Your arrival coincided with the 125th anniversary of this magazine. With a bit of luck and the right genes, you might see the next 125 years. How will you and the next generation of machines grow up together? We asked more than a dozen experts to imagine your joint future. We explained that this would be a thought experiment.

Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To bridge this gap, we introduce PixelProse, a comprehensive dataset of over 16M (million) synthetically generated captions, leveraging cutting-edge vision-language models for detailed and accurate descriptions. To ensure data integrity, we rigorously analyze our dataset for problematic content, including child sexual abuse material (CSAM), personally identifiable information (PII), and toxicity. We also provide valuable metadata such as watermark presence and aesthetic scores, aiding in further dataset filtering. We hope PixelProse will be a valuable resource for future vision-language research.

When he looks back on it now – gambling his house, battling Robert Maxwell, turning up in the Soviet Union on a wing and a prayer – Henk Rogers still insists that he never considered giving up. "People ask me how much naivety was involved?" he recalls. "I would say 20% naivety/stupidity and 80% determination." That may be the key to success in many aspects of life. In Rogers's case, he was a video game publisher who knew he had discovered the next big thing: Tetris, a strangely addictive puzzle in which players must arrange falling bricks of differing shapes to form a solid wall.

A cat-shaped robot server has worked its first shift in a UK restaurant to help address staff shortages. The BellaBot, a delivery robot created by Chinese tech company Pudu, was recruited by the Bella Italia branch at Center Parcs Whinfell Forest in Cumbria. The robot can zip between tables and the kitchen, with human staff or customers only required to load and unload the trays. The trial by Big Table Group, owner of Bella Italia, Café Rouge, Las Iguanas and other UK restaurant chains, marks the first time the BellaBot has been used in a large restaurant setting. The company said it launched the trial in an effort to'boost innovation and elevate the dining experience', and that it was now'the largest restaurant chain in the UK to introduce robot technology'.

Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. The science behind making machines talk just like humans is very complex, because our speech patterns are so nuanced. "The voice is not easy to grasp," says Klaus Scherer, emeritus professor of the psychology of emotion at the University of Geneva. "To analyze the voice really requires quite a lot of knowledge about acoustics, vocal mechanisms and physiological aspects. So it is necessarily interdisciplinary, and quite demanding in terms of what you need to master in order to do anything of consequence."

Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. The science behind making machines talk just like humans is very complex, because our speech patterns are so nuanced. "The voice is not easy to grasp," says Klaus Scherer, emeritus professor of the psychology of emotion at the University of Geneva. "To analyze the voice really requires quite a lot of knowledge about acoustics, vocal mechanisms and physiological aspects. So it is necessarily interdisciplinary, and quite demanding in terms of what you need to master in order to do anything of consequence."

Welcome to our August 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we cover IJCAI 2021, find out about new grants for climate research, hear about RoboCupJunior, and celebrate a significant birthday. The big event this month was the International Joint Conference on Artificial Intelligence (IJCAI-21), held virtually from 19-26 August. Gathertown played host to proceedings and participants were treated to eight invited talks, panel discussions, poster sessions, and more. We're covering the invited talks in a series of articles.

An artist can draw a baby daikon radish wearing a tutu and walking a dog, even if they've never seen one before. But this kind of visual mashup has long been a trickier task for computers. Now, a new artificial-intelligence model can create such images with clarity -- and cuteness. This week nonprofit research company OpenAI released DALL-E, which can generate a slew of impressive-looking, often surrealistic images from written prompts such as "an armchair in the shape of an avocado" or "a painting of a capybara sitting in a field at sunrise." (And yes, the name DALL-E is a portmanteau referencing surrealist artist Salvador Dalí and animated sci-fi film "WALL-E.") A new AI model from OpenAI, DALL-E, can create pictures from the text prompt "an illustration of a baby daikon radish in a tutu walking a dog".

Autocorrect is the saving grace for us all. The number of times I've gone to type a message and it would come out as if I am drunk then autocorrect intercedes on my behalf -- Oh, how I love you autocorrect (sometimes). To define Autocorrect more formally, it is a software function that suggests or makes corrections for spelling or grammatical errors automatically whilst we type. We all use autocorrect, but this post will teach you how it works. However, in these notes, we will only be covering spelling errors and not contextual errors.

They say your 20s is the most important decade of your life. Let's look back at what happened in the past 25 years of digital advertising's formative history. Over the years, due largely in part to advancements in ad technology, advertisers have become more focused on the customer and more aware of the journey between awareness and conversion. It's not necessarily surprising, but always exciting to watch as marketers continue to get smarter about what ad formats and channels to leverage for each part of this journey--or what combination of cross-channel efforts led to the ultimate conversion. Their strategies are constantly evolving as technology advances and new formats are introduced. With emerging technology like visual/voice search, chatbots, machine learning, and AR, we have a lot to look forward to in the next 25 years.