The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. Several technical solutions are being proposed to address the issue. Hardware security modules in cloud data centres appear limited to enterprise customers due to their complexity, while general multi-party computation techniques require a large number of message exchanges. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i) only require additively homomorphic encryption algorithms, (ii) limit interactions to a mere request and response, and (iii) that can be used directly for important machine-learning algorithms such as logistic regression and SVM classification. The basic protocols are then extended and applied to feed-forward neural networks.

With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are relevant for a classifier's decision. However, highlighting of image parts (e.g., an eye) cannot capture the relevance of a specific feature value for a class (e.g., that the eye is wide open). Furthermore, highlighting cannot convey whether the classification depends on the mere presence of parts or on a specific spatial relation between them. Consequently, we present an approach that is capable of explaining a classifier's decision in terms of logic rules obtained by the Inductive Logic Programming system Aleph. The examples and the background knowledge needed for Aleph are based on the explanation generation method LIME. We demonstrate our approach with images of a blocksworld domain. First, we show that our approach is capable of identifying a single relation as important explanatory construct. Afterwards, we present the more complex relational concept of towers. Finally, we show how the generated relational rules can be explicitly related with the input image, resulting in richer explanations.

Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis a patient may have. Despite the recent progress in machine learning, unsupervised learning remains largely at open, especially in the healthcare domain. In this work, we present a two-step unsupervised representation learning scheme to summarize the multi-modal clinical time series consisting of signals and medical codes into a patient status vector. First, an auto-encoder step is used to reduce sparse medical codes and clinical time series into a distributed representation. Subsequently, the concatenation of the distributed representations is further fine-tuned using a forecasting task. We evaluate the usefulness of the representation on two downstream tasks: mortality and readmission. Our proposed method shows improved generalization performance for both short duration ICU visits and long duration ICU visits.

We present an experimental comparison of the efficiency of three General Game Playing systems in their current versions: Regular Boardgames (RBG 1.0), Ludii~0.3.0, and a Game Description Language (GDL) propnet. We show that in general, RBG is currently the fastest GGP system. For example, for chess, we demonstrate that RBG is about 37 times faster than Ludii, and Ludii is about 3 times slower than a GDL propnet. Referring to the recent comparison [An Empirical Evaluation of Two General Game Systems: Ludii and RBG, CoG 2019], we show evidences that the benchmark presented there contains a number of significant flaws that lead to wrong conclusions.

Brian Nord wants to build a self-driving telescope. The Fermilab astrophysicist envisions an instrument that, when presented with a hypothesis about the nature of the Universe, figures out the best observations to make on its own. He anticipates that it could take up to thirty years to understand and put together the project's nuts and bolts. One known component is artificial intelligence (AI)--algorithms similar to those that underpin facial recognition software and nascent self-driving car technology. Building toward his telescope dream, Nord has begun applying AI to problems in astronomy, such as identifying unusual astronomical objects known as gravitational lenses.

This is not the same book as The Math of Machine Learning, also published by the same department at Berkeley, in 2018, and also authored by Garret Thomas. I hope they will add sections on Ensemble Methods (combining multiple techniques), cross-validation, and feature selection, and then it will cover pretty much everything that the beginner should know. Other popular free books, all written by top experts in their fields, include Foundations of Data Science published by Microsoft's ML Research Lab in 2018, and Statistics: New Foundations, Toolbox, and Machine Learning Recipes published by Data Science Central in 2019.

We are at the threshold of a fully connected, intelligent world, where innovation happens in the blink of an eye, and the impact on every person, home, and organization is nothing short of profound. An intelligent world is right around the corner, with wide application of AI and 5G across all industries. More and more companies have come to realize the value of AI and 5G, and with eagerness to leverage them, are now focusing on how technologies can accelerate their migration to the cloud.

We know that they have the same sensors – called nociceptors – that cause us to flinch or cry when we are hurt. And they certainly behave like they are sensing something unpleasant. When a chef places them in boiling water, for instance, they twitch their tails as if they are in agony. But are they actually "aware" of the sensation? Or is that response merely a reflex?

Kustomer, the SaaS platform that is reimagining enterprise customer service, today introduced KustomerIQ, embedding Artificial Intelligence and Machine Learning across the Kustomer platform to enhance the customer service experience of companies competing in today's on-demand world. KustomerIQ uniquely integrates Machine Learning models and other advanced AI capabilities with the Kustomer platform's powerful data, workflow, and rules engines to enable companies to provide smarter, automated customer experiences that are more personalized, efficient, and effortless. The Kustomer platform stands out among customer service solutions for the comprehensiveness of available customer data and its business process automation that is driven by branchable, multi-step workflows and custom business logic. "In today's crowded market, excellent customer service is often the differentiator that builds loyalty and trust between one brand to another," said Brad Birnbaum, Co-Founder and CEO of Kustomer. "With KustomerIQ and the inclusion of Artificial Intelligence and Machine Learning into our omnichannel platform, Kustomer will now go even further in helping brands automate their business processes, while making it easier for their agents to take action on customer information, ultimately developing a stronger and more profitable customer relationship."

As more companies realize the great need for data scientists to develop, experiment, and deploy artificial intelligence (AI), IBM designed a certification program. It offered it to the company workforce, and incentivized employees completed the program through coursework, skills training, and apprenticeships. IBM's certification and related programs "will speed the journey to AI and help improve business performance, efficiency and growth," said Martin Fleming, IBM vice president and chief economist. The demand for data scientists is recognized in the tech industry which "actually identifies the demand for data scientists as one of the industry's most pressing needs." Fleming cites social-media career platform LinkedIn's 2018 report, which found 151,000 US data scientist positions unfilled. "More companies are looking inward for ways to build the skills among their existing workforces," he said.