Attempts to develop AI systems are based on ontological and epistemological assumptions that guide the development explicity or implicitly. Philosophical understanding can guide the development of AI systems the tasks of which are inherently linked within human language and knowledge. On the other hand, the development of AI systems may potentially provide understanding of philosophical questions that would be difficult or even impossible without the use of computational methods and models. In this presentation, some elements of Wittgenstein's philosophical work and its development is considered in parallel with the developments that have taken place in AI research over the past half a century. Ways on how computational modeling could be used as empirical theoretical philosophy are considered. In addition, some current and hypothetical future means for studying philosophical works through computational means is discussed.

Innovation Birmingham-based 15-year-old digital tech entrepreneur Kari Lawler is set to pitch in front of UK Space Agency heads in June after winning the UK Space SatelLife Challenge 2018. The Challenge was launched by the UK Space Agency, offering the chance for 11-22-year-olds to get expert advice around their ideas on how satellites can improve life on Earth. As well as being shortlisted alongside eight promising young talents from across the UK, Kari has been awarded £5,000 and has been recognised as the second youngest innovator within the finalists of the prestigious challenge. Opening up opportunities for financial support, access to satellite data, resources and mentoring from industry advisors and experts, the pitch day is to be held on June 26th and will provide an arena for Kari to showcase her innovative'Capturing Earth's Changes' artificial intelligence (AI) proposition. Touching on machine learning, the "deep artificial neural application" will analyse and digest Earth's observation data, detecting patterns across the globe to identify the causes of natural disasters.

I had a new talk presented at Codemotion Amsterdam 2018 this week. I discussed the relation of Apache Kafka and machine learning to build a machine learning infrastructure for extreme scale. As always, I want to share the slide deck. The talk was also recorded. I will share the video as soon as it is published by the organizer.

Many of the biggest names in the technology industry are consumed with developing an artificial general intelligence, or AGI. Unlike today's leading artificial intelligence software, an AGI wouldn't need flesh-and-blood trainers to figure out how to translate English to Mandarin or spot tumors in an X-ray. In theory, it would have some measure of independence from its creators, solve complex, novel problems on its own, and herald an era in which humankind is no longer superior to machines. The consensus among our pitiful fleshbrains is that if humans ever manage to create an AGI, it'll arise in Mountain View, Calif., Beijing, or Moscow. All three cities are near world-class AI research universities and are home to companies that have pumped billions into the AGI race. There exists, however, a chance that the breakthrough will come from the Swiss city of Lugano. The picturesque slice of Switzerland's southern tip is home to about 60,000 people, including a computer scientist named Jürgen Schmidhuber. He's a professor, a researcher, and the co-founder of a 25-employee AI startup called Nnaisense.

Structural causal models are a popular tool to describe causal relations in systems in many fields such as economy, the social sciences, and biology. In this work, we show that these models are not flexible enough in general to give a complete causal representation of equilibrium states in dynamical systems that do not have a unique stable equilibrium independent of initial conditions. We prove that our proposed generalized structural causal models do capture the essential causal semantics that characterize these systems. We illustrate the power and flexibility of this extension on a dynamical system corresponding to a basic enzymatic reaction. We motivate our approach further by showing that it also efficiently describes the effects of interventions on functional laws such as the ideal gas law.

Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are defined in terms of covariance and cross-covariance operators and have been shown to be closely related to the conditional mean embedding framework developed by the machine learning community. The goal of this paper is to show how the dominant eigenfunctions of these operators in combination with gradient-based optimization techniques can be used to detect long-lived coherent patterns in high-dimensional time-series data. The results will be illustrated using video data and a fluid flow example.

The analysis of complex systems such as inventory control, financial markets, waste-to-energy plants and computer networks is difficult because of the inherent uncertainties in these systems. Risk-aware optimization offers a possible remedy by giving stronger reliability guarantees than the risk-neutral case. Furthermore, it allows expression of the risk attitude of the decision maker. Risk awareness is especially important in sequential decision making because of the dynamic nature of the uncertainty. Markov decision processes (MDPs) introduced by Bellman in [10] provide a mathematical framework for modeling sequential decision making in situations where outcomes are partly random and partly under the control the decision maker. However, in many cases the exact model of the underlying Markov decision process is not known and one can only observe the trajectory of states, actions, and rewards/costs.

This paper develops a theory of clustering and coding which combines a geometric model with a probabilistic model in a principled way. The geometric model is a Riemannian manifold with a Riemannian metric, ${g}_{ij}({\bf x})$, which we interpret as a measure of dissimilarity. The probabilistic model consists of a stochastic process with an invariant probability measure which matches the density of the sample input data. The link between the two models is a potential function, $U({\bf x})$, and its gradient, $\nabla U({\bf x})$. We use the gradient to define the dissimilarity metric, which guarantees that our measure of dissimilarity will depend on the probability measure. Finally, we use the dissimilarity metric to define a coordinate system on the embedded Riemannian manifold, which gives us a low-dimensional encoding of our original data.

One such application is intrusion detection in computer systems, where data points are typically digital messages transmitted over a network, and messages that are detected as outliers are considered likely to carry a threat [13, 17]. Another application is obstacle detection in autonomous car driving [18]. The ability to detect outliers is also important in scientific applications, where points detected as such are intrinsically more interesting than inliers, and should therefore be given more attention [59, 28]. A number of techniques can be used for outlier detection [12, 21, 36, 41, 51]. In practice, it is not only important to be able to detect outliers and inliers with high accuracy, one would also like to be able to explain why a machine learning model considers a sample as inlier or outlier. An interpretable explanatory feedback can indeed be used by a human operator for appropriate decision making. The data point could either be considered as benign and possibly incorporated to the dataset, or appropriate action might be taken. The problem of outlier explanation is shown schematically in Figure 1.

Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance improves rapidly when the context is taken into account. We propose an utterance-level attention-based bidirectional recurrent neural network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances to classify the current one. In our setup, the BiRNN is given the input set of current and preceding utterances. Our model outperforms previous models that use only preceding utterances as context on the used corpus. Another contribution of the article is to discover the amount of information in each utterance to classify the subsequent one and to show that context-based learning not only improves the performance but also achieves higher confidence in the classification. We use character- and word-level features to represent the utterances. The results are presented for character and word feature representations and as an ensemble model of both representations. We found that when classifying short utterances, the closest preceding utterances contributes to a higher degree.