African AI researchers would like better code switching, maps, to accelerate research:…The research needs of people in Eastern Africa tells us about some of the ways in which AI development will differ in that part of the world…*Shopping lists contain a lot of information about a person, and I suspect the same might be true of *scientific shopping lists that come from a particular part of the world. For that reason a paper from Caltech which outlines requests for machine learning research from members of the East African Tech Scene gives us better context when thinking about the global impact of AI. Research needs: **Some of the requests include:
-
Support for** code-switching **within language models; many East Africans rapidly code-switch (move between multiple languages during the same sentence) making support for multiple languages within the same model important.
-
Named Entity Recognition with multiple-use words; many English words are used as names in East Africa, eg “Hope, Wednesday, Silver, Editor”, so it’s important to be able to learn to disambiguate them.
-
Working with contextual cues; many locations in Africa don’t have standard addressing schemes so directions are contextual (eg, my house is the yellow one two miles from the town center) and this is combined with numerous misspellings in written text, so models will need to be able to fuse multiple distinct bits of information to make inferences about things like addresses.
-
Creating new maps in response to updated satellite imagery to help augment coverage of the East African region, accompanied by the deliberate collection of frequent ground-level imagery of the area to account for changing businesses, etc.
-
Due to poor internet infrastructure, spotty cellular service, and the fact “electrical power for devices is carce” one of the main types of request is for more efficient systems, such as models that are designed to run on low-powered devices, and on thinking about ways to add adaptive learning to processes involving surveying so that researchers can integrate new data on-the-fly to make up for its sparsity.Reinforcement learning, what reinforcement learning? **“No interviewee reported using any reinforcement learning methods”. Why it matters;** AI is going to be developed and deployed globally, so becoming more sensitive to the specific needs and interests of parts of the world underrepresented in machine learning should further strengthen the AI research community. It’s also a valuable reminder that many problems which don’t generate much media coverage are where the real work is needed (for instance, supporting code-switching in language models).** Read more: **Some Requests for Machine Learning Research from the East African Tech Scene** (Arxiv).**
DeepMap nets $60 million for self-driving car maps:…Mapping startup raises money to sell picks and shovels for another resource grab…A team of mapmakers who previously worked on self-driving-related efforts at Google, Apple, and Baidu, have raised $60 million for DeepMap, in a Series B round. One notable VC participant: Generation Investment Management, a VC firm which includes former vice president Al Gore as a founder. “DeepMap and Generation share the deeply-held belief that autonomous vehicles will lead to environmental and social benefits,” said DeepMap’s CEO, James Wu, in a statement. Why it matters: If self-driving cars are, at least initially, not winner-take-all-markets, then there’s significant money to be made for companies able to create and sell technology which enables new entrants into the market. Funding for companies like DeepMap is a sign that VCs think such a market could exist, suggesting that self-driving cars continue to be a competitive market for new entrants.** Read more:** DeepMap, a maker of HD maps for self-driving cars, raised at least $60 million at a $450 million valuation (Techcrunch).
Spotting thefts and suspicious objects with machine learning:…Applying deep learning to lost object detection: promising, but not yet practical…New research from the University of Twente, Leibniz University, and Zheijiang University shows both the possibility and limitations of today’s deep learning techniques applied to surveillance. The researchers attempt to train AI systems to detect abandoned objects in public places (eg, offices) and try to work out if these objects have been abandoned, moved by someone who isn’t the owner, or are being stolen. How does it work: The system takes in video footage and compares the footage against a continuously learned ‘background model’ so it can identify new objects in a scene as they appear, while automatically tagging these objects with one of three potential states: “if a object presents in the long-term foreground but not in the short-term foreground, it is static. If it presents in both foreground masks, it is moving. If an object has ever presented in the foregrounds but disappears from both of the foregrounds later, it means that it is in static for a very long time.” The system then links these objects with human owners by identifying the people that spend the largest amount of time with them, then they track these people, while trying to guess at whether the object is being abandoned, has been temporarily left by its owner, or is being stolen.** Results: They evaluate the system on the PETS2006 benchmark, as well as on the more challenging new SERD dataset which is composed of videos taken from four different scenes of college campuses. The model outlined in the paper gets top scores on PETS2006, but does poorly on the more modern SERD dataset, obtaining accuracies of 50% when assessing if an object is moved by a non-owner, though it does better at detecting objects being stolen or being abandoned. “The algorithm for object detection cannot provide satisfied performance,” they write. “Sometimes it detects objects which don’t exist and cannot detect the objects of interest precisely. A better object detection method would boost the framework’s performance.” More research will be necessary to develop models that excel here, or potentially to improve performance via accessing large datasets to use during pre-training. Why it matters:** Papers like this highlight the sorts of environments in which deep learning techniques are likely to be deployed, though also suggest that today’s models are still inefficient for some real-world use cases (my suspicion here is that if the SERD dataset was substantially larger we may have seen performance increase further).** Read more: **Security Event Recognition for Visual Surveillance (Arxiv).