

To build a chatbot, you shouldn't need any coding skills. There are many chatbot-building platforms that you can use to develop your bot. However, there are a few aspects that we usually discuss with customers before starting the process of building their bots. The three main elements that we focus on are the needs of the business and potential problems to solve, proper flow and content, and finally, the integrations. From experience, I can say that many questions are repetitive, and most of them can have straightforward answers.

This article is part of a Machine Learning series. Our fourth expert is Dr. Eibe Frank, Associate Professor (Computer Science) at the University of Waikato, New Zealand. In this article, he talks about Weka and reveals what's under its hood. The idea behind Weka was to provide a uniform interface to a collection of machine learning algorithms in Java. This includes a graphical user interface, a command-line interface, and an API.

The evolution of AI and AIOps today is being driven by five key trends which, while each important on their own, are much more striking when taken together. As a collective it forces a concentration on the kinetic aspects of AI. Let me outline the five biggest trends affecting AI and AIOps today, and explain why IT organizations should track their development and their implications. The first trend is widely recognised: DevOps has made a continuously evolving IT environment a reality. IT has always evolved rapidly, a constant feature of the industry. But now, with DevOps adopted in many organizations, we're going to another level.

Finance is one of the most conservative sectors and one of the latest technology adopters for a good reason: it requires stability, predictability, and risk hedging. Even if there are significant opportunities presented by technology, financial companies will be lagging behind because they should wait for a technology to enter its maturity stage as well as for somebody else to test it first and absorb associated risks. However, once a technology is stable enough and has gained mass adoption among businesses, it becomes appealing for the finance sector. This is the case of machine learning (ML), which has ceased to be just a buzz word now. SEE ALSO: Meet Manifold: Uber's machine learning model debugging tool goes open source Machine learning uses statistics to recognize patterns and associate them with entities or cases.

AI is developing at whirlwind rates. While nobody can say for certain how it will impact our work and personal lives, we can make a good few educated guesses. Also, with COVID-19 limiting human interaction in the built environment, advancements in AI and automation are on course to accelerate (providing funding is available, of course). The age-old fear among some of the population is that AI will displace workers, leading to high levels of unemployment. A report by management consulting firm McKinsey shows that between 400 million and 800 million individuals across the globe could be "replaced" by automation and need to find new jobs by 2030.

In the world of enterprises, AI technology plays a significant role. According to the recent study by PwC, 72% of executives believe that this technology will proffer sizeable advantages for enterprise companies in the near future. Not only this, but it can also make every area of business less of a headache. AI has already set its sights on hiring or recruitment process. More enterprise companies are looking forward to the use of AI-solutions to accelerate their work process and mitigate shortfalls.