Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at \url{https://github.com/lola63/HDE-Python}

With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes (some) GANs, adversarial training, and other recent algorithms. We show that SM-games are amenable to analysis and optimization using first-order methods.

To bridge the gap between the data we're collecting and the way organizations interface with it, we need to address some uncomfortable realities. As we step into the next decade, there's a growing sense – almost an inevitable momentum – that we're headed towards a golden age of AI. Over the past year, we've witnessed incredible advances in applying artificial intelligence techniques to image recognition, language processing, planning, and information retrieval. There are more amusing applications, too, including one team teaching AI how to craft puns. See also: Will the Consumerization of AI Set Unrealistic Expectations?

An agreed upon definition of machine learning is, a computer program is said to have learned when it's performance measure P at task T improves with experience E. Under the definition of Supervised Learning, we get this diagram. Here the experience would be the training data required to improve the algorithm. In practice we put this data into the Design Matrix. Design Matrix [dəˈzīn ˈmātriks]: term -- if a single input can be represented as a vector, putting all of the training examples, i.e the vectors, into 1 matrix makes the entire input aspects of the training data. This is not all of the experience. We still need the labels, if the examples are the inputs.

Artificial intelligence(AI) has been an atypical technology trend. In a traditional technology cycle, innovation typically begins with startups trying to disrupt industry incumbents. In the case of AI, most of the innovation in the space has been coming from the big corporate labs of companies like Google, Facebook, Uber or Microsoft. Those companies are not only leading impressive tracks of research but also regularly open sourcing new frameworks and tools that streamline the adoption of AI technologies. In that context, Uber has emerged as one of the most active contributors to open source AI technologies in the current ecosystems.

Researchers of the ICAI Group–Computational Intelligence and Image Analysis–of the University of Malaga (UMA) have designed an unprecedented method that is capable of improving brain images obtained through magnetic resonance imaging using artificial intelligence. This new model manages to increase image quality from low resolution to high resolution without distorting the patients' brain structures, using a deep learning artificial neural network –a model that is based on the functioning of the human brain–that "learns" this process. "Deep learning is based on very large neural networks, and so is its capacity to learn, reaching the complexity and abstraction of a brain," explains researcher Karl Thurnhofer, main author of this study, who adds that, thanks to this technique, the activity of identification can be performed alone, without supervision; an identification effort that the human eye would not be capable of doing. Published in the scientific journal "Neurocomputing," this study represents a scientific breakthrough, since the algorithm developed by the UMA yields more accurate results in less time, with clear benefits for patients. "So far, the acquisition of quality brain images has depended on the time the patient remained immobilized in the scanner; with our method, image processing is carried out later on the computer," explains Thurnhofer.

Artificial Intelligence (AI) has the capability to provide radiologists with tools to help improve their productivity and decision making, possibly leading to quicker diagnosis and improved patient outcomes. As evidenced by the great number of vendors entering the market, it is initially deploying as a diverse collection of assistive applications and tools. These are allowing radiologists to augment, quantify and stratify the information available to them and has the promise to provide major opportunities to enhance and augment the radiology reading and richness of the resulting reports. It is also improving access to medical record information with the goal of helping to give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular have promise to transform the foundation for decision making and workflow, as these types of algorithms have the ability to "learn" by example to execute a task as well as interpret new data.

Many regions around the globe that are most susceptible to natural disasters are also missing critical maps that emergency response workers need to get supplies in, and people out. Intel teamed up with the American Red Cross to build a deep learning algorithm that could significantly speed up mapping of these regions using satellite imagery.

From facial recognition to self-driving vehicles, machine learning is taking over modern life as we know it. It may not be the flying cars and world-dominating robots we envisioned 2020 would hold, but it's still pretty futuristic and frightening. The good news is if you're one of the pros making these smart systems and machines, you're in good shape. And you can get your foot in the door by learning the basics with this Essential AI and Machine Learning Certification Training Bundle. This training bundle provides four comprehensive courses introducing you to the world of artificial intelligence and machine learning.

Artificial Intelligence (AI) is more linked to dopamine-reinforced learning than you may think. That's a mouthful, so for now just think of Pavlov's dog study. DeepMind AI published a blog post on their discovery that the human brain and AI learning methods are closely linked when it comes to learning through reward. Their findings were also published in the journal Nature on Wednesday. It's been a well-known fact for a while now that we humans, and many animals, learn through reward. We are motivated by external and internal factors to learn more.