Kathleen Siminyu is a data scientist & machine learning engineer who is Regional Coordinator for the Artificial Intelligence for Development – Africa Network. She is Co-Founder of the Nairobi Women in Machine Learning & Data Science community, and part of the Deep Learning Indaba Steering Committee. Her other interests include natural language processing for African languages and low-cost hardware robotics. We share this story as a demonstration of how AI can indirectly bring people together and empower communities instead of downgrade, divide, or discriminate against them. We believe that community leaders have an important role to play in defining humanity's place in a world of algorithms.

WITH the cost of bringing a new drug to market now an average US$2.6bn1 and one-in-ten drug candidates failing to make it to market despite successfully completing Phase I trials2, it is no wonder that pharmaceutical companies have seized on the unparalleled data-processing potential of artificial intelligence (AI) systems. Their use in identifying compounds, some of which may have completed clinical trials already, that could be re-purposed to treat alternative diseases quickly and comparatively cheaply, is well documented. But as research scientists are beginning to find, AI systems are capable of achieving so much more. The potential applications of AI in drug discovery are almost endless, but one of the main areas of focus to date has been repurposing existing drugs. Typically, this involves finding new uses for drugs that have already attained market and regulatory approvals for the treatment of a specific disease.

Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants' social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, active perception and learning) and contextualize them in practical applications. We review current computational models mainly based on the simulation and teleological theories, and robotic implementations to identify the limitations of ToM functions in current robotic architectures and suggest a possible future developmental pathway. Finally, we propose future studies to create innovative computational models integrating the properties of the simulation and teleological approaches for an improved adaptive ToM ability in robots with the aim of enhancing human-robot interactions and permitting the application of robots in unexplored environments, such as disasters and construction sites. To achieve this goal, we suggest directing future research towards the modern cross-talk between the fields of robotics and developmental psychology.

Fox News Flash top headlines for August 30 are here. Check out what's clicking on Foxnews.com Sooner or later, the robots will make chimps of us. That's the warning from Elon Musk, who claimed on Thursday that artificial-intelligence will eventually create robots that aren't merely as smart as humans, but much smarter. At a Shanghai event with Alibaba's billionaire founder Jack Ma, Tesla's eccentric CEO compared the intelligence gap between humans and the AI-powered robots of the future to the gap between humans and chimpanzees.

Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning. We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trained an algorithm to detect individual buildings, then examined building clusters to identify groupings suggestive of communities. The approach was validated in southeastern Liberia, by comparing algorithmically generated results with community location data collected manually by enumerators and community health workers. The deep learning approach achieved 86.47% positive predictive value and 79.49% sensitivity with respect to individual building detection. The approach identified 75.67% (n 451) of communities registered through the community enumeration process, and identified an additional 167 potential communities not previously registered. Several instances of false positives and false negatives were identified.

The hype around AI is a media frenzy. If we aren't careful, we will ruin the name, due to a lack of knowledge, before it has a chance to prove itself. AI is a beautiful concept of futuristic computing that the tech industry and academic research is leading in a way that will one day see dramatic changes to the way we live. It will pivot the human race into a new digital era. Computers are not thinking for themselves nor are they able to live on their own.

Machine Learning Digest is a curated weekly news overview for those who are concerned about the Machine Learning development across a spectrum of industries. It provides brief summaries and links to articles and news, describing the most remarkable events in the ML sphere. Banana is the world's most popular fruit and its global population is going to amount to 10 billion in 2050. Banana is a crucial source of nutrition and is an essential fruit for many people. Still, a number of pests and diseases are to damage the plants.

Our sequential non-text information is best harnessed in a Bidirectional LSTM – a type of sequential model described in more detail here and here – that allows the model to learn end-of-sequence and beginning-of-sequence behavior. This maps to domain experts' knowledge that distinctive behavior at the end of the subscription period presages churn. It also captures the patterns in the progression of events over time that can be used to predict eventual churn. On the other hand our textual and categorical data need a separate model to learn from this differently structured data. We have several options here.

At Hugging Face, we experienced first-hand the growing popularity of these models as our NLP library -- which encapsulates most of them -- got installed more than 400,000 times in just a few months. However, as these models were reaching a larger NLP community, an important and challenging question started to emerge. How should we put these monsters in production? How can we use such large models under low latency constraints? Do we need (costly) GPU servers to serve at scale?

India's higher education sector has supplied some of the world's best talent. The CEOs of some of the biggest Fortune 500 companies--Microsoft, Google, Mastercard, and Adobe--are a product of the Indian higher education system. The landscape has also expanded over the past decade--from 436 universities in 2009–10 to 903 in 2017–18 and from 26,000 colleges to over 39,000.1 Student enrolment, at 36.6 million, is the third-largest in the world, next to China and the United States.2 Besides, India is already in the middle of the "demographic dividend" with a surge in its younger and working-age population, which is estimated to become the world's largest by 2030.3 India is expected to account for about 20 percent of the total young talent pool supplied by the non–Organisation for Economic Cooperation and Development (OECD) G-20 countries.4