Machine learning and deep learning, in particular, are developing at amazing speeds. Today, machine learning can be used to solve ever more complex tasks that have been considered impractical just a few years ago. Examples include autonomous cars, AlphaGo's win against the world's Go champion, the photo-realistic transformation of pictures, and neural machine translation systems. In this blog post, we describe a simple system to recognize monetary amounts on Swiss payment slips. The user interface is implemented using Eclipse Scout and we build, train, and run the deep neural net using Deeplearning4j.

Advances in natural language processing have led to a variety of successful tools and techniques for solving problems such as understanding, generating, and translating natural languages. Given the success of these techniques, a natural question is whether they can also be applied to programming languages. However, the initial research has been mixed. Researchers attempting to translate between programming languages by employing statistical machine translation (SMT) found that a large percentage of the translated programs were not syntactically valid. On the other hand, SMT has been successfully employed to recover identifiers in obfuscated JavaScript code. In this paper, we discuss several differences between natural languages and programming languages that can thwart successful application of NLP techniques to program transformation. We also discuss several strategies to cope with these differences in practice, using our own experiences with using SMT to assign meaningful identifier names to variables in decompiled C programs as an example.

We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.

Microsoft has so far released its artificial intelligence technologies largely through its well-known software platforms, such as the Cortana voice assistant on Windows 10, automated language translation in Microsoft Office, and AI-powered speech, vision, search and language technologies for developers on Microsoft Azure. Artificial intelligence specialists at the company are now working closely with its devices group, said Harry Shum, the executive vice president of Microsoft's AI and Research group, in a broader interview with GeekWire about the next phase of the company's AI initiatives. Without giving details, Shum said he expects some "very, very exciting devices" to result from the work by the company's AI engineers and devices group. Shum mentioned this as an aside, not to get the gadget blogs buzzing but to underscore the scope of what Microsoft is trying to do. As part of the massive engineering reorganization announced by CEO Satya Nadella last week, the company is attempting to bring artificial intelligence into everything it does.

Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow color hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.

WORLDWIDE revenue from AI will surge past US$46 billion in 2020, according to research firm IDC. In Asia-Pacific, this is projected to rise to US$6.8 billion by 2021. Though researchers have been working on AI decades, development has accelerated in the past few years thanks to three factors – the ubiquitous availability of data, the growing capabilities of cloud computing, and more powerful algorithms developed by AI researchers. Most recently, a team of Microsoft researchers have developed the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Throughout history, the emergence of new technologies has been accompanied by dire warnings about human redundancy.

Microsoft revealed on Wednesday it has reached a monumental milestone in artificial intelligence-powered translation software, declaring the creation of a system that can translate news article sentences from Chinese to English just as good as a human. "Hitting human parity in a machine translation task is a dream that all of us have had," Xuedong Huang, technical fellow in charge of the company's translation efforts, said in a statement. "We just didn't realize we'd be able to hit it so soon." The breakthrough is the latest in a race to develop human-like translations. Google has improved its translation tools over time, parsing whole sentences with a November 2016 update.

A team of Microsoft researchers said Wednesday that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and U.S. labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last fall. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang, a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

Machine learning has transformed major aspects of the modern world with great success. Self-driving cars, intelligent virtual assistants on smartphones, and cybersecurity automation are all examples of how far the technology has come. But of all the applications of machine learning, few have the potential to so radically shape our economy as language translation. The content of language translation is the perfect model for machine learning to tackle. Language operates on a set of predictable rules, but with a degree of variation that makes it difficult for humans to interpret.

The time has come when machine translation, first conceptualized in the 1960s, is finally useful. Google and hundreds of smaller companies have developed algorithms, mined data, used human translation examples and employed syntactic and contextual analysis and every tool in the book to come up with software that essentially breaks down the language barrier completely. You can now automatically translate every website, and in most languages the result is acceptable. Twitter offers a link under every foreign-language tweet to translate it into a foreign language to translate. Some social networks always appear in your preferred language, translating content on the fly. WeChat has a translation option for every chat that is surprisingly good.