Samsung is giving away a free 65-inch 4K TV right now when you pre-order a 2024 TV, and yes, you read that right. Pre-order a 2024 Samsung 4K, OLED or QLED display, and the retailer will throw in a 65-inch 4K TV for FREE, making this one of the best TV deals of 2024.
This is an incredible offer that only applies to Samsung’s newest flagship TVs, which include the Neo 4K and 8K models, the S95D OLED, and the 2024 The Frame TV. The free 65-inch TV that’s included in the pre-order deal is Samsung’s TU690T Crystal 4K smart TV, which currently retails for $449.99. While it’s an older model TV, you’re still getting Samsung’s ‘Crystal’ UHD 4K processor, an easy-to-use Tizen operating system, and full HDR support. That’s not all, though; Samsung is also offering a $100 discount on the 2024 TV, which will be applied to your cart.
This rare deal from Samsung is a limited-time offer that ends on April 11. It’s unprecedented for a retailer to give away a big-screen TV and discount a new 2024 display, which is why this offer could be the best TV deal of 2024.
The best TV deal of 2024: get a free 65-inch 4K TV
Amazon Web Services (AWS) has unveiled new plans to support AI startups, particularly those partnered with Y Combinator, by setting aside $500,000 in credits per startup for Amazon Bedrock.
Previously, AWS provided startups partnered with Activate Provider with $100,000 in free credits; however, the cloud giant’s latest initiative increases this fivefold for the more recent cohort of Y Combinator-funded startups.
Amazon isn’t the only tech giant to be offering startups an AI lifeline, but the latest credit announcement exceeds the $150,000 set aside by Microsoft Azure and $350,000 by Google Cloud.
AWS boosts its AI credit offering for eligible startups
Announcing the boost in credits, AWS says that experimenting is both vital and expensive for startups, hence its offer of free credits. Since launching AWS Activate, the company claims to have provided more than $6 billion in credits to help startups experiment on AWS cloud “with little-to-no upfront cost.”
With the announcement, AWS is allowing credit recipients to redeem their AWS currency against third-party models on Amazon Bedrock from companies including AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, and Stability AI.
The extension of free credits not only aims to help cash-strapped startups but also helps secure partners’ revenue streams. Notably, Anthropic recently received a significant $4 billion investment from AWS, the maker of the Claude LLM family.
Y Combinator Group Partner Michael Seibel commented: “With virtually every startup quickly becoming an AI startup, our partnership with AWS has never been more relevant to the companies getting into our program.”
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As well as spending credits on third-party FMs on Amazon Bedrock, the latest Y Combinator Cohort (January 2024) can use them for Amazon Trainium, AWS Inferentia, and a reserved capacity of up to 512 Nvidia H100 GPUs via Amazon EC2.
Giving birth shifts a person’s DNA markings back toward a more youthful state, but this trend is less noticeable in new birth parents with obesity.Credit: Chicago Tribune/Getty
Aches and pains aren’t all that pregnancy shares with ageing. Brewing a baby leads to changes in the distribution of certain chemical markers on a pregnant person’s DNA — changes similar to those that are a hallmark of getting older. But new research shows that, several months after a person gives birth, the chemical patterns revert to an earlier state1. The results strengthen previous work in mice and preliminary results in humans2.
It’s not surprising that pregnancy takes a toll, but the reversal was “somewhat unexpected”, says perinatal-health specialist Kieran O’Donnell at Yale University in New Haven, Connecticut, a co-author of the study. It was published 22 March in Cell Metabolism.
Aged DNA
The chemical tags analysed in the study are called methyl groups, and they are added to DNA in a process called methylation. They are one example of the ‘epigenome’, features of DNA that change gene activity without altering the genetic code.
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DNA-methylation patterns can be used to estimate a person’s ‘biological age’, which reflects the physiological stresses that a person’s body has accrued over time. Some research has found that biological age is a better predictor of health problems such as cardiovascular disease3 and dementia4 than a person’s chronological age.
But unlike chronological age, “biological age is quite flexible; it’s a fluid parameter. It can go up and down”, says biomedical scientist Vadim Gladyshev at Harvard Medical School in Boston, Massachusetts. Last year, his team published a study in Cell Metabolism2 that noted a decrease in biological age after pregnancy in mice and suggested that there could be a similar effect in humans. Cessation of several other stressful conditions also reversed biological age.
Obesity’s effect
The new study confirmed Gladyshev and colleagues’ results in humans and also showed that not everyone bounces back from pregnancy to the same degree. People who were at the cusp of obesity before pregnancy shed fewer years of biological age in the three months after birth than did people who had a body weight classified as “normal,” O’Donnell and his colleagues found. Meanwhile, people who breastfed exclusively experienced a greater reduction in biological age than did those who used formula or a mix of formula and breast milk.
Some participants’ biological ages were a few years younger postpartum than in early pregnancy. That’s “one thing that caught my eye”, says ageing-biologist Yousin Suh at Columbia University in New York City, who was not involved in the work.
The researchers didn’t measure the biological age of participants before pregnancy, so “we can’t claim that this is a rejuvenation effect”, O’Donnell says. But the data are suggestive, and he’d like to follow up with the participants in the future.
Not to worry
Interpreting Gladyshev and O’Donnell’s findings is tricky, some researchers say. Methylation clearly changes during pregnancy, but “we would be wrong to assume pregnancy is a state of accelerated ageing”, says Dena Dubal, a physician-scientist and specialist in ageing at the University of California, San Francisco. Dubal thinks that methylation might not be a hallmark of ageing but could instead underlie some of the sweeping changes that the body must undergo to support a growing fetus, such as altered gene expression.
Suh isn’t so sure. “Methylation is, thus far, one of the most robust markers of biological age,” she says.
‘Inflammation clock’ can reveal body’s biological age
Whether a reversible state can truly be called “age” is “a really important point”, O’Donnell says. “Perhaps as we begin to focus on pregnancy as a new area for ageing research, maybe there’s new terms and terminology that will need to be developed.”
In the end, people shouldn’t worry about any pregnancy-related increase in their biological age, scientists say. “We are talking about, you know, changes of about two, three years,” Gladyshev says.
And Dubal points out that pregnancy should not be conceptualized as a biological problem, even for people who don’t maximize recovery by breastfeeding. “While the benefits of breast feeding are many, its absence is not a dangerous predicament,” she says.
If you’ve ever marveled at the human brain’s remarkable ability to store and recall information, you’ll be pleased to know that researchers are hard at work trying to imbue artificial intelligence with similar capabilities. Enter Sparse Priming Representation (SPR), a cutting-edge technique designed to make AI’s memory storage and retrieval as efficient as ours. In this comprehensive guide, we’ll delve deep into the world of SPR and how it could be a game-changer for the future of AI.
What is Sparse Priming Representation (SPR)?
To put it simply, SPR is a memory organization method that seeks to emulate how human memory works. This technology distills complex thoughts, ideas, and knowledge into concise, context-driven lists of statements. By doing so, it allows machines, as well as human experts, to grasp and recall these complex ideas quickly and efficiently.
Here are a couple of main features
Minimalistic Representation: Stores complex ideas using minimal keywords or phrases.
Context Preservation: Maintains the surrounding context for accurate reconstruction.
Quick Retrieval: Facilitates rapid recall of stored information.
If you’re familiar with terms like “data overload” and “information glut,” you’ll understand the pressing need for efficient memory systems in AI. As machine learning models grow larger and more sophisticated, so does the volume of data they have to process and remember. This is where SPR comes in to save the day. Applications of SPR include:
Artificial Intelligence: Enhances memory organization in Large Language Models (LLMs).
Information Management: Simplifies the categorization and retrieval of data.
Education: Helps students and professionals understand and retain complex subjects.
What is Data Overload?
We live in a world where tons of data are created every day, from tweets to weather updates. For AI, data overload happens when there’s too much information coming in to handle properly. Think of it like trying to find a book in a messy library; the more books there are on the floor, the harder it is to find the one you need.
What is Information Glut?
This term is about having so much information that it becomes hard to know what really matters. It’s like getting a bunch of notifications on your phone, but only one or two are actually important, like a message from your boss. The rest are just distractions.
This is where Sparse Priming Representation (SPR) comes in. SPR helps AI sort through all that data and focus on what’s important. It’s like having a few key books in the messy library tagged, so you can find what you’re looking for easily. This doesn’t just make AI faster; it makes it better at the jobs it’s supposed to do.
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AI training
In case you’re curious how SPR fits into the bigger picture of AI training, let’s briefly discuss the existing methods:
Initial Bulk Training: Ludicrously expensive and often impractical.
Fine-tuning: Limited utility for knowledge retrieval.
Online Learning: Commercial viability is still in question.
In-context Learning: The most viable current solution.
SPR’s major contribution lies in its token-efficiency, which optimizes memory organization. This becomes invaluable, especially when we deal with constraints like the context window in Retrieval-Augmented Generation (RAG) systems. Simply put, SPR can be the ultimate way to teach LLMs how to better remember and apply information.
Most people underestimate the power of the latent space in AI models. SPR capitalizes on this underutilized feature, enabling what is known as associative learning. With just a few keywords or statements, SPR can “prime” an AI model to understand complex ideas—even those that were outside its original training data. So if you’re struggling to make your AI model understand concepts like “Heuristic Imperatives” or the “ACE Framework,” SPR could be the secret sauce you’ve been missing.
Sparse Priming Representation (SPR) benefits and features
SPR is a technique for organizing memory that mimics the structure and recall patterns observed in human memory.
Objective: To distill complex ideas, memories, or concepts into minimal sets of keywords, phrases, or statements for efficient storage and retrieval.
Applicability: Used by subject matter experts and large language models (LLMs) to reconstruct complex concepts quickly.
Human Memory Efficiency:
Stores information in compressed, contextually relevant forms.
Utilizes sparse, interconnected representations for quick recall and synthesis of new ideas.
SPR Methodology:
Focuses on reducing information to its most essential elements.
Retains the context necessary for accurate reconstruction using short, complete sentences.
Practical Applications:
Domains include artificial intelligence, information management, and education.
Can improve LLM performance, optimize memory organization, and facilitate effective learning and communication tools.
Limitations in Teaching LLMs:
Initial bulk training: Expensive.
Fine-tuning: May not be useful for knowledge retrieval.
Online Learning: Uncertain commercial viability.
In-context Learning: Currently the only viable method.
Current Trends:
Retrieval Augmented Generation (RAG) is popular, using vector databases and Knowledge Graphs (KGs).
Common question: “How to overcome context window limitations?” Short answer: you generally can’t.
Role of Latent Space:
LLMs possess a unique capability similar to human associative learning.
Can be “primed” to think in a certain way or to understand complex, novel ideas outside their training distribution.
Token-Efficiency with SPR:
SPRs are used to convey complex concepts efficiently for in-context learning.
Stored as metadata in Knowledge Graph nodes and fed to the LLM at inference, bypassing the need for raw, human-readable data.
As we continue to push the boundaries of what AI can achieve, it’s techniques like SPR that take us closer to creating machines that can think and learn more like humans. Whether you’re a researcher, a student, or simply an AI enthusiast, understanding the potential of SPR could significantly enhance your experience with this revolutionary technology.
In the rapidly evolving landscape of AI, the promise of SPR as a human-like approach to memory storage and retrieval is not just exciting—it truly is revolutionary. It stands as a bridge between the worlds of human cognition and machine intelligence, ensuring that as our computers grow smarter, they also grow more efficient and relatable. To learn more about SPR jump over to the official GitHub repository more details.
Filed Under: Technology News, Top News
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