Bitcoin (BTC) price is trading with a bullish bias following a successful breakout and retest of a symmetrical triangle pattern. Ripple (XRP) also follows in BTCs footsteps as it continues to hold on to its major support level. Ethereum (ETH) price, on the other hand, shows signs of an impending correction.

Bitcoin price broke out of the symmetrical triangle pattern on June 4, which was formed by connecting the three lower highs and three higher lows, formed since May 19 using trend lines. The breakout, however, was rejected by the weekly resistance around the $71,280 level.

Currently, BTC finds support around the upper boundary of the symmetrical triangle pattern around the $68,500 level.

If this support holds, then BTC could rally 7% to its all-time high of $73,777.

BTC/USDT 1-day chart

However, if BTC breaks below the lower boundary of the triangle and closes below $67,147, the bullish thesis could be invalidated, leading to an 8% crash to its daily support level of $61,293.

Ethereum price broke above a falling wedge pattern on the daily chart on May 20, leading to a 21% rally. However, it is encountering resistance from a bearish order block established on March 12, which ranges from $3,980 to $4,093, posing a challenge for ETH bulls.

Investors considering buying ETH should watch the following levels:

If the conditions mentioned above play out, then the Ethereum price could revisit its previous resistance level at $4,000.

ETH/USDT 1-day chart

Conversely, if Ethereum's daily candlestick closes below the $2,864 level, it would create a lower low and indicate a disruption in the market structure. This development would negate the previously discussed bullish outlook and could potentially lead to an additional 9% decline, reaching the prior support level of $2,600.

Ripple price bounces off from its daily support level of around $0.467 on Friday.

If the daily support at $0.467 support holds, XRP could move to the upside and retest its previous resistance level at $0.571.

XRP/USDT 1-day chart

However, if the Ripple daily candlestick closes below $0.467, then XRP could crash an additional 10% to its previous support at $0.419.

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Top 3 Price Prediction Bitcoin, Ethereum, Ripple: BTC likely to take markets on a rollercoaster - FXStreet

Spot Bitcoin exchange-traded funds (ETFs) in the United States acquired the equivalent of around two months worth of the cryptocurrencys mining supply in the first week of June.

With inflows of approximately $1.83 billion, the 11 ETFs bought 25,729 Bitcoin (BTC)in the trading week between June 3 and 7 around eight times more than the 3,150 new BTC mined over the same time, according to data from HODL15Capital.

The amount of Bitcoin acquired in the week alone was almost as much as the entire of May, 29,592 BTC, per HODL15Capitals count, and is the biggest week of buying since mid-March when Bitcoin hit its current all-time high of $73,679.

The 11 ETFs have seen $15.69 billion in net inflows since their January launch, including the $17.93 billion in net outflows from Grayscales fund, with total assets under management (AUM) of around $61 billion.

Bitcoin proponents have long touted cryptocurrency as digital gold due to its built-in scarcity mechanism, which sees only 21 million BTC ever beingissued.

Related: Bitcoin ETF flows will send BTC price into parabolic run, traders say

ETF Store president Nate Geraci noted in a June 9 X post that Bitcoin ETF AUM is around 60% that of the countrys gold ETFs, despite gold ETFs being around for 20 years and Bitcoin ETFs for only five months.

Bitcoin touched a highof $71,093on June 5amid the surge of inflows to the U.S. Bitcoin ETFs, the first time the asset has been above $71,000 since May 21, according to Cointelegraph Markets Pro.

The cryptocurrency has struggled to pass its current high, as its price is more heavily influenced by macroeconomic factors and geopolitical events, crypto exchange co-founder Radar Bear told Cointelegraph on June 7.

Magazine: Bitcoin ETFs make Coinbase a honeypot for hackers and governments Trezor CEO

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Bitcoin ETFs sucked up 2 months of BTC mining supply in first week of June - Cointelegraph

In recent times, the cryptocurrency market has been beset by volatility, providing a fascinating, yet unpredictable, landscape for those involved. One such crypto that has been making waves recently is the Binance Coin (BNB). Im going to delve into why BNB price is up today and what the future could potentially hold for this cryptocurrency heavyweight.

Binance Coins notable price surge can largely be attributed to a few factors. Firstly, Binance is one of the worlds leading cryptocurrency exchanges, and this naturally contributes to the popularity of its native token, BNB. Theres always an inherent interest in native tokens of top exchanges, and BNB is not an exception to this phenomenon.

Another major factor is the Binance Smart Chain. Binance Smart Chain is an independent blockchain that runs parallel to the Binance Chain and extends its functionality to enable the execution of smart contracts and the staking mechanism for BNB. This has contributed significantly to the BNBs value by increasing its functionality and use cases.

To make an objective prediction for BNBs future, one should examine market trends and speak to experts. That said, its important to remember that even the most careful analysis cant produce absolute certainty in such a volatile market. Based on available information, however, BNB has experienced consistent growth since its inception in 2017, which could suggest a positive future trajectory.

As we all know, the value of crypto does not rest solely on its market price. Its utility plays an equally crucial role. In terms of utility, BNB seems poised for continued success. The Binance Smart Chains increased functionality gives developers an attractive base for new decentralized applications (dApps), a factor that could further drive the popularity and therefore value of BNB.

In the end, the cryptocurrency market is an ever-evolving landscape, colored by sporadic ups and downs. A close eye should be kept on trends and developments, such as those related to Binance Coin. Remember that critical thinking and risk management should always be at the forefront of investment decisions. At the same time, never dismiss the value of information and market analysis. The future is unpredictable but understanding trends and factors can help us navigate through the intriguing world of cryptocurrencies.

Jake Morrison is an insightful cryptocurrency journalist and analyst, renowned for his deep understanding of the volatile and fascinating world of digital currencies. At 30 years old, Jake combines a background in Computer Science, with a degree from a reputable tech college, and a passion for decentralized finance, making him a prominent figure in the crypto journalism landscape.

Starting his career as a software developer with a focus on blockchain technologies, Jake quickly realized that his true calling lay in educating others about the potential and pitfalls of cryptocurrencies. Transitioning to journalism, he now serves as a leading voice for a major online financial news platform, specializing in the crypto category.

Jakes articles are a blend of technical analysis, market predictions, and feature stories on the latest in blockchain innovation. He has a talent for breaking down complex crypto concepts into understandable terms, making his writing accessible to both seasoned traders and crypto novices alike. His coverage spans a wide range, from Bitcoin and Ethereum to lesser-known altcoins, as well as the evolving regulatory landscape surrounding digital currencies.

What sets Jake apart is his critical approach to the hype that often surrounds the crypto space. He emphasizes the importance of due diligence and risk management, providing his readers with the tools they need to navigate the market intelligently. His investigative pieces on crypto scams and security breaches have been instrumental in raising awareness about the importance of security in digital asset investments.

Beyond his writing, Jake is an active participant in crypto conferences and online forums, where he shares his expertise and engages with the community. He also hosts a popular podcast that delves into the latest crypto trends, featuring interviews with leading figures in the blockchain space.

Jakes commitment to transparency and education in the cryptocurrency world has made him a trusted source of information and analysis. Through his work, he aims to foster a more informed and cautious approach to cryptocurrency investment, contributing to the maturity of the space.

Original post:

Exploring the rise of Binance Coin: factors behind its surging value and future prospects - The National - The National

[PRESS RELEASE Monaco City, Monaco, June 10th, 2024]

Memereum, a groundbreaking Binance Smart Chain token, is excited to highlight the success of its ongoing presale for its innovative services.

Memereum is designed to offer the first blockchain-based insurance, positioning itself as a potential next 100x crypto investment opportunity due to its robust technology backbone. With over 21 million tokens already sold, the Memereum team is optimistic about Memereums growth potential.

Key Features and Benefits

Memereums blockchain insurance solution and unique offerings distinguish it in the market. For detailed information on key features and benefits, users can visit Memereums website.

Presale Performance

The Memereum team is optimistic about the potential for Memereum to achieve a high return on investment, driven by strong fundamentals, innovative technology, and growing market demand for secure and scalable blockchain solutions. The team sees the sale of over 21 million tokens as a reflection of the strong interest and confidence from Memereums community.

Users can join Memereums presale here.

We are thrilled to highlight the ongoing success of the Memereum presale, which has attracted significant interest from investors worldwide. Our team has developed a product that not only enhances security but also offers extensive utility for various blockchain applications, said Oliver Sanchez, CEO of Memereum. With over 21 million tokens already sold, the presale presents a unique opportunity for early investors to potentially realize significant returns. We are confident in Memereums ability to drive innovation in the cryptocurrency space.

Memereum is a groundbreaking Binance Smart Chain token at the forefront of blockchain technology, dedicated to developing innovative solutions that harness the power of blockchain to solve real-world problems. With a team of experienced professionals and a commitment to excellence, Memereum aims to lead the way in the cryptocurrency and blockchain industry.

MemeSwap First Decentralized Exchange with Insurance Coverage

In addition to its groundbreaking blockchain insurance cryptocurrency, Memereum introduces MemeSwap, the first decentralized exchange with automatic insurance coverage. MemeSwap offers users added security and confidence in their transactions, further enhancing the Memereum ecosystem.

For more information, users can visit Memereums website.

LIMITED OFFER 2024 at BYDFi Exchange: Up to $2,888 welcome reward, use this link to register and open a 100 USDT-M position for free!

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Memereum Surpasses 21 Million Tokens Sold in Presale, Pioneers Blockchain-Based Insurance on Binance Smart ... - CryptoPotato

BNB price is on the cusp of breaking above $700, printing new all-time highs. Binance launchpools, analysts claim, are behind the rise of the Binance Smart Chain heres whats going on.

Bitcoin, Ethereum, and Solana have dominated headlines primarily because of their impressive performance.

The launch of spot Bitcoin ETFs drove most altcoins to all-time highs, and Binance Coin (BNB) benefited. However, after that, attention fizzled as analysts focused on ETH and SOL.

This is fast changing, and in the top 5, BNB has been overly resilient despite the drama surrounding Binance and its executives, mostly the resignation of Changpeng Zhao, the founder, back in November 2023.

There are many metrics to measure this impressive performance, but analysts often choose to look at price.

As Artemis data shared by one analyst reveals, BNB has undoubtedly been one of the top performers this year despite on-chain activity dropping.

(Artemis)

To put this into perspective, BNB is now the fourth most valuable coin after BTC, ETH, and USDT, commanding a market cap of over $93 billion.

Over the last year alone, analysts note that BNB has spiked by over 180%, rising from around $250 to over $625. If anything, the coin is at the cusp of breaking all-time highs at spot rates, highlighting just how resilient and rewarding for HODLers BNB has been.

In a post on X, one analyst said the spike in valuation is due to a surge in retail demand stemming from Binances popular offerings like Launchpools.

Launchpools are a kind of fundraising where Binance vets and allows Binance Smart Chain projects to raise capital from its user base. However, there is a catch: interested investors must hold BNB to have exclusive access to these token offerings.

Since BNB is at the backbone of the broader Binance ecosystem, including in the exchange and the BNB Chain, its utility further bolsters prices.

DISCOVER: What Are The Best Penny Crypto to Buy in June 2024

At current price levels, BNB is tantalizingly close to breaching 2021 highs of $700. The coin is on the cusp of breaking above the rising wedge, the bull flag. It is diverging from the middle BB, which points to high volatility.

(BNBUSDT)

Notably, BNB is pushing higher in early June after over five weeks of sideways movement, mirroring the general performance seen in Bitcoin and Ethereum.

That BNB is shaking off FUD, especially in H2 2023, following the arrest and subsequent imprisonment of Zhao, coupled with the success of Launchpool offerings, investors continue to bank heavily on even more gains in the days and weeks ahead.

Nonetheless, going forward, the upcoming Markets in Crypto Assets (MiCA) regulations in Europe pose a potential challenge, especially to Launchpool investors.

While Binance has assured users that they wont delist unauthorized stablecoins from spot trading, European clients will have restricted access to Binance products, mostly Launchpool and Earn.

Explore: Notcoin Price Explodes 227% Dominates the Market And This Learn-2-Earn Gem is Next To Skyrocket

Disclaimer: Crypto is a high-risk asset class. This article is provided for informational purposes and does not constitute investment advice. You could lose all of your capital.

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Is BNB Price Move To $1,000 Programmed? Bulls Pumping On Binance Launchpool Success - 99Bitcoins

The recent surge in Binance coin price has propelled it to its highest level in history. This price of the native asset of the Binance ecosystem has exceeded its 2021 peak and is now aiming for further highs. The bullish Bitcoin price action is favoring the investor sentiment around BNB and other altcoins.

On Thursday, BTC is changing hands above the $71,000 level but the traders remain cautious due to the likelihood of another rejection from the key level. As a result, most altcoins are trading sideways with a few coins like BRETT, STX, MNT and SUI outperforming the broader market in the last 24 hours.

Despite a strong surge in the BNB price, the total locked value (TVL) on Binance Smart Chain (BSC) remains far below its all-time high. According to the latest data from DeFi Llama, the network TVL currently stands at $5.84 billion. This is 6.5% down from the yearly peak and a 71.7% decline from its 2021 all-time high.

This suggests that the ongoing rally in Binance coin price is not supported by a similar growth in the network adoption. Another cause of concern is that the recent surge in the exchange coin is not backed by strong trading volume.

On April 30, Binance founder and ex-CEO, Chengpeng Zhao (CZ) was sentenced to four months in prison by a US federal judge. The billionaire founder of the biggest US exchange reported to a federal prison in California earlier this week to start this sentence.

For a better understanding of the BNB/USD price action, lets analyze its chart on the weekly timeframe. The following chart tells us that since its bottom in Jun 2022, the price has soared 283%. The chart also reveals a major supply zone above $670 which is acting as a resistance.

For Binance coin price prediction to flip bullish, this supply zone must be turned into a demand zone. Although the price is currently trading above this level, this weeks candle closure will be very critical. In case of a closure above $700, the immediate bullish target can be $737. However, if Bitcoin puts a new high, an extended rally toward $969 is also on the cards.

This bullish target comes from the 1.618 fib retracement level when connecting the November 2021 peak to the June 2022 low.

Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.

Link:

Binance Coin Price To Hit $1,000 Soon? Latest BNB Price Prediction - Analytics Insight

Binance Coin (BNB), the native currency of Binance Chain, experienced a significant surge in value, increasing over 6% within 24 hours to reach an all-time high of $712.

This remarkable growth aligns with the expanding activities on the BNB network, particularly through the Binance Launchpool and Launchpad platforms.

According to on-chain data from Lookonchain, several wallets have been actively purchasing the meme coin $WHY on the Binance Smart Chain (BSC).

In the past 24 hours, five wallets collectively withdrew 779 $BNB, equivalent to $534,000, from Binance to acquire 14.92 trillion $WHY, which is currently valued at $1.3 million.

Wallet 0x2eb6 spent 150 $BNB ($103K) to buy 3.75 trillion $WHY.

Wallet 0x886a spent 180 $BNB ($124K) to buy 3.25 trillion $WHY.

Wallet 0x3288 spent 153 $BNB ($105K) to buy 3.21 trillion $WHY.

Wallet 0x4bfd spent 150 $BNB ($103K) to buy 2.57 trillion $WHY.

Wallet 0x298F spent 145 $BNB ($99.5K) to buy 2.13 trillion $WHY.

This surge in BNBs value and the corresponding buying activity in $WHY highlight the growing interest and investment in the Binance ecosystem. The Binance Launchpool and Launchpad have been instrumental in fostering this growth, providing platforms for new projects and tokens to launch, thereby attracting more users and investments to the BNB network.

As BNB continues to reach new heights, the broader market is keeping a close watch on the activities within the Binance ecosystem, recognizing its potential for further growth and innovation in the cryptocurrency space. The active participation of large investors in tokens like $WHY underscores the dynamic nature of the market and the opportunities it presents for savvy traders.

Disclosure: This is not trading or investment advice. Always do your research before buying any cryptocurrency or investing in any services.

Follow us on Twitter@nulltxnewsto stay updated with the latest Crypto, NFT, AI, Cybersecurity, Distributed Computing, andMetaverse news!

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Binance Coin (BNB) Surges Over 6%, Hits All-Time High Amid Network Growth - NullTX

In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta and now available on Amazon SageMaker, this state-of-the-art LLM promises to revolutionize the way developers and data scientists approach coding tasks.

Code Llama 70B is a variant of the Code Llama foundation model (FM), a fine-tuned version of Metas renowned Llama 2 model. This massive language model is specifically designed for code generation and understanding, capable of generating code from natural language prompts or existing code snippets. With its 70 billion parameters, Code Llama 70B offers unparalleled performance and versatility, making it a game-changer in the world of AI-assisted coding.

Mixtral 8x7B is a state-of-the-art sparse mixture of experts (MoE) foundation model released by Mistral AI. It supports multiple use cases such as text summarization, classification, text generation, and code generation. It is an 8x model, which means it contains eight distinct groups of parameters. The model has about 45 billion total parameters and supports a context length of 32,000 tokens. MoE is a type of neural network architecture that consists of multiple experts where each expert is a neural network. In the context of transformer models, MoE replaces some feed-forward layers with sparse MoE layers. These layers have a certain number of experts, and a router network selects which experts process each token at each layer. MoE models enable more compute-efficient and faster inference compared to dense models.

Key features and capabilities of Code Llama 70B and Mixtral 8x7B include:

Amazon SageMaker, a fully managed machine learning service, provides a seamless integration with Code Llama 70B, enabling developers and data scientists to use its capabilities with just a few clicks. Heres how you can get started:

The following figure showcases how code generation can be done using the Llama and Mistral AI Models on SageMaker presented in this blog post.

You first deploy a SageMaker endpoint using an LLM from SageMaker JumpStart. For the examples presented in this article, you either deploy a Code Llama 70 B or a Mixtral 8x7B endpoint. After the endpoint has been deployed, you can use it to generate code with the prompts provided in this article and the associated notebook, or with your own prompts. After the code has been generated with the endpoint, you can use a notebook to test the code and its functionality.

In this section, you sign up for an AWS account and create an AWS Identity and Access Management (IAM) admin user.

If youre new to SageMaker, we recommend that you read What is Amazon SageMaker?.

Use the following hyperlinks to finish setting up the prerequisites for an AWS account and Sagemaker:

With the prerequisites complete, youre ready to continue.

The Mixtral 8x7B and Code Llama 70B models requires an ml.g5.48xlarge instance. SageMaker JumpStart provides a simplified way to access and deploy over 100 different open source and third-party foundation models. In order to deploy an endpoint using SageMaker JumpStart, you might need to request a service quota increase to access an ml.g5.48xlarge instance for endpoint use. You can request service quota increases through the AWS console, AWS Command Line Interface (AWS CLI), or API to allow access to those additional resources.

While Code Llama excels at generating simple functions and scripts, its capabilities extend far beyond that. The models can generate complex code for advanced applications, such as building neural networks for machine learning tasks. Lets explore an example of using Code Llama to create a neural network on SageMaker. Let us start with deploying the Code Llama Model through SageMaker JumpStart.

Additional details on deployment can be found in Code Llama 70B is now available in Amazon SageMaker JumpStart

Note: This blog post section contains code that was generated with the assistance of Code Llama70B powered by Amazon Sagemaker.

Let us walk through a code generation example with Code Llama 70B where you will generate a transformer model in python using Amazon SageMaker SDK.

Prompt:

Response:

Code Llama generates a Python script for training a Transformer model on the sample dataset using TensorFlow and Amazon SageMaker.

Code example: Create a new Python script (for example, code_llama_inference.py) and add the following code. Replace with the actual inference endpoint name provided by SageMaker JumpStart:

Save the script and run it:

python code_llama_inference.py

The script will send the provided prompt to the Code Llama 70B model deployed on SageMaker, and the models response will be printed to the output.

Example output:

Input

> Output

You can modify the prompt variable to request different code generation tasks or engage in natural language interactions with the model.

This example demonstrates how to deploy and interact with the Code Llama 70B model on SageMaker JumpStart using Python and the AWS SDK. Because the model might be prone to minor errors in generating the response output, make sure you run the code. Further, you can instruct the model to fact-check the output and refine the model response in order to fix any other unnecessary errors in the code. With this setup, you can leverage the powerful code generation capabilities of Code Llama 70B within your development workflows, streamlining the coding process and unlocking new levels of productivity. Lets take a look at some additional examples.

Lets walk through some other complex code generation scenarios. In the following sample, were running the script to generate a Deep Q reinforcement learning (RL) agent for playing the CartPole-v0 environment.

The following prompt was tested on Code Llama 70B to generate a Deep Q RL agent adept in playing CartPole-v0 environment.

Prompt:

Response: Code Llama generates a Python script for training a DQN agent on the CartPole-v1 environment using TensorFlow and Amazon SageMaker as showcased in our GitHub repository.

In this scenario, you will generate a sample python code for distributed machine learning training on Amazon SageMaker using Code Llama 70B.

Prompt:

Response: Code Llama generates a Python script for distributed training of a deep neural network on the ImageNet dataset using PyTorch and Amazon SageMaker. Additional details are available in our GitHub repository.

Compared to traditional LLMs, Mixtral 8x7B offers the advantage of faster decoding at the speed of a smaller, parameter-dense model despite containing more parameters. It also outperforms other open-access models on certain benchmarks and supports a longer context length.

Additional details on deployment can be found in Mixtral-8x7B is now available in Amazon SageMaker JumpStart.

Hyperparameters are external configuration variables that data scientists use to manage machine learning model training. Sometimes called model hyperparameters, the hyperparameters are manually set before training a model. Theyre different from parameters, which are internal parameters automatically derived during the learning process and not set by data scientists. Hyperparameters directly control model structure, function, and performance.

When you build complex machine learning systems like deep learning neural networks, exploring all the possible combinations is impractical. Hyperparameter tuning can accelerate your productivity by trying many variations of a model. It looks for the best model automatically by focusing on the most promising combinations of hyperparameter values within the ranges that you specify. To get good results, you must choose the right ranges to explore.

SageMaker automatic model tuning (AMT) finds the best version of a model by running many training jobs on your dataset. To do this, AMT uses the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that creates a model that performs the best, as measured by a metric that you choose.

Note: This blog post section contains code that was generated with the assistance of Mixtral 8X7B model, powered by Amazon Sagemaker.

Prompt:

Response:

There are instances where users need to convert code written in one programing language to another. This is known as a cross-language transformation task, and foundation models can help automate the process.

Prompt:

Response:

This Python code uses a built-in list data structure instead of the Java ArrayList class. The code above is more idiomatic and efficient in Python.

The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework for defining cloud infrastructure as code with modern programming languages and deploying it through AWS CloudFormation.

The three-tier architecture pattern provides a general framework to ensure decoupled and independently scalable application components can be separately developed, managed, and maintained (often by distinct teams). A three-tier architecture is the most popular implementation of a multi-tier architecture and consists of a single presentation tier, logic tier, and data tier:

Prompt:

Response:

The following are some additional considerations when implementing these models:

Delete the model endpoints deployed using Amazon SageMaker for Code Llama and Mistral to avoid incurring any additional costs in your account.

Shut down any SageMaker Notebook instances that were created for deploying or running the examples showcased in this blog post to avoid any notebook instance costs associated with the account.

The combination of exceptional capabilities from foundation models like Code Llama 70B and Mixtral 8x7B and the powerful machine learning platform of Sagemaker, presents a unique opportunity for developers and data scientists to revolutionize their coding workflows. The cutting-edge capabilities of FMs empower customers to generate high-quality code, infill missing sections, and engage in natural language interactions, all while using the scalability, security, and compliance of AWS.

The examples highlighted in this blog post demonstrate these models advanced capabilities in generating complex code for various machine learning tasks, such as natural language processing, reinforcement learning, distributed training, and hyperparameter tuning, all tailored for deployment on SageMaker. Developers and data scientists can now streamline their workflows, accelerate development cycles, and unlock new levels of productivity in the AWS Cloud.

Embrace the future of AI-assisted coding and unlock new levels of productivity with Code Llama 70B and Mixtral 8x7B on Amazon SageMaker. Start your journey today and experience the transformative power of this groundbreaking language model.

Shikhar Kwatrais an AI/ML Solutions Architect at Amazon Web Services based in California. He has earned the title of one of the Youngest Indian Master Inventors with over 500 patents in the AI/ML and IoT domains. Shikhar aids in architecting, building, and maintaining cost-efficient, scalable cloud environments for the organization, and supports the GSI partners in building strategic industry solutions on AWS. Shikhar enjoys playing guitar, composing music, and practicing mindfulness in his spare time.

Jose Navarro is an AI/ML Solutions Architect at AWS based in Spain. Jose helps AWS customersfrom small startups to large enterprisesarchitect and take their end-to-end machine learning use cases to production. In his spare time, he loves to exercise, spend quality time with friends and family, and catch up on AI news and papers.

Farooq Sabiris a Senior Artificial Intelligence and Machine Learning Specialist Solutions Architect at AWS. He holds PhD and MS degrees in Electrical Engineering from the University of Texas at Austin and an MS in Computer Science from Georgia Institute of Technology. He has over 15 years of work experience and also likes to teach and mentor college students. At AWS, he helps customers formulate and solve their business problems in data science, machine learning, computer vision, artificial intelligence, numerical optimization, and related domains. Based in Dallas, Texas, he and his family love to travel and go on long road trips.

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Code generation using Code Llama 70B and Mixtral 8x7B on Amazon SageMaker | Amazon Web Services - AWS Blog

Last month, Apple's tablets got a major revamp with the arrival of the M4 chip, two size options for the iPad Air, updates to the Magic Keyboard and a new iPad Pro packing a fancy Tandem OLED display. And now at WWDC 2024, Apple is looking to flesh out the iPad's software with the introduction of Apple Intelligence and a number of fresh features heading to iPadOS 18, which is due out sometime later this year.

To start, iPadOS is getting deeper customization options for your home screen including the ability to put app icons pretty much wherever you want. Apple's Control Center has also been expanded with support for creating multiple lists and views, resizing and rearranging icons and more. There's also a new floating tab bar that makes it easy to navigate between apps, which can be further tuned to remember your favorites. Next, SharePlay is getting the ability to draw diagrams on someone else's iPad or control someone else's device remotely (with permission) for times like when you need to help troubleshoot.

After years of requests, the iPad is also getting its own version of the Calculator app, which includes a new Math Notes feature that supports the Apple Pencil and the ability to input handwritten formulas. Math Notes will even update formulas in real time or you can save them in case you want to revisit things later. Alternatively, the Smart Script tool in the Notes app uses machine learning to make your notes less messy and easier to edit.

General privacy is also being upgraded with a new feature that lets you lock an app. This allows a friend or family member to borrow your device without giving them full access to everything on your tablet. Alternatively, theres also a new hidden apps folder so you can stash sensitive software in a more secretive way.

In Messages, Tapbacks are now compatible with all your emoji. Furthermore, you'll be able to schedule messages or send texts via satellite in case you aren't currently connected to Wi-Fi or a cellular network. Apple even says messages sent using satellite will feature end-to-end encryption.

The Mail and Photos apps are also getting similarly big revamps. Mail will feature new categorizations meant to make it easier to find specific types of offers or info (like plane flights). Meanwhile, the Photos app will sport an updated UI that will help you view specific types of images while hiding things like screenshots. And to better surface older photos and memories, there will be new categories like Recent Days and People and Pets to put similar types of pics all in a single collection.

Audio controls on iPads is also getting a boost with a new ability for Siri to understand gestures for Yes and No by either shaking or nodding your head while wearing AirPods. This should make it easier to provide Apple's digital assistant with simple responses in areas like a crowded bus or quiet waiting room where you might be uncomfortable talking aloud.

However, the biggest addition this year is that alongside all the iPad-specific features, Apples tablet OS is also getting Apple Intelligence. This covers many of the companys new AI-powered features like the ability to create summaries of websites, proofread or rewrite emails or even generate new art based on your prompts.

Apple says that to make its AI more useful, features will be more personalized and contextual. That said, to help protect your privacy and security, the company claims it wont build profiles or sell data to outside parties. Generally, Apple says it will use on-device processing for most of its tools, though some features require help from the cloud.

As its iconic digital assistant, Siri is getting a big refresh via Apple Intelligence too. This includes better natural language recognition and the ability to understand and remember context from one query to another. Siri will also be able to help you use your device, allowing you to ask your tablet how to perform certain tasks, search for files or control apps and features using your voice.

Some examples of what Apple Intelligence can do is highlight priority emails and put them at the top of your inbox so you don't miss important messages or events. Or if you're feeling more creative, you can use AI to create unique emoji (called Genmoji). And in photos, Apple Intelligence can help you edit images with things like the Clean Up tool. And for those who want the freedom to use other AI models, Apple is adding the option to integrate other services, the first of which will be Chat GPT.

Finally, other minor updates including a new Passwords app for stashing credentials across apps and websites, a new dedicated Game Mode with personalized spatial audio, expanded hiking results in Apple Maps and a new eye-tracking feature for improved accessibility.

Catch up here for all the news out of Apple's WWDC 2024.

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iPadOS 18's Smart Script uses machine learning to make your handwriting less horrible - Yahoo Movies Canada

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Researchers who had been using Fitbit data to help predict surgical outcomes have a new method to more accurately gauge how patients may recover from spine surgery.

Using machine-learning techniques, researchers worked to develop a way to more accurately predict recovery from lumbar spine surgery.

The results, published in the journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, show that their model outperforms previous models to predict spine surgery outcomes.

This is important because in lower back surgery and many other types of orthopedic operations, outcomes vary widely depending on the patients structural disease but also on varying physical and mental health characteristics across patients.

Surgical recovery is influenced by both physical and mental health before the operation. Some people may have excessive worry in the face of pain that can make pain and recovery worse. Others may suffer from physiological problems that worsen pain. If physicians can get a heads-up on the various pitfalls a patient faces, they can better tailor treatment plans.

By predicting the outcomes before the surgery, we can help establish some expectations and help with early interventions and identify high risk factors, says first author Ziqi Xu, a PhD student in the lab of Chenyang Lu, a professor in the McKelvey School of Engineering at Washington University in St. Louis.

Previous work in predicting surgery outcomes typically used patient questionnaires given once or twice in clinics, capturing a static slice of time.

It failed to capture the long-term dynamics of physical and psychological patterns of the patients, Xu says. Prior work training machine-learning algorithms focused on just one aspect of surgery outcome but ignored the inherent multidimensional nature of surgery recovery, she adds.

Researchers have used mobile health data from Fitbit devices to monitor and measure recovery and compare activity levels over time. But the new research has shown that activity data, plus longitudinal assessment data, is more accurate in predicting how the patient will do after surgery, says Jacob Greenberg, an assistant professor of neurosurgery at the School of Medicine.

The current work offers a proof of principle showing that, with multimodal machine learning, doctors can see a more accurate big picture of the interrelated factors that affect recovery. Before beginning this work, the team first laid out the statistical methods and protocol to ensure they were feeding the artificial intelligence system the right balanced diet of data.

Previously, the team had published work in the journal Neurosurgery showing for the first time that patient-reported and objective wearable measurements improve predictions of early recovery compared to traditional patient assessments.

In addition to Greenberg and Xu, Madelynn Frumkin, a PhD student studying psychological and brain sciences in Thomas Rodebaughs laboratory, was a co-first author on that work. Wilson Zack Ray, a professor of neurosurgery at the School of Medicine, was co-senior author, along with Rodebaugh and Lu. Rodebaugh is now at the University of North Carolina at Chapel Hill.

In that research, they show that Fitbit data can be correlated with multiple surveys that assess a persons social and emotional state. They collected that data via ecological momentary assessments (EMAs) that employ smartphones to give patients frequent prompts to assess mood, pain levels, and behavior multiple times throughout day.

We combine wearables, EMA, and clinical records to capture a broad range of information about the patients, from physical activities to subjective reports of pain and mental health, and to clinical characteristics, Lu says.

Greenberg adds that state-of-the-art statistical tools that Rodebaugh and Frumkin have helped advance, such as Dynamic Structural Equation Modeling, were key in analyzing the complex, longitudinal EMA data.

For the most recent study, they took all those factors and developed a new machine-learning technique of Multi-Modal Multi-Task Learning to effectively combine these different types of data to predict multiple recovery outcomes.

In this approach, the AI learns to weigh the relatedness among the outcomes while capturing their differences from the multimodal data, Lu adds.

This method takes shared information on interrelated tasks of predicting different outcomes and then leverages the shared information to help the model understand how to make an accurate prediction, according to Xu.

It all comes together in the final package, producing a predicted change for each patients post-operative pain interference and physical function score.

Greenberg says the study is ongoing as the researchers continue to fine-tune their models so they can take more detailed assessments, predict outcomes and, most notably, understand what types of factors can potentially be modified to improve longer-term outcomes.

Funding for the study came from AO Spine North America, the Cervical Spine Research Society, the Scoliosis Research Society, the Foundation for Barnes-Jewish Hospital, Washington University/BJC Healthcare Big Ideas Competition, the Fullgraf Foundation, and the National Institute of Mental Health.

Source: Washington University in St. Louis

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