In a study that used artificial intelligence to analyze design elements, such as artwork and wall colors, in pictures of living rooms posted to Airbnb, a popular home rental website, the researchers found that people tended to follow cultural trends when they decorated their interiors. In the United States, where the researchers had economic data from the U.S. Census, they also found that people across socioeconomic lines put similar efforts into interior decoration. "We were interested in seeing how other cultures decorated," said Clio Andris, assistant professor of geography, Penn State and an Institute for CyberScience associate. "We see maps of the world and wonder, 'What's it like living there,' but we don't really know what it's like to be in people's living rooms and in their houses. This was like people around the world inviting us into their homes."

This is something that's been on my mind for a while, and it's been hard to shake - even in beautiful New Zealand. Thought I'd use some of the New Year's energy to write it up. It's a bit long - despite my efforts to get it down to a manageable size, but let's start somewhere. There's a scene in the movie "I, Robot", inspired by Asimov's series of the same name, where the titular robot tells the main character that he cannot create a work of art. He does this while creating a rather striking sketch that most humans would be happy to have been the creator of. This movie stands out in my memory as unique because it briefly touches on what the purpose of existence might be to an artificial mind, unlike most I've seen. Intelligences in popular culture are often portrayed as villains, and even the ones on the side of humanity seem far too concerned with the same things we are - domination, power, control, even glimpses of happiness - that I'm given to wonder if it's been given any serious thought. It's something that's been stuck in my mind for a while, but I have to admit I haven't made any significant progress. That said I'm hoping I can repeat some of the questions I've had and wonder about what the answers might be without making myself any more lost than I already am. To start, let's consider what I think is the popular perception of AI. Before we do so, I'd like to borrow some terminology to define what I'm talking about as an Artificial General Intelligence (AGI), also called a strong AI. We'll explore the meaning of this term later, but for now let's consider it to be a general mind that can perform any intellectual task a human being can. This definition if you'll notice does not require consciousness or sentience, each of which is a concept just as complicated if not more. For now, let's forget about what an Artificial SuperIntelligence would be - which in my mind is an advanced AGI - and use the term'intelligence' and'artificial intelligence' to refer to an AGI.

The hardware segment is projected to witness the highest growth rate during the forecast period. Algorithm Segment Review Based on algorithm, it is classified into deep learning, querying method, natural language processing, and context aware processing. The deep learning segment is projected to grow at the highest CAGR during the forecast period, owing to increase in use of signal reduction, data mining, and image recognition, which are integral components of most AI protocols. Global AI in healthcare Market: Key Geographic Segment Based on region, the AI in healthcare market is divided into North America, Europe, Asia-Pacific, and LAMEA. North America accounted for the largest market share in the AI in healthcare market in 2016, and is expected to retain its dominance throughout the forecast period.

India could emerge as the third-largest market in the Asia-Pacific (APAC) region for IBM's artificial intelligence (AI)-powered workforce automation solution, launched in November last year. The Armonk-based software services giant expects large-sized and mid-sized enterprises from sectors such as banking, insurance and manufacturing to be among the first adopters of the solution. The solution, dubbed the Talent and Transformation suite of services, is one among several that have come out of IBM's global AI platform, Watson. "India is one of the largest markets for the solution in terms of opportunity after Australia and Singapore (in the APAC region)," Lula Mohanty, general manager for APAC at IBM Global Business Services, told TechCircle. "Only five per cent of chief executive officers (CEOs) think that they have embarked on a transformation journey, especially when it comes to human resources core functions and only 24% of CHROs (chief human resources officers) think that they have a lot of work to do in terms of improving their core functions. This is a positive change in terms of rising awareness in the country," she added.

Demand forecasting, for all of its importance in business, has had a mixed run in retail. Even in fairly predictable categories in general merchandise, it's far too easy for retailers to start the current year's plan by loading in all the assumptions made from the year before, rather than starting clean with a new demand forecast. In fact, according to RSR Research's benchmark, even though 68% of better-performing retailers ("Retail Winners") and 53% of all other retailers believe that starting with a demand forecast as the basis for the next year's plan is very valuable, only 49% of Winners and 29% of their peers actually do so today. Part of the reason why is because forecast error in retail is high, as high as 32% according to some estimates. And, the more sporadic or non-repeatable the demand is, the more forecast error occurs – thus, grocery retailers operating a replenishment strategy have a far easier time using a forecast than a fashion retailer introducing a high-fashion item that responds to a new trend. Additionally, not all products face the same demand profiles.

First responders will be able to use drones equipped with Augmented Reality technology to better deal with emergency situations. Drones have been getting a really bad rep of late, specially in the United Kingdom, after rogue operators managed to shut down operations at both Gatwick and Heathrow airports, effectively ruining Christmas for thousands of travellers and prompting widespread clamour for greater regulation against them. Yet like all technology, it's not the tech itself, but what you do with it that counts, and which makes it a force for evil – or for the greater good. The other side of all the fear and annoyance that drones can cause in the wrong hands are the life-saving applications that companies like Edgybees are working on. Edgybees was initially founded as AR video game enhancement software, then pivoted to specialize in rescue drone technology that collects geospatial data and overlays information onto video feeds to bring emergency responders accurate and real-time information.

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model's behavior and predictions, which are helpful for determining the reliability of the model's prediction. However, such methods do not fix and improve the model's reliability. In this paper, we teach our models to make the right prediction for the right reason by providing explanation training signal and ensuring alignment of the models explanation with the ground truth explanation. Our experimental results on multiple tasks and datasets demonstrate the effectiveness of the proposed method, which produces more reliable predictions while delivering better results compared to traditionally trained models.

In many social situations, a discrepancy arises between an individual's private and expressed opinions on a given topic. Motivated by Solomon Asch's seminal experiments on social conformity and other related socio-psychological works, we propose a novel opinion dynamics model to study how such a discrepancy can arise in general social networks of interpersonal influence. Each individual in the network has both a private and an expressed opinion: an individual's private opinion evolves under social influence from the expressed opinions of the individual's neighbours, while the individual determines his or her expressed opinion under a pressure to conform to the average expressed opinion of his or her neighbours, termed the local public opinion. General conditions on the network that guarantee exponentially fast convergence of the opinions to a limit are obtained. Further analysis of the limit yields several semi-quantitative conclusions, which have insightful social interpretations, including the establishing of conditions that ensure every individual in the network has such a discrepancy. Last, we show the generality and validity of the model by using it to explain and predict the results of Solomon Asch's seminal experiments.

Generalization to unknown/uncertain environments of reinforcement learning algorithms is crucial for real-world applications. In this work, we explicitly consider uncertainty associated with the test environment through an uncertainty set. We formulate the Distributionally Robust Reinforcement Learning (DR-RL) objective that consists in maximizing performance against a worst-case policy in uncertainty set centered at the reference policy. Based on this objective, we derive computationally efficient policy improvement algorithm that benefits from Distributionally Robust Optimization (DRO) guarantees. Further, we propose an iterative procedure that increases stability of learning, called Distributionally Robust Policy Iteration. Combined with maximum entropy framework, we derive a distributionally robust variant of Soft Q-learning that enjoys efficient practical implementation and produces policies with robust behaviour at test time. Our formulation provides a unified view on a number of safe RL algorithms and recent empirical successes.

The dramatic success in machine learning has led to an explosion of artificial intelligence (AI) applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations have, however, met with fundamental obstacles that cut across many application areas. One such obstacle is adaptability, or robustness. Machine learning researchers have noted current systems lack the ability to recognize or react to new circumstances they have not been specifically programmed or trained for. Intensive theoretical and experimental efforts toward "transfer learning," "domain adaptation," and "lifelong learning"4 are reflective of this obstacle. Another obstacle is "explainability," or that "machine learning models remain mostly black boxes"26 unable to explain the reasons behind their predictions or recommendations, thus eroding users' trust and impeding diagnosis and repair; see Hutson8 and Marcus.11 A third obstacle concerns the lack of understanding of cause-effect connections.