Machine learning and AI represent opportunities to not only automate but also transform financial services and create new business models. While these new digital transformation projects and innovation centers are exploring new use cases, companies are already putting AI to good use in areas including merger and acquisition due diligence, regulatory compliance and cyber risk insurance. Unfortunately, there are also opponents to AI who are determined to undermine society's trust as well as nation states and criminal organizations who will use AI to cause financial, reputational, and even physical harm. In this presentation, you will learn how artificial intelligence is being inspired by the self-learning intelligence of the human immune system and is being successfully deployed. This is especially applicable against ever-growing and evolving cyber-threats emanating from dark corners across the globe.

Videolectures.net is planning to present the TheyBuyforYou project in video lectures format. Initially you are kindly invited to read about its first results. We are happy to announce the first release of the knowledge graph for public procurement, integrating tender and company data. Public procurement affects organisations across all sectors. With tenders amounting to close to 2 trillion euros annually in the EU, it is critical that this market operates fairly and efficiently, supporting competitiveness and accountability.

A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can use a large context of recently observed words when making predictions. In this tutorial, you will discover how to develop a statistical language model using deep learning in Python. How to Develop a Word-Level Neural Language Model and Use it to Generate Text Photo by Carlo Raso, some rights reserved. The Republic is the classical Greek philosopher Plato's most famous work. It is structured as a dialog (e.g. The entire text is available for free in the public domain. It is available on the Project Gutenberg website in a number of formats. Download the book text and place it in your current working directly with the filename'republic.txt'

Emotional Intelligence(also known as Emotional Quotient, or EQ) training courses and workshops in Dubai, the UAE have become very popular recently among managers of all levels. What reason stands behind the popularity of emotional intelligence trainings? A lot of people dream about taking on a managerial or leadership role, but only a few think about the drawbacks and consequences of this role. The responsibility for success or failure always puts a heavy burden on the shoulders of the leader. To manage that pressure and successfully accomplish daily tasks, each leader must have a certain set of characteristics, skills, and traits.

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.

These results are important, as it highlights that the quality generated can and does vary across the run, even after the training process becomes stable. More training iterations, beyond some point of training stability may or may not result in higher quality images.

Taken from the InfoGan paper. Let's start off by developing the generator model as a deep convolutional neural network (e.g. a DCGAN). The model could take the noise vector (z) and control vector (c) as separate inputs and concatenate them before using them as the basis for generating the image. Alternately, the vectors can be concatenated beforehand and provided to a single input layer in the model. The approaches are equivalent and we will use the latter in this case to keep the model simple.

The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. It is an important extension to the GAN model and requires a conceptual shift away from a discriminator that predicts the probability of a generated image being "real" and toward the idea of a critic model that scores the "realness" of a given image. This conceptual shift is motivated mathematically using the earth mover distance, or Wasserstein distance, to train the GAN that measures the distance between the data distribution observed in the training dataset and the distribution observed in the generated examples. In this post, you will discover how to implement Wasserstein loss for Generative Adversarial Networks. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.

Judgment aggregation (JA) is concerned with aggregating sets of binary truth valuations assigned to logically related issues [27, 19]. Various collective decision making problems in artificial intelligence can be modelled as JA problems, e.g., problems of constructing agreements, such as finding a collective goal in multi-agent systems [36, 2]. In agreement reaching problems each agent in a group is a source of judgments and also typically affected by the collective choice resulting from the aggregation of individual judgments. For example, I am a citizen voting on a referendum that decided not to impose global warming curbing methods, but I am also a citizen that has to live with the consequences of that collective decision.

Ursula von der Leyen was elected on Tuesday as the new President of the European Commission by 383 Members of the new European Parliament. She is the first woman to hold the office. A centre-right politician and close ally of German Chancellor Angela Merkel, she was until this week Germany's defense minister. So what does her election mean for EU tech policy over the next five years? We did get a glimpse of her priorities in her Tuesday morning speech addressing the plenary of the European Parliament ahead of her confirmation vote.