On April 1, UT Austin's Machine Learning Lab held its inaugural public lecture, "AI For Accurate and Fair Imaging" with Alex Dimakis. The talk covered recent research into bias in AI imaging algorithms. In 2020, a deep learning generative model with groundbreaking performance was posted on the web. The model could turn low-resolution images to high-quality photos. A user uploaded a low-resolution image of President Obama and obtained an image that is now referred to as'White Obama'.

Researchers at the University of Texas at Austin are leading the way when it comes to fine-tuning the accuracy of artificial intelligence. UT's Machine Learning Lab held a public lecture Friday titled, "AI for Accurate and Fair Imaging." Researchers with the lab's Institute for Foundations of Machine Learning (IFML) have been working to improve the algorithm that in 2020 produced an internet-famous image of former President Barack Obama, dubbed "White Obama." AI that was meant to enhance a pixelated, low-resolution photo of the 44th president instead transformed him into a white man. "Even though it looked like a good image, a high resolution, realistic image of a person, it had a bias," said Alex Dimakis, IFML co-director.

ODSC APAC 2021 is right around the corner this September 15–16th, and while there's something for everyone, NLP is sticking out as one of the focal points of this conference. Natural language processing is indeed special in the APAC region, namely because there's a greater need for diverse datasets, due to the number of different languages spoken in the region. This has lead researchers to develop novel and exciting techniques to address these concerns. At ODSC APAC in a few weeks, you'll be able to hear from these data scientists about NLP, and hear from some research institutions that focus on NLP Natural language processing (NLP) has made truly impressive progress in recent years and is being deployed in an ever-increasing range of user-facing settings. Accompanied by this progress has been a growing realization of inequities in the performance of naively-trained NLP models for users of different demographics, with minorities typically experiencing lower performance levels.

OK, so here it is - the newest version of Neptune, a tool for building and deploying machine learning models. Run things in the cloud with a single command line neptune send, track your models with charts, compare & reproduce your previous models. We give you $100 for cloud computing in Google Cloud (we charge per second, so it's a lot of computing power). Let us know if you find Neptune helpful for your work (whether business projects, Kaggle competitions, some side projects or hands-on learning deep learning). We would be excited to hear your feedback, so we can keep improving Neptune.