Motivation
Machine learning (ML), a sub-field of artificial intelligence, involves teaching machines how to solve complex tasks at scale. These tasks can involve learning from language (natural language processing), imbuing robots with the ability to see (computer vision), and extracting insight from wearable sensors (signal processing). Concepts from machine learning, when deployed appropriately, can have a significant positive impact on multiple aspects of our societies. For example, within the field of healthcare, systems can predict the deterioration of a patient's health, and thus contribute to improved patient outcomes. Furthermore, systems can monitor, at scale, the rate with which climate-related events take place, and thus better inform policies that attempt to curb the detrimental impacts of climate change.
The tangible societal impacts of machine learning have, however, been mostly absent from the Arab world. In the words of William Gibson, "the future is already here, it is just unevenly distributed". We strongly believe that to overcome this challenge and to accelerate the impact of machine learning on our societies, we must shed light on region-specific technical challenges (e.g., designing intelligent machines that are adept with the Arabic language), and best position the next generation of machine learning researchers to address these challenges.
To that end, the Arabs in ML (AML) organization (or أمل, which means "hope" in Arabic) has a threefold mission, as outlined below.
Mission
Engage
Engage machine learning researchers both within the Arab world and those looking to contribute positively to Arab societies.
Educate
Educate and inspire the next generation of Arab machine learning researchers in order to address pressing societal challenges within the Arab world.
Amplify
Amplify the representativeness of Arab researchers within the broader machine learning community.
Monthly Research Highlight
Each month, our board members identify a machine learning research paper that is likely to be relevant to our broader community, and will post it on our website. This is an opportunity for members of our community to remain abreast of the latest research, learn more about a domain that they otherwise would not have explored, and potentially spur collaborations between individuals.
This month, we are sharing the work of Adam Yala et al. , published in Nature Medicine, who designed a reinforcement learning (RL) policy to better recommend mammography screenings while balancing between early detection and a high rate of false positives. You can find the link to their work here.
Opportunities
Join the Arabs in ML Organization
Please complete the following form if you are interested in being a part of the Arabs in ML (AML) organization.
