Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenges in understanding and debugging the optimization process. We present DeepCAVE, a tool for interactive visualization and analysis, providing insights into HPO. Through an interactive dashboard, researchers, data scientists, and ML engineers can explore various aspects of the HPO process and identify issues, untouched potentials, and new insights about the ML model being tuned. By empowering users with actionable insights, DeepCAVE contributes to the interpretability of HPO and ML on a design level and aims to foster the development of more robust and efficient methodologies in the future.

Disney is adding another layer to its AI and extended reality strategies. As first reported by Reuters, the company recently formed a dedicated emerging technologies unit. Dubbed the Office of Technology Enablement, the group will coordinate the company's exploration, adoption and use of artificial intelligence, AR and VR tech. It has tapped Jamie Voris, previously the CTO of its Studios Technology division, to oversee the effort. Before joining Disney in 2010, Voris was the chief technology officer at the National Football League.

With world-class research institutes in artificial intelligence (AI), Sweden keeps up with all the latest ideas – and sometimes even steps out ahead. But blue-sky research doesn't always lead to practical solutions that can be used by industry. That's where AI Sweden plays a key role. An important first step in the development of AI Sweden came when Mikael Ljungblom was working as a political advisor to the Swedish minister for digital development. While travelling to see what other countries were doing, Ljungblom and his colleagues saw that countries such as Japan and China were investing in AI. Given the importance of the technology for competitiveness and societal development, they sensed a need to develop an AI centre in Sweden.

Before it was even released, Homecoming was notable for many reasons. For one, it was the new project from Mr. Robot mastermind Sam Esmail. For another, it marked Julia Roberts' first turn leading an episodic television show. And finally, the Amazon original series was one of Hollywood's first big bets on adapting podcasts for the screen. Sam Esmail's Homecoming Is Nothing Like Mr. Robot Where Is Hollywood Looking for Its Next Hit?

Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bound achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics, or even improving one, is an NP-hard problem. In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs. We apply the approach to both the Maximum Independent Set Problem and the Maximum Cut Problem. Experimental results on synthetic instances show that the deep reinforcement learning approach, by achieving tighter objective function bounds, generally outperforms ordering methods commonly used in the literature when the distribution of instances is known. To the best knowledge of the authors, this is the first paper to apply machine learning to directly improve relaxation bounds obtained by general-purpose bounding mechanisms for combinatorial optimization problems.

There have been some exciting developments of late in the Semantic Web and Technology space. Semantic Technology trends in 2018 will continue to advance many of the trends discussed in 2017 and build upon a number of new changes just entering the marketplace. This fall, in fact, the Elsevier 2017 Semantic Web challenge focused on Knowledge Graphs. The winner was IBM Socrates by Michael Glass, Nandanda Mihindukulasooriya, Oktie Hassanzadeh, and Alfio Gliozzo of IBM Research AI. "Knowledge Graphs are currently among the most prominent implementations of Semantic Web technologies," an Elsevier press release stated. "Innovative integration of additional Artificial Intelligence techniques such as Natural Language Processing (NLP) and Deep Learning over multiple web sources to find and check facts. Their knowledge graph outperformed the state of the art."

Bart de Boer of the VUB Artificial Intelligence Laboratory in Belgium then turned the information into a computer model that could predict and simulate a macaque's vocal range based on the physical attributes. Human speech stems from a source sound produced by the larynx that is changed by the positions of the vocal anatomy such as the lips and tongue. They found that a macaque could produce comprehensible vowel sounds -- and even full sentences -- with its vocal tract if it had the neural ability to speak. "This new result tells us that there's still a big mystery concerning where human speech came from," said Laurie Santos, a psychology professor at Yale University. "The paper opens whole new doors for finding the key to the uniqueness of humans' unparalleled language ability. "If a species as old as a macaque has a vocal tract capable of speech, then we really need to find the reason that this didn't translate for later primates into the kind of speech sounds that humans produce.

Are you hearing the term "Semantic Web" as often as you may have in the past? There's no denying the importance of the technologies, standards, concepts, and collaborations that define the Semantic Web proper and all that is affiliated with it or grown out of it. But if anything, the terms "Semantic Web" or "Semantic Web technologies" are receiving less attention, points out Amit Sheth, educator, researcher, and entrepreneur whose roles include being the executive director of Kno.e.sis--the Ohio Center of Excellence in Knowledge-enabled Computing. As we head into 2017, DATAVERSITY wanted to follow up the state of the Semantic Web and Semantic technologies (both standards-body related and not). In addition to Sheth, Michael Bergman, co-founder of knowledge-based Artificial Intelligence startup Cognonto (see our recent article here) and CEO of Structured Dynamics, and David Wood, CTO of 3 Round Stones, Director of Technology at Ephox TinyMCE and author of books including Linking Enterprise Data, also participated.

That's the direction taken by startup Cognonto, co-founded by Michael Bergman, a man whose history in the AI, Machine Learning, Semantic technologies, Internet search and data arenas goes back a long way. That includes his additional duties as CEO of Structured Dynamics, birthplace of UMBEL (Upper-level Mapping and Binding Exchange Layer), a knowledge graph and vocabulary for interoperating Web-accessible information, which had its latest update in May. As far as the new Cognonto venture, whose initial fruits are the Cognonto Platform and KBpedia knowledge structure, Bergman says it's been in gestation for about eight years. "The'aha' moment came when we realized how many of the large-scale QA systems were basing their knowledge structure around Wikipedia," Bergman says. "We realized this was a huge storehouse of very useful information, but one that everyone reinvented every time they brought in their own system," from Siri to Viv to IBM Watson and the Google Knowledge Graph.

That's the direction taken by startup Cognonto, co-founded by Michael Bergman, a man whose history in the AI, Machine Learning, Semantic technologies, Internet search and data arenas goes back a long way. That includes his additional duties as CEO of Structured Dynamics, birthplace of UMBEL (Upper-level Mapping and Binding Exchange Layer), a knowledge graph and vocabulary for interoperating Web-accessible information, which had its latest update in May. As far as the new Cognonto venture, whose initial fruits are the Cognonto Platform and KBpedia knowledge structure, Bergman says it's been in gestation for about eight years. "The'aha' moment came when we realized how many of the large-scale QA systems were basing their knowledge structure around Wikipedia," Bergman says. "We realized this was a huge storehouse of very useful information, but one that everyone reinvented every time they brought in their own system," from Siri to Viv to IBM Watson and the Google Knowledge Graph.