It's no surprise by now that voice assistants like Google Home and Amazon Echo were some of the biggest sellers of the latest holiday season. In addition, people are using Siri on their Apple iPhones to ask questions and receive answers. And some people now prefer to ask Google questions directly with their voice while working on a laptop or desktop rather than type in a question. So how can you get out in front of this voice search trend? Your first step should be to think in terms of the long tail keywords that people are actually using in their voice queries.

Nothing kills a bad idea faster than good advertising. Yet, the diffusion of information into a system can be essential--especially in medicine. So the balance between the kind of stuff that "sticks to the roof of your customer's brain" and valuable information can be tricky and even contradictory. For most of us, the introduction of Watson's skill set wasn't as a peer-reviewed paper published in a top academic journal--it was a guy name Ken Jennings and the popular TV game show Jeopardy. After a winning streak of 74 shows, Jennings took on IBM Watson and the rest is history.

A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annotations specific for visual grounding is difficult and expensive. In this work, we demonstrate that we can effectively train a VQA architecture with grounding supervision that can be automatically obtained from available region descriptions and object annotations. We also show that our model trained with this mined supervision generates visual groundings that achieve a higher correlation with respect to manually-annotated groundings, meanwhile achieving state-of-the-art VQA accuracy.

Attention mechanisms in biological perception are thought to select subsets of perceptual information for more sophisticated processing which would be prohibitive to perform on all sensory inputs. In computer vision, however, there has been relatively little exploration of hard attention, where some information is selectively ignored, in spite of the success of soft attention, where information is re-weighted and aggregated, but never filtered out. Here, we introduce a new approach for hard attention and find it achieves very competitive performance on a recently-released visual question answering datasets, equalling and in some cases surpassing similar soft attention architectures while entirely ignoring some features. Even though the hard attention mechanism is thought to be non-differentiable, we found that the feature magnitudes correlate with semantic relevance, and provide a useful signal for our mechanism's attentional selection criterion. Because hard attention selects important features of the input information, it can also be more efficient than analogous soft attention mechanisms. This is especially important for recent approaches that use non-local pairwise operations, whereby computational and memory costs are quadratic in the size of the set of features.

IBM (NYSE: IBM) has announced that several new European clients have selected IBM Watson Internet of Things (IoT) technologies, the company said. New contracts signed with Spanish electricity grid operator Red Elà ctrica de Espaà a, Italian elderly care provider Cooperativa Sole, Dutch telecommunications operator Tele2 and Israeli manufacturer of smart air conditioning Electra Group are examples of IBM s commitment to transforming business and improving operations with the power of Artificial Intelligence (AI)-enabled, IBM Cloud-based Internet of Things (IoT) technologies. Red Elà ctrica de Espaà a (http://www.ree.es/en), the sole transmission agent and operator of the national electricity system in Spain has selected IBM Watson IoT technologies as part of its Intelligent Asset Management initiative project. Israel s manufacturer and distributor of consumer goods, is collaborating with IBM to create smart air conditioning solutions, which incorporate Watson IoT technology.

IBM's artificial intelligence software, Watson, isn't ready to replace your doctor just yet. Medical experts working with the tech giant on its Watson for Oncology system discovered that it made'often inaccurate' and'unsafe' treatment recommendations, according to internal documents reviewed by Stat News. However, no patients were reportedly harmed despite Watson's missteps. Medical experts working with IBMon its Watson for Oncology system discovered that it made'often inaccurate' and'unsafe' treatment recommendations, internal documents showed The documents were included in two presentations given in June and July 2017 by IBM Watson's former deputy health chief Andrew Norden. In one case, a 65-year-old patient was diagnosed with lung cancer and said he had developed severe bleeding.

Next time you fire up the Apple Store app to look up the MacBooks, iPhones and compatible accessories you can buy, just say the keyword out loud. Cupertino has rolled out voice search for its Store application on iOS -- you only need to tap the mic icon in the search bar to get the app to recognize your words. If you've ever used the application in the past instead of exclusively visiting the website on a browser, you'll also notice that Apple has refreshed its interface. It now shows trending searchers, perhaps in an effort to entice you to look at products and features you might not check out or know otherwise. Plus, search results now come in card-like boxes so they're easier to digest.

Last year, studies presented at the American Society of Clinical Oncology's annual meeting showed that IBM Watson was pretty darn good at creating treatment plans for cancer patients. Turns out, however, that the AI is still far from perfect: according to internal documents reviewed by health-oriented news publication Stat, some medical experts working with IBM on its Watson for Oncology system found "multiple examples of unsafe and incorrect treatment recommendations." In one particular case, a 65-year-old man was diagnosed a drug that could lead to "severe or fatal hemorrhage" even though he was already suffering from severe bleeding. The report puts the blame on the IBM engineers and the Memorial Sloan Kettering (MSK) Cancer Center doctors who helped train the AI. They reportedly fed Watson hypothetical patients' data and treatment recommendations by MSK doctors instead of real patients' information.

IBM's Watson supercomputer gave unsafe recommendations for treating cancer patients, according to documents reviewed by Stat. The report is the latest sign that Watson, once hyped as the future of cancer research, has fallen far short of expectations. In 2012, doctors at Memorial Sloan Kettering Cancer Center partnered with IBM to train Watson to diagnose and treat patients. But according to IBM documents dated from last summer, the supercomputer has frequently given bad advice, like when it suggested a cancer patient with severe bleeding be given a drug that could cause the bleeding to worsen. "This product is a piece of s--," one doctor at Jupiter Hospital in Florida told IBM executives, according to the documents. "We bought it for marketing and with hopes that you would achieve the vision.

Internal company documents from IBM show that medical experts working with the company's Watson supercomputer found "multiple examples of unsafe and incorrect treatment recommendations" when using the software, according to a report from Stat News. Stat reviewed documents that were included in two presentations given in June and July 2017 by IBM Watson's former deputy health chief Andrew Norden. The documents were reportedly shared with IBM Watson Health management. According to Stat, those documents provided strong criticism of the Watson for Oncology system, and stated that the "often inaccurate" suggestions made by the product bring up "serious questions about the process for building content and the underlying technology." One example in the documents is the case of a 65-year-old man diagnosed with lung cancer, who also seemed to have severe bleeding.