Audio-Visual Question Answering (AVQA) is a complex multi-modal reasoning task, demanding intelligent systems to accurately respond to natural language queries based on audio-video input pairs. Nevertheless, prevalent AVQA approaches are prone to overlearning dataset biases, resulting in poor robustness. Furthermore, current datasets may not provide a precise diagnostic for these methods. To tackle these challenges, firstly, we propose a novel dataset, MUSIC-AVQA-R, crafted in two steps: rephrasing questions within the test split of a public dataset (MUSIC-AVQA) and subsequently introducing distribution shifts to split questions. The former leads to a large, diverse test space, while the latter results in a comprehensive robustness evaluation on rare, frequent, and overall questions.

When searching for talent, sometimes the best person for the job is a machine. Robots make sense for repetitive and dangerous tasks, but they also work well as a check against bias. Artificial intelligence already outperforms judges in choices about setting bail because humans on the bench tend to overthink the defendants' demeanor, a poor predictor of flight risk. Likewise, hiring algorithms do better than recruiters at screening resumes because humans in HR show too much favoritism for traditional applicants. Unfortunately, smart technology also has blind spots.

Racial bias in [READ MORE…] Recommended For You Revolutionizing IoT Through AI: Why They're Perfect Together Artificial Intelligence seen as key technology game-changer but implementation challenges remain, finds Frost & Sullivan No "significant" loss of jobs from Artificial Intelligence in China HRM Asia Guest Opinion: Here's how to regulate artificial intelligence properly Brainerd Dispatch Callaway Mavrik metalwoods use artificial intelligence to push face and head designs to optimize specific player needs – Golf Digest CES 2020: virtual reality, artificial intelligence and a small glimpse of the future China – The First Artificial Intelligence Superpower Mobile Artificial Intelligence Market From 2020-2029: Growth Analysis by Manufacturers Apple NVIDIA Corporation, Huawei Technologies Co Ltd, Samsung Electronics Co Ltd CategoriesArtificial Intelligence TagsAI, Artificial Intelligence, Deep Learning, Machine Intelligence, Machine Learning, Mixed Learning, Smart Devices Guest Opinion: Here's how to regulate artificial intelligence properly Brainerd Dispatch

As humans, we are naturally flawed. Our ability to make mistakes and learn from them is what makes us who we are. Nowhere is this more apparent than in technology. From systems mis-registering genders based on job titles, to prejudiced photo auto-tagging and facial recognition software struggling to recognise people of colour, our own bias, whether conscious or not, seeps into the technology we create. Whenever someone mentions the diversity challenges facing the technology industry, they always turn to the aforementioned examples.

Most people talk too much about values relative to facts, as they care more about showing off their values than about learning facts. So I usually avoid talking values. But I'll make an exception today for this value: expanding rather than fighting about possibilities. On the x-axis you, or your group, get more of what you want. On the y-axis, others get more of what they want. The blue region is a space of possibilities, the blue curve is the frontier of best possibilities, and the blue dot is the status quo, which happens if no one tries to change it.

Imagine an innovation in pipes. If this innovation were general, something that made all kinds of pipes cheaper to build and maintain, the total benefits could be large, perhaps even comparable to the total amount we spend on pipes today. And if most of the value of pipe use were in many small uses, then that is where most of these economic gains would be found. In contrast, consider an innovation that only improved the very largest pipes. This innovation might, for example, cost a lot to use per meter of pipe, and so only make sense for the largest pipes.

Imagine an innovation in pipes. If this innovation were general, something that made all kinds of pipes cheaper to build and maintain, the total benefits could be large, perhaps even comparable to the total amount we spend on pipes today. And if most of the value of pipe use were in many small uses, then that is where most of these economic gains would be found. In contrast, consider an innovation that only improved the very largest pipes. This innovation might, for example, cost a lot to use per meter of pipe, and so only make sense for the largest pipes.

The Economic Singularity: Artificial intelligence and the death of capitalism .. This new book from best-selling AI writer Calum Chace argues that within a few decades, most humans will not be able to work for money. This book mentions me by name 15 times, especially on my review of Martin Ford's Rise of the Robots, wherein I complain that Ford's main evidence for saying "this time is different" is all the impressive demos he's seen lately. This seems to be Chace's main evidence as well: Faster computers, the availability of large data sets, and the persistence of pioneering researchers have finally rendered [deep learning] effective this decade, leading to "all the impressive computing demos" referred to by Robin Hanson in chapter 3.3, along with some early applications. But the major applications are still waiting in the wings, poised to take the stage. It's time to answer the question: is it really different this time?