Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days.

In the field of neuroimaging, two heads are better than one. So radiologists around the globe are exploring the use of AI tools to share their heavy workloads -- and improve the consistency, speed and accuracy of brain scan analysis. "We often refer to manual annotation as the gold standard for neuroimaging, when it's actually probably not," said Tim Wang, director of operations at the Sydney Neuroimaging Analysis Centre, or SNAC. "In many cases, AI provides a more consistent, less biased evaluation than manual classification or segmentation." An Australian company co-located with the University of Sydney's Brain and Mind Centre, SNAC conducts neuroimaging research as well as commercial image analysis for clinical research trials.

It is the 21st century, and technology is changing faster than ever. To meet the market current opportunities, we should know Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science. These concepts have become buzzwords and lucrative career options. But we often see them being thrown around, when in fact, the terms are not interchangeable. The need to empower readers like you to tell them apart is what births this article.

Automated Machine Learning (AutoML) has received significant interest recently. We believe that the right automation would bring significant value and dramatically shorten time-to-value for data science teams. Databricks is automating the Data Science and Machine Learning process through a combination of product offerings, partnerships, and custom solutions. This talk will focus on how Databricks can help automate hyperparameter tuning. For both traditional Machine Learning and modern Deep Learning, tuning hyperparameters can dramatically increase model performance and improve training times.

The number of companies using artificial intelligence in healthcare has increased from less than 20 in 2012 to 100 last year, according to GlobalData Healthcare estimates. Growth is expected to accelerate with the AI healthcare market set to reach $6.6bn by 2021, a 40 percent growth from its current size, research from Accenture shows. The three most cost-saving uses of AI in healthcare are robot assisted surgery, virtual nursing assistants, and administrative workflow assistance, Accenture has found. Although healthcare AI is widely used in the US, take up has been slower in the UK though healthcare apps are gaining traction. Babylon is an AI app which uses speech recognition to check symptoms and connect patients with doctors while MedyMatch helps A&E departments make better decisions under extreme pressure.

In a key step toward making large-scale optical neural networks practical, researchers have demonstrated a first-of-its-kind multilayer all-optical artificial neural network. Generally, this type of artificial intelligence can tackle complex problems that are impossible with traditional computational approaches, but current designs require extensive computational resources that are both time-consuming and energy intensive. For this reason, there is great interest developing practical optical artificial neural networks, which are faster and consume less power than those based on traditional computers. In Optica, The Optical Society's journal for high-impact research, researchers from The Hong Kong University of Science and Technology, Hong Kong detail their two-layer all-optical neural network and successfully apply it to a complex classification task. "Our all-optical scheme could enable a neural network that performs optical parallel computation at the speed of light while consuming little energy," said Junwei Liu, a member of the research team.

Natural language processing (NLP) is the ability to extract insights from and literally understand natural language within text, audio and images. Language and text hold huge insight, and that data is often prevalent and widespread in many organizations. The ability to process language systematically, effectively and at scale lends itself to numerous applications across almost any organization with application to customer-facing products and services and customer support through to big process changes in the back office. NLP applications can apply to speech-to-text, text-to-speech, language translation, language classification and categorization, named entity recognition, language generation, automatic summarization, similarity assessment, language logic and consistency, and more. There have been a range of techniques applied to NLP, but deep learning, in particular, is showing some exceptional results. Deep learning has been applied to NLP for several years, and research and development breaks new ground so quickly that new methods and increasingly capable models are rapidly occurring.

Nominate graduate students and early-career professional colleagues for the opportunity to participate -- free of charge -- in the Women's Early Career Accelerator. This by-invitation-only training and networking event takes place on Monday, November 4. The day-long program, designed for women just getting started in deep learning, will introduce the fundamentals through a hands-on workshop and connect them with women doing important work in the field. The ideal candidate is a developer, data scientist, engineer, researcher, or student in a technical field who wants a hands-on introduction to deep learning and computer vision. Participants should be familiar with basic programming fundamentals like functions and variables, but no prior experience with deep learning is necessary. These sponsored registrations are available to a limited number of people.

The most frequently used AI applications are voice recognition and assistance systems, each of which was mentioned by 40 percent of those surveyed. Bots and robotics (30 percent each) are still lagging behind. Optimizing internal processes (37 percent) and improved efficiency (36 percent) are the key goals pursued through the use of ML solutions. Only about one-fourth views the technology as a means to develop new products and services.

LIKE other human champions facing a machine opponent, Grzegorz "MaNa" Komincz rated his chances. "A realistic goal would be 4-1 in my favour," he told an interviewer before the match. One of the world's best players of video game StarCraft II, Komincz was at the height of a successful esports career. Artificial intelligence company DeepMind invited him to face its latest AI, a StarCraft II-playing bot called AlphaStar, on 19 December 2018. Komincz was expected to be a tough opponent. After being thrashed 5-0, he was less cocky.