Culture.trace in an experiment and public research engagement designed to demonstrate an alternative approach to AI, where humans are regarded with kindness, empathy and imagination rather than historical biases and stereotypes. The events will aim to demonstrate how technology, and the people it analyses, is most interesting when approached through a non-judgemental and positive lens. Over two days, you are invited to engage with a new inclusive AI tool, watch a documentary on AI Bias and learn about the affects of AI on minority communities. You will be asked to share your questions and thoughts surrounding AI as a display is built showcasing public contributions to the subject. On Friday 8 November there will be a Q&A with journalists, technologists and academics where you can join the debate.

Earlier this year, Swedish distillery Mackmyra launched the world's first whisky created using artificial intelligence in a collaboration with Finnish …

The fact that millions of people still need to go through months/years/decades of (expensive) physiotherapy to (maybe) get better illustrates how primitive medical science still is in many regards. Whatever issue these people have ideally should be curable right then and there using targeted technology but we're likely centuries away from anything even remotely resembling that. It's also possible we may never be able (or bother) to cure some of these ailments.

PyTorch Geometric is a great library and people should definitely give it a go for themselves. Both libraries implement some of the same algorithms. One of the main differences is that StellarGraph is Tensorflow-based and PyTorch Geometric is, obviously, PyTorch-based. Also, the selection of algorithms is not exactly the same. We are carefully selecting algorithms that achieve state-of-the-art results on common benchmark datasets but also we aim for variety.

The kernel trick can be used with any algorithm from the broad class of algorithms known as kernel machines. The most popular kernel machines are the support vector machine and logistic regression. Essentially, the optimisation objective of a generic kernel machine is formulated in such a way that it depends only on dot products between input vectors. This allows us to swap these dot products with a kernel computation of the dot product into some higher-dimensional (possibly infinite dimensional) space. The key to a kernel function is that it MUST have the following property: K(x_i x_j) g(x_i), g(x_j) for some g.

I've created tutorial that shows how to create web service in Python and Django to serve multiple Machine Learning models. There can be several ML models available at the same endpoint with different versions. What is more, there can be many endpoint addresses defined.

Excited to launch a new machine learning competition! The goal is to be able to a better job creating disaster response plans based on detailed maps of communities. In order to do this, we need to understand the risk to structures, which we can do by understanding what kind of roof a building has. Come use your machine learning skills for a good cause! Plus it's got interesting geo data, novel imagery, and the opportunity to develop new methods.

Is reinforcement learning practical at this point for industry work? The most prominent examples we see are from DeepMind (AlphaStar, AlphaGo), but the team are world-class researchers (over 40 of them) who also worked closely with expert Starcraft 2 players with a ton of computing resources. As someone who hasn't had much experience in RL, I see potential applications but am unsure of the amount of work or practicality of it. For example, one potential application for RL is to learn fraudulent behavior in an online retailer system (i.e. Amazon, EBay) and proactively find methods of fraud before they happen.

Microsoft and Nokia collaborate to accelerate digital transformation and Industry 4.0 for communications service providers and enterprises Companies announce their first joint solutions combining Microsoft cloud, AI and machine learning expertise with Nokia's leadership across mission-critical networking and communications REDMOND, Wash., and ESPOO, Finland -- Nov. 5, 2019 -- Microsoft and Nokia today announced a strategic collaboration to accelerate transformation and innovation across industries with cloud, Artificial Intelligence (AI) and Internet of Things (IoT). By bringing together Microsoft cloud solutions and Nokia's expertise in mission-critical networking, the companies are uniquely positioned to help enterprises and communications service providers (CSPs) transform their businesses. As Microsoft's Azure, Azure IoT, Azure AI and Machine Learning solutions combine with Nokia's LTE/5G-ready private wireless solutions, IP, SD-WAN, and IoT connectivity offerings, the companies will drive industrial digitalization and automation across enterprises, and enable CSPs to offer new services to enterprise customers. BT is the first global communications service provider to offer its enterprise customers a managed service that integrates Microsoft Azure cloud and Nokia SD-WAN solutions. BT customers can access this through a customer automated delegated rights service, which enables BT to manage both the customer Azure vWAN and the unique Agile Connect SD-WAN, based on Nokia's Nuage SD-WAN 2.0.

Then left, another left, then left again. That's the typical movement your thumb might go through if you're trying to find "the one" on any dating app. In some circles, it's becoming more common to hear friends say they met their significant other via dating apps Tinder, Bumble or Hinge, but what about Spotify? For two emerging artists, the music-streaming service helped them connect, and fast forward, they're getting married. The lovebirds are trying to find out who's responsible.