With great biological havoc comes great opportunity. Breakthroughs, discoveries, and DIY tips sent every weekday. About 445 million years ago, our planet completely changed. Massive glaciers formed over the supercontinent Gondwana, sucking up sea water like an icy sponge. Now called the Late Ordovician mass extinction (LOME), Earth's first major mass extinction wiped out about 85 percent of all marine species as the ocean chemistry radically changed and Earth's climate turned bitter cold. However, with great biological havoc also comes opportunity.

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks, including cases of domain generalization and contextual bias. Code is available at https://github.com/lisadunlap/ALIA.

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity.

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity.

OpenAI's management chaos in November could have long-lasting effects on its business as some of the company's customers say it was a wake-up call about the risks of being too reliant on one company's tech. Executives at companies that use OpenAI's software say they are increasingly looking to also use others' technology to protect themselves from the risks of problems at any one. OpenAI's competitors are using the opportunity to sign up wary customers.

I've been watching this trade pop up all over my newsfeed and I am so intrigued. I have a background in computer science and programming and I know enough to be dangerous, but this seems like it would take some serious skill. So what is the deal with automated trading bots? How can a bot make 1% daily profit? This post will answer all of your questions about automating cryptocurrency trading with artificial intelligence (AI) bots.

ACCOUNTANTS have been slow to adopt technology in recent years, although sophisticated software, tools, and solutions are now on the verge of reinventing the profession entirely. If accountants want to keep their edge, they need to learn to use technology -- at least to automate simple tasks and put them back in the driver's seat when it comes to managing the organization's financial strategy. RPA or robotic process automation is one technology they can quickly benefit from. It's easy to implement, affordable, and very effective. The technology could easily make repetitive, monotonous tasks redundant and free them up to get involved with data interpretation and management, allowing them to investigate errors and anomalies in data which requires their time, energy, and attention and has the potential to save the business money.

The problem is, simply put, out of control. Just because a company or individual can create and distribute content on a platform, doesn't mean they should. I've had the opportunity to analyze content marketing strategies from huge brands, desperately trying to build audiences online leveraging content marketing. In almost every case, each one made the same mistake. When an organization decides to fund a content marketing strategy, the initial stages are always exciting.