Black in AI
Black in AI is a place for sharing ideas, fostering collaborations and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence. If you are in the field of AI and self-identify as Black, please request to join our Google group here and our Facebook group here. Tell us about your interests/work in AI in a couple of sentences when you ask to join. If you currently work in the field of AI and identify as an ally, please request to join the Google Group here. Tell us what area of AI you work in and one thing you’ve done or plan to do in your role as an ally. Like our Facebook Page and follow us on Twitter
Workshop at NIPS 2017
The first Black in AI event will be co-located with NIPS 2017 at the Marriott Renaissance Long Beach Hotel in Long Beach, California on December 8th from 1:00pm to 6:00pm, followed by dinner 6:30-8:30, location TBD. We invite Black AI researchers from around the world to share their work and learn about others’ research. The workshop will have invited talks from prominent researchers, oral presentations, and a poster session. There will also be socials to facilitate networking, discussion of different career opportunities in AI, and sharing of ideas to increase participation of Black researchers in the field. We invite all Black researchers, including undergraduates, graduate students, faculty, and researchers in industry to participate in this workshop. People of all races are also invited to attend the workshop to learn about the research being conducted by Black researchers across the world. Deadline for registration is October 31, 2017 and can done here. Please register as soon as possible to help us figure out headcount.
Call for Participation
- October 13, 2017: Abstract submission deadline
- October 13, 2017: Travel grant application deadline
- October 29, 2017: Notification of acceptance
- October 31, 2017: Workshop registration deadline
- December 8th from 1:30pm-5:30pm: Workshop
- TBD: Dinner
We welcome theoretical, empirical, and applied work in machine learning and artificial intelligence, including, but not limited to, search, planning, knowledge representation, reasoning, natural language processing, computer vision, robotics, multiagent systems, statistical reasoning, and deep learning. Work may be previously published, completed, or ongoing. Submissions will be peer-reviewed by at least 2 reviewers. The workshop will not publish proceedings. The presenter must be a Black researcher in AI, and does not need to be first author.
Submissions can be up to two page abstracts and must state the research problem, motivation, and technical contribution. Submissions must be self-contained and include all figures, tables, and references.
- Submission page: Black in AI CMT Page
- Travel Grants: In order to be considered for a travel grant, please select yes to the question Do you need a travel grant?
Need-based travel grants will be awarded to workshop participants. The travel grant can be used for covering costs associated with the workshop such as NIPS registration, accommodation and travel. Please note that the travel grants may not cover all of your costs and we may not be able to award them to all applicants. The amount of money we award to each person will depend on the number of applicants and the location each applicant will be traveling from.
If you are a student who has not conducted research and would like a travel grant to attend our workshop, you may do so by either:
- Submitting an abstract, and a paragraph describing financial need on the conference submission page OR
- Submitting a one page statement describing your research interests in AI and reasons for participating in the workshop, and a paragraph describing financial need on the conference submission page
These must be done by the deadline, October 13, 2017.
Location: Marriott Renaissance Long Beach Hotel
Date: December 8th, 2017
1:00 - 1:05 pm Introduction
1:05 - 1:25 pm Keynote - Ciira Maina: Leveraging Machine Learning, Low Cost Devices and Open Science for Impact in the Developing World: An Example In Ecology
Ciira Maina, Dedan Kimathi University of Technology
Ciira Maina graduated from the University of Nairobi, Kenya with a Bsc. degree in Electrical Engineering (First class honors) in 2007 and with a Ph.D. from Drexel University in Philadelphia, USA in September 2011. At Drexel he was a member of the Adaptive Signal Processing and Information Theory Research Group where he conducted research on robust speech processing. Between October 2011 and August 2013 he was a postdoctoral researcher in computational Biology working with Prof. Magnus Rattray and Prof. Neil Lawrence at the University of Sheffield. Since September 2013 he has been a Lecturer in Electrical Engineering at Dedan Kimathi University of Technology in Nyeri, Kenya where he conducts research on bioacoustic approaches to environmental monitoring, sensor systems for livestock monitoring and novel approaches to electrical engineering instruction. In addition he serves on the organising committee for Data Science Africa, an organisation that runs an annual data science and machine learning summer school and workshop in Africa.
1:25 - 1:55 pm Oral Session 1
Bonolo Mathibel, IBM Research Africa
Title:Towards Impactful Artificial Intelligence on the African Continent
Abstract: In recent years, machine learning has been applied to solve diverse sets of challenges on the African continent. This includes reducing road traffic congestion in the face of failing road infrastructure in South Africa, drought modeling in the Horn of Africa, transfer learning for cassava disease detection in sub-Saharan Africa, and galaxy count extraction from radio telescopes. The vast majority of research conducted in the field of Artificial Intelligence (AI) occurs outside of the African continent, and the few studies that have been applied to the African context are based on bespoke datasets generated to solve the problem at hand. We therefore propose three pillars of representation that are foundational to achieving impactful, sustainable, and scalable AI research and product development for and on the African continent. Our aim is to increase the number of AI studies conducted in Africa and encourage researchers and AI practitioners to consider both science and impact when selecting problems to work on.
George W Musumba, Dedan Kimathi University of Technology
Title: Modelling Virtual Enterprises Using a Multi-Agent Systems Approach: A Case of Construction Industry for Third World Countries
Abstract: Nowadays enterprises work together towards a common goal by sharing responsibilities and profits as is the case for construction related projects. The construction sector’s potential contribution to the economic growth of developing countries can be enhanced if the challenges facing the sector that include delayed completion of projects, frequent collapse of buildings, lack of ethics, incompetent design, use of inappropriate materials, poor coordination and management of contractors are effectively addressed. These can be attributed to poor choice of partner enterprises for the tasks due to insufficient information available about them and lack of facilitation techniques. Selection of best partner among many for construction project is a Multi-Criteria Decision Making (MCDM) process. Existing MCDM techniques cannot be used to select right partners for construction projects. Fuzzy Analytical Hierarchy Process (FAHP) and Group Fuzzy Analytical Hierarchy Process (GFAHP), MCDM algorithms that learns partner attributes (machine learning technique incorporated), were designed and applied. A Multi-Agent Systems(MAS) approach was used for simulations. The approach provide efficient decision-making support for human beings using software agents. Results show that this technique is both efficient and effective. Validation of the system, carried out by stakeholders, show that it is approximately 99.7% accurate in the evaluation and selection of partners and partners’s performance evaluation.
Charles Onu, McGill University
Title: Saving Newborn Lives at Birth through Machine Learning
Abstract: Every year, 3 million newborns die within the first month of life. Birth asphyxia and other breathing-related conditions are a leading cause of mortality during the neonatal phase. Current diagnostic methods are too sophisticated in terms of equipment, required expertise, and general logistics. Consequently, early detection of asphyxia in newborns is very difficult in many parts of the world, especially in resource-poor settings. We are developing a machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia through automated analysis of the infant cry. Deployed via smartphone and wearable technology, Ubenwa will drastically reduce the time, cost and skill required to make accurate and potentially life-saving diagnoses.
1:55 - 2:15 pm Keynote - Danielle Belgrave: TBD
Danielle Belgrave, Microsoft Research
Danielle Belgrave is a Researcher at Microsoft Research Cambridge. She also holds a tenured Research Fellowship (Assistant Professor) at Imperial College London. Her research focuses on developing probabilistic and causal graphical modelling frameworks to understand disease progression over time. The aim of this research is to use machine learning to identify distinct subtypes of disease evolution (endotypes) and to understand the underlying mechanisms of these subtypes so as to develop personalized disease management strategies. She has a BSc in Business Mathematics and Statistics from the London School of Economics and an MSc in Statistics from University College London. She was awarded a Microsoft PhD Scholarship to complete her PhD in Statistics and Machine Learning applied to Health (2010-2013) at The University of Manchester. She received a Medical Research Council (UK) Career Development Award in Biostatistics (2015 – 2020) for the project “Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies”.
2:15 - 4:15 pm Poster Session and Coffee Break
4:15 - 4:35pm Keynote - Debo Olaosebikan: How to automate the creation of software
Debo Olaosebikan, Gigster
Debo is co-founder and CTO of Gigster, a software development marketplace that seeks to automate the creation and delivery of software while creating a productive workplace of the future for engineers. Gigster logs data about code, projects, and people throughout the software development lifecycle and uses patterns in that data to drive increases in reliability and efficiency. Gigster aims to apply machine learning to challenging problems like software cost and time estimation, optimal team formation, predicting the future (risk) on projects, and ultimately code generation. Gigster is backed by Andreessen Horowitz, Redpoint, Y Combinator, and Greylock.
Debo has founded multiple marketplace, energy, and data startups. He is on leave from a physics PhD at Cornell where he worked on silicon nanophotonics and theoretical physics. He was once a radio featured musician and was the young Nigerian scientist of 2011. Debo advises startups and helps young founders as a Thiel Fellowship mentor.
4:35 - 5:05 pm Oral Session 2
Ousmane Dia, ElementAI
Title: Adversarial Functionality-Preserving Training in the Malware Domain
Abstract: Multiple approaches of generating adversarial examples have been proposed to deceive deep neural networks into predicting an incorrect target for a given observation [1, 2, 4, 7, 8, 10]. Most of the existing techniques that deal with images involve either computing the gradients of a loss function with respect to the images pixels [3, 7, 10], or they inject some noise generally sampled from a random or a normal distribution [1, 4, 8] into a true case in the hope that the network will take an unexpected decision. While for images, the adversarial examples are generated in a way to be identical to the true cases, the precise locations of some details in a true image may still not be preserved in the perturbed one . However, exact locations of those fine details are not usually important for perceptual image recognition or validation due to images high-entropy . In Security, and specifically in malware detection, however, where the cases in hand usually consist of raw bytes or sequences of system calls, this rarely holds. In Security, being able to generate new examples that preserve the functionalities (or malignant properties) of some true cases is paramount due to the difficulty of gathering large enough quantities of data for modeling purposes. We posit that the reasons the adversary generated examples may not preserve such properties are because the noise that is injected into the true cases is not necessarily sampled within the manifold of the true cases or that the gradients that are exploited are not selected in the neighborhood of the true examples.
In this study, we explore a new approach of generating adversarial malware cases. We make use of variational autoencoders (VAEs) (similar in spirit to ) to generate functionality-preserving mutations of true malware and extend Stein variational gradient descent  where the distribution of the latent samples are approximated using the true cases data-generating distribution. We also provide two ways to assess that the generated cases are functionality-preserving mutations of true malware: 1) by sampling sequences of bytes from the (vector representation of the) adversarial cases that we validate using as Oracle the Cuckoo Sandbox , and 2) by comparing specific sections of our generated mutations against true cases of malware. Because our architecture is generic enough, we evaluate our approach further with existing work on adversarial training of images and audio and compare our results.
Adji Bousso Dieng, Columbia University
Title: A Recurrent Neural Network with Long-Range Semantic Dependency
Abstract: Language modeling is crucial to many NLP tasks. Applications include machine translation and speech recognition. Traditional n-gram and feed-forward neural network language models fail to capture long-range word dependencies. Previous work by Mikolov et al. has shown that adding context to a Recurrent Neural Network (RNN) language model is a promising direction to solve this issue. In this talk I will briefly review traditional language models and topic models before diving into the more recent contextual RNN-based language models. In particular, I will discuss the TopicRNN model, a RNN-based language model that captures long-range semantic dependencies using topic features. I will also highlight some results on word prediction and sentiment analysis using the TopicRNN model.
Flora Ponjou Tasse, University of Cambridge
Title: ShapeSearch: A Generic Engine for 3D Models, Images, and Sketches
Abstract: We present ShapeSearch, a generic search engine for shapes that supports queries such as 3D models, images, sketches, and text. Online repositories of images and 3D objects are growing at an exponential rate, used by growing communities of makers and artists. Moreover, the proliferation of Augmented Reality platforms is creating new communities of content creators and developers in need of 3D content. However, search features in the large 3D repositories are still limited to text. On the other hand, the research community has made significant progress in context-based shape retrieval, but current methods are typically limited to one modality such as images or sketches. We propose a generic search engine able to retrieve relevant shapes based on a wide range of modalities by leveraging the latest machine learning advances in Graphics, Vision, and NLP.
5:05 - 5:25 pm Keynote
Haben Girma, Harvard Law School
The first Deafblind person to graduate from Harvard Law School, Haben Girma advocates for equal opportunities for people with disabilities. President Obama named her a White House Champion of Change, and Forbes recognized her in Forbes 30 Under 30. Haben travels the world consulting and public speaking, teaching clients the benefits of fully accessible products and services. Haben is a talented storyteller who helps people frame difference as an asset. She resisted society’s low expectations, choosing to create her own pioneering story. Because of her disability rights advocacy she has been honored by President Obama, President Clinton, and many others. Haben is also writing a memoir that will be published by Grand Central Publishing in 2019.
5:25 - 5:55 pm Panel
5:55 - 6:00 pm Closing Remarks
6:30 - 9:00pm Dinner
Nyalleng is a senior Data Science researcher at South Africa’s national science lab, Council for Scientific and Industrial Research (CSIR), with the Modeling and Digital Sciences Unit. In her capacity at CSIR, she works on projects ranging from: rhino poaching prevention with park rangers, working with news outlets to understand social media sentiments, and searching for Biomarkers in African cancer proteomes. Before the CSIR, she was a computer science lecturer at Fort Hare University and a software engineer at Thomson Reuters. Moorosi is an active member of Women in Machine Learning, Black in Artificial Intelligence, and an organising member of the Deep Learning Indaba - a yearly workshop that gathers African researchers in one space to share ideas and grow machine learning and artificial intelligence capabilities.
Dr Simon Osindero is a pioneer in the field of machine learning. His 2006
post-doctoral research on deep belief networks in collaboration with Geoff Hinton
and Yee Whye Teh helped kickstart the deep learning revolution.
He is currently a scientist at DeepMind, where he researches machine learning methods with the goal of developing Artificial General Intelligence. In his previous role as an A.I. Architect at Yahoo, he led computer vision and machine learning R&D at Flickr. He joined Yahoo in 2013 after it acquired LookFlow, a company he cofounded in 2009 to productize cutting-edge research from the fields of machine learning and human-computer interaction. Prior to starting LookFlow, he worked with a Montreal-based start-up, Idilia, designing machine learning algorithms for natural language processing.
He holds a MSci in physics and a BA/MA in natural sciences (physics, biochemistry, molecular biology, mathematics) from Cambridge University (1st Class) and a PhD in Computational Neuroscience from the Gatsby Unit, University College London. He has also worked as a visual and new-media artist, and holds degrees in Photography and Digital Design from Concordia University.
Thanks to our corporate sponsors, the workshop is free to attendees and we are able to provide inclusive travel funding to select participants.
We thank the following institutions for sponsoring their students to attend the workshop
- Cornell University
- Duke University
- Harvard University
- Stanford University
- University of California, Berkeley
- University of Illinois at Urbana-Champaign
Rediet Abebe, Cornell University
Sarah M. Brown, University of California, Berkeley
Mouhamadou Moustapha Cisse, Facebook AI Research
Timnit Gebru, Microsoft Research
Sanmi Koyejo, University of Illinois, Urbana-Champaign
Lyne P. Tchapmi, Stanford University
Thanks to the following members of the Black in AI community and supportive allies for helping review the submissions.
- Rediet Abebe
- Justice Amoh
- Silèye Ba
- Irwan Bello
- Samy Bengio
- Sarah M. Brown
- Joy Buolamwini
- Diana Cai
- Moustapha Cisse
- Charles Cearl
- Tewodros Dagnew
- Hal Daumé III
- Ousmane Dia
- Ashley Edwards
- Oluwaseun Francis Egbelowo
- Dylan Foster
- Fisseha Gidey Gebremedhin
- Timnit Gebru
- Christan Grant
- Alvin Grissom II
- Bernease Herman
- Jack Hessel
- Abigail Jacobs
- Emmanuel Johnson
- Sanmi Koyejo
- Ciira Maina
- nyalleng moorosi
- George Musumba
- Ndapa Nakashole
- Ehi Nosakhare
- Billy Okal
- Charles Onu
- Forough Poursabzi-Sangdeh
- Alexandra Schofield
- Frank Lanke Fu Tarimo
- Lyne P. Tchapmi
- Kale-ab Tessera
- Basiliyos Tilahun BETRU
- Wil Thomason
- Marcelo Worsley