Where do you guys rent compute resources for training? What are your primary selection criteria (cost/reliability/bandwidth/ data location), for your particular use case? Do you also own your own AI/ML gears for consistent workload, in addition to the cloud? I am asking this as I am building an exchange where people can share quality compute resources at-cost or near-cost. Would this be something that you are interested in?

Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting.

But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.

But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.

Sudbury's Centre for Excellence in Mining Innovation (CEMI) has gone international in signing a memorandum of understanding (MOU) with an industry technology centre in Australia. CEMI and METS Ignited of Brisbane signed an agreement to establish a vehicle for each organization to collaborate and accelerate the commercialization of mining innovations in Canada and Australia. "We have boots on the ground in Australia now," said Charles Nyabeze, CEMI's vice-president of business development and commercialization, in a Sept. 13 phone interview. "We will have access to game-changing solutions not only for our Canadian mines, but also for the mines we work with globally." The two organizations intend to cross-promote each other in their respective countries when it comes to mining-related exploration, extraction, transportation; tailings, waste and water management technologies; and digitalization of mine operations using analytics, artificial intelligence, automation and robotics.

McDonald's announced it will McBuy the Bay Area voice-recognition startup Apprente for an undisclosed amount. According to McDonald's, Apprente's "sound-to-meaning" technology handles "complex, multilingual, multi-accent and multi-item conversational ordering," and believes the technology will help streamline the drive-thru process -- even faster food, you say?? As the earth turns and the centuries change, so does the way people wish to order a Big Mac, and Micky D's has the cash to listen. Back in March, the company bought Dynamic Yield, which customizes drive-thru menus based on factors like weather, time of day, and customer order profiles. A month later, it invested in New Zealand app-designer Plexure, which will help connect customers to its new smart drive-thrus, among other things.

In an age of uncertainty with the arrival of artificial intelligence (AI) tools and technologies in the healthcare field, many in the industry question how the addition of AI will impact their careers. One particular area is not immune to these changes: radiography. We spoke with Dr. Nick Woznitza, a reporting radiographer at Homerton University Hospital and a clinical academic at Canterbury Christ Church University in the United Kingdom, to gain some insight into what kind of effect AI will have on radiographers' tasks, workflow, and training and what the future holds for the field of radiography. Which routine tasks do radiographers perform that AI cannot assist with? Effective and compassionate communication is a core skill of all radiographers.

AI startup Adarga has closed a £5 million Series A fundraising by Allectus Capital. But this news rather cloaks the fact that it has been building up a head of steam since its founding in 2016, building up what they say is a £30 million-plus sales pipeline through strategic collaborations with a number of global industrial partners and gradually building its management team. The proceeds will be used to continue the expansion of Adarga's data science and software engineering teams and to roll out internationally. Adarga, which comes from the word for an old Moorish shield, is a London and Bristol-based startup. It uses AI to change the way financial institutions, intelligence agencies and defence companies tackle problems, helping crunch vast amounts of data to identify possible threats even before they occur.

Traditionally, off-policy learning algorithms (such as Q-learning) and exploration schemes have been derived separately. Often times, the exploration-exploitation dilemma being addressed through heuristics. In this article we show that both the learning equations and the exploration-exploitation strategy can be derived in tandem as the solution to a unique and well-posed optimization problem whose minimization leads to the optimal value function. We present a new algorithm following this idea. The algorithm is of the gradient type (and therefore has good convergence properties even when used in conjunction with function approximators such as neural networks); it is off-policy; and it specifies both the update equations and the strategy to address the exploration-exploitation dilemma. To the best of our knowledge, this is the first algorithm that has these properties.

Time series classification (TSC) is the area of machine learning interested in learning how to assign labels to time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE is infeasible to use in many applications because of its very high training time complexity in O(N^2*T^4) for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 72,000s to learn from a small dataset with N=700 time series of short length T=46. Deep learning, on the other hand, has now received enormous attention because of its high scalability and state-of-the-art accuracy in computer vision and natural language processing tasks. Deep learning for TSC has only very recently started to be explored, with the first few architectures developed over the last 3 years only. The accuracy of deep learning for TSC has been raised to a competitive level, but has not quite reached the level of HIVE-COTE. This is what this paper achieves: outperforming HIVE-COTE's accuracy together with scalability. We take an important step towards finding the AlexNet network for TSC by presenting InceptionTime---an ensemble of deep Convolutional Neural Network (CNN) models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime slightly outperforms HIVE-COTE with a win/draw/loss on the UCR archive of 40/6/39. Not only is InceptionTime more accurate, but it is much faster: InceptionTime learns from that same dataset with 700 time series in 2,300s but can also learn from a dataset with 8M time series in 13 hours, a quantity of data that is fully out of reach of HIVE-COTE.