Abstract: We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders. MixNMatch requires bounding boxes during training to model background, but requires no other supervision. Through extensive experiments, we demonstrate MixNMatch's ability to accurately disentangle, encode, and combine multiple factors for mix-and- match image generation, including sketch2color, cartoon2img, and img2gif applications.

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

A lot of the core tech of self-driving cars uses a type of machine learning called deep learning so I've been working with it the past few years.

A lot of work in the field of AI focuses on making machine learning systems better at generalizing and learning from fewer examples, Domingos said.

But years before Alexa and VR porn Her understood that as technology evolved, the lines between human intimacy and machine learning would fade.

Long-time reader, but first-time poster: If this post should go into r/cscareerquestions, or be deleted, just let me know. I have the opportunity to start a PhD in Computer Vision/ML with a potential focus on SLAM. I am looking for topics related to society that can guide and especially motivate the research. A counterexample would be face-recognition which is technically fascinating, but IMHO has significant negative potential from a political perspective. A possible example would be "helping the blind" where computer vision should generally be beneficial; however, I currently assume that most challenges in this area are more user-interface- and product-design-related.

Anything you do online can come back to bite you. It's been a decade full of lessons: who to trust, when to speak out and how to stream big events online after you've broken up with your cable company. In 2010, the first iPhone was only three years old. Uber and Lyft didn't exist, and neither did Google Assistant and Siri, Instagram or streaming video. We've come a long way since then, but the next 10 years won't be easy.

For parents looking to prise their children away from a life online, there are plenty of tangible, inventive, educational and/or entertaining physical products out there for the tech-savvy children of 2015. Whether you're a parent, carer, relative or family friend – and whatever your price range – you're spoiled for choice when it comes to tech-related presents this year. Here are some of the best examples. If your children are constantly trying to get their hands on your tablet, it may make sense to buy them their own – with suitable rules about how much they use it, of course. Amazon's child-focused tablet is well worth a look.