Today marked the kickoff of Xiaomi's annual Mi Developer conference in Beijing, and the tech giant wasted no time in announcing updates across its AI portfolio. It took the wraps off the latest release of Mobile AI Compute Engine (MACE), its open source machine learning framework, and it demoed an improved version of its Xiao AI voice assistant (Xiao AI 3.0). Xiao AI, which Xiaomi says is used by 49.9 million users each month, will soon support multi-turn conversations à la Alexa Conversations and Google's Continued Conversation. This will be enabled on select phones, including the Xiaomi Mi 9 Pro and the Xiaomi Mi 9 via a software update, and it will allow for interruptions of the assistant at any time with new requests or commands. Xiao AI 3.0 also boasts improved voice shortcut functionality and a voice reply feature that will let users respond to incoming calls with transcribed text messages. That's in addition to a command to find missing devices, like smartphones, and a male voice option.

Being able to spell, checking your profile for typos and using correct grammar could determine whether you get a match on a dating site, a new study has revealed. Researchers worked with members of an online dating service and asked them to read fake profiles that had language errors as well as ones that had no mistakes. The study of more than 800 people on a dating site in the Netherlands found that errors such as writing'teh' for'the' and putting capital letters in the wrong place suggested sloppiness and a lack of attention to detail. If a profile has spelling mistakes and grammatical errors they were more likely to be perceived as less intelligent and so were less likely to be a match. 'In the case of online dating, inattentiveness can be interpreted as a lack of effort and interest in putting time and effort in constructing a dating profile.

As Frank Newport, senior scientist at Gallup, expressed it: "Whether they know it or not, AI has moved into a big percentage of Americans' lives in one way or another already." Newport made the comment in light of the results of a 2018 Gallup consumer survey. It found almost nine of ten (85%) US adults use at least one service regularly that features some element of artificial intelligence. Almost half (47%) said they used smartphone personal assistants while 32% use ride-sharing apps, such as Uber and Lyft. Twenty-two percent have home personal assistants, such as Alexa and Google Home, and 20% use smart home devices, such as smart lights or smart thermostats.

In the formative years of a digital transformation journey, customer experience is the primary source of derived business value, as organizations see value in deploying strategic digital initiatives to augment and improve every customer interaction, with the goal of increasing customer growth and retention. Customer experience is followed closely by cost reduction, as organizations are continuously looking for opportunities to leverage technologies for increasing process efficiency, improving decision making, and automating more tasks. "As per a recent Gartner survey, by 2020, at least 40% of people will interact primarily with people-literate technologies, removing much of the perceived need to invest further in improving computer literacy. Additionally, virtual private assistants (VPAs) and voice response systems, such as Alexa, Cortana, Google Assistant, and Siri, are increasingly becoming the norm for consumer interaction with search engines, along with many other services that would previously have required interaction via web browsers." In the subsequent section, we will go through a few use cases that are now being increasingly deployed across industries to unlock untapped customer value. Additionally, we will see that how some of these use cases are industry agnostic, and can be implemented across other industries with minor tweaks to augment customer experience.

Artificial Intelligence Application is a subject widely prevalent in the IT world for more than 4 decades and it was in the laboratory all through. AI is all about building intelligence into machines to enable it to perform human tasks with precision for the known operations. It also packs machines with a lot of data points for the machines to learn and handle unknown situations. AI simulates machines to work, learn, think and react like human beings. In this article let's see the applications developed using AI technologies.

The smart home is dead. I'm not sure exactly when the time of death should have been called, but it happened at some point between Google trying to rebrand the smart home as "the helpful home" and the publication of this article, which expresses dismay that at five years of age, Amazon's Alexa offers little more than a new way of interacting with things, without deep functionality or truly new use cases. This week in New York, at an IoT Consortium event, I listened to executives of dozens of companies associated with the smart home talk around its death but never address the fact directly. Instead, they talked about a lack of compelling use cases, how to move beyond a device-specific mindset, and the ways they are trying to handle consumer demand for interoperability in the smart home without actually providing such interoperability. For example, Google's Mark Spates, who works in the smart display and speaker division, said onstage, "I don't think we've done a good job explaining our value proposition to consumers. We have to come up with new stories that isn't just'Go buy another Mini.'"

Finance teams are integral and essential to the smooth running of any business. The role of the finance team is beginning to increase in scope, and this means that finance professionals are beginning to be concerned about what they can't see or control. Finance teams of the past have left the company's decisions around implementing new technology to others, seeing it as beyond their remit. If the IT was not broken, then the finance team was certainly not looking for a way to fix it. Now, however, the situation has changed.

To address the complexities that arose while solving large & complicated scenarios, this manual training process fell short in potential. But with the advent of AI & ML, the complete cyberspace has taken a giant leap forward and the process of training these Algorithmic bots has been automated. They don't rely on humans to supervise or train them. Once their artificial neural structure (ANN) achieves a certain level of maturity, they start learning on their own when exposed to different training sets/ data sets. Meanwhile, the effort that goes into training a bot in its nascent stage can't just be denied.

We live in a world of exponentially increasing technology advancements. Never in the history of mankind have so many such advancements emerged in parallel and in combination, carrying so much impact. The phenomena are marked in time: The timing from what once seemed impossible to possible and functional can become extremely short – sometimes measured in just days or weeks. Ever heard the maxim that internet years are like dog years, where one in actual elapsed time equates to seven? Exponential advancement is the reason.

BOTs are known by different names; including Internet Robots, Spiders, Crawlers, and Web BOTs. All of these refer to the same technology. So, what exactly are these BOTs? These are automated systems on the internet that are used for various reasons. In the past, these BOTs simply performed repetitive jobs like indexing a search.