Critics of the notch on Apple's iPhone X will probably be peeved at Google's next smartphone. New images purportedly show the yet-to-be-released Pixel 3 XL in a white colorway, according to XDA-Developers. They reveal that the Pixel 3 XL is likely to include a large notch, as well as a sizable'chin,' or space at the bottom of the phone's display. Critics of the notch on Apple's iPhone X are likely to be peeved by Google's next smartphone. The photos provide more details after another leak in early June claimed to show the Pixel 3 XL in black. Like the photos shared earlier this year, these show off a Pixel phone with a larger form-factor, an orange power button on the right side of the device, single-lens rear camera, dual front-facing cameras and stereo speakers.

Assistants are a big part of the future of human-computer interaction. So how you address the your artificially intelligent friend in the cloud is important. Star Trek's "computer" is accurate, but cold and sterile. Of the current leading assistant technologies, three have aimed at friendly first-name approachability. Amazon has Alexa, which sounds fun, female, and friendly.

Imagine you were traveling on business and you just arrived into town and had an emergency. Your luggage was sent to the wrong city or you were late for a critical meeting and you needed directions to the convention center. The check-in counter has a dozen guests waiting and the concierge is busy. Well, you may be in luck, because a four-foot robot with an open tablet computer and no waiting line is standing in the corner, and just may be able to come to your assistance. Pepper is an intelligent assistant that costs a bit more than a smart speaker, and can engage you in ways that go beyond ordering pizza or playing your favorite Top 40 tunes on the radio.

Some call artificial intelligence the fourth industrial revolution -- and it fascinates us, raises many questions, excites us, even scares us a little. But there is no denying that AI makes our daily lives so much easier than it was a few years ago, and much more than we imagined. When we talk about AI, we immediately think about what we use every day, such as virtual assistants or chatbots (e.g., Siri or Cortana), smartphones that identify us through fingerprint or facial recognition, cars that are able to detect pedestrians and to park themselves (often better than humans do). We also think about computers that recognize and analyze documents automatically. AI is also widely present in the business environment.

It is amply established that whether in science or business, machines will represent the greatest collaborators for human beings. Some of the early wins that have been witnessed across sectors is almost complete disintermediation, but, will AI ever be cognitive to customer needs? A click away from your next decision: The Indian e-commerce players were some of the natural early adopters of AI in India, with not just bots taking over from humans, but also building in an experience that the customer now expects and appreciates. They were followed by brick-and-mortar retailers to gather stock keeping unit (SKU)-performance and customer preferences to personalise their product promotions. Today, it is no longer restricted to e-commerce; a lot of other sectors, especially traditionally inclined ones like real estate and even B2B (business to business) firms are seeing the value of an online experience that counsels customers into making informed decisions, while still keeping it human. Bots at play: From virtual tours of a place to understanding your carbon-footprint based on where you choose to stay, to farmers getting AI assistants to predict weather patterns and when to sow next, these bots have clearly taken over much of the mundane tasks with very little human intervention.

Leviton is no stranger to the light switch, and it was an early mover into the smart home space. That kind of maturity is good news for those seeking product stability, but it also means shoppers face a daunting, complex market, as Leviton now sells more than a dozen smart switch products based on Bluetooth, HomeKit, Z-Wave, and--in this case--Wi-Fi protocols. We looked several models in Leviton's Decora Smart Wi-Fi lineup earlier, but largely found them wanting because of their limited options: You could control them with Amazon Alexa or Google Assistant voice commands (or by manipulating the devices themselves), and that was about it. Leviton has since updated this product line to add compatibility with IFTTT (If This Then That) and some Nest devices (specifically, Nest Learning Thermostats, Nest Cams, and the Nest Protect smoke/carbon-monoxide detector). The Leviton app has also been updated with home and away occupancy modes.

It's no secret that Tesla wants to use more artificial intelligence in its business. Elon Musk even left Open AI's board due to potential conflict with Tesla's own AI effort. Now we learn that Musk wants Tesla to have a KITT-like AI in its vehicles. Last year, Tesla hired Andrej Karpathy to lead its computer vision and AI team. We also learned that the automaker is working on its own new AI chip. If you take into account Tesla's several artificial intelligence efforts and Musk's desire to expand voice command features, it's not surprising that Tesla would want to implement an AI assistant in its vehicles.

About half of smartphones sold globally this year will have an Artificial Intelligence (AI)-powered assistant, a report by an industry consulting firm showed Sunday. According to Strategy Analytics (SA) Inc., 47.7 per cent of smartphones sold on the global market will be equipped with some kind of on-device AI assistant, up from 36.6 per cent last year, Yonhap news agency reported. On-device AI is growing fast among smartphone vendors and by 2023, around 90 per cent of smartphones will have a built-in AI assistant, according to the Strategy Analytics report. In 2017, Google Assistant rose to be the top AI assistant with a 46.7 per cent market share, followed by Apple's Siri with 40.1 per cent, it said. The report said the market share of Google's AI assistant is expected to climb to 51.3 per cent this year and 60.6 per cent by 2023.

Conversational AI is a subfield of artificial intelligence focused on producing natural and seamless conversations between humans and computers. We've seen several amazing advances on this front in recent years, with significant improvements in automatic speech recognition (ASR), text to speech (TTS), and intent recognition, as well as the rocketship growth of voice assistant devices like the Amazon Echo and Google Home, with estimates of close to 100 million devices in homes in 2018. But we're still a long way away from the fluent human-machine conversation promised in science fiction. Here are some key advances we should see over the next decade that could get us closer to that long-term vision. Machine learning, and in particular deep learning, has become an extremely popular technique within the field of AI over the past few years.

The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.