Artificial intelligence has made its place in all our lives, from correcting our bad grammar, personalizing our music on apps, to automating work in several industries. AI holds a massive potential to transform the future of work. But to understand this disruptive technology, the general public needs to have a working knowledge of the capabilities. To start slow and avoid the feeling of being overwhelming, here are 10 books that will help you grasp the concept. This book is beginner-friendly and gives a less technical overview of several AI topics.

The reality is that humans need AI to survive, and vice-versa. As a race, we have progressed so much that we now need AI to extend our intelligence and inspire our creativity. We have been able to build tremendous things. Anyone with access to a computer and an internet connection can read this article. AI is exceptional at learning patterns and automating tasks.

Sometimes the truth has an expiry date. When a time-limited claim (such as'masks are obligatory on public transport') emerges in search engine rankings, its apparent'authoritative' solution can outstay its welcome even by many years, outranking later and more accurate content on the same topic. This is a by-product of search engine algorithms' determination to identify and promote'long-term' definitive solutions, and of their proclivity to prioritize well-linked content that maintains traffic over time – and of an increasingly circumspect attitude to newer content in the emerging age of fake news. Alternately, devaluing valuable web content simply because the timestamp associated with it has passed an arbitrary'validity window' risks that a generation of genuinely useful content will be automatically demoted in favor of subsequent material that may be of a lower standard. Towards redressing this syndrome, a new paper from researchers in Italy, Belgium and Denmark has used a variety of machine learning techniques to develop a methodology for time-aware evidence ranking.

Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied, prompting us to ask the question: what is the best way to sample episodes? In this paper, we first propose a method to approximate episode sampling distributions based on their difficulty. Building on this method, we perform an extensive analysis and find that sampling uniformly over episode difficulty outperforms other sampling schemes, including curriculum and easy-/hard-mining. As the proposed sampling method is algorithm agnostic, we can leverage these insights to improve few-shot learning accuracies across many episodic training algorithms. We demonstrate the efficacy of our method across popular few-shot learning datasets, algorithms, network architectures, and protocols.

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One of the more startling and instructive documentaries of the recent past is 2020's Coded Bias, which explores a thorny dilemma: in modern society, artificial-intelligence systems increasingly govern and surveil people's lives--algorithms now routinely make decisions about health care, housing, insurance, education, employment, banking, and policing--yet racial and gender biases are deeply embedded in many of these AI systems (for more background, read "Artificial Intelligence and Ethics," January-February 2019, page 44). The film, which premiered at Sundance and is now streaming on Netflix, begins with MIT Media Lab researcher and MIT doctoral candidate Joy Buolamwini recounting an experience from her first semester there in 2015: working on an art project that used AI facial-recognition software, she was confused at first when the computer didn't seem to register her face. During a striking moment early in the documentary, Buolamwini, who is African American, demonstrates the problem: holding a white mask over her own face, she turns toward her computer, which trills and lights up in response; when she lowers the mask, the computer sits eerily silent. The documentary presents a damning portrait of AI's flaws and the efforts under way to improve them, weaving together research and interviews of those who study the field, including several with Harvard connections: Berkman Klein faculty associate Zeynep Tufekci, former Nieman visiting fellow Amy Webb, data scientist Cathy O'Neil, Ph.D. '99, author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016). Buolamwini herself is a former Adams House tutor (and performed her spoken-word poem, "AI, Ain't I A Woman?" at a Harvard conference in 2019).