To send a link to this page you must be logged in. The high tech company DeepMind is paying to set up a scholarship programme for those wanting to graduate at the Mile End campus. The Masters degree is supported by the Institute of Coding and backed by the government to correct "the gender imbalance" in advanced technology which is said to be under-represented by women by three-to-one. "Queen Mary is determined to do its part to break down the barriers that discourage women from digital education," the university's programme manager Isobel Bates said. "The scholarship programme will play a role in helping us tackle the gender imbalance by encouraging women take up the subject at graduate level."

After nearly a year of suspense and controversy, any day now the team of artificial intelligence (AI) researchers at OpenAI will release the full and final version of GPT-2, a language model that can "generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization--all without task-specific training." When OpenAI first unveiled the program in February, it was capable of impressive feats: Given a two-sentence prompt about unicorns living in the Andes Mountains, for example, the program produced a coherent nine-paragraph news article. At the time, the technical achievement was newsworthy--but it was how OpenAI chose to release the new technology that really caused a firestorm. There is a prevailing norm of openness in the machine learning research community, consciously created by early giants in the field: Advances are expected to be shared, so that they can be evaluated and so that the entire field advances. However, in February, OpenAI opted for a more limited release due to concerns that the program could be used to generate misleading news articles; impersonate people online; or automate the production of abusive, fake or spam content.

About the Author: Alexandre Hubert began his career as a trader in the city of London, and shifted to become a data scientist after four years. He has worked on a wide range of use cases, from creating models that predict fraud to building specific recommendation systems. Alex has also worked on loan delinquency for leasing and refactoring institutions as well as marketing use cases for retailer bankers. Alex is a lead data scientist at Dataiku, located in Singapore.

Generative Adversarial Networks (GANs) have revolutionized high-fidelity image generation, making global headlines with their hyperrealistic portraits and content-swapping, while also raising concerns with convincing deepfake videos. Now, DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis. Text-to-Speech (TTS) is a process for converting text into a humanlike voice output. One of the most commonly used TTS network architectures is WaveNet, a neural autoregressive model for generating raw audio waveforms. But because WaveNet relies on the sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers.

OpenAI, a non-profit co-founded by Elon Musk, recently unveiled its newest trick: A robot hand that can'solve' Rubik's Cube. Whether this is a feat of science or mere prestidigitation is a matter of some debate in the AI community right now. In case you missed it, OpenAI posted an article on its blog last week titled "Solving Rubik's Cube With a Robot Hand." Based on this title, you'd be forgiven if you thought the research discussed in said article was about solving Rubik's Cube with a robot hand. Don't get me wrong, OpenAI created a software and machine learning pipeline by which a robot hand can physically manipulate a Rubik's Cube from an'unsolved' state to a solved one. But the truly impressive bit here is that a robot hand can hold an object and move it around (to accomplish a goal) without dropping it.

AI and Blockchain are cutting-edge technologies and Mazenet has a power-packed curriculum. The Blockchain is the stored data in an encrypted immutable format. Artificial Intelligence is developed to make the machine capable of intelligent tasks. Mazenet's Artificial Intelligence & Deep Learning with TensorFlow is for aspiring Data Scientists who want to have rich hands-on training in various deep learning projects. Deep Learning is an AI function that emulates the human brain in creating patterns and processing information for decision making.

"Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition." We often use human consciousness as the ultimate benchmark for artificial exploration. The human brain is ridiculously intricate. While weighing only three pounds, it contains about 100 billion neurons and 100 trillion connections between those.

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A deep learning algorithm can accurately detect acute intracranial hemorrhage (ICH) on head CT on par with highly trained neuroradiologists, in some cases identifying subtle abnormalities overlooked by the radiologists, new research shows. Only a "handful" of artificial intelligence (AI) applications in medical image interpretation have achieved this level of accuracy, Esther Yuh, PhD, of the University of California, San Francisco (UCSF), told Medscape Medical News. The study was supported by the California Initiative to Advance Precision Medicine and was published online October 21 in the Proceedings of the National Academy of Sciences. Head CT is the "workhorse" medical imaging modality for diagnosing neurologic emergencies, such as acute traumatic brain injury, stroke, and aneurysmal hemorrhage, the investigators note. "However, these gray scale images are limited by low signal-to-noise, poor contrast, and a high incidence of image artifacts. A unique challenge is to identify tiny subtle abnormalities in a large 3D volume with near-perfect sensitivity," they write.

Disclaimer: This is a third-party implementation. The original authors stated that they will be releasing code soon. A recent research showed that fully-convolutional GAN called MelGAN can invert mel-spectrogram into raw audio in non-autoregressive manner. They showed that their MelGAN is lighter & faster than WaveGlow, and even can generalize to unseen speakers when trained on 3 male 3 female speakers' speech. I thought this is a major breakthrough in TTS reserach, since both researchers and engineers can benefit from this fast & lightweight neural vocoder.