While supervised neural nets trained on huge datasets can achieve impressive performances in tasks such as computer vision and speech recognition, they are often criticized because their internal representations are lacking in interpretability. In order to address some of these concerns, work by scientist Charlie Tang proposes models which add domain-specific knowledge in the form of structured latent variables to standard deep learning methods, leading to good results in one-shot face recognition under illumination variations.

The telescope offers one of the most seductive suggestions a technological object can carry: the idea that humans might pick up a thing, peer into it, and finally solve the riddle of the heavens as a result. Unraveling that mystery requires its own kind of refraction in perspective, collapsing the distance between near and far as a way to understand our planet and its place in the universe. This is why early astronomers didn't just gaze up each night to produce detailed sketches of celestial bodies. They also tracked the movement of those bodies across the sky over time. They developed an understanding of Earth's movement as a result.

The future of bots is sitting in thousands of documents folders, waiting to be born. At least, that's the premise of Albert, a bot and bot-creation tool from NoHold, which released a pro version on Monday. The premise behind Albert is straightforward: upload a document, and then ask the Albert-generated bot to answer questions with information based on that document. Albert is a product of the modern era of chatbots, but Albert's origins are, by tech standards, positively ancient: the key work dates back to a patent filed in 1999. "Do you know what a bucaneve is?" asks Diego Ventura, the CEO and Founder of NoHold.

A fresh Texas Hold'em-playing AI terror has emerged barely a month after a supercomputer-powered bot claimed victory over four professional poker players. But instead of relying on a supercomputer's hardware, the DeepStack AI has shown how it too can decisively defeat human poker pros while running on a GPU chip equivalent to those found in gaming laptops. The success of any poker-playing computer algorithm in heads-up, no-limit Texas Hold'em is no small feat. This version of two-player poker with unrestricted bet sizes has 10160 possible plays at different stages of the game--more than the number of atoms in the entire universe. But the Canadian and Czech reseachers who developed the new DeepStack algorithm leveraged deep learning technology to create the computer equivalent of intuition and reduce the possible future plays that needed to be calculated at any point in the game to just 107.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value.

Building upon his success in getting a humanoid robot to perform an action he was aiming for, Kuniyoshi has now turned his attention to how fetuses develop intelligence to explore the fundamental principles of intelligence of our species. For his studies, Kuniyoshi has modeled a virtual fetus consisting of approximately 400 muscles and a skeleton, gestating in an environment resembling a womb filled with amniotic fluid to run computer simulations. This fetus does not possess "innate behaviors," namely a pre-existing process that spawns specific movements. However, when vibrations sent through neural signals from the spinal cord to random muscles reached the body's other muscles through the skeleton or feedback of pressure from the amniotic fluid and uterine wall, the muscles and corresponding spinal circuits began coordinating their movements, leading to the emergence of actions resembling those of an actual fetus in the womb (movie 4). Recently, Kuniyoshi used a computer model of a fetus in the 32nd week of gestation with cerebral neural circuits, inside the womb of a woman, to observe how the cerebrum receives sensory information and the neural circuits learn about the body through touch and somatic sensation. Furthermore, he compared a fetus that was raised inside the womb with one raised outside it, and found that learning occurring inside the womb led to more enhanced development of neural circuits than that occurring outside it.

The application of AI to education has been the subject of academic research for more than 30 years, with the aim of making "computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit"3. The evidence from existing AI systems that assess learning as well as provide tutoring is positive with respect to their assessment accuracy4. AI is a powerful tool to open up the'black box of learning', by providing a deep, fine-grained understanding of when and how learning actually happens. In order to open this black box of learning, AI assessment systems need information about: (1) the curriculum, subject area and learning activities that each student is completing; (2) the details of the steps each student takes as they complete these activities; and (3) what counts as success within each of these activities and within each of the steps towards the completion of each activity. AI techniques, such as computer modelling and machine learning, are applied to this information and the AI assessment system forms an evaluation of the student's knowledge of the subject area being studied.

Artificial intelligence is coming to Aylesbury Vale... and being used by our council. AVDC say they're going to be using things like Amazon's new Echo system to help deliver services better. "So the idea with this is that people can talk to a device and actually engage directly with the council, rather than having to come in, ring us or do anything else. "So it's about using the clever things that are out there today to make things better." You won't be able to do this straight away, this is all part of a long-term plan. The five year plan called'Connected Knowledge', focuses on ensuring customers continue to have the best possible experience, by providing a digital programme that's more efficient, flexible and better value for money. AVDC was one of the first councils to adopt a cloud IT strategy, saving around £6 million. Connected Knowledge sees its digital systems evolve further, working towards fully integrated and connected transactions for customers. And through the use of Amazon Echo technology, developed with Arcus Global (AVDC's development partner), AVDC will become the first council in the UK to use AI (artificial intelligence) and AI powered voice control, to serve residents' needs. "Arcus Global is delighted to be collaborating with AVDC on the very first Amazon Echo integration with a local authority platform.

Building on its promise to give the entrenched a run for their money, conversational AI startup Ozlo is making its meticulously crafted knowledge layer available for purchase today. Ozlo's new suite of APIs that includes tools for both expressing knowledge and understanding language will help to democratize the creation of conversational AI assistants. In the spirit of the expert systems of the 1980's, knowledge graphs are about leveraging massive fact databases to build intelligent AI tools. The problem is that intelligence isn't just a matter of knowing facts. Though no truly intelligent AI has emerged from well organized information, Google, Microsoft and others have made use of the knowledge graph to reduce the barriers to getting desired information -- think of the search widgets that tell you Barack Obama's age so you don't have to click on Wikipedia.

Prisma is a wildly popular iOS and Android app that uses a mix of neural networks, artificial intelligence and cloud-based machine learning to apply a range of filter effects to photos and videos. The resulting images mimic styles of artists such as Munch or Picasso. However, even though Prisma effects tend to be located at the more interesting end of the filter spectrum, there is a chance you won't quite find what you are looking for when planning to enhance your images or videos. Thankfully, Prisma is now expanding the number of filters on offer by adding a filter-store to the Android and iOS versions of its app. This addition allows you to download new filters and remove existing ones from your app, creating your personal filter collection.