While polyhedral compilers have shown success in implementing advanced code transformations, they still have challenges in selecting the most profitable transformations that lead to the best speedups. This has motivated the use of machine learning to build cost models to guide the search for polyhedral optimizations. State-of-the-art polyhedral compilers have demonstrated a viable proof-of-concept of this approach. While such a proof-of-concept has shown promise, it still has significant limitations. State-of-the-art polyhedral compilers that use a deep-learning cost model only support a small subset of affine transformations, limiting their ability to apply complex code transformations. They also only support simple programs that have a single loop nest and a rectangular iteration domain, limiting their applicability to many programs. These limitations significantly impact the generality of such compilers and autoschedulers and put into question the whole approach. In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep-learning based cost model and covers a large set of affine transformations and programs. It supports the exploration of a large set of affine transformations, allowing the application of complex sequences of polyhedral transformations. It also supports the optimization of programs with multiple loop nests and with rectangular and non-rectangular iteration domains, allowing the optimization of an extensive set of programs. We implement and evaluate LOOPer and show that it achieves speedups over the state-of-the-art. On the Polybench benchmark, LOOPer achieves a geometric mean speedup of 1.59x over Tiramisu. LOOPer also achieves competitive speedups with a geometric mean speedup of 1.34x over Pluto, a state-of-the-art polyhedral compiler that does not use a machine-learning based cost model.

From the huge craze about AI art to human robots, 2022 witnessed a paradigm shift in tech. Meta AI, a pioneer in the domain, made sure it wasn't left behind in transforming the industry. Here are the innovations by Meta AI that helped it remain at the white-hot epicentre of AI in 2022! A picture is worth a thousand words. With text-to-image generation, a few words may be enough to create a thousand pictures.

Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users and systems, e.g., compute infrastructure. For broader adoption, this practice must (i) accommodate software engineers without ML backgrounds, and (ii) provide mechanisms to optimize for product goals. In this work, we describe general principles and a specific end-to-end ML platform, Looper, which offers easy-to-use APIs for decision-making and feedback collection. Looper supports the full end-to-end ML lifecycle from online data collection to model training, deployment, inference, and extends support to evaluation and tuning against product goals. We outline the platform architecture and overall impact of production deployment. We also describe the learning curve and summarize experiences from platform adopters.

On a warm day in April 2013, I was sitting in a friend's kitchen in Paris, trying to engineer serendipity. I was trying to get my computer to write music on its own. I wanted to be able to turn it on and have it spit out not just any goofy little algorithmic tune but beautiful, compelling, mysterious music; something I'd be proud to have written myself. The kitchen window was open, and as I listened to the sounds of children playing in the courtyard below, I thought about how the melodies of their voices made serendipitous counterpoint with the songs of nearby birds and the intermittent drone of traffic on the rue d'Alésia. In response to these daydreams, I was making a few tweaks to my software--a chaotic, seat-of-the-pants affair that betrayed my intuitive, self-taught approach to programming--when I saw that Bill Seaman had just uploaded a new batch of audio files to our shared Dropbox folder. I had been collaborating with Bill, a media artist, on various aspects of computational creativity over the past few years. I loaded Bill's folder of sound files along with some of my own into the software and set it rolling. I was thrilled and astonished.

On a warm day in April 2013, I was sitting in a friend's kitchen in Paris, trying to engineer serendipity. I was trying to get my computer to write music on its own. I wanted to be able to turn it on and have it spit out not just any goofy little algorithmic tune but beautiful, compelling, mysterious music; something I'd be proud to have written myself. The kitchen window was open, and as I listened to the sounds of children playing in the courtyard below, I thought about how the melodies of their voices made serendipitous counterpoint with the songs of nearby birds and the intermittent drone of traffic on the rue d'Alésia. In response to these daydreams, I was making a few tweaks to my software--a chaotic, seat-of-the-pants affair that betrayed my intuitive, self-taught approach to programming--when I saw that Bill Seaman had just uploaded a new batch of audio files to our shared Dropbox folder. I had been collaborating with Bill, a media artist, on various aspects of computational creativity over the past few years. I loaded Bill's folder of sound files along with some of my own into the software and set it rolling. I was thrilled and astonished.

The chatbot is the new black: exponentially growing in popularity as the world becomes an increasingly connected place. And as we trundle along this path towards nirvana where everything and everyone is connected, all the time, we'll grow more and more comfortable with interacting with these machines online. Inevitably chatbots and artificial intelligence (AI) will dominate the online landscape and be involved with almost everything we do online. While most chatbot tasks are quite basic at the moment, continued innovation in AI technology means that they're getting smarter and smarter. Some research suggests that only about one-third of all customer service queries will require human attention.