We get fascinated these days by looking at the output from Machine learning and AI-driven models, but after spending close to a decade in this field, my reaction to this is getting more apparent, and it just recalls me of my childhood days when our super hero's use to be the character of Ramayan and Mahabharat:) One of my favorite part of Ramayan is the "Sundar Kand" of Ramayan. I always feel that this Kand could have been named "Bhishan Agni kand" or "Jwala Kand," but it was named "Sundar Kand" and the reason could be due to the very first line with which it got started .. line goes as - The true lesson for any management firm that as soon as leadership decides or commits delivery i.e. soon, you get the go-ahead from your senior leadership like "Jamwant Ji" in this case, you should start taking the project and deliverables ahead with full force and best of your capability. The biggest disasters like "Lanka Dhen", Major fights between both armies happened during this Kand but given employees are moving ahead with full trust in Senior management and with a go-getter attitude that made this Kand to be named "Sundar Kand". Veda's, Upanishad, and Puran used to be taught at Gurukul by great saints of that time. This teaching material is similar to modern-day Machine learning libraries, which were crafted as a single package for anyone with an interest can use them to perform data science.

The start of the Democratizing AI Newsletter which focuses in the first edition on "Artificial Intelligence a Key Exponential Technology in the Smart Technology Era" coincides with the launch of BiCstreet's "AI World Series" Live event, which kicks off both virtually and in-person (limited) from 10 March 2022, where this theme, amongst others, will be discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. See the 10 Weekly Program here: https://www.BiCstreet.com)). We live in tremendously exciting times where we already experience the disruptive and far-reaching impact of a smart technology revolution that seems to be on track to comprehensively change how we live, work, play, interact, and relate to one another.

Senior Technical Fellow PeopleTec, Inc. Huntsville, AL, USA ABSTRACT For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements and sensor inputs. To simplify the timeaccuracy comparisonbetween 27 different algorithms, we treat the imbalance between normal and failing states with nominal under-sampling. We identify 3 promising regression and discriminant algorithms with both higher accuracy (96%) and twenty-fold faster execution times than previous work. Because predictive maintenance success hinges on input features prior to prediction, we provide a methodology to rank-order feature importance and show that for this dataset, error counts prove more predictive than scheduled maintenance might imply solely based on more traditional factors such as machine age or last replacement times. INTRODUCTION Successful predictive maintenance is challenging not only because failures can prove multifactorial but also because maintenance forecasters often lack good training data.

The fundamental drivers of economic growth in the past 250 years have been technological innovations. The most important of these general-purpose technologies include the steam engine, electricity, information technology, and now artificial intelligence (AI). The First Machine Age, formally known as the Industrial Revolution, was when humans overcame the limitations of muscle power. We are now approaching the early stages of the Second Machine Age, in which humans are overcoming the limitations of mental capacity. The essence of AI is learning.

When a technology has its breakthrough, can often only be determined in hindsight. In the case of artificial intelligence (AI) and machine learning (ML), this is different. ML is that part of AI that describes rules and recognizes patterns from large amounts of data in order to predict future data. Both concepts are virtually omnipresent and at the top of most buzzword rankings. Personally, I think – and this is clearly linked to the rise of AI and ML – that there has never been a better time than today to develop smart applications and use them.

Mommas, don't let your babies grow up to be truck drivers. Or pretty much anything that a machine or a robot could do, if you want them to have a job. The list of those things will continue to get longer – in some cases rapidly – extending well beyond the assembly line on a factory floor. The forecast is not all gloomy – artificial intelligence (AI), machine learning (ML) and automation are also expected to create jobs that will likely be much more interesting and creative than the repetitive tasks of the industrial age. Indeed, it has been a growing component of cybersecurity technology, and therefore cybersecurity jobs, for several years.

There is a rising debate about recent Artificial Intelligence (AI) improvements -- is it a peril for humanity?

We are in the midst of a revolution, one that dwarfs the so-called industrial revolutions that preceded it. What we are experiencing today bears striking similarities in size and scope to the Scientific Revolution of the 16th century. The discoveries of Copernicus and Galileo, which challenged our understanding of the world around and beyond us, inspired others to ask deep questions about the nature of humanity and how societies should be organized and governed. The Scientific Revolution disrupted the way the human race thought about itself. We now have a chance to embrace today's revolution for what it is: a powerful, defining moment to rethink what it means to be human.

Artificial Intelligence – a technology which has continually broken promises. AI technology has always been the star striker of the technology field, hugely expensive and much hyped. However, AI has never scored the big goals promised by those working on the sidelines, with a Minority Report-esque world remaining in the realms of fiction. However, no one can ignore the renewed enthusiasm that AI is today courting – companies like Salesforce, Intel and Apple are rushing to acquire AI start-ups, bolstering their in-house offerings, while others are creating secretive labs in order to get a jump on competitors. Everyone seems to be throwing their hat into a driverless, smart, self-learning, AI project or company, indicating that the much lauded'Machine Age' may be closer than critics think.

The threat of automation is a very real one, but will robots actually end up replacing workers? It is now widely accepted that technological advances, especially ones that make machines more like humans – such as robotization or artificial intelligence – are putting people out of work and will only destroy more jobs in the future. The wealth will accrue to those who own the machines, not to what's known as the middle class today. There's some good news for humans, though: The evidence of our displacement by machines is sketchy, and we should be able to adjust to the new technological era if we put our minds to it. Eric Brynjolfsson and Andrew McAfee of the Massachusetts Institute of Technology labeled this "the great decoupling": according to them, advances in productivity, mainly driven by the development of digital technology, and the resulting economic growth, no longer cause employment and workers' incomes to rise.