Investing.com -- Apollo Global Management (NYSE:APO) Inc. is reportedly in discussions to spearhead a financing package of approximately $35 billion for Meta Platforms Inc (NASDAQ:META), according to a Thursday report by Bloomberg.

Apollo Global reportedly in talks to finance Meta’s US data centers By Investing.com

The event was hosted by Shi Yang, founder of Wuyang Technology and partner at BUDAO LABS, who welcomed all guests on behalf of the organizers and expressed great expectations for the innovative development of the Web3 ecosystem.

The Hong Kong Consensus Conference concluded successfully, discussing the future of Web 3.0 public chains at Meta Earth - ChainCatcher

About 30 researchers and engineers at Thinking Machines Lab come from rivals such as OpenAI, Meta, and Mistral, with ex-OpenAI staff making up two-thirds of the team.

Ex-OpenAI CTO Mira Murati launches AI startup, recruits top talent from rivals – Computerworld

Yahoo Finance host Seana Smith tracks several of the day's biggest stocks in this Market Minute: Nvidia (NVDA) leads gains among Magnificent Seven stocks, while Meta Platforms (META) shares could be on track to snap their 20-day winning streak. Baidu (BIDU) stock falls after reporting advertising ...

Nvidia gains, Baidu earnings, Walgreens: Market Minute

Santa Clara, Feb. 17, 2025 (GLOBE NEWSWIRE) -- Santa Clara, California - Interview Kickstart, a leader in professional upskilling, has garnered praise for its comprehensive Data Science course—designed to prepare professionals for high-stakes technical interviews.

Data Science Course 2025 Interview Kickstart - Roadmap to Top Data Scientist Jobs at Google Meta Amazon Apple Netflix

Published first on TheFly – the ultimate source for real-time, market-moving breaking financial news.

Meta plans ‘major investment’ into humanoid robots, Bloomberg says

Last year, Figure AI said it raised $675 million in a funding round, with investors such as Nvidia, Microsoft and Amazon.com founder Jeff Bezos, valuing the company at $2.6 billion. Major tech companies, including Nvidia, Meta Platforms and Tesla, alongside various startups, are diving into ...

Robotics startup Figure AI in talks for new funding at $39.5 billion valuation, Bloomberg News reports

Major tech companies, including Nvidia, Meta Platforms and Tesla, alongside various startups, are diving into the competitive field of humanoid robotics, driven by advanced AI models that are accelerating innovation in robotics and automation. Meta Platforms is establishing a new division within ...

Artificial intelligence (AI) investing is still the market's most prevalent theme. When it comes to the companies to buy, Nvidia (NASDAQ: NVDA), Meta Platforms (NASDAQ: META), and Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL) top the list.

3 Artificial Intelligence (AI) Stocks I'm Buying and 1 I'm Avoiding in February

Meta is expediting the hiring process for machine-learning engineers as it starts to cut thousands of jobs.

Meta speeds up its hiring process for machine-learning engineers as it cuts thousands of 'low performers'

LinkedIn Actually Cares About Spam and Scammers—Unlike Meta (Facebook), Who Couldn’t Care Less

LinkedIn gee eintlik om vir strooipos en swendelaars - anders as Meta (Facebook), wie kan nie minder omgee nie

LinkedIn Chiqpachans Spam ukat Estafadores ukanakat llakisi —Meta (Facebook) ukar uñtasita, khitinakatix jan juk’amp llakisipkaspati

LinkedIn kümmert sich tatsächlich um Spam und Betrüger – im Gegensatz zu Meta (Facebook), dem das völlig egal ist

A LinkedIn le importan el spam y los estafadores, a diferencia de Meta (Facebook), a quien no le importa en lo más mínimo

LinkedIn todella välittää roskapostista ja huijareista – toisin kuin Meta (Facebook), joka ei voisi vähempää välittää

LinkedIn Aktyèlman Care Sou Spam ak SCAMMERS-Kontrèman ak Meta (Facebook), Ki moun ki pa t 'kapab pran swen mwens

LinkedIn はスパムや詐欺師を本当に気にしている — Meta (Facebook) は気にしていない

LinkedIn ពិតជាយកចិត្តទុកដាក់ចំពោះ Spam និងអ្នកបោកប្រាស់ - មិនដូច Meta (Facebook) ដែលមិនអាចយកចិត្តទុកដាក់តិចជាង

LinkedIn ເປັນຫ່ວງເປັນໄຍກ່ຽວກັບ spam ແລະ scammers—ບໍ່ເຫມືອນກັບ Meta (Facebook), ຜູ້ທີ່ບໍ່ສາມາດສົນໃຈຫນ້ອຍ

O LinkedIn realmente se importa com spam e golpistas — ao contrário do Meta (Facebook), que não se importa nem um pouco

I-LinkedIn eneneni ikhathalele i-Spam kunye nabaqhatha-ngokungafaniyo ne-Meta (i-Facebook), ngubani ongakhathaleli okuncinci

I-LinkedIn Empeleni Iyabakhathalela Ugaxekile Nabakhwabanisi—Ngokungafani ne-Meta (Facebook), Ubani Obengakwazi Ukukhathalela Kancane

India is considered WhatsApp’s largest market, with many Indians using WhatsApp as their primary communication tool and hundreds of millions of monthly active users. Despite the app’s immense popularity in the market, it has faced significant regulatory challenges from the Indian government, with a Delhi High Court mandating traceability (resulting in the breaking of WhatsApp’s end-to-end encryption) in April 2024, followed by a five-year ban on sharing information collected from WhatsApp by India’s Competition Commission in November 2024. Despite these regulatory challenges, WhatsApp received approval from India’s National Payments Corporation of India (NPCI) in December 2024 to extend its UPI services through WhatsApp Pay to its entire Indian user base. The NPCI, which oversees the country's retail payments and settlement infrastructure, granted this permission, marking a significant expansion for WhatsApp Pay in its largest market. This ruling speaks to the importance of WhatsApp’s role in Indian digitalization but also highlights the increasing footprint, and therefore, a potentially Meta-branded super app, within India. The Indian digital payments landscape In 2016, the NPCI introduced the Unified Payments Interface (UPI), a real-time, instant payment system that facilitates both peer-to-peer and merchant transactions. UPI’s core strength in enabling intermediate funds transfers through a phone number or UPI ID, along with its ability to integrate with third-party applications, has made it the de-facto backbone of all digital payments in India. UPI is also playing a significant role in the implementation of India’s digital rupee central bank digital currency (CBDC), highlighting India’s appetite for digitalization. Popular e-payment applications in India today include Google Pay, Walmart-backed PhonePe, Paytm, CRED, and BharatPe, which all utilize UPI to facilitate transactions. Since their respective launches, all of these applications have integrated a variety of additional digital payment features, including offline transactions, utility bill payments, Before this decision, WhatsApp had been limited in onboarding UPI users for Whatsapp Pay, with an initial cap of 1 million users in 2020 (later raised to 100 million users in 2022), which required WhatsApp Pay to take a phased approach to expand its UPI user base. Therefore, despite currently having a relatively small share of the payments market, the removal of the user cap for WhatsApp will enable significant market share capture by India’s top communication application, with the merging of both communications and payments services potentially establishing WhatsApp’s, and a Meta-developed super app’s, dominance in the digital Indian market. Superapps in India Most popularized throughout the globe by WeChat and AliPay, superapps have played a big role in the digitization of Southeast Asian countries, with Singapore’s Grab, Indonesia’s GoJek, and South Korea’s KakaoTalk revolutionizing everything from ride-hailing to food delivery to digital payments, among many other services. Despite these positive trends elsewhere throughout the world, superapps in India have not received much success, with one prime case study being Tata Group’s Tata Neu, launched in 2022, which has experienced exponentially declining interest due to many different problems. This failure could be due to market saturation from entrenched players, lack of interest from local audiences, high market share capture costs, and/or a myriad of other factors. However, the removal of barriers against WhatsApp, combined with its existing popularity, especially when combined with capabilities offered by other Meta products, could help supercharge a Meta-built platform to superapp status in India. What’s next for WhatsApp (and Meta)? The Indian social media market is already dominated by Meta products such as Facebook and WhatsApp, with Indians being the primary driver behind Meta-owned platform downloads. As a result, Meta’s foothold in India remains strong, giving rise to the potential of a Meta-built application with WhatsApp integration being India’s first undisputed superapp. Meta is deeply entrenched in India in both private and public contexts. On the private front, Meta announced an expansion of its business subscription program to Indian users in July 2024, followed by an October 2024 announcement of a Meta partnership focusing on generative artificial intelligence (AI) with global conglomerate Infosys. One recent public sector partnership included the September 2024 announcement of Meta to work with the Telangana government to provide Indians with emerging technologies such as AI to enhance e-governance and citizen services. Another recent public partnership included the October 2024 announcement to partner with India’s Ministry of Skill Development and Entrepreneurship (MSDE) to integrate AI and VR into Skill India to increase and incubate talent Indian talent. Conclusion While an independently-created Meta-developed superapp, or even enhancements on WhatsApp, may seem far-fetched to some, the fact remains that Meta controls India’s most popular communication and social media platforms. The removal of restrictions on onboarding users to digital payments for one of those platforms, WhatsApp, highlights an immense opportunity for Meta to develop India’s first widely-adopted super-app. However, regardless of whether a Meta-built superapp is in development or not, it is clear that WhatsApp has and will continue to play an important role in Indian digitalization, whether through its status as India’s #1 communication platform or as a growing digital payments application. Image source: PYMNTS.com India is considered WhatsApp ’s largest market , with many Indians using WhatsApp as their primary communication tool and hundreds of millions of monthly active users. Despite the app’s immense popularity in the market, it has faced significant regulatory challenges from the Indian government, with a Delhi High Court mandating traceability (resulting in the breaking of WhatsApp’s end-to-end encryption) in April 2024 , followed by a five-year ban on sharing information collected from WhatsApp by India’s Competition Commission in November 2024 . WhatsApp largest market largest market primary communication tool primary communication tool April 2024 April 2024 November 2024 November 2024 Despite these regulatory challenges, WhatsApp received approval from India’s National Payments Corporation of India (NPCI) in December 2024 to extend its UPI services through WhatsApp Pay to its entire Indian user base. The NPCI, which oversees the country's retail payments and settlement infrastructure, granted this permission, marking a significant expansion for WhatsApp Pay in its largest market. received approval This ruling speaks to the importance of WhatsApp’s role in Indian digitalization but also highlights the increasing footprint, and therefore, a potentially Meta-branded super app, within India. The Indian digital payments landscape In 2016 , the NPCI introduced the Unified Payments Interface (UPI), a real-time, instant payment system that facilitates both peer-to-peer and merchant transactions. UPI’s core strength in enabling intermediate funds transfers through a phone number or UPI ID, along with its ability to integrate with third-party applications, has made it the de-facto backbone of all digital payments in India. UPI is also playing a significant role in the implementation of India’s digital rupee central bank digital currency (CBDC), highlighting India’s appetite for digitalization. 2016 2016 significant role Popular e-payment applications in India today include Google Pay, Walmart-backed PhonePe, Paytm, CRED, and BharatPe, which all utilize UPI to facilitate transactions. Since their respective launches, all of these applications have integrated a variety of additional digital payment features, including offline transactions, utility bill payments, include include Before this decision, WhatsApp had been limited in onboarding UPI users for Whatsapp Pay, with an initial cap of 1 million users in 2020 (later raised to 100 million users in 2022 ), which required WhatsApp Pay to take a phased approach to expand its UPI user base. 2020 2020 2022 2022 Therefore, despite currently having a relatively small share of the payments market, the removal of the user cap for WhatsApp will enable significant market share capture by India’s top communication application, with the merging of both communications and payments services potentially establishing WhatsApp’s, and a Meta-developed super app’s, dominance in the digital Indian market. Superapps in India Most popularized throughout the globe by WeChat and AliPay, superapps have played a big role in the digitization of Southeast Asian countries, with Singapore’s Grab, Indonesia’s GoJek, and South Korea’s KakaoTalk revolutionizing everything from ride-hailing to food delivery to digital payments, among many other services. Most popularized Most popularized superapps superapps Despite these positive trends elsewhere throughout the world, superapps in India have not received much success, with one prime case study being Tata Group’s Tata Neu, launched in 2022 , which has experienced exponentially declining interest due to many different problems . This failure could be due to market saturation from entrenched players, lack of interest from local audiences, high market share capture costs, and/or a myriad of other factors. 2022 2022 different problems different problems However, the removal of barriers against WhatsApp, combined with its existing popularity, especially when combined with capabilities offered by other Meta products, could help supercharge a Meta-built platform to superapp status in India. What’s next for WhatsApp (and Meta)? The Indian social media market is already dominated by Meta products such as Facebook and WhatsApp, with Indians being the primary driver behind Meta-owned platform downloads. As a result, Meta’s foothold in India remains strong, giving rise to the potential of a Meta-built application with WhatsApp integration being India’s first undisputed superapp. dominated dominated Meta primary driver primary driver Meta is deeply entrenched in India in both private and public contexts. On the private front, Meta announced an expansion of its business subscription program to Indian users in July 2024 , followed by an October 2024 announcement of a Meta partnership focusing on generative artificial intelligence (AI) with global conglomerate Infosys. One recent public sector partnership included the September 2024 announcement of Meta to work with the Telangana government to provide Indians with emerging technologies such as AI to enhance e-governance and citizen services. Another recent public partnership included the October 2024 announcement to partner with India’s Ministry of Skill Development and Entrepreneurship (MSDE) to integrate AI and VR into Skill India to increase and incubate talent Indian talent. July 2024 July 2024 October 2024 October 2024 September 2024 September 2024 October 2024 October 2024 Conclusion While an independently-created Meta-developed superapp, or even enhancements on WhatsApp, may seem far-fetched to some, the fact remains that Meta controls India’s most popular communication and social media platforms. The removal of restrictions on onboarding users to digital payments for one of those platforms, WhatsApp, highlights an immense opportunity for Meta to develop India’s first widely-adopted super-app. However, regardless of whether a Meta-built superapp is in development or not, it is clear that WhatsApp has and will continue to play an important role in Indian digitalization, whether through its status as India’s #1 communication platform or as a growing digital payments application. Image source: PYMNTS.com Image source: PYMNTS.com PYMNTS.com

Meta's Superapp Ambitions Take Shape as WhatsApp Expands UPI Access in India

Forget Bad Business—Meta’s Forgotten What Business They’re In

Забравете лошия бизнес – Meta са забравили в какъв бизнес се занимават

Zaboravite loš posao—Meta je zaboravila kojim poslom se bave

Ξεχάστε την κακή επιχείρηση—Η Meta έχει ξεχάσει σε ποια επιχείρηση ασχολείται

Olvídese de los malos negocios: Meta ha olvidado en qué negocio está

Ahaztu negozio txarra—Meta-k ahaztu egin du zer negoziotan ari diren

Bliye move biznis—Meta a bliye ki biznis yo ye

悪いビジネスは忘れろ。Meta は自分たちがどんなビジネスをしているのかを忘れている

បំភ្លេចអាជីវកម្មមិនល្អ - Meta's ភ្លេចអ្វីដែលពួកគេកំពុងធ្វើអាជីវកម្ម

Жаман бизнести унутуңуз - Мета алар кандай бизнесте экенин унутуп калды

Adinoy ny raharaham-barotra ratsy—Hadinon'i Meta hoe inona no orinasa iasany

Заборавете на лошиот бизнис - Мета заборави во кој бизнис се занимаваат

खराब व्यवसाय बिर्सनुहोस् - मेटाले बिर्सिएको छ कि तिनीहरू कुन व्यवसायमा छन्

What Is The Deal With Meta's Threads

I Led Dozens of Meta System Design Interviews, Here's The Right Way to Prep

I'm excited to tell you about Meta's Llama 3.1, a powerful AI language model you can use for free. In this article, I'll show you how to install and run Llama 3.1 on your computer. This means you can use it without the internet and save money on AI costs. https://youtu.be/UdcmnhmruIs?si=9okWfpSuMgd1AYOZ&embedable=true What is Llama 3.1? Llama 3.1 is Meta’s newest AI model that’s open for anyone to use. It comes in three sizes: A huge 405-billion parameter version
A medium 70 billion parameter version
A smaller 8 billion parameter version Llama 3.1 is special because it works really well. It can even do better than some private AI models. You can read more about it here:https://llama.meta.com/ Why Llama 3.1 is Great It’s free.
Anyone can change and improve it.
It works as well as expensive private models.
It understands many languages.
It can handle long texts. It can work with up to 128,000 words at once, which is great for long writing tasks. Challenges with Llama 3.1 It needs a lot of computer power: The biggest version needs very powerful computers to run.
Might have biases: Like all AI, it might repeat unfair ideas from its training data.
Possible safety issues: Because anyone can see how it works, there might be some safety risks if not used carefully. Where to Try Llama 3.1 Here are some ways you can try out Llama 3.1 for free: Meta.ai Chatbot: Meta’s own platform offers access to Llama 3.1, including the 405B model, along with image generation capabilities. However, this is currently limited to U.S. users with a Facebook or Instagram account.
Perplexity: This AI-powered search tool offers access to Llama 3.1 405B, but it requires a paid subscription.
Cloud Platforms: Various cloud providers offer access to Llama 3.1, including: Amazon Bedrock
Microsoft Azure AI
Cloudflare Workers AI
Snowflake
DataBricks
Nvidia AI Foundry
IBM Cloud In this tutorial, I’ll show you how to use Llama 3.1 for free on your own machine. How to Run Llama 3.1 on Your Computer The big 405 billion version needs a very powerful computer. But I’ll show you how to run the 8-billion version. Here’s what you need: 16 GB of RAM
8-core CPU
20 GB of free space Step 1. Download and Install the Ollama Tool Go to https://ollama.com/ and download the tool for your operating system (OS). For Linux, I’ll use this command:curl -fsSL https://ollama.com/install.sh | sh The command will install Ollama, which is a tool for running and managing large language models (LLMs) locally on your machine. Step 2. Download the Llama 3.1 Model On the same website, click on the link “Models” in the top right corner. Then, find Llama 3.1 in the list. Click on the link and then select the desired model from the drop-down menu. Copy the command and paste it into your terminal. Terminal command: ollama run llama3.1 Wait for it to download (it might take 30 minutes or more). If everything is okay, you can use LLM inside your terminal. Enjoy! :) Using Llama 3.1 Without Internet One cool thing about having Llama 3.1 on your computer is you can use it without the internet. This is great if you need to keep your data private. Things to Remember There are a few downsides to know about: The 8 billion model only knows things up to 2021
It can’t look at files on your computer or websites
It might be slower than online AI services How to Give Llama 3.1 Information The model can’t read your files or websites, but you can: Copy and paste text into it
Use things like Dropbox to share files
Summarize what you want it to know in your question Wrapping Up Running Llama 3.1 on your own computer is a great way to use powerful AI without the internet or ongoing costs. It has some limits, but it’s still amazing to have such a smart AI tool on your own machine. If you like this tutorial, please click like and share. You can follow me on YouTube, join my Telegram, or support me on Patreon. Cheers! :) I'm excited to tell you about Meta's Llama 3.1, a powerful AI language model you can use for free. In this article, I'll show you how to install and run Llama 3.1 on your computer. This means you can use it without the internet and save money on AI costs. https://youtu.be/UdcmnhmruIs?si=9okWfpSuMgd1AYOZ&embedable=true https://youtu.be/UdcmnhmruIs?si=9okWfpSuMgd1AYOZ&embedable=true What is Llama 3.1? Llama 3.1 is Meta’s newest AI model that’s open for anyone to use. It comes in three sizes: A huge 405-billion parameter version A medium 70 billion parameter version A smaller 8 billion parameter version A huge 405-billion parameter version A medium 70 billion parameter version A smaller 8 billion parameter version Llama 3.1 is special because it works really well. It can even do better than some private AI models. You can read more about it here: https://llama.meta.com/ https://llama.meta.com/ https://llama.meta.com/ Why Llama 3.1 is Great It’s free. Anyone can change and improve it. It works as well as expensive private models. It understands many languages. It can handle long texts. It can work with up to 128,000 words at once, which is great for long writing tasks. It’s free. Anyone can change and improve it. It works as well as expensive private models. It understands many languages. It can handle long texts. It can work with up to 128,000 words at once, which is great for long writing tasks. Challenges with Llama 3.1 It needs a lot of computer power: The biggest version needs very powerful computers to run. Might have biases: Like all AI, it might repeat unfair ideas from its training data. Possible safety issues: Because anyone can see how it works, there might be some safety risks if not used carefully. It needs a lot of computer power: The biggest version needs very powerful computers to run. Might have biases: Like all AI, it might repeat unfair ideas from its training data. Possible safety issues: Because anyone can see how it works, there might be some safety risks if not used carefully. Where to Try Llama 3.1 Here are some ways you can try out Llama 3.1 for free: Meta.ai Chatbot: Meta’s own platform offers access to Llama 3.1, including the 405B model, along with image generation capabilities. However, this is currently limited to U.S. users with a Facebook or Instagram account. Perplexity: This AI-powered search tool offers access to Llama 3.1 405B, but it requires a paid subscription. Cloud Platforms: Various cloud providers offer access to Llama 3.1, including: Meta.ai Chatbot: Meta’s own platform offers access to Llama 3.1, including the 405B model, along with image generation capabilities. However, this is currently limited to U.S. users with a Facebook or Instagram account. Perplexity: This AI-powered search tool offers access to Llama 3.1 405B, but it requires a paid subscription. Cloud Platforms: Various cloud providers offer access to Llama 3.1, including: Amazon Bedrock Microsoft Azure AI Cloudflare Workers AI Snowflake DataBricks Nvidia AI Foundry IBM Cloud Amazon Bedrock Microsoft Azure AI Cloudflare Workers AI Snowflake DataBricks Nvidia AI Foundry IBM Cloud In this tutorial, I’ll show you how to use Llama 3.1 for free on your own machine. How to Run Llama 3.1 on Your Computer The big 405 billion version needs a very powerful computer. But I’ll show you how to run the 8-billion version. Here’s what you need: 16 GB of RAM 8-core CPU 20 GB of free space 16 GB of RAM 8-core CPU 20 GB of free space Step 1. Download and Install the Ollama Tool Go to https://ollama.com/ and download the tool for your operating system (OS). https://ollama.com/ https://ollama.com/ For Linux, I’ll use this command: curl -fsSL https://ollama.com/install.sh | sh curl -fsSL https://ollama.com/install.sh | sh The command will install Ollama, which is a tool for running and managing large language models (LLMs) locally on your machine. Step 2. Download the Llama 3.1 Model On the same website, click on the link “Models” in the top right corner. “Models” Then, find Llama 3.1 in the list. Click on the link and then select the desired model from the drop-down menu. Copy the command and paste it into your terminal. Terminal command: ollama run llama3.1 ollama run llama3.1 Wait for it to download (it might take 30 minutes or more). If everything is okay, you can use LLM inside your terminal. Enjoy! :) Using Llama 3.1 Without Internet One cool thing about having Llama 3.1 on your computer is you can use it without the internet. This is great if you need to keep your data private. Things to Remember There are a few downsides to know about: The 8 billion model only knows things up to 2021 It can’t look at files on your computer or websites It might be slower than online AI services The 8 billion model only knows things up to 2021 It can’t look at files on your computer or websites It might be slower than online AI services How to Give Llama 3.1 Information The model can’t read your files or websites, but you can: Copy and paste text into it Use things like Dropbox to share files Summarize what you want it to know in your question Copy and paste text into it Use things like Dropbox to share files Summarize what you want it to know in your question Wrapping Up Running Llama 3.1 on your own computer is a great way to use powerful AI without the internet or ongoing costs. It has some limits, but it’s still amazing to have such a smart AI tool on your own machine. If you like this tutorial, please click like and share. You can follow me on YouTube , join my Telegram , or support me on Patreon . YouTube YouTube Telegram Telegram Patreon Patreon Cheers! :)

No More AI Costs: How to Run Meta Llama 3.1 Locally

The fall and rise of have been nothing short of astonishing in recent years. After shedding more than 70% of its value amid 2022’s tech stock sell-offs, the stock surged 180% in 2023–and the tech giant is far from finished. Meta It’s difficult to find the right superlatives for Meta [NASDAQ: META]. The stock became one of the best performers on the global stage last year, and the company’s measured approach to embracing artificial intelligence formed a key component of its impressive growth. Today, Meta has almost recovered all of its losses amid severe economic headwinds in late 2021 and 2022 and has recaptured a market capitalization of almost $1 trillion. The stock’s impressive movements throughout the past 12 months have seen it become one of the stars of the ‘Magnificent Seven,’ a collective of leading tech stocks including Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, and Tesla. But as the generative AI boom rumbles on and ideation shifts towards implementation for the emerging technology, can Meta continue to facilitate its growth throughout 2024? Let’s take a deeper look at why Meta has become the stock that investors are coveting most this year: Finding Strength in Fundamentals It’s impossible to ignore Meta’s underlying financial strength as a key attribute to 2023’s impressive Wall Street growth. “The company has demonstrated strong financial performance, with a 23% growth in revenue to $34.1 billion in the third quarter of 2023, surpassing expectations,” highlights Maxim Manturov, head of investment research at . Freedom Finance Europe “Meta's vision for the effectiveness of the company has translated into reality, and its diverse user base supports profitable, targeted advertising, a significant revenue driver.” In addition to its impressive revenue growth, Meta is an attractive proposition for advertisers, thanks to its extensive data reservoirs that pave the way for more targeted campaigns. As a result, the firm’s advertising revenue has increased 23% in Q3 to $33.6 billion. However, it appears to be Meta’s inroads in the field of artificial intelligence that will help to leverage growth in 2024. Meta’s AI offerings currently include Quest 3, AI-powered Ray-Ban Stories, and the AI Studio platform. With a significant free cash flow (FCF) balance of $13.6 billion by the end of Q3 2023, analysts are expecting AI investments to improve stock performance in the new year, anticipating revenue rising 13% year-over-year to $151 billion, while earnings are expected to rise 21% over the same time frame. According to a team of , there could be three catalysts to push Meta’s stock higher in 2024. The first comes in the form of a conservative consensus for sales growth, which may prove to be stronger thanks to better monetization of Reels short videos and growing interest from Chinese advertisers. Mizuho Securities analysts led by James Lee Secondly, the analysts believe that guidance on operating expenditures could fall over the financial year, which is a trend with some precedence. Finally, WhatsApp messaging performance may contribute to the revenue base by as much as a third due to the implementation of AI for automated customer service. Finding Strength in AI So far, much of the positive sentiment surrounding Meta in 2024 stems from optimism surrounding generative AI. There’s a good reason for this. While Meta has long utilized AI within its businesses in the form of identifying faces posted on Facebook and delivering curated content and ads for its users, the future of the company lies in its utility of generative AI services. In early 2023, Meta launched a for firms to bolster their social media advertising. Through an Ads Manager, images and text can be adapted from user to user to target their products more effectively. suite of free AI-powered marketing tools Crucially, this launch of AI tools has helped to make Facebook an even more attractive prospect for companies seeking to improve their marketing ROI. For Meta CTO Andrew Bosworth, the arrival of generative AI was an opportunity for Meta to ramp up its operations. “AI has always been a major component of our thinking,” . “But like all technologies, you’re in these S curves. We’ve known for three or four years that these generative AI, large language models were a big deal, but they still felt like infrastructure. That’s why I think the OpenAI introduction of ChatGPT is so interesting.” Bosworth explained “I’m not trying to say we were prescient. We had been investing, and we believed in this technology. For all of that belief, it still snuck up on us. It was like, ‘Oh my god, it’s here. We’ve got to do things differently. We’ve got to change it up.’” Will Meta Lead the ‘Magnificent Seven’ in 2024? To better understand Meta’s position among the Magnificent Seven, it’s worth exploring its PEG ratio, which refers to the stock’s price/earnings in comparison to the company’s growth. With a , Meta features the second-best ratio of the seven Wall Street leaders, falling behind Nvidia with an impressive 0.95 PEG. PEG ratio of 1.2 As another stock that’s heavily exposed to generative AI, Nvidia’s growth in 2024 will hinge on the development of the emerging technology and the success of the implementation process as more enterprises seek to embrace the GenAI boom. Meta, on the other hand, will rely on its rich history of innovation to harness the power of generative AI based on its products and services. With Meta’s history of resilience and strategic acumen in often unpredictable tech markets, we could see the stock outpacing its Magnificent Seven rivals in 2024, and with solid fundamentals continuing to drive growth, there’s no reason why Meta shouldn’t be at the top of investor priority lists for the year ahead.

Meta’s Meteoric Rise in 2023 Shows No Sign of Slowing This Year

2023 সালে মেটার উল্কা উত্থান এই বছর ধীর হওয়ার কোন লক্ষণ দেখায় না

Metas kometenhafter Aufstieg im Jahr 2023 zeigt in diesem Jahr keine Anzeichen einer Verlangsamung

El meteórico ascenso de Meta en 2023 no muestra signos de desaceleración este año

L’ascension fulgurante de Meta en 2023 ne montre aucun signe de ralentissement cette année

2023 में मेटा की उल्कापिंडीय वृद्धि इस वर्ष धीमी होने का कोई संकेत नहीं दिखाती है

2023 年のメタの流星的上昇は今年も衰える兆しなし

2023년 메타의 급격한 상승세는 올해도 둔화될 조짐을 보이지 않습니다

A ascensão meteórica do Meta em 2023 não mostra sinais de desaceleração este ano

Метеоритный взлет Меты в 2023 году не покажет никаких признаков замедления в этом году

Meta'nın 2023'teki Meteorik Yükselişi Bu Yıl Yavaşlama İşareti Göstermiyor

Sự gia tăng thiên thạch của Meta vào năm 2023 không có dấu hiệu chậm lại trong năm nay

Meta 在 2023 年的迅速崛起今年没有放缓的迹象

Quality is better than quantity when it comes to fine-tuning LLMs — Emu. “ ” aka. is a released by Meta to generate highly aesthetic images. It achieves this by introducing a fine-tuning technique called which goes on to show that as little as two thousand high-quality images are all that is needed to achieve the objective, hence the paper title, “ ” Enhancing Image Generation Models Using Photogenic Needles in a Haystack, Emu paper quality tuning, needles in a haystack. In this article, let’s look at the model architecture and the process of collating the quality dataset to train the , and, lastly, some of the mind-blowing results achieved by Emu. Emu model Visual Explanation If you are a visual learner like me, you might prefer watching the Emu model explained through a video here: https://youtu.be/W2qjoM3cHf4?feature=shared&embedable=true Approach Emu is trained in two stages, namely and quality . Though this may be a well-known recipe, the main message from this paper is that it all comes down to quality rather than quantity. So the authors have not only curated such a quality fine-tuning dataset but shown that such a fine-tuning does not compromise on the generality of the model as measured by the faithfulness metric. pre-training fine-tuning Architecture The first contribution of the paper is the modifications to the latent diffusion architecture. In that first comes the autoencoder, which has the encoder and a decoder. The autoencoders typically use four channels in their intermediate layers, thereby compressing the data and restricting their ability to represent fine details. In this work, the authors have increased the number of layers in the intermediate channels from 4 to 8 and 16. To increase the input channels further, they do a Fourier feature transform to lift the input channel dimension from 3 (RGB) to even higher dimensions. With these modifications, they tested the reconstruction of images and evaluated reconstruction scores such as FID and peak signal-to-noise ratio. We can notice that as the channels increase, these metrics show an improvement. Even visually, the quality of images seems to get far better as the number of channels increases: The next modification to the architecture is that of the U-Net used for denoising. To increase the capacity of the U-Net, they increase the channel size and the number of stacked residual blocks in each stage. And finally, they modify the decoder to output images at a high resolution of 1024 by 1024. Pre-Training With those architectural changes, they start with the first stage of training, which is pre-training. Pretraining needs a massive dataset, and so they pre-train with 1.1 billion images. But the point to note here is that they employ progressive training by gradually increasing the resolution of the image. This seems to help the model learn fine details in the images slowly but steadily. The other thing is the introduction of noise offset in the final stages of pre-training. It was introduced in which states that simply modifying the noise with a small offset enables Stable Diffusion to generate very dark or light images easily. , this blog The offset can be introduced with 1 line of code modification in PyTorch, where we add a small offset of 0.1 or similar to the noise generation process. Fine-tuning The second stage of the training is Fine-tuning. The pre-trained model obtained in the first stage is now subject to quality-tuning. But for this, we need the quality dataset in the first place. So, how do we curate such a quality dataset for quality tuning? For this, the authors of the paper employ a two-stage filtering process, namely and . automatic filtering manual filtering In automatic filtering, they use algorithms to remove images that have too many texts using the OCR recognition algorithm. They also employ CLIP to keep only results with good image-text alignment. Lastly, they also remove images with bad aspect ratio and image size and end up with a dataset of 200 thousand images which is a lot less than the billions we started with. In the next step of manual or human filtering, humans manually assess the images for aesthetic appeal. For example, this image satisfies the rule of thirds in photography so it is aesthetically appealing. Similarly, portraits taken using professional photography gear are quite appealing compared to ordinary images because the background is all blurred, creating what is called a bokeh in photography. So it’s much more aesthetically appealing compared to a selfie taken on, say, a mobile phone. The human filtering step resulted in just two thousand images. They then composed captions to those images. The image-caption pairs were then used for training the Emo model. Because there are just 2k samples, they fine-tuned for just 15K iterations with early stopping. The results are incredible, as seen in the next section. Metrics For evaluation, they were mainly concerned about the two metrics — and . They ditch the FID scores as many recent papers argue that FID scores do not correlate well with human assessment of the performance of generative models. visual appeal faithfulness Visual appeal is subjective, so the generated images were shown to 5 annotators, usually with generated images from two models shown side by side, and the annotators chose which one of the two was more appealing. For example, if we are comparing the pre-trained model with the quality-tuned model (as shown in the figure above), then these two images are shown to the annotators who are asked to choose which one of the two images is visually appealing. I would personally choose the quality-tuned one straightaway. Text faithfulness refers to the degree of similarity between a generated image and a text caption. Again, the annotators were asked to ignore the visual appeal of the images and asked to choose which ones best describe the caption with choices “A,” “B,” and “Both.” For example, the above image is for the caption, “A teddy bear working on AI research,” which is more faithful compared to “A teddy bear playing with a computer.” Results Lastly, the paper shows that quality tuning is not just restricted to the latent diffusion models but is applicable to other models like pixel diffusion and masked generative transformers. As seen from the figure above, quality tuning, when applied to these models, also improves the visual aesthetics of the model. Conclusion In my opinion, this paper is a clear eye-opener to show Emu generates results that are so good that the generated images can be used in commercial applications. how small the fine-tuning dataset can be to tune LLMs or VLMs. There are a couple of extensions of this paper, namely Emu Edit and Emu Video. I will be reviewing them in the upcoming posts. Until then, I am signing off, and I will see you in my next… till then, take care!

Meta's Emu: The Foundational Model for Emu Edit and Emu Video

The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 48 of 100. HackerNoon’s Legal PDF Series here COUNT III: FALSE OR MISLEADING STATEMENTS BY META IN VIOLATION OF BUSINESS AND PROFESSIONS CODE SECTION 17500 (BY THE PEOPLE OF THE STATE OF CALIFORNIA) 865. The People of the State of California (California) reallege and incorporate herein by reference each of the allegations contained in the preceding paragraphs 1 through 859 as though fully alleged in this cause of action. 866. From a date unknown to California and continuing to the present, Meta has engaged in and continues to engage in acts or practices that constitute violations of California Business and Professions Code section 17500 et seq., by making or causing to be made untrue or misleading statements with the intent to induce members of the public to use Meta’s platforms when such statements were likely to mislead members of the public about the nature and safety of Meta’s platforms. Meta’s untrue or misleading representations include, but are not limited to, the representations described in paragraph 846. 867. At the time the untrue or misleading representations were made, Meta knew or by the exercise of reasonable care should have known that the representations were untrue or misleading. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

The Pursuit of Veracity: California Accuses Meta of Touting Falsehoods

The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 35 of 100. HackerNoon’s Legal PDF Series here d. Users under age 13 are an “intended audience” of Instagram. 771. Under 16 C.F.R. § 312.2, “evidence regarding the intended audience” of an online service is relevant to determining whether an online service, or a portion thereof, is directed to children. A platform is “directed to children” if it targets children as “one of its audiences—even if children are not the primary audience.”[38] 772-777. [Redacted] 778. [Redacted] Gen Alpha is the generation with birth years beginning in the 2010s (in 2020, this would have included children aged 10 and under). 779-786. [Redacted] 787. At times, Meta even publicly acknowledges that focusing on Instagram users under age 13 is a part of its business strategy. 788. For example, in September 2018, Meta released a “guide” for parents, urging them to allow their children to join Instagram, lest the children risk “social marginalization.” Commentators noted that the guide suggested that children under the age of 13 already use Instagram 789-791. [Redacted] 792. Despite its widespread efforts to secure the market of users under the age of 13, externally, Meta denies that it designs Instagram to appeal to children; for example, Davis testified to Congress on September 30, 2021 that Meta’s Platforms are not designed for children 12 and under. 793. Meta’s communications expressing its intent to reach under-13 users are one way that it reveals itself to be “directed to children.” [38] July 2020 COPPA Guidance, supra note 35. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

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