Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches. While longer sequences inherently offer richer information for enhanced predictive precision, prevailing studies often respond by escalating model complexity. These intricate models can inflate into millions of parameters, resulting in prohibitive parameter scales. Our study demonstrates, through both theoretical and empirical evidence, that decomposition is key to containing excessive model inflation while achieving uniformly superior and robust results across various datasets. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks, using over 99\% fewer parameters than the majority of competing methods.

Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. However, MGMs still underperform in image synthesis compared to recent well-developed continuous diffusion models with similar size in terms of quality and diversity of generated samples. A key factor in the performance of continuous diffusion models stems from the guidance methods, which enhance the sample quality at the expense of diversity. In this paper, we extend these guidance methods to generalized guidance formulation for MGMs and propose a self-guidance sampling method, which leads to better generation quality. The proposed approach leverages an auxiliary task for semantic smoothing in vector-quantized token space, analogous to the Gaussian blur in continuous pixel space.

Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties. Assessing these two competencies independently is crucial for model refinement, despite the inherent difficulty due to the intertwined nature of seeing and reasoning in existing VLMs. To tackle this issue, we present Prism, an innovative framework designed to disentangle the perception and reasoning processes involved in visual question solving. Prism comprises two distinct stages: a perception stage that utilizes a VLM to extract and articulate visual information in textual form, and a reasoning stage that formulates responses based on the extracted visual information using a Large Language Model (LLM). This modular design enables the systematic comparison and assessment of both proprietary and open-source VLM for their perception and reasoning strengths.

DOD is working to develop AI capabilities--computer systems capable of tasks that normally require human intelligence. We found that DOD's efforts …

Fredrik Nilsson is Vice President of the Americas for Axis Communications, overseeing the company's operations in North and South America. What is the general public's impression of artificial intelligence (AI)? It isn't always easy to gauge. AI-powered voice assistants like Siri and Alexa are in our phones, our homes and even our cars, making them a part of everyday life. Yet most people don't expect them to be particularly accurate.

President TrumpDonald John TrumpGillibrand backs federal classification of third gender: report Former Carter pollster, Bannon ally Patrick Caddell dies at 68 Heather Nauert withdraws her name from consideration for UN Ambassador job MORE issued an executive order this week directing federal agencies to support the development of artificial intelligence. It couldn't have come at a better time. That's because the U.S. is in a race against China to develop cutting-edge artificial intelligence technology, and it's a race we can't afford to lose. "Artificial Intelligence" (AI) may bring to mind any number of futuristic pop culture references, from "Star Wars" to "Westworld", and it may seem like something that's decades or even centuries away. The reality is that AI is already here – it's in the apps we use to navigate through traffic, it protects us from spam emails and more nefarious online security threats, and it's what responds when we say "OK Google..." and "Alexa?"

Digital computers have transformed work in almost every sector of the economy over the past several decades (1). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a "general purpose technology," like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities (2), there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be "suitable for ML" (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some.

Perhaps no other technology was as disruptive as artificial intelligence in 2017. 'Machine learning,' 'neural network,' and'data bias' became commonplace terms in the headlines of mainstream media outlets, signifying the machines had arrived. And with them comes an uncertain future. We reached out to several experts to tell us what to expect from AI in 2018. These educational robots make a lot of sense from a business perspective-- there's a big market for STEM education toys and they're more affordable to produce as they don't require massive CPU, lots of sensors or advanced AI.

Perhaps no other technology was as disruptive as artificial intelligence in 2017. 'Machine learning,' 'neural network,' and'data bias' became commonplace terms in the headlines of mainstream media outlets, signifying the machines had arrived. And with them comes an uncertain future. We reached out to several experts to tell us what to expect from AI in 2018. These educational robots make a lot of sense from a business perspective-- there's a big market for STEM education toys and they're more affordable to produce as they don't require massive CPU, lots of sensors or advanced AI.

Microsoft is adding new artificial intelligence (AI) and machine learning capabilities to its Office 365 productivity suite aimed at simplifying complicated Excel spreadsheets, demystifying corporate Jargon and ensuring that its users never miss a meeting again. The company announced that it would be adding AI features to its productivity suite and to its search engine, Bing at an AI event in San Francisco. Microsoft has decided to infuse its core products with new intelligent technologies in an effort to make AI available to everyone to help users create their best work. A new service called Insights will leverage machine learning to automatically detect and highlight patterns in their data within Excel. This feature will first be made available in a preview for Office insiders that will roll out this month.