AI hype is at fever pitch. It's slated to fundamentally change everything about our world, from our economies to the way we get around cities. But how much of the hype is credible, and how much will AI change the nature of business in the near future? Will AI completely take over the realm of human expertise? Algorithms are changing the world.

This is a guest blog post by Andrea Tabacchi, the Solution Architects team lead at Memsource. Memsource is always looking out for exciting new integrations that enhance its cutting-edge translation solutions. With machine translation (MT) continuing to be a hot topic in the localization industry, Memsource is focusing on integrating with innovative MT engines that meet customers' growing MT needs. In particular, Memsource strives to offer neural machine translation (NMT) engines, such as Amazon Translate. NMT is proving to be a highly influential technology. The quality of NMT output continues to improve, making it a powerful productivity tool and therefore more in demand.

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.

A number of recent machine learning papers work with an automated style transfer for texts and, counter to intuition, demonstrate that there is no consensus formulation of this NLP task. Different researchers propose different algorithms, datasets and target metrics to address it. This short opinion paper aims to discuss possible formalization of this NLP task in anticipation of a further growing interest to it.

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.

AI has evolved dramatically in the last two decades. Technologies like image recognition and machine translation are now a part of everyday life for millions. AI has transformed industries all over the world, and created entirely new ones. And in the process, it promises an increase in quality of life and work never before imagined. But there's still much more we can do--after all, AI is still a nascent field of many opportunities and challenges.

Summer Lab Diversity Coordinator Jihane Lamouri believes that having a variety of perspectives, as the program encourages, is crucial to the development of AI: If a society is biased, so, too, are its machines. At the Lab's closing event, Lamouri referenced the alleged sexism that machine translation services like Google Translate or Microsoft's Bing Translator exhibit. When translating phrases from gender-neutral languages like Finnish or Turkish, machine translators may assign gender pronouns illustrative of a gender bias to the English translation. Users have complained that in the hands of a machine translator the phrase "they are engineer" becomes "he is an engineer," whereas the phrase "they are a nurse" becomes "she is a nurse." Lamouri hopes that having more women in the industry will lead to the identification of gender bias in AI.

The ability to generate natural language sequences from source code snippets can be used for code summarization, documentation, and retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine translation (NMT), have achieved state-of-the-art performance on these tasks by treating source code as a sequence of tokens. We present ${\rm {\scriptsize CODE2SEQ}}$: an alternative approach that leverages the syntactic structure of programming languages to better encode source code. Our model represents a code snippet as the set of paths in its abstract syntax tree (AST) and uses attention to select the relevant paths during decoding, much like contemporary NMT models. We demonstrate the effectiveness of our approach for two tasks, two programming languages, and four datasets of up to 16M examples. Our model significantly outperforms previous models that were specifically designed for programming languages, as well as general state-of-the-art NMT models.

I sometimes also carry an Iridium satellite phone, and I always travel with a power strip in case there aren't enough outlets where I am. I try to keep my backpack light in Gaza. Other items -- armor-plated flak jacket, Kevlar helmet, gas mask and spare filters, and a trauma kit -- add lots of weight to my load. Probably the most vital tech tool I carry is the lightest: a paper clip to switch SIM cards. I've been here less than a year and finally got a local number from the Palestinian provider Jawwal, which has good coverage across Gaza and can be quickly replenished at countless retail shops.

You probably don't want to make grammar errors in your emails (or blog posts), but every now and then, they do slip in. Your standard spell-checking tool won't catch them unless you use an extension like Grammarly. Well, Grammarly is getting some competition today in the form of a new machine learning-based grammar checker from Google that's soon going live in Google Docs. These new grammar suggestions in Docs, which are now available through Google's Early Adopter Program, are powered by what is essentially a machine translation algorithm that can recognize errors and suggest corrections as you type. Google says it can catch anything from wrongly used articles ("an" instead of "a") to more complicated issues like incorrectly used subordinate clauses.