The images are so crafted and "painterly" that you may not realise at first they have been dreamed up by a machine in just a few minutes. Maybe you've seen one already, but not realised what it was. It may have looked like something you'd seen before in an art book or a museum. These images are the product of a new AI-generated art scene that's exploded thanks to the development of free and easy-to-use tools that require (at the very least) short text prompts to create unique pictures. The image in the tweet above, for example, was created by giving the text prompt "a summer day" to an AI tool.

If you were into video games in the 1980s or 90s, then along with your computer, your QuickShot joystick and your tape player, there was one other vital component of your set-up: a games magazine. For me it was Zzap! 64, a glossy mag dedicated to the Commodore 64 with brilliant, opinionated writers, excellent features, and an exhaustive tips section. I would rush to the newsagent on publication day, bring it home with almost religious reverence, then read it from cover to cover. And then I would go back and read it again. This was how I discovered new games such as Sentinel, Elite and Leaderboard, but also, through the letters page and competitions, joined a community of players, years before the world wide web allowed us all to get in contact. In the 80s, video game magazines were the internet.

Advances in artificial intelligence (AI) over the years has become foundational technology in autonomous vehicles and security systems. Now, a team of researchers at the University of Stanford are teaching computers to recognise not just what objects are in an image, but also how those images make people feel. The team has trained an algorithm to recognise emotional intent behind great works of art like Vincent Van Gogh's Starry Night and James Whistler's Whistler's Mother. "The ability will be key to making AI not just more intelligent, but more human," a researcher said in the study titled'ArtEmis: Affective Language for Visual Art'. The team built a database of 81,000 WikiArt paintings and over 4 lakh written responses from 6,500 humans indicating how they felt about a painting.

Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (A WD) method developed by Lichtenberger (2009) [1]. This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by A WD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to A WD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset. 1 Introduction Lightning strokes create powerful electromagnetic pulses that result in Very Low Frequency (VLF) waves propagating along the magnetic field lines of the earth. Due to the dipole shape of the geomagnetic field, these waves travel upward from the stroke location out through portions of the plasmasphere and back to the Earth's surface at the field line foot point in the opposite hemisphere. VLF antenna receivers set up at various high and middle latitude locations can detect whistler waves generated by these lightning strokes. The propagation time delay of these waves is dependent on the plasma density along the propagation path. This enables the use of whistler wave observations for characterising the plasmasphere in terms of particle number and energy density. The dynamics of energetic particle populations in the plasmasphere are an important factor in characterising the risk to spacecraft in orbit around Earth. Annual global lightning flash rates are on the order of 45 flash/s [2].

The Fourth International Conference on Knowledge Capture was held October 28-31, 2007, in Whistler, British Columbia. K-CAP 2007 included two invited talks, technical papers, posters, and demonstrations. Topics included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixed-initiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice-taking systems. This was the fourth in a series of meetings; the first was held in Victoria, British Columbia, in 2001; the second was collocated with the ISWC meeting and was held on Sanibel Island, Florida, in October 2003; and the third meeting was held in Banff, Alberta, in October 2005. The conference was held at the Fairmont Chateau in Whistler.

It all started at a small academic get-together in Whistler, British Columbia. The topic was speech recognition, and whether a new and unproven approach to machine intelligence--something called deep learning--could help computers more effectively identify the spoken word. Microsoft funded the mini-conference, held just before Christmas 2009, and two of its researchers invited the world's preeminent deep learning expert, the University of Toronto's Geoff Hinton, to give a speech. Hinton's idea was that machine learning models could work a lot like neurons in the human brain. He wanted to build "neural networks" that could gradually assemble an understanding of spoken words as more and more of them arrived.

The Fourth International Conference on Knowledge Capture was held October 28-31, 2007, in Whistler, British Columbia. The topics covered in the invited talks, technical papers, posters, and demonstrations included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixedinitiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice-taking systems. These events, which were from web-based game-playing systems. The title of his talk was "Human Ken Barker and John Gennari Derek Sleeman noted in his introductory Etzioni's invited talk and had primary responsibilities for comments, knowledge capture is gave some technical details of the systems the conference and workshop programs. In the The best technical paper Since the K-CAP series was initiated, last decade or so, knowledge capture award was presented to Kai Eckert, the K-CAP and European Knowledge has again expanded its horizons significantly Heiner Stuckenschmidt, and Magnus Acquisition Workshop (EKAW) meetings to embrace information-extraction Pfeffer for their paper "Interactive have been held in alternate years, techniques, and more recently Thesaurus Assessment for Automatic with the K-CAP meetings taking place the web and enhanced connectivity Document Annotation."