AI/ML supporting hometowns of international students: what, how and why?

Nearly everyone around by now has either heard or used artificial intelligence (AI) and machine learning (ML) in some form or fashion. Some students are already publishing papers in the field while other students are applying various AI techniques in their research, internships, or just for fun. Professionals in the industry have either incorporated some form of AI/ML into their product or services or are currently considering it. Either way, AI and ML have a lot to offer but not without a good amount data, significant processing power, right skillsets, and a lot of patience with design and execution of such projects. For that problem, AutoML is a promising new technique in the field that allows researchers and professionals to make use of pre-trained models and cloud-based services to roll out AI solutions much more rapidly than building machine learning models from scratch. AutoML provides the methods and processes to apply, integrate, deploy, and scale machine learning intelligence without requiring expert knowledge. Major AI platforms, starting with Google and followed by Microsoft, H20.ai, and others are priming AutoML as the next evolutionary frontier in artificial intelligence so that humans can spend zero time recreating machine learning models from scratch, and, instead, focus on applying the models while letting machines take care of building them.

References: -PwC 2017 report “PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution” https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

-National Foundation for American Policy (October 2017) “The Importance of International Students to American Science and Engineering” http://nfap.com/wp-content/uploads/2017/10/The-Importance-of-International-Students.NFAP-Policy-Brief.October-20171.pdf

English on non-English NLP and machine learning projects

Whenever I ask a bilingual (English + another language) students or professionals working on machine learning or artificial intelligence if they considered doing a project in AI for their nonEnglish mother tongues language, such as Hindi or Spanish or Arabic, they look at me puzzled and surprised. Yes, there are a lot of publications on all sorts of languages but how often do you see innovative products in the market for non-English customers even in English-speaking nations? The US has a huge immigration population and houses neighborhoods that don’t even speak English. Why not develop more intelligent products with the aid of deep learning that targets nonEnglish recipients and not just comes up with another translation software every time? We need to think beyond the status-quo of research, products, software, and publications that are predominately English. It is challenging; I admit because it is so easy to code in English with an English programming language syntax, editor, OS, GUI and it is also hard to find nonEnglish corpus. Mandarin is an exception in all this here. It is not impossible to do more for nonEnglish speaking societies.