Artificial intelligence is crossing new barriers every day -- while some tech companies are working on giving it imagination, others are working on making it capable of even more complex tasks -- for instance, judging short stories. A new study by Disney's research team and researchers at the University of Massachusetts at Boston has attempted to create artificial intelligence which will be able to understand and evaluate short stories. "Our neural networks had some success in predicting the popularity of stories. You can't yet use them to pick out winners for your local writing competition, but they can be used to guide future research," stated Boyang Li, a research scientist at Disney Research. The target of the research is to create an AI-based automated evaluation of content, although it does not have a reference point of large human evaluated story databases since not many exist.

Last week, when asking for a cup of yogurt, my three-year-old son called me Alexa. It's a funny, modern problem to have your child mistake you for a disembodied A.I. voice--in this case, the one powering Amazon's Echo family of devices. But it's also a good example of what it's like living in a home powered by a smart speaker. Too busy to lift a remote, we bark commands into the void expecting something to respond. Using a voice assistant to run your home feels beamed-in-from-Star Trek cool, making mundane actions magical.

Remember, in many cases, the application of your AI and deep learning will be to improve the customer's banking experience, provide proactive …

With the potential of providing'smart' solutions for a variety of challenges, artificial intelligence (AI) and machine learning are getting a lot of attention.

Tata Consultancy Services' (TCS) global report states that 68% of companies use artificial intelligence (AI) for IT functions. However, 70% believe AI's greatest impact by 2020 will be in functions outside of IT such as aspects of customer service and marketing. AI is transforming the media industry, using chatbots to deliver services and optimising media buying spends. In India, customers have been able to experience subtle interventions of AI in their day-to-day functions, such as during interactions with their handheld device or recommendations from e-commerce portals and news feeds on their social media. However, the business ecosystem in India is yet to incorporate the full effectiveness of AI.

Hate crimes unfortunately happen all over the world, such as in America, and according to Google, it is difficult to track since apparently very little official data is collected on such incidents, which makes it harder for journalists to do their jobs on reporting on such issues since there isn't much to go on. However that's something that Google wants to address using machine learning. The company has announced that Google News Lab will be working with ProPublica in launching a new machine learning tool that will help track hate crimes, and in turn will also help journalists find information that they can use for their reporting. According to Google, "The new Index will help make this data easier to understand and visualize. It is one of the first visualisations to use machine learning to generate its content using the Google Natural Language API, which analyses text and extracts information about people, places, and events. In this case, it helps reporters by digging out locations, names and other useful data from the 3,000-plus news reports."

I'm not sure either of these scooped anyone. I think last week I read about an example applied to moss classification, and even there I was unimpressed - it's the obvious mirror to doing random crops for data augmentation, which has been around for years, and I would bet dollars to donuts that'Cutout'/'Random Erasing' have been used before and might be implemented already in libraries because they're so obvious.

Should I bother trying to turn the 3rd one into a paper? I considered it as my architecture/method was somewhat original but there is such an overflow of machine learning/deep learning papers right now that I'm wary of dumping more on the slush pile (and I'm not in academia so there is no pressure on me to publish).

It's 2050 and the world revolves around you. From the contents of your fridge to room temperature -- digital assistants ensure your home runs smoothly. Your screens know your taste and show channels you want to see as you enter the room. Your car is driverless and your favourite barman may just be an android. Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes -- he helped develop technology that evolved into predictive texting and Apple's Siri.

Google has opened up its Home smart speaker in a big way. Today the search giant has added Bluetooth audio to the device for everyone, after teasing it back at I/O in May and slowly rolling out to select users after that. This is perfect if your app of choice doesn't support Google Cast. You know, like Apple Music, or maybe a legacy piece of audio gear. And here you probably thought that the recent music-related Google Home news would stop with Spotify.