Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects of the world that are not captured by the system’s models). This article discusses recent progress in AI and then describes eight ideas related to robustness that are being pursued within the AI research community. While these ideas are a start, we need to devote more attention to the challenges of dealing with the known and unknown unknowns. These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world

Zuckerberg, who spent the summer sparring with Tesla's Elon Musk over the risks of ever-advancing artificial intelligence in our technology, got some support from Google's head of search and AI, John Giannandrea, who spoke recently about what he called the "huge amount of unwarranted hype around AI right now." Speaking at the TechCrunch Disrupt conference in San Francisco on Tuesday, Giannandrea echoed some of the Facebook co-founder's recent statements dismissing doomsday scenarios in which AI-empowered machines pose an inherent existential threat to their human creators. "This leap into, 'Somebody is going to produce a superhuman intelligence, and then there's going to be all these ethical issues' is unwarranted and borderline irresponsible," Giannandrea said at the conference. Google's AI chief added: "I'm definitely not worried about the AI apocalypse." Giannandrea went on to explain the importance of machine learning and artificial intelligence in revolutionizing the technology industry. Google uses AI to power features like Google Translate, the online tool that can instantly translate both spoken words and typed text, as well as products that help users search for new jobs online and provide you with ready-made replies to messages in Google's Gmail, among countless other applications.

We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.

Today I want to show an example of Sequence to Sequence model with all the latest TensorFlow APIs [as of TF 1.3]. Seq2Seq models are very useful when both your input and output have some structure or time component. Most popular applications are all in the language domain, but one can use it to process time series, trees, and many other intrinsically structured data. Translation has been domain where this models advanced the most, as it has a large enough dataset to train large and complicated models and provides a clear value from advancing state-of-the-art. If you haven't seen, here are few papers on Neural Language Translation with Seq2Seqs: https://arxiv.org/abs/1409.3215,

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RM-SProp, or SGD with and without Momentum on a ConvNet model. We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system.

Every day we use different technologies without even knowing how exactly they work. In fact, it's not very easy to understand engines powered by machine learning. The Statsbot team wants to make machine learning clear by telling data stories in this blog. Today, we've decided to explore machine translators and explain how the Google Translate algorithm works. Years ago, it was very time consuming to translate the text from an unknown language.

Geospatial imagery as well as facial recognition and other biometrics are driving the intelligence community's research into artificial intelligence. Other intelligence activities, such as human language translation and event warning and forecasting, also stand to gain from advances being pursued in government, academic and industry research programs funded by the community's research arm. The Intelligence Advanced Research Projects Activity (IARPA) is working toward breakthroughs in artificial intelligence, or AI, through a number of research programs. All these AI programs tap expertise in government, industry or academia. IARPA is one of the biggest financial backers of AI research, states its director, Jason Matheny, and imagery is the biggest growth area for intelligence AI.

Google's latest take on machine translation could make it easier for people to communicate with those speaking a different language, by translating speech directly into text in a language they understand. The team trained its system on hundreds of hours of Spanish audio with corresponding English text. After a learning period, Google's system produced a better-quality English translation of Spanish speech than one that transcribed the speech into written Spanish first. And text translation service Google Translate already uses neural networks on its most popular language pairs, which lets it analyse entire sentences at once to figure out the best written translation.

We are excited to share our recent work on supporting a recurrent neural network (RNN). We did not support RNN models at our open source launch in April. So, over the last several months, we have developed state-of-the-art RNN building blocks to support RNN use cases (machine translation and speech recognition, for example). Using Caffe2, we significantly improved the efficiency and quality of machine translation systems at Facebook. We got an efficiency boost of 2.5x, which allows us to deploy neural machine translation models into production.

"The World is a small place". You may have heard this phrase many times in many places. One pillar that holding the stability and sustainability of this community is communication. The answer to that simple question is "using languages". The linguistic research estimates that there are 5000 to 7000 languages in this world.