On one screen, an urban professional in Oslo taps through ultra-secure banking apps, relies on an AI-powered personal assistant, and streams media seamlessly over high-speed 5G using their iPhone. On the other screen, a farmer in Malawi scrolls through a modest Android phone -- likely costing less than a week's wages -- just to read the news, check tomorrow's weather, and send WhatsApp messages over a patchy mobile connection. These very different experiences highlight the divide between the Global North and the Global South. These terms refer not only to geographic locations but also to the world's wealthiest and most industrialized regions -- such as Europe, North America, and parts of East Asia -- and economically developing nations across much of Africa, Latin America, South Asia, and Oceania. Technology symbolizes innovation, convenience, and seamless connectivity in the Global North.

Large language models have exhibited significant proficiency in languages endowed with extensive linguistic resources, such as English and Chinese. Nevertheless, their effectiveness notably diminishes when applied to languages characterized by limited linguistic resources, particularly within the Southeast Asian linguistic landscape, such as Indonesian. The scarcity of linguistic resources for these languages presents challenges associated with inadequate training, restricted vocabulary coverage, and challenging evaluation processes. In response to these exigencies, we have introduced CompassLLM, a large multilingual model specifically tailored for Southeast Asian languages, with the primary aim of supporting the developmental requirements of Shopee. Our methodology encompasses several key strategies. To progressively enhance multilingual proficiencies, we implemented a multi-stage pre-training strategy integrated with curriculum learning, gradually intensifying the focus on low-resource languages. Concurrently, to better accommodate low-resource human instructions, we curated and generated a repository of high-quality multilingual human instructions, culminating the CompassLLM-SFT model through supervised instruction fine-tuning. Finally, to reinforce the model's alignment with human preference behaviors, we have embraced the principle of Direct Preference Optimization (DPO) to obtain CompassLLM-DPO model. Preliminary evaluation of the CompassLLM model yields promising results, with our model surpassing benchmark models like Vicuna-7b-v1.5, Sealion, Falcon and SeaLLM, across diverse evaluation tasks, as verified through both automated and human-driven assessments. Notably, our model exhibits its superior performance in South-east Asia languages, such as Indonesian language.

Efforts on the research and development of OCR systems for Low-Resource Languages are relatively new. Low-resource languages have little training data available for training Machine Translation systems or other systems. Even though a vast amount of text has been digitized and made available on the internet the text is still in PDF and Image format, which are not instantly accessible. This paper discusses text recognition for two scripts: Bengali and Nepali; there are about 300 and 40 million Bengali and Nepali speakers respectively. In this study, using encoder-decoder transformers, a model was developed, and its efficacy was assessed using a collection of optical text images, both handwritten and printed. The results signify that the suggested technique corresponds with current approaches and achieves high precision in recognizing text in Bengali and Nepali. This study can pave the way for the advanced and accessible study of linguistics in South East Asia.

Geopolitical strife and tight financing conditions will slow global economic growth, while artificial intelligence (AI) will increase inequality, according to leading economists. The survey, released by the World Economic Forum (WEF) on Monday before its annual meeting in the Swiss resort of Davos, weighed the analysis of 60-plus chief economists from both the private and public sectors. More than half of the economists surveyed (56 percent) predict weakened global economic conditions but with differences across regions. The majority foresee moderate or stronger growth in China and the United States, weak or very weak growth in Europe, and at least moderate growth in South Asia, East Asia and the Pacific. "While technological advances may give new impetus to global productivity, policies that enhance good-quality growth are needed to revive global momentum and balance the impact across the income groups," the survey stated.

I built a data analytics business organically in 2019, and I took it down in 2020. In 2021, I left Canada for South East Asia and I became a full-time writer. Every Tuesday I share my journey, thoughts, and ideas as a remote writer living in South East Asia. For any inquiries you can contact me on LinkedIn or Twitter.

Professor Kyunghyun Cho (Computer Science - Courant and Center for Data Science) is one of five recipients of the Inaugural Samsung AI Researcher of the Year Award. Professor Cho is donating the $30,000 prize money to MILA, an AI research institute in Quebec, for the support of incoming female students from Latin America, Africa, South Asia, South East Asia, and Korea. (November, 2020)

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To help the company and its customers gain value from this data deluge, the Dell IT organization manages a massive data lake and a world-class set of tools for data analytics, machine learning, deep learning and artificial intelligence. At the heart of this data environment is a Greenplum database, a massively parallel data platform for structured data analytics, machine learning and AI. In a typical use case, this raw data gets parsed in Hadoop into a structured format, and then that structured data gets pumped into the Greenplum database, so business and IT users can consume it in analytics applications. The data is used by Dell Technologies employees and customers in the Americas, Europe, the Middle East, Asia and other geographic regions, according to Darryl Smith, chief data platform architect and distinguished engineer at Dell Technologies. For the full story, see the Dell Technologies case study "Analytics and AI in a massive data lake."

Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it's one of those vague terms thrown around a lot for students, it's actually fairly simple to define. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems. I'm not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.

Kellogg's is perhaps best known for its breakfast cereals from its Corn Flakes to Frosties to Fruit Loops and more, and before the COVID-19 pandemic hit the APAC region, its dominant positioning was within the quick prework/school breakfast consumption occasion. But with the onset of the pandemic and most consumers having to stay home for both work and school, this consumption occasion became less attractive, and the company rightly realised a need to interpret consumer behaviour in a new light to pivot and operate accordingly. "We saw a big transformation where most consumers started to increase their at-home consumption significantly, which also included a lot more cooking at home as many found their inner chef – so we wanted to find a way to offer them more options to use our products in a way closer to their culture and habits," Kellogg's South East Asia Chief Marketing Officer Sanjib Bose told FoodNavigator-Asia. "Before COVID-19, we would have done this via normal interviews with consumers but realised this was now not possible, and we also wanted to go deeper to understand them better by finding out what they wanted directly from conversations they were leading online. "We felt that AI technology was a good way forward for this as it also helped us solve challenges such as language barriers, as most consumers will post on social media in different languages especially in Asia where there are so many different languages; and also to make sense of the huge volume of data from all of these online conversations." Kellogg's partnered with AI-specialist firm Ai Palette to do this, and apart from the use of AI to process data, the firm's technology is also language agnostic, which allowed it to help with the language challenge. "Our language agnostic model allowed us to gather and process data from various locations and across various diverse languages such as Bahasa Indonesia and Bahasa Melayu, whereas image analytics also helped to accurately identify data relevant to Kellogg's," Ai Palette CEO Somsubhra GanChoudhuri told us. "The big data found that now new consumption occasions for cereals have gone beyond breakfast – these are being used in proper recipes for cooking and baking, as a result of increased interest in home cooking and home baking during the pandemic.