The advent of neural machine translation (NMT) has revolutionized cross-lingual communication, yet preserving stylistic nuances remains a significant challenge. While existing approaches often require parallel corpora for style preservation, we introduce Babel, a novel framework that enhances stylistic fidelity in NMT using only monolingual corpora. Babel employs two key components: (1) a style detector based on contextual embeddings that identifies stylistic disparities between source and target texts, and (2) a diffusion-based style applicator that rectifies stylistic inconsistencies while maintaining semantic integrity. Our framework integrates with existing NMT systems as a post-processing module, enabling style-aware translation without requiring architectural modifications or parallel stylistic data. Extensive experiments on five diverse domains (law, literature, scientific writing, medicine, and educational content) demonstrate Babel's effectiveness: it identifies stylistic inconsistencies with 88.21% precision and improves stylistic preservation by 150% while maintaining a high semantic similarity score of 0.92. Human evaluation confirms that translations refined by Babel better preserve source text style while maintaining fluency and adequacy.

Large language models (LLMs) have revolutionized natural language processing (NLP), yet open-source multilingual LLMs remain scarce, with existing models often limited in language coverage. Such models typically prioritize well-resourced languages, while widely spoken but under-resourced languages are often overlooked. To address this disparity, we introduce $\texttt{Babel}$, an open multilingual LLM that covers the top 25 languages by number of speakers, supports over 90% of the global population, and includes many languages neglected by other open multilingual LLMs. Unlike traditional continue pretraining approaches, Babel expands its parameter count through a layer extension technique that elevates Babel's performance ceiling. We introduce two variants: $\texttt{Babel-9B}$, designed for efficient inference and fine-tuning, and $\texttt{Babel-83B}$, which sets a new standard for open multilingual LLMs. Extensive evaluations on multilingual tasks demonstrate its superior performance compared to open LLMs of comparable size. In addition, using open-source supervised fine-tuning datasets, Babel achieves remarkable performance, with Babel-9B-Chat leading among 10B-sized LLMs and Babel-83B-Chat setting a new standard for multilingual tasks, reaching the same level of commercial models.

Sensing technology is widely used for comprehending the physical world, with numerous modalities explored in past decades. While there has been considerable work on multi-modality learning, they all require data of all modalities be paired. How to leverage multi-modality data with partially pairings remains an open problem. To tackle this challenge, we introduce the Babel framework, encompassing the neural network architecture, data preparation and processing, as well as the training strategies. Babel serves as a scalable pre-trained multi-modal sensing neural network, currently aligning six sensing modalities, namely Wi-Fi, mmWave, IMU, LiDAR, video, and depth. To overcome the scarcity of complete paired data, the key idea of Babel involves transforming the N-modality alignment into a series of two-modality alignments by devising the expandable network architecture. This concept is also realized via a series of novel techniques, including the pre-trained modality tower that capitalizes on available single-modal networks, and the adaptive training strategy balancing the contribution of the newly incorporated modality with the previously established modality alignment. Evaluation demonstrates Babel's outstanding performance on eight human activity recognition datasets, compared to various baselines e.g., the top multi-modal sensing framework, single-modal sensing networks, and multi-modal large language models. Babel not only effectively fuses multiple available modalities (up to 22% accuracy increase), but also enhance the performance of individual modality (12% averaged accuracy improvement). Case studies also highlight exciting application scenarios empowered by Babel, including cross-modality retrieval (i.e., sensing imaging), and bridging LLM for sensing comprehension.

FROM the very beginnings of recorded history, there has been a desire to create a single language that could unite humankind. Allegorised in the biblical story of the Tower of Babel, as well as in origin myths from cultures around the world, the belief has always been that the diversity of languages – there are over 7000 spoken today – is a problem for which we need to find a solution. This has led, down through the centuries, to many a scheme trying to craft some form of truly universal communication. To date, however, none of these have properly succeeded.

I've always found the fact that the acronym for artificial intelligence in English, AI, is surprisingly similar to the first two characters for that word in both simplified Chinese -- '人工智能'. The first two characters together, 人工, mean'people' and'work' individually, but when put together mean'artificial' while '智能' means'intelligent.' This is quite a fascinating linguistic experiment, and it's interesting that the two most widely used languages in the world came up a similar acronym or character for one of the most important technologies ever invented by man. Perhaps there is some weird universal synergy going on or maybe there's an easy answer hidden somewhere deep within the linguistic annals of these two languages. Either way, this got me thinking about language.

In the past two years, Swiggy, the Naspers, DST Global and Bessemer Ventures-funded restaurant aggregator, has been on a tear. The number of interactions on its platform since October 2017 has gone from 2 billion (across consumers, riders and restaurants) to 40 billion in January 2019. In that time, Swiggy has gone from a business working with 12,000 restaurants to over 55,000; from seven cities to 70; from delivery staff of 15,000 to 120,000. The Bengaluru-based venture has become far more valuable, too -- from $700 million in February 2018 to $3.3 billion by the end of the year. This dizzying growth has meant that Swiggy, a firm founded as recently as 2014, has to look beyond human intervention to keep pace.

Across all Wikipedia language editions, millions of images augment text in critical ways. This visual encyclopedic knowledge is an important form of wikiwork for editors, a critical part of reader experience, an emerging resource for machine learning, and a lens into cultural differences. However, Wikipedia research--and cross-language edition Wikipedia research in particular--has thus far been limited to text. In this paper, we assess the diversity of visual encyclopedic knowledge across 25 language editions and compare our findings to those reported for textual content. Unlike text, translation in images is largely unnecessary. Additionally, the Wikimedia Foundation, through the Wikipedia Commons, has taken steps to simplify cross-language image sharing. While we may expect that these factors would reduce image diversity, we find that cross-language image diversity rivals, and often exceeds, that found in text. We find that diversity varies between language pairs and content types, but that many images are unique to different language editions. Our findings have implications for readers (in what imagery they see), for editors (in deciding what images to use), for researchers (who study cultural variations), and for machine learning developers (who use Wikipedia for training models).

In the myth about the Tower of Babel, people conspired to build a city and tower that would reach heaven. Their creator observed, "And now nothing will be restrained from them, which they have imagined to do." According to the myth, God thwarted this effort by creating diverse languages so that they could no longer collaborate. Language remains a barrier in business and marketing. Even though technological devices can quickly and easily connect, humans from different parts of the world often can't.

Anthony Levandowski has set up a religious nonprofit organization called Way of the Future and devoted to the worship of artificial intelligence (AI). "Our mission is to develop and promote the realization of a Godhead based on artificial intelligence and through understanding and worship of the Godhead contribute to the betterment of society," the organization's founding documents say. The new god is seen by US media as an almighty bot that will cater to all of its adherents' wishes. What will make it different from biblical God, however, is that it will be genuinely kind and will not punish mortals. The Transhumanist Christians, who established their religion in 2013, espouse the idea that people can and should use science and technology to make the world better. They believe that this fully conforms to the Bible where Jesus says, "Be ye perfect as your Father in heaven is perfect," so this is probably why they so happily jumped on Levandowski's AI bandwagon.

Did you notice that Siri sounds a little more sprightly today? Apple's ubiquitous virtual assistant has had a little virtual work done on her virtual vocal cords, and her newly dulcet-ized tones went live today as part of iOS 11. (Check out a few more lesser-known iOS 11 features here.) It turns out a lot of work went into this little upgrade. The old methods of creating speech from text produced the familiar but stilted voices we're all familiar with from the last decade or two. Basically you took a big library of voice sounds -- "ah," "ess," etc. -- and stuck them together to make words.