Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing. Keywords: P systems, Neural-symbolic integration, Membrane computing 1. Introduction In the last years, the scientific community has paid more and more attention to artificial neural networks due to the doubtless success of such devices in many real-world problems.

As soon as it was possible to scan and load medical images into a computer, researchers have attempted to built system to automate the analysis of such images. Initially, from 1970s to 1990s, medical image analysis was done using sequential application of low level pixel processing(edge and line detector filters) and mathematical modeling to construct a rule-based system that could solve only particular task. At the same time there were some agents based on if-else rules, popular in field of Artificial Intelligence commonly known as GOFAI (Good Old Fashioned Artificial Intelligence) agent. Towards the end of 1990s, supervised techniques were becoming popular in which training data was used to train models and they were becoming increasingly popular in the field of medical image analysis. Examples may include active shape model, atlas method.

The increasing popularity of cell phones has made them the most personal and ubiquitous communication devices nowadays. Typically, the ringing notifications of mobile phones are used to inform the users about the incoming calls. However, the notifications of inappropriate incoming calls sometimes cause interruptions not only for the users but also the surrounding people. In this paper, we present a data-driven approach to infer the opportune moments for such phone call interruptions based on user's unavailability, i.e., when a user is unable to answer the incoming phone calls, by analyzing individual's past phone log data, and to discover the corresponding phone silent mode configuring rules for the purpose of minimizing call interruptions in an automated intelligent system. Experiments on the real mobile phone datasets show that our approach is able to identify the opportune moments for call interruptions and generates corresponding silent mode configuring rules by capturing the dominant behavior of individual users' at various times-of-the-day and days-of-theweek. Received on XXXX; accepted on XXXX; published on XXXX Keywords: Mobile phones, phone log data, temporal context, user modeling, phone ringer mode, interruptions, unavailability, personalization, intelligent systems.

The computer can't tell you the emotional story. It can give you the exact mathematical design, but what's missing is the eyebrows. Analytics has never been sexier in the world of business. Big data, artificial intelligence (AI) and machine learning are all terms that fill executives with excitement at their potential, or with dread at falling behind. Yet as recently as three years ago, an online job search would have returned very few AI-titled jobs.

Artificial Intelligence (AI) is proving its powers to prevent and detect everything gripping them from routine employee theft, frauds, insider trading and business risks. Many large corporations, business enterprises have been employing AI to detect and prevent money laundering and widespread frauds. Machine learning has been increasingly deployed by social media platforms to block illicit content such as child pornography and fake news. Businesses have been using AI for higher risk management and responsive fraud detection towards prevention and prediction of crimes. The earlier monitoring systems used by the industries need manual interference and are often not cent percent accurate.

Note: This is part-4 of a series of articles on'Security and Privacy in Artificial Intelligence & Machine Learning'. In this article we will take a closer look at use of AI&ML in various security-related use cases. We will cover not only cybersecurity but also some general security scenarios and how solutions based on AI&ML are becoming increasingly prevalent in all such areas. Towards the end we will also explore ways that attackers are likely to circumvent these security techniques. So let us begin with a look at some interesting areas where security features are benefiting from ML&AI. When we consider cybersecurity, one of the most common areas where AI&ML gets a mention is addressing the'needle in the haystack' problem in security of a large scale environment.

Despite the emergence of apps like Slack and Yammer for internal employee communication, email is still the dominant form of external employee communication for enterprises. "In a similar way that computers, servers and devices communicate with one another through data packets transmitted via TCP/IP, employees communicate with one another through natural language and documents shared via email," says Bishop. Why are account takeovers on the rise? And how can businesses prevent this method of attack? Asaf Cidon, from Barracuda Networks, helps Information Age answer these questions. "When email was created in the early 1970s it was the first'killer app' for the web.

Famously, in 2014 Prof. Stephen Hawking told the BBC: "The development of full artificial intelligence could spell the end of the human race." The ethical questions around Artificial Intelligence were discussed at a meeting led by the BCS President Chris Rees in London on October 2nd. This is also an area covered by KuppingerCole under the heading of Cognitive Technologies and this blog provides a summary of some of the issues that need to be considered. Firstly, AI is a generic term and it is important to understand precisely what this means. Currently the state of the art can be described as Narrow AI.

Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to the existing annotation guidelines with a goal of making the extracted app features more useful and informative to the app developers. We test the proposed changes via simulating the application of the new annotation guidelines and then evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to the app developers. Secondly, we are interested in what kind of annotated training data is necessary for training an automatic app feature extraction model. In particular, we explore whether the training set should contain annotated app reviews from those apps/app categories on which the model is subsequently planned to be applied, or is it sufficient to have annotated app reviews from any app available for training, even when these apps are from very different categories compared to the test app. Our experiments show that having annotated training reviews from the test app is not necessary although including them into training set helps to improve recall. Furthermore, we test whether augmenting the training set with annotated product reviews helps to improve the performance of app feature extraction. We find that the models trained on augmented training set lead to improved recall but at the cost of the drop in precision.

This article is part of Demystifying AI, a series of posts that (try) to disambiguate the jargon and myths surrounding AI. A while ago, while browsing through the latest AI news, I stumbled upon a company that claimed to use "machine learning and advanced artificial intelligence" to collect and analyze hundreds of data touch points to improve user experience in mobile apps. On the same day, I read about another company that predicted customer behavior using "a combination of machine learning and AI" and "AI-powered predictive analytics." Some people refer to AI and machine learning as synonyms and use them interchangeably, while other use them as separate, parallel technologies. In many cases, the people speaking and writing about the technology don't know the difference between AI and ML. In others, they intentionally ignore those differences to create hype and excitement for marketing and sales purposes.