Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products. Thermal analysis generally needs complex simulation, which leads to an unaffordable computational burden to layout optimization as it iteratively evaluates different schemes. Surrogate modeling is an effective way to alleviate computation complexity. However, temperature field prediction (TFP) with complex heat source layout (HSL) input is an ultra-high dimensional nonlinear regression problem, which brings great difficulty to traditional regression models. The Deep neural network (DNN) regression method is a feasible way for its good approximation performance. However, it faces great challenges in both data preparation for sample diversity and uniformity in the layout space with physical constraints, and proper DNN model selection and training for good generality, which necessitates efforts of both layout designer and DNN experts. To advance this cross-domain research, this paper proposes a DNN based HSL-TFP surrogate modeling task benchmark. With consideration for engineering applicability, sample generation, dataset evaluation, DNN model, and surrogate performance metrics, are thoroughly studied. Experiments are conducted with ten representative state-of-the-art DNN models. Detailed discussion on baseline results is provided and future prospects are analyzed for DNN based HSL-TFP tasks.

"I last sat down in 2005," Geoffrey Hinton often says, "and it was a mistake". Now 73, the British computer scientist has spent his entire career driving forward the field of artificial intelligence (AI), doing almost all of his work while standing due to back injuries beginning in his teens. By the time he reached his 50s, Mr Hinton's back problems were so severe that he decided to just stop sitting down. These days, when travelling by car he lies sprawled across the back seat - and he eats "like a monk at the altar" by kneeling on a foam cushion before the table. "If you let it completely control your life, it doesn't give you any problems," he told Cade Metz, a journalist for The New York Times, who has detailed his life in the book Genius Makers.

Google's Area 120 startup incubator has launched a couple of of video apps that borrow from existing platforms like Houseparty and TikTok to target influencers and vloggers. The video service, which launches today as a website and Chrome extension, lets you record clips or your screen to share with team mates. Though you can create longer videos that last up to 70 minutes, Threadit is designed for short-form content which Google says gets more "engagement and viewership." Taking a page from Google Workspace, Threadit lets you control whether recipients can respond to a clip by limiting them to viewing or allowing them to add their own video. There are also 17 video templates that essentially create longer threads based on the task.

Artificial intelligence (AI) is entering the artistic arena, and it is fundamentally changing creativity and culture as we know it, a UNSW researcher says. Oliver Bown is Associate Professor and Co-Director of the Interactive Media Lab at the School of Art & Design. He says that as algorithms get smarter, computers will play more of a role in creating music, art, and other cultural artifacts. "While much of the discussion about artificial intelligence centres around automation and labour from an economic perspective, this particular case is in some ways more salient to our lives," A/Prof. "We're cultural beings, and artificial intelligence has the potential to fundamentally impact the way we do culture and the way we might understand ourselves."

People have been trying to make machine generated music for a long time. Some of the earliest examples were musicians punching holes in piano roles to create complex melodies unplayable by humans (see Conlon Nancarrow, 1947). More recently, it's looked like electronic music in the form of MIDI files, where, by specifying various attributes --the instrument, pitch, duration, and timing--songs can be symbolically represented. But what does it look like for AI to run the whole generation process? This article explores generative audio techniques, training OpenAI's Jukebox on hours of house music.

This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. Hi everyone, This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. From this video, you will learn how you can apply Linear regression, Decision Tree classification, Gradient boost classification, and random forest classification model.

In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 168 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.

There have been various types of pretraining architectures including autoregressive models (e.g., GPT), autoencoding models (e.g., BERT), and encoder-decoder models (e.g., T5). On the other hand, NLP tasks are different in nature, with three main categories being classification, unconditional generation, and conditional generation. However, none of the pretraining frameworks performs the best for all tasks, which introduces inconvenience for model development and selection. We propose a novel pretraining framework GLM (General Language Model) to address this challenge. Compared to previous work, our architecture has three major benefits: (1) it performs well on classification, unconditional generation, and conditional generation tasks with one single pretrained model; (2) it outperforms BERT-like models on classification due to improved pretrain-finetune consistency; (3) it naturally handles variable-length blank filling which is crucial for many downstream tasks. Empirically, GLM substantially outperforms BERT on the SuperGLUE natural language understanding benchmark with the same amount of pre-training data. Moreover, GLM with 1.25x parameters of BERT-Large achieves the best performance in NLU, conditional and unconditional generation at the same time, which demonstrates its generalizability to different downstream tasks.

It's widely presumed that artificial intelligence (AI) will play a dominant role in future wars. However, the way the future unfolds might be nothing like that. AI developments increasingly led by machine learning-enabled technologies, seem to be going in another direction. AI is a fairly plastic term. Its meaning has shifted over time, reflecting changes in both our understanding of what intelligence is and in the technology available to mimic this.

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. You can now use Studio notebooks to securely connect to Amazon EMR clusters and prepare vast amounts of data for analysis and reporting, model training, or inference. You can apply this new capability in several ways. For example, data analysts may want to answer a business question by exploring and querying their data in Amazon EMR, viewing the results, and then either alter the initial query or drill deeper into the results.