Back translation is one of the most widely used methods for improving the performance of neural machine translation systems. Recent research has sought to enhance the effectiveness of this method by increasing the 'diversity' of the generated translations. We argue that the definitions and metrics used to quantify 'diversity' in previous work have been insufficient. This work puts forward a more nuanced framework for understanding diversity in training data, splitting it into lexical diversity and syntactic diversity. We present novel metrics for measuring these different aspects of diversity and carry out empirical analysis into the effect of these types of diversity on final neural machine translation model performance for low-resource English$\leftrightarrow$Turkish and mid-resource English$\leftrightarrow$Icelandic. Our findings show that generating back translation using nucleus sampling results in higher final model performance, and that this method of generation has high levels of both lexical and syntactic diversity. We also find evidence that lexical diversity is more important than syntactic for back translation performance.

Coral reefs are among Earth's most stunning and biodiverse ecosystems. Yet, due to human-induced climate change resulting in warmer oceans, we are seeing growing numbers of these living habitats dying. The urgency of the crisis facing coral reefs around the world was highlighted in a recent study that showed that 91% of Australia's Great Barrier Reef had experienced coral bleaching in the summer of 2021–22 due to heat stress from rising water temperatures. Determining reef health is key to gauging the extent of the problem and developing ways of intervening to save these ecosystems, and a new artificial intelligence (AI) tool has been developed to measure reef health using… sound. Research coming out of the UK is using AI to study the soundscape of Indonesian reefs to determine the health of the ecosystems.

Have you ever gone to the dentist and been unsure if that spot on your tooth the doctor is looking at is really a cavity? Or maybe you've gone to get a second opinion, only to have the new practice tell you that you need a crown on a completely different tooth? The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. Unfortunately, this story is all too common in dentistry -- in fact, there's a well-known story about a Reader Digest reporter who went to see 50 different dentists and received nearly 50 different diagnoses.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program .

Virtual children that play with you, cuddle you, and even look like you will be commonplace in 50 years, and could help to combat overpopulation, an artificial intelligence expert has claimed. These computer-generated offspring will only exist in the immersive digital world known as the'metaverse', which is accessed using virtual reality technology such as a headset to make a user feel as if they're face-to-face with the child. They will cost next to nothing to bring up, as they will require minimal resources, according to Catriona Campbell, one of the UK's leading authorities on AI and emerging technologies. In her new book, AI by Design: A Plan For Living With Artificial Intelligence, she argues that concerns about overpopulation will prompt society to embrace digital children. She describes them as the'Tamagotchi generation' -- a reference to the handheld digital pets that became wildly popular among Western youngsters in the late 1990s and the 2000s.

Since 2002, Quantium have combined the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Our solutions make sense of what has happened and what will, could or should be done to re-shape industries and societies around the needs of the people they serve. As one of the world's fully diversified data science and AI leaders we operate across every sector of the economy and we're growing fast - with growth comes opportunity! We're passionate about building out our team of smart, fun, diverse and motivated people. We combine a team of experts that spans data scientists, actuaries, statisticians, business analysts, strategy consultants, engineers, technologists, programmers, product developers, and futurists – all dedicated to harnessing the power of data to drive transformational outcomes for our clients.

The co-founder of a fast-growing conversational AI start-up attributes its ongoing success to the commitment to building a diverse and trusting company culture almost as much as to the quality of the technology it is offering. Andrei Papancea is CEO of conversational AI specialist NLX, which has expanded from five to 25 staff in a little over a year. The small team is geographically spread across the world, from New York to Seattle to Queensland, and Berlin, and speaks 19 different languages, including Arabic, Mandarin, Korean and Spanish. Andrei says that his mission is to combine the best of AI with the best of human support to create extraordinary, memorable self-service experiences for users by building the world's go-to platform to create human conversational AI applications. "In all the jobs I had throughout my career, I always disliked it when good people – my colleagues, my peers, and my friends – quit. They always left for one of three reasons: they weren't paid well, they didn't feel heard or respected, or they didn't have interesting and engaging work to do. In building NLX, I've done the best I can to avoid losing good people because of these three reasons," explains Andrei.

Strong Compute, a Sydney, Australia-based startup that helps developers remove the bottlenecks in their machine learning training pipelines, today announced that it has raised a $7.8 million seed round. The round includes a total of 30 funds and angels, including the likes of Sequoia Capital India, Blackbird, Folklore and Skip Capital, as well as Y Combinator, Starburst Ventures and founders and engineers from companies like Cruise, Waymo, Open AI, SpaceX and Virgin Galactic. The company, which was part of Y Combinator's Winter '22 batch, promises that its optimizations can speed up the training process by 10x to 1000x, depending on the model, pipeline and framework. As Strong Compute founder Ben Sands, who previously also co-founded AR company Meta, told me, the team has recently made some breakthroughs where it was able to take Nvidia's reference implementation, which its customer LayerJot used, to run 20 times faster. "That was a big win," Sands said.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.