Artificial Intelligence (AI) also referred to as Machine Intelligence is a hot topic, which is loved by many, feared by few, but cannot be avoided by any. Now let's take a look at certain statistics that will help you to understand the popularity of this emerging exponential technology. By 2020 One Billion Video Cameras to be Connected to AI Nvidia just announced that its AI Imaging System can reconstruct or repair corrupted photos with minute level of accuracy. Its system "Metropolis", an AI-platform for smart cities is getting popular at lightning speed. Huawei and Alibaba are early adopters of Metropolis.

The European Union's executive body, the EC, has taken a first pass at drawing up a strategy to respond to the myriad socio-economic challenges around artificial intelligence technology -- including setting out steps intended to boost investment, support education and training, and draw up an ethical and legal framework for steering AI developments by the end of the year. It says it's hoping to be able to announce a "coordinated plan on AI" by the end of 2018, working with the bloc's 28 Member States to get there. "The main aim is to maximise the impact of investment at the EU and national levels, encourage cooperation across the EU, exchange best practices, and define the way forward together, so as to ensure the EU's global competitiveness in this sector," writes the Commission, noting it will also continue to invest in initiatives it views as "key" for AI (specifically name-checking the development of components, systems and chipsets designed to run AI operations; high-performance ...

For hundreds of years, new materials were discovered through trial and error, or luck and serendipity. Now, scientists are using artificial intelligence to speed up the process. Recently, researchers at Northwestern University used AI to figure out how to make new metal-glass hybrids 200 times faster than they would have doing experiments in the lab. Other scientists are building databases of thousands of compounds so that algorithms can predict which ones combine to form interesting new materials. Others yet are using AI to mine published papers for "recipes" to make these materials.

A startup that uses artificial intelligence to help emergency dispatchers identify signs of cardiac arrest over the phone will begin testing its software across Europe this summer. Danish firm Corti says its algorithms can recognize out-of-hospital cardiac arrests (those that occur in the home or public) more quickly and accurately than humans. The software has already been deployed in Copenhagen, but this year, it will start four new pilots in as-yet-unnamed European cities in partnership with the European Emergency Number Association (EENA). Quick recognition of cardiac arrests is vital, as every minute that passes without treatment reduces an individual's chances of survival by 7 to 10 percent. Corti's software works by listening in during emergency calls and looking out for a number of "verbal and non-verbal patterns of communication." These include cues like a caller's tone of voice and whether or not the subject is breathing.

Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cut costs or both. Web Mining is the application of data mining techniques to discover patterns from the World Wide Web. It can be divided into three different types - Web usage mining, Web content mining and Web structure mining. Web usage mining itself can be classified further depending on the kind of usage data considered: Web Server Data, Application Server Data, and Application Level Data. Web log Mining includes three main stages: Data Pre-Processing, Pattern Discovery and Pattern Analysis. A) Data Pre-Processing: Web Server Data contains information such as who accessed the web site, what pages were accessed, Time of request etc. In pre-processing [3] stage, irrelevant data fields are removed and unique users are identified [4]. Transaction table is created through the user sessions.

The standard approach in AI is to take a set of positive examples and a set of negative examples. We seek for a function that says "YES" for the positive examples given, and "NO" for the negative examples given. Using the function found, we begin to predict the right answer for examples which we do not know whether are positive or negative. In essence, the standard approach in AI represents an approximation. What is sought for is an approximation function. It is usually sought for in a given set of functions.

Planning for autonomous driving in complex, urban scenarios requires accurate trajectory prediction of the surrounding drivers. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between their trajectories. We present a probabilistic prediction framework based on a dynamic Bayesian network, which represents the state of the complete scene including all agents and respects the aforementioned dependencies. We propose Markovian, context-dependent motion models to define the interaction-aware behavior of drivers. At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference. Secondly, we perform a probabilistic forward simulation of the network's estimated belief state to generate the different combinatorial scene developments. This provides the corresponding trajectories for the set of possible, future scenes. Our framework can handle various road layouts and number of traffic participants. We evaluate the approach in online simulations and real-world scenarios. It is shown that our interaction-aware prediction outperforms interaction-unaware physics- and map-based approaches.

We generalize, by a progressive procedure, the notions of conjunction and disjunction of two conditional events to the case of $n$ conditional events. In our coherence-based approach, conjunctions and disjunctions are suitable conditional random quantities. We define the notion of negation, by verifying De Morgan's Laws. We also show that conjunction and disjunction satisfy the associative and commutative properties, and a monotonicity property. Then, we give some results on coherence of prevision assessments for some families of compounded conditionals; in particular we examine the Fr\'echet-Hoeffding bounds. Moreover, we study the reverse probabilistic inference from the conjunction $\mathcal{C}_{n+1}$ of $n+1$ conditional events to the family $\{\mathcal{C}_{n},E_{n+1}|H_{n+1}\}$. We consider the relation with the notion of quasi-conjunction and we examine in detail the coherence of the prevision assessments related with the conjunction of three conditional events. Based on conjunction, we also give a characterization of p-consistency and of p-entailment, with applications to several inference rules in probabilistic nonmonotonic reasoning. Finally, we examine some non p-valid inference rules; then, we illustrate by an example two methods which allow to suitably modify non p-valid inference rules in order to get inferences which are p-valid.

This chapter offers an accessible introduction to the channel-based approach to Bayesian probability theory. This framework rests on algebraic and logical foundations, inspired by the methodologies of programming language semantics. It offers a uniform, structured and expressive language for describing Bayesian phenomena in terms of familiar programming concepts, like channel, predicate transformation and state transformation. The introduction also covers inference in Bayesian networks, which will be modelled by a suitable calculus of string diagrams.

Data-driven spatial filtering approaches are commonly used to assess rhythmic brain activity from multichannel recordings such as electroencephalography (EEG). As spatial filter estimation is prone to noise, non-stationarity effects and limited data, a high model variability induced by slight changes of, e.g., involved hyperparameters is generally encountered. These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as, e.g., in the field of rehabilitation. We propose a data-driven method to identify groups of reliable and functionally relevant oscillatory components computed by a spatial filtering approach. Therefore, we initially embrace the variability of decoding models in a large configuration space before condensing information by density-based clustering of components' functional signatures. Exemplified for a hand force task with rich within-trial structure, the approach was evaluated on EEG data of 18 healthy subjects. We found that functional characteristics of single components are revealed by distinct temporal dynamics of their event-related power changes. Based on a within-subject analysis, our clustering revealed seven groups of homogeneous envelope dynamics on average. To support introspection by practitioners, we provide a set of metrics to characterize and validate single clusterings. We show that identified clusters contain components of strictly confined frequency ranges, dominated by the alpha and beta band. Our method is applicable to any spatial filtering algorithm. Despite high model variability, it allows capturing and monitoring relevant oscillatory features. We foresee its application in closed-loop applications such as brain-computer interface based protocols in stroke rehabilitation.