Radix Trading is a proprietary firm focused on quantitative research and scientific trading. We're one of the most active liquidity providers on electronic exchanges globally, and have leveraged a culture of open, collaborative innovation to scale the reach of our ideas and pace of iteration, without having to scale our headcount (currently, we're around 125 people across Chicago, New York, and Amsterdam). In our industry, the vast majority of ideas will fail. So, since inception, we've focused on continuous enhancement of our automated research platform and cutting-edge technology, allowing us to fail faster than the day prior, glean insights from each idea, and leverage individual contributions to the fullest across our entire organization. We're led by Ben Blander and Michael Rauchman, who played key roles in the rise of electronic trading, but both recognized a major gap in the industry -- a true focus on research processes coupled with an open organizational structure that fosters collaboration.

Artificial intelligence has already beaten chess. Hell, the most sophisticated AI systems have a very good chance against top players in the incredibly complicated game of Go. But, in the uber-complicated car-based soccer game of Rocket League, can an AI do a boosted 360 aerial bicycle kick power shot from the midline? Can it pinch a ball off the side ramp so precisely it sails into the goal at 90 MPH? No, at least not yet, but AI can apparently dribble like a madman. For more than a week, players have been driven up the wall (sometimes literally, in game) by machine learning-based AI that's been hacked into games of Rocket League.

Levels of 14 proteins in the blood of critically ill COVID-19 patients are associated with survival. A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité – Universitätsmedizin Berlin, Germany, and colleagues. Healthcare systems around the world are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19. In the new study, researchers studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centers in Germany and Austria.

Machine learning is awesome. Who doesn't want to build a cool AI that you can teach to do anything. The only problem is machine learning is very confusing. In this video I breakdown what a neural network is, how you can create one, and how to train it. By the end of this video you will have a fully functional AI.

Sign in to report inappropriate content. Help keep the channel: Patreon: https://www.patreon.com/Upscale Downlaod for patreon: Resolution 7680 x4320 pixels Frame Rate: 24 Video Format: H.265/HEVC Bitrate: 149 Mbps This is the official cinematic trailer for World of Warcraft's second expansion, Wrath of the Lich King.

There is a general debate going on how ethical or unethical the use of AI is, however not many people are talking about the challenges in adoption of AI by Small and Medium-sized enterprises. So, before we go one pondering about how people will lose their jobs due to AI, or before we actually start looking for new careers without actually knowing what AI is about, let me take you through a few challenges we are facing in the implementation of Machine learning and Deep learning programs and apps developed on AI platforms, in the real world especially by the majority of businesses around the globe. AI phobia is not a new kind of fear, it is a fear which we have been living with all our lives due to the irrational works of fiction writers and movies. This fear has been around long before the technology was even developed if you have watched movies like Terminator, you know exactly what I am talking about. This phobia is so rampant that even great minds like Stephen Hawkings and Elon Musk have been very vocal about their irrational fear of AI.

The convergence of three emergent technologies: Intelligent Learning Systems (AI, ML), Quantum Computing (QC) and Distributed Ledgers (DLT) will likely have a transforming impact on our society and shape the future of Information Technology. What will be the direction of future AI, ML technology initiatives in the financial industry, in a broad spectrum of research, development & commercialisation efforts for ML, AI and DLT in the quantum domain? As an example, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. This will have a profound impact on Investment Portfolio Management, High Frequency Trading, Loan Origination and processing, Fraud Detection, Risk Modelling and calculating credit ratings. Future use cases that can leverage these three technologies include a secure and transparent distributed personal financial data (PII) marketplace, and a secondary mortgage platform with a tokenised exchange combining DLT, post-QC cryptographic algorithms and deep learning technologies.

AI. It's a term that used to conjure up images from science fiction movies – the ones filled with machines that could think and act with human-like intelligence. In fact, for most of the last 40 or so years AI has been closely linked with things like the Turing test, that famous assessment for measuring a machine's ability to exhibit intelligent behavior equivalent to or indistinguishable from that of a human. Oh, how times have changed. That once-prestigious definition is long gone. AI is now nothing more than a marketing term used for any software application displaying even the most rudimentary intelligence.

This is going to be the most important focus area for 2018. Most enterprises have done proof of concepts on ML and are looking to realize the full value of their data with full fledged production implementations of the algorithms. The key technologies in this space may be Clipper. Clipper is the state-of-art ML serving system from Rise labs, Berkeley university and uses distributed computing concepts to scale models, containerized model deployment to handle models created in any platform and also performs cross-framework caching and batching to leverage parallel architectures like GPUs. Finally, Clipper can also perform cross-framework model composition using ML techniques like ensembling and multi-armed bandits.

Even though Walmart was founded in 1962, it's on the cutting edge when it comes to transforming retail operations and customer experience by using machine learning, the Internet of Things (IoT) and Big Data. In recent years, its patent applications, position as the second largest online retailer and investment in retail tech and innovation are just a few reasons they are among the retail leaders evolving to take advantage of tech to build their business and provide better service to their customers. Lauren Desegur, VP of customer experience engineering at WalmartLabs said, "We're essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores." While its arch nemesis in business may be Amazon.com,