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The modern world seems really fast and dynamic with a multitude of new products being launched. Marketing agencies are making fortune by monitoring the markets and delivering reports on consumers' opinions. For today, the feedback analysis is a separate area, let's say a growing industry with an array of products and services. And the prices for those services are pretty exorbitant. So, do vendors have a chance to cut down expenses? Without any doubts, there's always an opportunity to start personal volcanic activities on feedback collection and analysis.

When Apple Inc. (NASDAQ:AAPL) released Siri on the iPhone 4S back in 2011, the personal digital assistant managed to earn mixed reviews from users. On one side, Siri was seen as a revolutionary feature, as a personal digital assistant that had the potential to change the way we use devices. However, numerous users felt as if Apple had blown the hype surrounding its digital personal assistant out of proportion, as Siri regularly failed to function the way it was meant to. The worst part about Siri was that it actually managed to regress under Apple, as the software that was released by its original developers was more capable then the one released by the Cupertino based tech giant. Moreover, it was apparent that one of Apple's biggest rivals Google managed to design a superior AI system as compared to what Siri had to offer.

Almost two years ago (to the day), Salesforce plunked down 392 million for RelateIQ, a specialist in software that automates sales tasks--like picking the best time to call a sales prospect or filling out call reports--using artificial intelligence. Since that time, the business software giant has snapped up at least a half-dozen other startups specializing in machine learning, predictive analytics, and other AI technologies. The latest one was just one week ago: BeyondCore, a specialist in statistical analysis. It turns out that Salesforce crm is cobbling all of these technologies together as part of project dubbed Salesforce Einstein, which will be detailed during the company's upcoming Dreamforce conference in late September, according to an article published this week by Forbes. The grand scheme is to endow Salesforce's core applications for sales management, marketing automation, and commerce with AI software that "learns" from the data it is collecting to spot trends or identify areas that might require attention from managers.

Nonmedical use of prescription medications/drugs (NMUPD) is a serious public health threat, particularly in relation to the prescription opioid analgesics abuse epidemic. While attention to this problem has been growing, there remains an urgent need to develop novel strategies in the field of "digital epidemiology" to better identify, analyze and understand trends in NMUPD behavior. We conducted surveillance of the popular microblogging site Twitter by collecting 11 million tweets filtered for three commonly abused prescription opioid analgesic drugs Percocet (acetaminophen/oxycodone), OxyContin (oxycodone), and Oxycodone. Unsupervised machine learning was applied on the subset of tweets for each analgesic drug to discover underlying latent themes regarding risk behavior. A two-step process of obtaining themes, and filtering out unwanted tweets was carried out in three subsequent rounds of machine learning.

Whether you're blogging, publishing a video, or sharing an image, you are contributing to the 2.5 quintillion bytes of data that is made everyday! The old method of publishing tons of content isn't as effective as it used to be. Many more are publishing great content nowadays to the point that it's becoming increasingly difficult to be heard over all that digital noise. It's time to blow off that dust and apply a shiny new coat of machine learning polish to your content strategy. As a sub-set of artificial intelligence, machine learning occurs when computer algorithms are programmed to learn from the data and information it inputs.

A group of researchers at Google appear to have taken a leaf out of HBO comedy Silicon Valley's book for its latest project. The team has developed a way to use neural networks that mimic the workings of the human brain to compress images more efficiently than traditional methods. The researchers trained an AI system (built using Google's TensorFlow, which the company open sourced last year) to learn how compression works using 6 million photos for reference. It broke these images into 32 x 32 pixel pieces and selected 100 pieces with the least effective compression to learn from; the idea is that training with these difficult bits would make it a cakewalk to handle the rest of the image. The AI then predicts how a image would look after it's compressed and generates that result.

Check out these smart home startups. A lot of these use AI behind the scene to get smarter over time. This has been a weird recovery -- sluggish and slow to produce jobs and higher wages. In addition to a bunch of unusual international circumstances, the global economy has been incorporating exponential technology, particularly all the artificial intelligence applications above. While the bots are eating away at some predictable job categories, all this technology has yielded frustratingly slow productivity growth.

A pair of recent columns show that Apple (AAPL) is paying close attention to two of the largest consumer mobile software trends: The use of artificial intelligence to make apps and cloud services smarter and more powerful, and an explosion in social content-sharing, much of it via messaging apps. But the articles also show that Apple is addressing these trends in a way that accounts for other company interests, both for better and for worse. Tech site Backchannel has published an in-depth column that looks at Apple's growing investments in AI, and features remarks from software chief Craig Federighi, Internet software/services chief Eddy Cue and marketing/App Store chief Phil Schiller. It follows announcements at Apple's June developer's conference regarding the use of AI by iOS 10 (due to launch in September), and the company's recent purchase of Turi, a provider of software that developers can use to create AI-powered apps and services. Backchannel observes the Siri voice assistant has been using AI since 2014 to better understand user requests, and that Apple has also used machine learning -- a type of AI in which algorithms are trained to detect patterns, and get better at it as they take in more data -- to provide many other features and services. Examples include providing a list of apps a user is likely to open, identifying a caller who isn't in a user's contact book by analyzing e-mail data, selecting news stories, finding faces and locations in photos and figuring out whether an Apple Watch user is exercising.

BOULDER, Colo.--(BUSINESS WIRE)--Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.