Some days ago I was interviewing a candidate for a data-related position: after a couple of technical questions I asked him what algorithm he would have used to have a reliable starting point for a random classification problem. I was just curious to understand how used he was in doing some data science and if he knew some state-of-the-art algorithms and techniques. He told me that he would have gone with a simple decision tree because it's somehow easy to explain and interpret. That answer surprised me a little: I mean, why a decision tree in 2019 when you can get way better and, above all, more robust results using more advanced algorithms? As always happens, once you notice something you see it everywhere, and from that day I keep seeing and reading here and there blog posts about interpretability, explicability and how all of these concepts are connected to machine learning and trust.

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise, and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts, have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems.

In this paper, we introduce an extension of our presented cognitive-based emotion model [27][28]and [30], where we enhance our knowledge-based emotion unit of the architecture by embedding a fuzzy rule-based system to it. The model utilizes the cognitive parameters dependency and their corresponding weights to regulate the robot's behavior and fuse their behavior data to achieve the final decision in their interaction with the environment. Using this fuzzy system, our previous model can simulate linguistic parameters for better controlling and generating understandable and flexible behaviors in the robots. We implement our model on an assistive healthcare robot, named Robot Nurse Assistant (RNA) and test it with human subjects. Our model records all the emotion states and essential information based on its predefined rules and learning system. Our results show that our robot interacts with patients in a reasonable, faithful way in special conditions which are defined by rules. This work has the potential to provide better on-demand service for clinical experts to monitor the patients' emotion states and help them make better decisions accordingly.

This paper formally compares some central notions from two well-known formalisms for rule-based argumentation, DeLP and ASPIC+. The comparisons especially focus on intuitive adequacy and inter-translatability, consistency, and closure properties. As for differences in the definitions of arguments and attack, it turns out that DeLP's definitions are intuitively appealing but that they may not fully comply with Caminada and Amgoud's rationality postulates of strict closure and indirect consistency. For some special cases, the DeLP definitions are shown to fare better than ASPIC+. Next, it is argued that there are reasons to consider a variant of DeLP with grounded semantics, since in some examples its current notion of warrant arguably has counterintuitive consequences and may lead to sets of warranted arguments that are not admissible. Finally, under some minimality and consistency assumptions on ASPIC+ arguments, a one-to-many correspondence between ASPIC+ arguments and DeLP arguments is identified in such a way that if the DeLP warranting procedure is changed to grounded semantics, then DeLP notion of warrant and ASPIC+'s notion of justification are equivalent. This result is proven for three alternative definitions of attack.

Let's explore the complexity and vulnerability of IT infrastructure and how to build a modern IT infrastructure monitoring solution, using a combination of time series databases with machine learning. Check out ZDNet's series of articles detailing the outages. The outage caused disruptions to the likes of YouTube, Snapchat, and Gmail, among others. We have quickly embraced the cloud as more resilient than on-premise infrastructure, so this news is sobering. It also shows the vulnerability of the IT infrastructures, both cloud-based and on-premise, that power much of our software-dependent world -- a world that now includes entertainment and personal, as well as professional connections.

A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Appendicitis) which essentially assigns the patient to a class(es) of patients with similar body failures. However, two patients having the same disease label(s) with high probability may still have differences in their feature manifestation patterns implying differences in the required treatments. Additionally, in many cases, the labels of the primary diagnoses leave some findings unexplained. Medical diagnosis is only partially about probability calculations for label X or Y. Diagnosis is not complete until the patient overall situation is clinically understood to the level that enables the best therapeutic decisions. Most machine learning models are data centric models, and evidence so far suggest they can reach expert level performance in the disease labeling phase. Nonetheless, like any other mathematical technique, they have their limitations and applicability scope. Primarily, data centric algorithms are knowledge blind and lack anatomy and physiology knowledge that physicians leverage to achieve complete diagnosis. This article advocates to complement them with intelligence to overcome their inherent limitations as knowledge blind algorithms. Machines can learn many things from data, but data is not the only source that machines can learn from. Historic patient data only tells us what the possible manifestations of a certain body failure are. Anatomy and physiology knowledge tell us how the body works and fails. Both are needed for complete diagnosis. The proposed Double Deep Learning approach, along with the initiative for Medical Wikipedia for Smart Machines, leads to AI diagnostic support solutions for complete diagnosis beyond the limited data only labeling solutions we see today. AI for medicine will forever be limited until their intelligence also integrates anatomy and physiology.

IRCAM, the world's largest research center dedicated to both musical expression and scientific research, is a partner of the Ars Electronica Festival 2019. The institution is also involved in the STARTS Initiative as coordinator of the STARTS Residencies and will present these activities at the STARTS Day. Hugues Vinet, head of research activities at IRCAM, told us in an interview how the institute works, why AI has social relevance and what role he and his team will play at the festival. Hugues Vinet: My background is signal processing. I worked from the mid 1980s at the Musical Research Group (GRM) in Paris on the first real-time audio workstations and I designed the early versions of the GRM Tools product which made creative audio processing tools broadly available on personal computers.

There's no denying the crisis created by a sudden rise in automated phishing attacks. And while IT leaders are using AI to take security to the next level, what if this technology falls into the wrong hands- the bad guys? The dawn of the internet and advances in computing means we're able to trigger an exact solution to complex problems in diverse areas – ranging from astrophysics and biological systems to automation and precision. But at the same time, these systems are inherently vulnerable to cyber threats. In this fast-paced world where innovations come and go in the blink of the eye, cybersecurity remains top-of-mind, especially for companies dabbling with data-rich transformations such as the Internet of Things (IoT).

You can certainly write rules with machine learning models. That is one approach: to create a machine learning model that is a rule or a set of rules.

We present a comprehensive case study in the automated synth esis of list sorting algorithms: two main proofs produce the most popular sorting algorithms (min-so rt, quick-sort, insert-sort, merge-sort) and trigger all the proofs necessary for producing the needed au xiliary functions for inserting, splitting, and merging. This is a continuation of our work on exploring in pa rallel the theories of multisets, lists, and binary trees, for the purpose of developing proof method s for the synthesis of algorithms on these domains. In one related paper [12] we already investigated a lgorithms for deletion from lists and binary trees using multisets. We follow the proof-based approach to automated synthesis: first one proves automatically a synthesis conjecture which is based on the specification (input and output conditions) of the desired function, then the algorithm is extracted automatically from the proo f, in form of conditional rewrite rules. The theoretical basis and the correctness of this scheme is well -known [6] and we used earlier in [11, 15].