UNESCO just released its new report on Artificial Intelligence and Gender Equality, which sets forth proposed elements of a Framework on Gender Equality and AI for further consideration, discussion and elaboration amongst various stakeholders. Advancing gender equality through education, the sciences, culture, information and communication lies at the heart of UNESCO's mandate, with Gender Equality constituting one of the two Global Priorities of the Organization since 2008. UNESCO is therefore keen to adopt a gender equality lens in its ongoing work on artificial intelligence in all its programme areas. Research, including UNESCO's 2019 report I'd Blush if I Could: closing gender divides in digital skills through education, unambiguously shows that the gender biases found in AI training data sets, algorithms and devices have the potential of spreading and reinforcing harmful gender stereotypes. These gender biases risk further stigmatizing and marginalizing women on a global scale.

In the digital age, the line between the material world and simulation often blur. The realm of simulacra is very much intermeshed within an increasingly virtual reality. The proliferation of synthetic media has created a vast realm of possibilities from deepfake-enabled political misinformation to a wholly new type of computer-generated Instagram influencer. Moving forward, synthetic media and artificial intelligence (AI) could transform the way companies target global audiences and provide internal core competency training. Digital education avatars could also boost engagement and memory retention in the virtual classroom.

After Covid-19 impeded schools from operating and examining regularly, the UK Department of Education attempted to grade students' A-level and GCSE exams with a third-party machine learning algorithm. Britain's A-levels largely determine students' chances to attend higher education and thus have life-long consequences. The applied algorithm predicted students' grades based on their individual performance in earlier -- and somewhat irrelevant and deviating -- mock exams as well as on their school's relative performance to others in the previous year. Many critics labeled this approach as inaccurate and unfair, resulting in significant downgrading and favoring private schools. In fact, over 40% of students received lower grades than predicted by their teachers, compared to only 2% whose scores improved (Heaven, 2020).

Amba Kak was in law school in India when the country rolled out the Aadhaar project in 2009. The national biometric ID system, conceived as a comprehensive identity program, sought to collect the fingerprints, iris scans, and photographs of all residents. It wasn't long, Kak remembers, before stories about its devastating consequences began to spread. "We were suddenly hearing reports of how manual laborers who work with their hands--how their fingerprints were failing the system, and they were then being denied access to basic necessities," she says. "We actually had starvation deaths in India that were being linked to the barriers that these biometric ID systems were creating. So it was a really crucial issue."

NOTE: This course is a continuation of XCS229i: Machine Learning. Though not strictly required, it is highly recommended to take XCS229i before enrolling in XCS229ii, as assignments assume knowledge of topics in the first course. As machine learning models grow in sophistication, it is increasingly important for its practitioners to be comfortable navigating their many tuning parameters. Through video lectures and hands-on exercises, this course will equip you with the knowledge to get the most out of your data. You will learn the concepts and techniques you need to guide teams of ML practitioners.

Have you been thinking about getting up to speed with deep learning or applied data ethics? Well, look no further than the latest free courses from fast.ai. Fast.ai recently announced some exciting new releases. Part 2 of the deep learning course shows how to build a state of the art deep learning model from scratch. It covers many topics from the foundations of implementing matrix multiplication and back-propagation, through to high performance mixed-precision training, and the latest neural network architectures and learning techniques. This course focusses on ethics issues that are both urgent and practical.

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Deep learning and artificial intelligence are one of the hottest topics in the world today. We see an ever-increasing number of applications that employ deep learning: facial recognition, speech recognition (Siri, 'OK Google', Alexa), Self-Driving Cars, and the list goes on and on. So as a student, fresh employee, team manager, senior management, we get curious: Will this ever-rising wave of AI technology eventually make my job or future career less relevant? That was actually how I stumbled upon the world of Deep Learning years ago, and ended up where I am today: pursuing a postgraduate degree in this field of research. We all have been conditioned to learn things in a certain way: search for information online, sign up for courses, watch tutorials on YouTube. However, the massive and ever-increasing amount of information on this subject is scary, and many of us give up on the pursuit of new, relevant skills quickly.

The government will strengthen support for non-Japanese children in need of Japanese-language education with a planned doubling of the budget as part of efforts to ensure they are not missing out on learning opportunities, the education ministry said Thursday. Based on a survey conducted last year, the ministry estimates that more than 19,000 out of the around 124,000 non-Japanese children of elementary or junior high school-age in Japan do not attend school at all. It also found that there is growing demand for Japanese-language education in public elementary and junior high schools. In accounting for the lack of attendance, the ministry said some children and guardians may not possess sufficient command of the Japanese language and support also varies among local governments, with many only sending notices regarding enrollment in Japanese. The ministry allocated a budget of around ¥700 million ($6.6 million) this fiscal year for support measures, which included covering one-third of the labor costs of Japanese-language tutors and assistants to provide advice in children's native tongues.

In the cognitive sciences, it is common to distinguish between crystal intelligence, the ability to utilize knowledge acquired through past learning or experience and fluid intelligence, the ability to solve novel problems without relying on prior knowledge. Using this cognitive distinction between the two types of intelligence, extensively-trained deep networks that can play chess or Go exhibit crystal but not fluid intelligence. In humans, fluid intelligence is typically studied and quantified using intelligence tests. Previous studies have shown that deep networks can solve some forms of intelligence tests, but only after extensive training. Here we present a computational model that solves intelligence tests without any prior training. This ability is based on continual inductive reasoning, and is implemented by deep unsupervised latent-prediction networks. Our work demonstrates the potential fluid intelligence of deep networks. Finally, we propose that the computational principles underlying our approach can be used to model fluid intelligence in the cognitive sciences.