If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Valentine's Day is only two days away. You could go off and overspend on getting a last-minute flower delivery, or you could get your significant other a gift that's guaranteed to show up on time and doesn't come with added delivery fees. Everyone knows Amazon offers free 2-day shipping on virtually everything for Prime members, but they're not the only retailer that can get your gift to your loved one on time.

Romeo, an 11-year-old frog from Cochabamba City, Bolivia, has been given his own online dating profile in a bid to save his species. The Sehuencas water frog, who is the last known individual of his species, has not had a partner for more than 10 years. Conservation groups have teamed up with Match.com to give Romeo a platform and raise awareness for his story as well as funds for an expedition to find him a mate. If all else fails, one of the researchers on the project wont rule out cloning as a means of preserving this amphibian species which, like many others, is threatened by climate change, habitat loss and other environmental and ecological issues. Romeo, an 11-year-old frog from Cochabamba City, Bolivia, has been given his own online dating profile in a bid to save his species.

The number of companies using artificial intelligence in healthcare has increased from less than 20 in 2012 to 100 last year, according to GlobalData Healthcare estimates. Growth is expected to accelerate with the AI healthcare market set to reach $6.6bn by 2021, a 40 percent growth from its current size, research from Accenture shows. The three most cost-saving uses of AI in healthcare are robot assisted surgery, virtual nursing assistants, and administrative workflow assistance, Accenture has found. Although healthcare AI is widely used in the US, take up has been slower in the UK though healthcare apps are gaining traction. Babylon is an AI app which uses speech recognition to check symptoms and connect patients with doctors while MedyMatch helps A&E departments make better decisions under extreme pressure.

In 2018, disruptive technologies like artificial intelligence, natural language processing, and computer vision are maturing, going from game-changing ideas to foundational tools for business. This year, we'll see these and other technologies drive how business gets done and what new products will launch in the near future. To get a sense of what's ahead for this year, we looked at the technologies experts say are most likely to affect a wide variety of organizations as they undergo digital transformations. Pros in these fields gave us their top picks for what should be on your radar, as well as some insight into the implications of adopting these new technologies. Last month, Amazon, Berkshire Hathaway and JPMorgan Chase announced a joint venture with a focus on using technology to offer their employees and their families "simplified, high-quality and transparent healthcare at a reasonable cost."

It depends who you ask. AI might be a hot topic but you'll still need to justify those projects. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to recognise what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI.

How we search is undergoing a sea change. The Next Revolution of Search report, produced in association with Microsoft, explores the shifts taking place at the intersection of marketing and cutting-edge artificial intelligence technology, taking readers through insights from our survey of more than 2500 consumers of search technology. The survey of 2,687 American adults supports insight gleaned from more than 25 interviews with futurists, scientists, and marketing practitioners. The Next Revolution of Search covers privacy issues in an unprecedented future, how new technologies will affect consumer attitudes, the skills marketers will need to learn, and other areas of concern for marketers over the next decade. From improved mobile assistants, to electronic aids that predict which products we will want and order them without human input, "search," in one form or another, will pervade modern life.

That's the idea behind Pour Reception, an art project by Tore Knudsen, Simone Okholm Hansen, and Victor Permild, featured recently on Creative Applications. It's a radio that you tune by filling, drinking, and tapping on two glasses of water. By pouring water from one glass to the other, you scrub through frequencies to find a new station. By touching a jar, you can fine-tune its signal and remove distortion. Finally, to turn the music down, you just plunge your finger right into the water.

Innovators in the science field are not the only ones concerned with Artificial intelligence. It is also of great interest, exploration, and expansion to those in the business world and mobile app development in particular. When most people hear AI, they immediately picture Apple's Siri and Google's Assistant. However, they're not the only Artificial intelligence systems out there as there seems to be a growing demand for AI this year. It is no surprise to, therefore see that a lot of Top app development companies are integrating artificial intelligence into their apps.

The newest artificial intelligence out of San Francisco-based tech firm Aeolus Robotics is a revolutionary robot that can pretty much second guess your family's movements, their identity, and even perform household duties from moping the floor to getting drinks out of the fridge. While the flying cars that were promised to us in The Jetsons are lamentably still on the drawing board, a digital domestic goddess by the likes of Rosie The Robot has actually arrived. The mechanical mate, which is expected to be available by the end of the year, will reportedly cost as much as a car at around $US20,000 ($25,000) and is described by Aeolus as being the height and weight of a 12 year old -- however undoubtedly more house-trained. Just like a creepy scene out of (insert favourite Sci Fi film here) this amazing android can distinguish between family members' faces, recognise where household items are supposed to go (then put them back in place) and can keep a sly eye open for emergencies like a fire or notice a change in posture and possibly prevent a fall. The yet to be officially named "Aeolus Robot" can also move furniture, find lost items and even learn the household schedule via an information sharing network.

Model-based methods for recommender systems have been studied to provide more precise results. In systems with large corpus, the amount of calculation for learnt model to predict all user-item pairs' preferences is tremendous, which makes the model difficult to be directly employed in recommendation candidate generation stage. To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item's latent factors as the preference) and index like hashing to perform efficient approximate k-nearest neighbor search. However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation. In this paper, we focus on the problem how arbitrary advanced models can be introduced to generate recommendations from large corpus. We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size with more expressive deep neural networks. The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes top-down and making decisions whether to pick up each node to user. Furthermore, we show that the tree structure can also be jointly learnt towards better compatible with user interests' distribution, to facilitate both training and prediction. Experiments in two large-scale real-world datasets indicate that the proposed model significantly outperforms traditional methods. And online A/B test results in Taobao display advertising platform prove the effectiveness of the tree-based deep model in production.