A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

In Australia, five major companies are involved in a trial run of eight principles developed as part of the government AI Ethics Framework. The idea behind the principles is to ensure that AI systems benefit individuals, society and the environment; respect human rights; don't discriminate; and uphold privacy rights and data protection.

Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In order to assess the importance of such a manuscript, it is vital to know who wrote it. In this work, we aim to develop a machine learning framework to effectively determine authorship. We formulate the task as a single-label multi-class text categorization problem and propose a supervised machine learning framework incorporating stylometric features. This task is highly interdisciplinary in that it takes advantage of machine learning, information retrieval, and natural language processing. We present an approach and a model which learns the differences in writing style between $50$ different authors and is able to predict the author of a new text with high accuracy. The accuracy is seen to increase significantly after introducing certain linguistic stylometric features along with text features.

Audio Player failed to load. Try to Download directly (2.19 MB) Space to play or pause, M to mute, left and right arrows to seek, up and down arrows for volume. Australia's Human Rights Commission is calling for a moratorium on the introduction of some new artificial intelligence technologies, until the rights of humans can be safeguarded. And many of those inside the industry agree that the technology is taking off too fast for our legal system to keep up. The commission wants to better regulate artificial intelligence like facial recognition to protect people's privacy and to prevent society's most vulnerable from being further disadvantaged.

Every time some genius decides to apply AI where it doesn't belong, the world collectively rolls its eyes and puts another ballot in the AI-Is-A-Fad box. If your dictionary defines AI as magic or robots (or magical robots), of course you'll be disappointed when it doesn't deliver the cure to all that ails you. Let's look at three common gripes using simple examples everyone can grasp. A respectable software engineer once asked me with a straight face, "Can AI know that Canada is a country?" Hold your horses there, cowboy.

YouTube has launched a new free-to-watch documentary series about artificial intelligence fronted by "Iron Man" star Robert Downey Jr. The YouTube Original series debuted on the platform on Wednesday, and is called "The Age of AI." Its stated aim is to demystify misconceptions around AI. One of the main focuses of the first episode is a New Zealand-based company called Soul Machines, which specialises in making digital avatars. Its founder Mark Sagar is an award-winning visual effects artist who's worked on films like "Rise of the Planet of the Apes" and "Avatar." Sagar is working on a project he calls "Baby X," in which he is using AI to simulate a human baby, modelled after his own daughter.

At the company's GPU Technology Conference (GTC) in Suzhou, China, Nvidia CEO Jensen Huang took to the stage to introduce Drive AGX Orin, the next generation SoC in the company's automotive portfolio. Orin follows Drive AGX Xavier, launched just under 2 years ago at CES 2018. Xavier is Nvidia's current flagship SoC for AI acceleration in vehicles. Orin, at 17 billion transistors, is almost double the size of Xavier, which had 9 billion, and it offers nearly 7x the performance (200 TOPS for INT8 data). Despite its size, Orin also offers 3x the power efficiency of Xavier, the company said.

The University of Adelaide plans to achieve substantial growth in its student population within five years, and one of the teams responsible for achieving this very aggressive goal has a new staff member this year: a chatbot. It helps answer the critical question, "Am I eligible to attend the university?" Catherine Cherry, the school's director of prospect management, is putting innovative technologies to work to help meet that goal. The University of Adelaide uses a chatbot to let prospective students know whether they're eligible to apply. Prior to the introduction of the chatbot, the university's admissions office couldn't easily answer the eligibility question for prospective students from outside of Australia who were curious about whether they could attend.

New Zealand's financial sector is primed for artificial intelligence, according to new national research. The AI Forum of New Zealand (part of the NZTech ecosystem) research study says the country's financial and insurance sectors are better prepared to incorporate and reap rewards from AI implementation than other industries. According to Emma Naji, AI Forum executor director, the report identifies New Zealand urgently needs to increase its focus on the core foundations needed to operate in an AI enabled future. This is especially important relating to investment, skills and talent, research, trusted data, ethics and regulation, she says. "The report shows how AI-driven solutions can be used to improve New Zealand's wellbeing, productivity and sustainability," says Naji. "Unsurprisingly, financial institutions have been quick to capitalise on the opportunities and new techniques that AI offers."