Like the fabled tower of Babel, AI researchers have for years sought a mathematical representation that would encapsulate all natural language. Tuesday, Facebook announced it is open-sourcing "LASER," a PyTorch tool for "Language-Agnostic SEntence Representations." The code underlies a stunning research report Facebook unleashed in December, titled, "Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond." The work showed how Facebook were able to train a single neural network model to represent the structure of 93 different languages in 34 different alphabets. That research was able to develop a single "representation," a mathematical transformation of sentences, in the form of vectors, that encapsulates structural similarities across the 93 languages.

WASHINGTON - The Federal Aviation Administration said on Wednesday that 43 flights into New Jersey's Newark Liberty International Airport were required to hold after drone sightings at a nearby airport on Tuesday, while nine flights were diverted. The incident comes as major U.S. airports are assessing the threat of drones and have been holding meetings to address the issue. The issue of drones impacting commercial air traffic came to the fore after London's second-busiest airport, Gatwick Airport, was severely disrupted in December when drones were sighted on three consecutive days. An FAA spokesman said that Tuesday's event lasted for 21 minutes. The flights into Newark, the 11th-busiest U.S. airport, were suspended after a drone was seen flying at 3,500 feet over nearby Teterboro Airport, a small regional airport about 17 miles (27.3 km) away that mostly handles corporate jets and private planes.

Machine Learning is the new cool kid in the block. It has huge application from Space Exploration to Finances, Medicine and Science. It is also estimated that demand for Machine Learning experts is going to increase for the foreseeable future, with an estimated increase of about 60% this year alone. On top of that, the industry has gone through huge transformation over the last few years. Before, to be a machine learning expert, you needed to have a PHD (or some high education level), but that's no longer the case.

I can't find it, but there was an interesting midi sound library that I would've thought for sure someone would've used to make a melody maker AI before. Is this type of thing possible, if anyone has done it I'd really love it if you could link me to some code.

The loudest industry buzz has been about using big data and artificial intelligence (AI) for predictive maintenance, or turning unscheduled events into scheduled ones by forecasting likely failures. But surprise events still occur, and AI can also help troubleshoot them faster and more effectively. Any tool that enables predictive maintenance also helps troubleshooting, as it often points to causes of likely failures. But to provide maximum diagnostic benefits, some AI techniques can also be used in different ways. For example, natural language processing can translate mechanics' plain-spoken inquiries into text that helps find answers.

Automation: AI can handle most tasks as accurately as humans, but it can do it much faster and more efficiently. There is no need for boring manual work anymore because the system automates the majority of processes. Business intelligence: Machine learning and neural language processing can derive meaningful conclusions from seemingly unrelated data sources. This drastically improves business intelligence and enables companies to make better decisions. Cost reduction: Automating jobs, AI saves businesses both time and money.

Cloud computing, multi-cloud management, in particular, will keep channel partners busy in 2019. But another cloud dimension is that of technology enabler. Emerging technologies such as AI and blockchain require infrastructure and technical acumen. IT services executives suggested organizations will harness the investments cloud service providers such as AWS, Azure, Google and IBM have made at the cutting edge. Customers, to whatever extent they tap blockchain or AI next year, will probably do so using the cloud as the underlying infrastructure, delivery vehicle and development environment.

Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts.

Consumer brands and retailers often struggle to fully understand ever-changing customer needs. That is why you mostly find XL sizes in your favorite fashion store and no M sizes. That is why you have to spend hours looking for the style you saw on Instagram and still not find it. That is why the cost of dead inventory to fashion retailers in the US alone is an estimated to be a whopping USD 50 billion. And that is part of the reason why the US generated 16 million tons of textile waste in 2014.

At Facebook, machine learning provides a wide range of capabilities that drive many aspects of user experience including ranking posts, content understanding, object detection and tracking for augmented and virtual reality, speech and text translations. While machine learning models are currently trained on customized datacenter infrastructure, Facebook is working to bring machine learning inference to the edge. By doing so, user experience is improved with reduced latency (inference time) and becomes less dependent on network connectivity. Furthermore, this also enables many more applications of deep learning with important features only made available at the edge. This paper takes a data-driven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.