Google today announced an update to its Cloud Speech-to-Text and Text-to-Speech APIs that introduces a few new features that should be especially interesting to enterprise users, as well as improved language support and a price cut. Most of these updates focus on the Speech-to-Text product, but Cloud Text-to-Speech is getting a major update with 31 new WaveNet and 24 new standard voices. The service now also supports seven new languages: Danish, Portuguese/Portugal, Russian, Polish, Slovakian, Ukrainian, and Norwegian Bokmål. These are all in beta right now and extend the list of supported languages to 21 total. The service now also features the ability to optimize audio playback for specific devices.

Just as many other technological advancements, Artificial Intelligence came to our lives from the pages of fairy tales and fiction books (think of the Tinman from The Wizard of Oz or Maria from Metropolis). People dreamt about machines able to solve problems and release some of the fast-compounding pressure of the 21st century. Less than 70 years from the day when the very term Artificial Intelligence came into existence, it's become an integral part of the most demanding and fast-paced industries. Forward-thinking executive managers and business owners actively explore new AI use in finance and other areas to get a competitive edge on the market. More often than not, we don't realize how much Artificial Intelligence is involved in our day-to-day life.

When you were getting ready to be a first-time parent, you might have read one or two pregnancy and baby books and maybe even took a couple of classes to prepare for the arrival of your little bundle of joy. But what happens after the first year of life when that baby turns into an independence-seeking toddler? To help you navigate the terrible twos and beyond, we consulted child psychologists, therapists, authors, and developmental experts to create a reading list of the best books on how to raise toddlers with patience and understanding. Our panel of experts include Sarah S. MacLaughlin, author of What Not to Say: Tools for Talking With Young Children and senior writer at Zero to Three; Dr. Stephanie Lee, a psychologist at the Child Mind Institute; Dr. Eileen Kennedy-Moore, author of forthcoming book What's My Child Thinking?; Maureen Healy, author of The Emotionally Healthy Child: Helping Children Calm, Center, and Make Smarter Choices; child psychologist Dr. George Sachs and author of The Mad Sad Happy Book: Emotional Literacy for Preschoolers; child and family psychotherapist Joseph Sacks; child therapist Michelle Paget; psychotherapist Matt Lundquist; Dr. Sarah Roseberry Lytle, director of outreach and education at the Institute for Learning and Brain Sciences; British parenting expert Sarah Ockwell-Smith, author of Gentle Discipline: Using Emotional Connection -- Not Punishment -- to Raise Confident, Capable Kids; and speech-language pathologist Gordy Rogers. As always, each title below was mentioned by at least two of our panelists -- and in the case of one our picks, by nearly half of them.

Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. Random Forest Algorithm in Machine Learning: Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Artificial Intelligence (AI) will nearly double the rate of innovation and employee productivity improvements in the Asia-Pacific by 2021 – at any rate, for those organisations that have actually adopted it. This according to a new study from Microsoft and IDC Asia-Pacific, which surveyed more than 3,000 business leaders workers across 15 regional markets. However, despite clear benefits in adopting the technology, only 41% of organisations in Asia Pacific today have embarked on their AI journeys. For these organisations, the top three business drivers were: better customer engagement (26%), higher competitiveness (19%), and higher margins (18%). Business leaders who are adopting AI face three top challenges: a lack of thought leadership and leadership commitment to invest in AI; a lack of tools and infrastructure to develop actionable insights; and a lack of skills, resources and continuous learning programs.

Last October, Google Developers brought their Machine Learning Bootcamp to Jakarta, Indonesia! ML Bootcamp is a one-stop solution to learn about Google's latest machine learning offerings from both Googlers and other industry experts. The 4-day intensive bootcamp consists of instructor-led trainings, hands-on codelabs, and saw 35 companies, as well as 12 startups represented from across Indonesia. If you're an aspiring ML developer, be sure to check out the following online courses: ML crash course with TensorFlow APIs http://bit.ly/2MLUDkU

I usually see artificial intelligence explained in one of two ways: through the increasingly sensationalist perspective of the media or through dense scientific literature riddled with superfluous language and field-specific terms. There's a less publicized area between these extremes where I think literature needs to step up a bit. News about "breakthroughs" like that stupid robot Sophia hype up A.I. to be something akin to human consciousness while in reality, Sophia is about as sophisticated as AOL Instant Messenger's SmarterChild. Scientific literature can be even worse, causing even the most driven researcher's eyes to glaze over after a few paragraphs of gratuitous pseudo-intellectual trash. In order to accurately assess A.I., the general population needs to know what it really is.

Traditionally undervalued in the tech industry, empathy -- which is the ability to read and respond to another person's feelings, thoughts and experiences -- is a trait hiring managers and C-level executives can no longer ignore. After all, in a world where artificial intelligence will take up to 5 million jobs away from humans by 2020, the McKinsey Global Institute predicts that up to 14% of human workers will need to adapt to new occupations to secure our future in the workforce. In other words, as we start sharing the workforce with more machines, human soft skills such as empathy will be at a premium. And, that premium is justified. Hiring employees who are empathetic helps companies increase productivity, develop strong leadership and retain high-performing talent.

Andrew NG is a computer scientist, executive, investor, entrepreneur, and one of the leading experts in Artificial Intelligence. He is the former Vice President and Chief Scientist of Baidu, an adjunct professor at Stanford University, the creator of one of the most popular online courses for machine learning, the co-founder of Coursera.com At Baidu, he was significantly involved in expanding their AI team into several thousand people. The book starts with a little story. Imagine, you want to build the leading cat detector system as a company.

We propose a new method for local distance metric learning based on sample similarity as side information. These local metrics, which utilize conical combinations of metric weight matrices, are learned from the pooled spatial characteristics of the data, as well as the similarity profiles between the pairs of samples, whose distances are measured. The main objective of our framework is to yield metrics, such that the resulting distances between similar samples are small and distances between dissimilar samples are above a certain threshold. For learning and inference purposes, we describe a transductive, as well as an inductive algorithm; the former approach naturally befits our framework, while the latter one is provided in the interest of faster learning. Experimental results on a collection of classification problems imply that the new methods may exhibit notable performance advantages over alternative metric learning approaches that have recently appeared in the literature.