The quality of training data is one of the crucial problems when a learning-centered approach is employed. This paper proposes a new method to investigate the quality of a large corpus designed for the recognizing textual entailment (RTE) task. The proposed method, which is inspired by a statistical hypothesis test, consists of two phases: the first phase is to introduce the predictability of textual entailment labels as a null hypothesis which is extremely unacceptable if a target corpus has no hidden bias, and the second phase is to test the null hypothesis using a Naive Bayes model. The experimental result of the Stanford Natural Language Inference (SNLI) corpus does not reject the null hypothesis. Therefore, it indicates that the SNLI corpus has a hidden bias which allows prediction of textual entailment labels from hypothesis sentences even if no context information is given by a premise sentence. This paper also presents the performance impact of NN models for RTE caused by this hidden bias.

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots' recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control.

We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.

Are the U.S., China, and Russia recklessly undertaking an "AI arms race"? Clearly, there is military competition among these great powers to advance a range of applications of robotics, artificial intelligence, and autonomous systems. So far, the U.S. has been leading the way. AI and autonomy are crucial to the Pentagon's Third Offset strategy. Its Algorithmic Warfare Cross-Functional Team, Project Maven, has become a "pathfinder" for this endeavor and has started to deploy algorithms in the fight against ISIS.

Have you seen the GE commercial about "Molly, the Kid Who Never Stops Inventing"? Every time I see it, I'm reminded about how robots are becoming a greater part of our workplace. And that's not a bad thing, but it does mean that we need to get more comfortable with today's technology concepts. That's why I'm very excited to share today's interview with you. A few weeks ago, I wrote about Kronos' new next generation workforce management solution called Workforce Dimensions.

In many manufacturing plants today, monitoring is a highly manual process. FOURDOTONE Teknoloji analyzes data from sensors to enable manufacturers to respond immediately to problems, and predict when machines are likely to fail. Downtime can be expensive, and in a tightly coupled manufacturing line a problem with one machine can have an impact on the entire factory. For many factories, avoiding downtime is a matter of luck rather than science: machine inspections are infrequent, and only capture what's visible to the eye. Data is gathered from the machines and analyzed in the factory, enabling an immediate response to emergencies or imminent problems.

Machine learning, the most fundamental form of artificial intelligence, has started infiltrating the medical field, and it seems machines can play a crucial role in improving our health. A study of over 50 executives in the healtcare sector by TechEmergence revealed that by 2025 AI will be adopted on a broader scale. If there's one thing the healthcare industry has in abundance, it's undoubtedly data. And machine learning algorithms work better if they are exposed to more data. The savings would also be huge.

But 83% are unaware of how to deploy artificial intelligence & machine learning to address specific business problems, so much benefit is still to be realised Zurich, London, 18 April 2018 – The use of artificial intelligence (AI) and machine learning (ML) in financial services (FS) is on the rise, with 83% of banks having evaluated AI & ML solutions, and 67% having actively deployed them, according to a new study out today. The research with 200 global tier one and tier two banks was conducted by capital market research firm TABB Group on behalf of augmented intelligence solutions provider Squirro, and revealed that AI is the most important'disrupter' for banks today. The study – 'Enhanced Bankers – The Impact of AI'- also highlighted a lack of understanding around AI & ML as specifically applied to improving business processes, with 83% of respondents still unaware of how to apply the technology to solve business problems. Using AI and machine learning to source new leads and opportunities is key to bankers, with 87% of respondents saying that it would be highly impactful if an AI engine could spot relevant events that led to engaging with a client and closing a deal. Bankers recognize that AI driven insights will have a tremendous impact when it comes to anticipate market events to stay ahead of the competition.

In this post I present a Python script that automatically generates suggestions for startup names. You feed it a text corpus with a certain theme, e.g. a Celtic text, and it then outputs similar sounding suggestions. I applied the script to "normal" texts in English, German, and French, and then experimented with corpora of Celtic songs, Pokemon names, and J.R.R. Tolkien's Black Speech, the language of Mordor. I've made a few longer lists of sampled proposals available here. You can find the code, all the text corpora I've used, and some pre-computed models in my GitHub repo: Recently, an associate and I started to found a software company, but most name ideas we came up with were already in use. We wanted a name with a Celtic touch, and we needed a large number of candidates to find one that was still available.

There is a lot you can do with Alexa. Here's how you can add skills. We spent some time at our local Best Buy store this week, checking out TVs, looking for models that talk. But just wait a few months. You'll be seeing them all over the showroom floor.