Nuance has released an upgraded version of its PowerScribe One cloud platform for radiologists. The solution leverages Nuance's conversational AI tech to facilitate a range of clinical and administrative functions. The latest update introduces an Ambient Mode that sorts free-form dictation into more organized reports, as well as a dark mode that is designed to reduce fatigue. The latter was designed over the course of 18 months with input from radiologists and user interface experts with an eye towards the long hours that radiologists spend in reading rooms. Other highlights include better data synchronization with third-party platforms, and an improved virtual assistant that allows radiologists to request electronic health records (EHR), send messages, and carry out other tasks with basic vocal commands.

Here's an interesting stat from the Pew Research Center: more than half of smart speaker owners in the US (54 percent) report saying "please" at least occasionally to their AI assistants, with one-in-five (19 percent) saying please frequently. Curiously, the question of AI politeness also breaks down along gender lines, with 62 percent of women reporting that they say "please" at least sometimes, versus 45 percent for men. One possible answer is that men are generally ruder to women, and this latter category now includes AI assistants coded as female. Experts have long noted that the design choices for AI bots could have misogynist effects by reinforcing gender stereotypes. "Because the speech of most voice assistants is female, it sends a signal that women are ... docile and eager-to-please helper," a report from the UN noted earlier this year.

In developing the ground-breaking Seeing AI app, Saqib Shaikh and the team at Microsoft were driven by this simple but powerful re-framing he articulated, "What if we could look at disability as an engine of innovation?" "There's so many examples where the technologies we rely on today where inspired or influenced by disability, from speech recognition and text to speech to the touch screen itself. There's this terminology of inclusive design where if you focus in on one person's needs, then actually doing that can help you create solutions which benefit a broader population. With seeing AI, we focus in on the needs of people who are blind or low vision, but in doing that I believe it also helps us make better products for all customers," said Shaikh. We spoke about the evolution of platform and how the team approached adding new features. "We're always listening to our customers (the low or no vision community) and understanding what are the challenges that they face. And then we're talking to the scientists and engineers at Microsoft to see what are the emerging technologies we can leverage. And with each of these, we consider the type of task you can complete," said Shaikh.

How does artificial intelligence-powered precision farming affect food sustainability? This is the question we asked our panel of experts. "Precision farming" is a bit of a buzz phrase; it is often used, but rarely defined. Generally, it means the widespread adoption of new technologies to accurately monitor and control agricultural activity. But which technologies are adopted and which consequences result?

In 2019, companies looking to gain an edge on competitors and insight into customers and trends have come to rely more heavily on data scientists to inform their business decisions. A good data scientist is invaluable to a company with any online presence. They will assess and interpret complex information and build out machine learning algorithms. Data volume keeps growing, and the amount of skill and effort needed to create data-driven initiatives is certainly keeping pace with that growth. Mistakes can produce huge consequences and, while the tools may change, the mistakes stay the same. Over the course of my career I've seen every permutation of these common mistakes, and my hope here is to help you identify and avoid them within your own teams.

Getting its world premiere at documentary festival IDFA in Amsterdam, Tonje Hessen Schei's gripping AI doc "iHuman" drew an audience of more than 700 to a 10 a.m. Many had their curiosity piqued by the film's timely subject matter--the erosion of privacy in the age of new media, and the terrifying leaps being made in the field of machine intelligence--but it's fair to say that quite a few were drawn by the promise of a Skype Q&A with National Security Agency whistleblower Edward Snowden, who made headlines in 2013 by leaking confidential U.S. intelligence to the U.K.'s Guardian newspaper. Snowden doesn't feature in the film, but it couldn't exist without him: "iHuman" is an almost exhausting journey through all the issues that Snowden was trying to warn us about, starting with our civil liberties. Speaking after the film--which he "very much enjoyed"--Snowden admitted that the subject was still raw for him, and that the writing of his autobiography (this year's "Permanent Record"), had not been easy. "It was actually quite a struggle," he revealed.

The steel industry has great impacts on the economy and the environment of both developed and underdeveloped countries. The importance of this industry and these impacts have led many researchers to investigate the relationship between a country's steel consumption and its economic activity resulting in the so-called intensity of use model. This paper investigates the validity of the intensity of use model for the case of Iran's steel consumption and extends this hypothesis by using the indexes of economic activity to model the steel consumption. We use the proposed model to train support vector machines and predict the future values for Iran's steel consumption. The paper provides detailed correlation tests for the factors used in the model to check for their relationships with the steel consumption. The results indicate that Iran's steel consumption is strongly correlated with its economic activity following the same pattern as the economy has been in the last four decades.

We study the phenomenon that some modules of deep neural networks (DNNs) are more critical than others. Meaning that rewinding their parameter values back to initialization, while keeping other modules fixed at the trained parameters, results in a large drop in the network's performance. Our analysis reveals interesting properties of the loss landscape which leads us to propose a complexity measure, called module criticality, based on the shape of the valleys that connects the initial and final values of the module parameters. We formulate how generalization relates to the module criticality, and show that this measure is able to explain the superior generalization performance of some architectures over others, whereas earlier measures fail to do so. 1 Introduction Neural networks have had tremendous practical impact in various domains such as revolutionizing many tasks in computer vision, speech and natural language processing. However, many aspects of their design and analysis have remained mysterious to this date. One of the most important questions is "what makes an architecture work better than others given a specific task?" Extensive research in this area has led to many potential explanations on why some types of architectures have better performance; however, we lack a unified view that provides a complete and satisfactory answer. In order to attain a unified view on superiority of one architecture over another in terms of generalization performance, we need to come up with a measure that effectively captures this. Analyzing the generalization behavior of neural networks has been an active area of research since Baum and Haussler (1989). Many generalization bounds and complexity measures have been proposed so far. Bartlett (1998) emphasized on the norm of the weights in predicting the generalization error.

Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just like humans, real-world AI systems have to coordinate and communicate with other agents in cooperative partially observable environments as well. These settings commonly require participants to both interpret the actions of others and to act in a way that is informative when being interpreted. Those abilities are typically summarized as theory of mind and are seen as crucial for social interactions. In this paper we propose two different search techniques that can be applied to improve an arbitrary agreed-upon policy in a cooperative partially observable game. The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy. In contrast, in multi-agent search all agents carry out the same common-knowledge search procedure whenever doing so is computationally feasible, and fall back to playing according to the agreed-upon policy otherwise. We prove that these search procedures are theoretically guaranteed to at least maintain the original performance of the agreed-upon policy (up to a bounded approximation error). In the benchmark challenge problem of Hanabi, our search technique greatly improves the performance of every agent we tested and when applied to a policy trained using RL achieves a new state-of-the-art score of 24.61 / 25 in the game, compared to a previous-best of 24.08 / 25. Introduction Real-world situations such as driving require humans to coordinate with others in a partially-observable environment with limited communication. In such environments, humans have a mental model of how other agents will behave in different situations (theory of mind). This model allows them to change their beliefs about the world based on why they think an agent acted as they did, as well as predict how their own actions will affect others' future behavior. Together, these capabilities allow humans to search for a good action to take while accounting for the behavior of others.

RDF [21] and Graph databases [27] are two approaches for data management that are based on modeling, storing and querying graph-like data. The database systems based on these models are gaining relevance in the industry due to their use in various application domains where complex data analytics is required [2]. RDF triplestores and graph database systems are tightly connected as they are based on graph data models. RDF databases are based on the RDF data model [21], their standard query language is SPARQL [15], and RDF Schema [8] allows to describe classes of resources and properties (i.e. the data schema). On the other hand, most graph databases are based on the Property Graph (PG) data model, there is no standard query language, and there is no standard notion of property graph schema [25]. Therefore, RDF and PG database systems are dissimilar in data model, schema constraints and query language.