AI Research News Update: Issue 3 (Dec 1-5, 2021)
Asif Razzaq
AI Research Editor | CEO @ Marktechpost | 1 Million Monthly Readers and 52k+ ML Subreddit
Facebook AI Introduces ‘NeuralProphet’: A Hybrid Forecasting Framework Based on PyTorch And Trained With Standard Deep Learning Methods
Meta/Facebook AI introduces ‘Neural Prophet‘, a simple forecasting package that provides a solution to some of the most prevalent needs of customers, seeking to maximize the scalability and flexibility of time series forecasts based on Meta’s own internal data scientists and requests from external industry practitioners. Whether it’s estimating infection rates for disease management programs or projecting product demand to store inventory properly, the expanding data size necessitates new methodologies.
Machine learning methods that are nonparametric do not make any assumptions regarding the type of mapping function. They must be both accurate and simple to understand. By not forming hypotheses, they can choose any functional form from the training data. Thanks to this nature of deep learning models, they are scalable to match complex datasets. However, their black-box nature proves to be a disadvantage when projections are used to guide commercial or operational choices.
Purdue University Researchers Introduce A Compositional Reader Model That Composes Multiple Documents In One Shot To Form A Unified Political Entity Representation
Natural language processing (NLP) is an area of computer science—more specifically, a branch of artificial intelligence (AI)—that deals with computers’ capacity to understand the text and spoken words in the same way that humans can. But even humans sometimes find it difficult enough to decipher the deeper meaning and context of social media and news items. It’s nearly impossible to ask computers to accomplish it.?Even C-3PO,?who can communicate in over 6 million different ways, misses the subtext a lot of the time.
Natural language processing analyses language using statistical methods frequently without considering the real-world context required to comprehend human society’s shifts and currents. To accomplish the task mentioned above, it must convert online communication and the context in which it occurs into a format that computers can understand and reason about.
Researchers at?Purdue University?are working on new approaches to model human language so that computers can understand people better. Part of the problem, according to them, is that so much of online communication relies on readers already knowing the context—whether it’s Twitter shorthand or the foundation for comprehending a meme. The context is an essential aspect of the message when analyzing it.
Researchers Introduce ‘PERSIA’: A PyTorch-Based System for Training Large Scale Deep Learning Recommendation Models up to 100 Trillion Parameters
Deep learning-based models dominate the contemporary landscape of production recommender systems. Modern recommender systems offer a plethora of real-world applications. Thanks to deep neural network models of ever-increasing size, they have made incredible progress.
However, the training of such models is challenging even within industrial-scale data centers. This challenge stems from the training computation’s startling heterogeneity—the model’s embedding layer could account for more than 99.99 percent of the overall model size. The entire process is exceedingly memory-intensive, while the rest of the neural network (NN) becomes progressively computation-intensive.
PERSIA?(parallel recommendation training?system with hybrid?acceleration), an efficient distributed training system based on a revolutionary hybrid training algorithm, has been unveiled by a research team from Kwai Inc., Kuaishou Technology, and ETH Zürich. This approach provides training efficiency and accuracy for extensive deep learning recommender systems with up to 100 trillion parameters. The researchers have carefully co-designed the optimization method and the distributed system architecture.
Microsoft And The University Of Washington Researchers Introduce A Proof-Of-Concept Molecular Controller In The Form Of A Tiny DNA Storage Writing Mechanism On A Chip
According to current forecasts, data storage consumption is expected to expand by?20.4 percent year over year?to around nine zettabytes by 2024. To put that figure in context, Windows 11, which initially takes up roughly?64 gigabytes?of storage space, would need to be installed on almost 15 billion machines to consume just one zettabyte of space. In comparison, since 2011, it is projected that little over?3 billion?personal computers have been supplied worldwide.
In the long run, available storage solutions are having trouble keeping up with the rising demand. Synthetic DNA, which is essentially a microscopic data storage device, can drastically reduce the amount of space and material required for future archive storage demands. Using the above growth prediction, it would take millions of tape cartridges—the current densest commercial storage media—to store nine zettabytes of data, whereas DNA would only take up the space of a tiny refrigerator.
Google Research Release Reinforcement Learning Datasets For Sequential Decision Making
Most reinforcement learning (RL) and sequential decision-making agents generate training data through a high number of interactions with their environment. While this is done to achieve optimal performance, it is inefficient, especially when the interactions are difficult to generate, such as when gathering data with a real robot or communicating with a human expert.?
领英推荐
This problem can be solved by utilizing external knowledge sources. However, there are very few of these datasets and many different tasks and ways of generating data in sequential decision making, so it has become unrealistic to work on a small number of representative datasets. Furthermore, some of these datasets are released in a format that only works with specific methods, making it impossible for researchers to reuse them.
Google researchers have released?Reinforcement Learning Datasets (RLDS)?and a collection of tools for recording, replaying, modifying, annotating, and sharing data for sequential decision making, including offline reinforcement learning, learning from demonstrations, and imitation learning. RLDS makes it simple to share datasets without losing any information. It also allows users to test new algorithms on a broader range of jobs easily. RLDS also includes tools for collecting data and examining and altering that data.?
Researchers From Imperial College London have Developed ‘NeatNet’: A Machine Learning Tool For Robots To Tidy Up Home Environments Similar To An Individuals preference
As robots become more advanced and less expensive, more people may incorporate them into their homes. As a result, several roboticists have been working to create systems that may effectively assist humans with household activities such as cleaning, cooking, and tidying up. Researchers at Imperial College London’s Robotic Studying Lab have just created?NeatNet, a novel Variational Autoencoder architecture using Graph Neural Network layers, that will allow robots to clean up home surroundings in ways tailored to individual customers’ tastes. This model is based on a new variational autoencoder structure with graph neural community layers, as described in the pre-published work on arXiv.
“Everyone arranges their house in a unique and particular fashion, which is determined by whether someone is left or right-handed, their aesthetic style, their routines, and even their cultural background,” according to one of the researchers. “We devised a mechanism for learning people’s preferences for how they want their homes to be set up so that a robot might clean it in a customized manner.”
Dr. Johns and his research team created NeatNet, which allows robots to study a customer’s unique cleaning preferences by seeing how they arrange their furniture and belongings in their home. The robots can then utilize these choices as guidance to clean the customer’s houses in ways that reflect their tastes.
An MIT Research Develops A New Machine Learning Model That Understands The Underlying Relationships Between Objects In A Scene
Deep learning models do not see the world the same way we humans do. Humans have the ability to see objects and interpret their relationships. However, DL models don’t comprehend the complex interactions between individual objects.?
To address this issue, researchers from MIT have developed a?machine learning approach?that understands the underlying relationships between objects in a scene. Firstly, this model illustrates individual relationships one at a time. Then it combines them to describe the complete picture.?
The new framework creates an image of a scene based on a text description of objects and their connections, such as “A wood table to the left of a blue stool.”?
About:?
Marktechpost?is a California-based AI News Platform providing easy-to-consume, byte size updates in machine learning, deep learning, and data science research. Our vision is to showcase the hottest research trends in AI from around the world using our innovative method of search and discovery.
Asif Razzaq:?Asif Razzaq is an AI Journalist and Cofounder of Marktechpost, LLC. He is a visionary, entrepreneur, and engineer who aspires to use the power of Artificial Intelligence for good.
You can also share your AI-related research with us either via LinkedIn message or email at [email protected]
Feel free to join our?37k+ AI Group on Facebook
Student at Cotton University
2 年Thanks for sharing such wonderful information Asif
margaris ventures I #VentureCapitalist I #StrategicAdvisor I #BoardMember I Global No. 1 #Finance, #Fintech & top #AI Thought Leader
2 年Good share
AI Growth Marketing Consultant
2 年Thanks for sharing