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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.

The day is coming--some say has already arrived--when artificial intelligence starts to invent things that its human creators could not. But our laws are lagging behind this technology, UNSW experts say. It's not surprising these days to see new inventions that either incorporate or have benefitted from artificial intelligence (AI) in some way, but what about inventions dreamt up by AI--do we award a patent to a machine? This is the quandary facing lawmakers around the world with a live test case in the works that its supporters say is the first true example of an AI system named as the sole inventor. In commentary published in the journal Nature, two leading academics from UNSW Sydney examine the implications of patents being awarded to an AI entity.

The images are so crafted and "painterly" that you may not realise at first they have been dreamed up by a machine in just a few minutes. Maybe you've seen one already, but not realised what it was. It may have looked like something you'd seen before in an art book or a museum. These images are the product of a new AI-generated art scene that's exploded thanks to the development of free and easy-to-use tools that require (at the very least) short text prompts to create unique pictures. The image in the tweet above, for example, was created by giving the text prompt "a summer day" to an AI tool.

Analysis - Two years ago this weekend, GPT-3 was introduced to the world and although you may not have heard of it there's a good chance you've read its work. It is likely that you have already read work composed by AI model, GPT-3. Or you may have used a website that runs GPT-3 code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model - a type of artificial intelligence - and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programmes are producing greater amounts of data, from words, to images, to code.

Two years ago this weekend, GPT-3 was introduced to the world. You may not have heard of GPT-3, but there's a good chance you've read its work, used a website that runs its code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model -- a type of artificial intelligence -- and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programs are producing greater amounts of data, from words, to images, to code. A lot of the time, this happens in the background; we don't see what the AI has done, or we can't tell if it's any good.

Artificial Intelligence (AI) systems have been involved in numerous scandals in recent years. For instance, take the COMPAS recidivism algorithm. The algorithm evaluated the likelihood that defendants will commit another crime in the future. It was widely used in the US criminal justice system to inform decisions about who can be set free at all stages of the process. In 2016, ProPublica exposed that COMPAS's predictions were biased: its mistakes favored white over black defendants. Black defendants were twice as likely to be labeled as high risk to reoffend but not actually reoffend.