Apple's Siri assistant may not boast as many third-party integrations as Amazon Alexa and Google Assistant, but it's still useful in its own right. Since the company opened up the platform last year, major apps have tapped into the platform, allowing iPhone and iPad users to perform tasks completely hands-free. WhatsApp, for example, has allowed users to send texts using their voice since September, but as part of its latest update, Apple's assistant can read messages aloud too. Once the app has been updated, a simple "Hey Siri, read my last WhatsApp message" request will have Siri dictate the most recent text. It'll no doubt help keep friends and family updated on the latest group chat happenings, but it will also promote vehicle safety by keeping eyes on the road and hands on the wheel.

If there wasn't already a mountain of pressure on Apple to deliver something spectacular with this year's iPhone update, there surely is now. If you haven't noticed, Samsung has released the Galaxy S8 and S8, and they're pretty remarkable. As a former iPhone 7 Plus user, the S8 might very well be the best phone I've ever used, with a stunning screen, speedy processor, and, yes, a gorgeous design. But what makes the S8 so amazing is how unique it is. I got to spend a week with it while writing my review, and I came away stunned.

When a robot almost looks human--almost, but not quite--it often comes across as jarringly fake instead of familiar. Robots that are clearly artificial, like WALL-E or R2-D2, don't have this problem. But androids like this one that imperfectly mimic human mannerisms and facial expressions are weird enough to be haunting. This phenomenon is known as the uncanny valley. It's a major obstacle for designers who try to make their robots look like people--and it may be just as much of a hurdle for developers who are creating bots that talk like people, but that don't have a body at all.

I will use {ordinal} clm() (and other cool R packages such as {text2vec} as well) here to develop a hybrid content-based, collaborative filtering, and (obivously) model-based approach to solve the recommendation problem on the MovieLens 100K dataset in R. All R code used in this project can be obtained from the respective GitHub repository; the chunks of code present in the body of the post illustrate the essential steps only. The MovieLens 100K dataset can be obtained from the GroupLens research laboratory of the Department of Computer Science and Engineering at the University of Minnesota. The first part of the study introduces the new approach and refers to the feature engineering steps that are performed by the OrdinalRecommenders_1.R script (found on GitHub). The second part, to be published soon, relies on the R code in OrdinalRecommenders_3.R and presents the model training, cross-validation, and analyses steps. The OrdinalRecommenders_2.R script encompasses some tireless for-looping in R (a bad habbit indeed) across the dataset only in order to place the information from the dataset in the format needed for the modeling phase.

Google Home device soon allowing you to use multiple Gmail accounts. A link has been sent to your friend's email address. Google Home device soon allowing you to use multiple Gmail accounts.

I keep hearing that 2017 will be the year bots really take off. There is no question that they offer numerous benefits, especially when it comes to reducing costs and improving customer service. More and more brands are waking up to the benefits bots deliver for enhancing customer engagement platforms. For many of the most common inquiries, delivering responses either through enhanced text messaging on mobile applications, through secure presentation of customer information and records, and through voice recognition and automated voice response can deliver a much faster and more satisfying experience for the consumer, while also dramatically lowering the cost to serve customers as fewer agents, and less infrastructure are required. That isn't the goal; instead bots are growing as cost-effective human helpers.

How in the world did we manage to survive for a few millennia without voice-activated virtual assistants? I mean, it wasn't that long ago that we drove around without being able to ask our phones for driving directions. But now the idea of being unable to do that is downright scary. More recently, if you needed to find a fact, you typed into Google and thousands of answers popped up, almost instantly. If you wanted to read movie or restaurant reviews, tap tap tap.

In comparison to the struggles the retail space has been having with the blurred lines between digital and physical, the healthcare sector appears to be the throes of a wide embrace of technologies that will alter the way doctors, pharmacies, insurers, and patients connect with each other. The alignment of the Internet of Things -- or in the case of a specific business segment identified by Accenture as the "Internet of Health Things" -- with artificial intelligence that powers "smart search" results provided by Amazon's Alexa or IBM's Watson is already altering the priorities of all facets of the health & wellness industry. To put a dollar figure on what all this change amounts to, Accenture cites an eMarketer's forecast saying the value of IoHT will reach $163 billion by 2020, with a Compound Annual Growth Rate (CAGR) of 38.1 percent between 2015 and 2020. And within the the next five years the healthcare sector is projected to be "number one" in the top 10 industries for Internet of Things app development. As a separate Accenture report notes, the insurance industry is primed for AI.

Since 2012, private robo-advisors have raised over $1.32B globally across 119 equity investments. Robo-advisors make up the largest sub-category of companies in wealth tech and account for roughly 30% of total funding. Three of the earliest robo-advisors firms and largest in terms of total funding are Betterment, Personal Capital, and Wealthfront. Though they lead in the US, expanding internationally is a challenge because of the complex international regulatory environment, differing investment practices, and other barriers to entry. Seeing the market opportunity outside the US, new early-stage (seed/angel or Series A) robo-advisors have been launching in many different markets and span at least 17 countries outside of the US.

Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules).