There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient zero-short learning method, ZeroGen. Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset. This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free and efficient, we argue that ZeroGen can also provide useful insights from the perspective of data-free model-agnostic knowledge distillation, and unreferenced text generation evaluation. Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference), show the effectiveness of ZeroGen.

AI technologies are being used in different areas like healthcare, pharmaceuticals, manufacturing, teaching, security, etc. However, the use of artificial intelligence is not just confined to some businesses and industries. Artificial Intelligence is also being used for saving the environment, and saving sea turtles as well. There are different ways to help the environment, and the use of AI software for saving sea turtles is one such way to use AI for conservation. The use of AI technologies has helped in protecting and saving sea turtles eggs from the feral pigs in North Queensland in Australia.

Across the world, companies are in a fierce battle to assemble the right mix of hardware and software technology to deliver autonomous vehicles. With dozens of companies working on one of the hardest problems, driverless vehicles will be here in the not too distant future. So how is Australia getting ready to facilitate their introduction and allow businesses and citizens to take advantage of the technology? Last Friday, the 16th meeting of Infrastructure and Transport Ministers was held and they have made a really important determination, available in the now-public documents at infrastruture.gov.au Automated vehicles Ministers agreed that the future Automated Vehicle Safety Law will be implemented through Commonwealth law.

At present, quantum computer systems make too many errors to ever be really helpful, however, a synthetic intelligence that may appropriate quantum errors might supply an answer. The duty is extra complicated in quantum computing as a result every qubit, or quantum bit, exists in a blended state of zero and 1, and any try to establish errors by instantly measuring qubits destroys the information. Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy. A technique to detect the tiniest deviations from the precise conditions needed to execute quantum algorithms using trapped ion and superconducting quantum computing hardware. These are the core technologies used by industrial quantum computing efforts at IBM, Google, Honeywell, and others.

Digital health is defined as the space where digital technologies, daily life, and health care intersect. The objective of digital health is to enhance the efficiency of healthcare delivery and make medicine more individualised and precise. Information and communication technologies are used to help address the health problems faced by people undergoing treatment. The interconnectedness of health systems development, improved use of computational analysis, smart devices, and communication media are the techniques and tools used in digital health to aid clinicians and their clients with managing illnesses and health conditions while simultaneously promoting good health and well being. Generally, digital health is concerned about the development of interconnected health systems to improve the use of computational technologies, smart devices, computational analysis techniques, and communication media to aid healthcare professionals and their clients manage illnesses and health risks, as well as promote health and wellbeing.

Chips to perform AI inference on edge devices such as smartphones is a red-hot market, even years into the field's emergence, attracting more and more startups and more and more venture funding, according to a prominent chip analyst firm covering the field. "There are more new startups continuing to come out, and continuing to try to differentiate," says Mike Demler, Senior Analyst with The Linley Group, which publishes the widely read Microprocessor Report, in an interview with ZDNet via phone. Linley Group produces two conferences each year in Silicon Valley hosting numerous startups, the Spring and Fall Processor Forum, with an emphasis in recent years on those AI startups. At the most recent event, held in October, both virtually and in-person, in Santa Clara, California, the conference was packed with startups such Flex Logix, Hailo Technologies, Roviero, BrainChip, Syntiant, Untether AI, Expedera, and Deep AI giving short talks about their chip designs. Demler and team regularly assemble a research report titled the Guide to Processors for Deep Learning, the latest version of which is expected out this month.

In the early hours of the morning, the news was breaking. Specialty coffee was being recognized as a vital part of the global coffee trade, and the industry was seeing huge growth. This was great news for coffee growers and roasters all over the world, and it was especially exciting for those in the know, who could look forward to even more opportunities to make a name for themselves. So, how did Sprudge uncover this scoop and break the news to the world? Thanks to the miracle of artificial intelligence, which some are calling AI.

Are you an Engineer who is passionate about using cutting-edge ML algorithms to create and deliver value using data? Poshmark is looking for a Senior ML Engineer who can independently drive innovation across our key AI products. As a market leader in Social Commerce, Poshmark faces a unique opportunity to utilize our massive multi-platform social and commerce data to shape social commerce and create value for our users. With 400 MM events per day and 30 terabytes of exponentially growing data, Poshmark presents a unique opportunity to build AI products using Big Data. This person will work on multiple ML initiatives and help solve some of the most challenging problems in social commerce like - Personalizing User Experience, Search Ranking, and many more.

That is, at least in the U.S., essentially still the case. However, there's been a significant volume of water that's passed under the policy and lawmaking bridge since then, so I wanted to revisit the question. First, let's back up a little. I have to admit that my reasoning in 2018 was narrow rather than broad. The work – and let's note that it doesn't have to be considered aesthetically "good" or have required a lot of skill – must simply be original (meaning that it was independently created and has at least a "modicum" of creativity) and an expression of some sort.

Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of questions from stories possible, the fully-automated approach excludes parent involvement, disregards educational goals, and underoptimizes for child engagement. Informed by need-finding interviews and participatory design (PD) results, we developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences. StoryBuddy's design highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy. The PD revealed varied assessment and educational goals of parents, which StoryBuddy addressed by supporting configuring question types and tracking child progress. A user study validated StoryBuddy's usability and suggested design insights for future parent-AI collaboration systems.