The future of social media will be defined by how we choose to wield its power.

1.	The Power of Social Media Algorithms	2.	How Social Media Shapes Public Opinion	3.	The Role of Viral Content in the Digital Age	4.	Misinformation and Its Impact on Society	5.	The Concept of Social Contagion	6.	The Influence of Social Media on Elections	7.	Balancing Privacy and Data in the Digital World	8.	The Economics Behind Social Platforms	9.	Regulating Social Media for Public Good	10.	The Future of Social Media and Its Global Impact

Ideas Explained

Social media algorithms determine what we see, shaping our perceptions and influencing behavior. Platforms optimize for engagement, which can have both positive and negative effects. 	“Algorithms don’t just predict our behavior; they shape it.”

Power of Social Media Algorithms

Social media is one of the most powerful tools for shaping public opinion, often polarizing users through echo chambers that reinforce existing beliefs. 	“Our opinions are no longer formed in isolation—they’re curated by the networks we participate in.”

How Social Media Shapes Public Opinion

Content that goes viral spreads rapidly through social networks, significantly impacting opinions, behavior, and even political outcomes. 	“Virality has the power to amplify voices, for better or for worse.”

The Role of Viral Content

Misinformation spreads faster than factual information on social media, creating confusion, distrust, and societal division. 	“Misinformation isn’t just a nuisance; it’s a societal threat.”

Misinformation & Its Impact on Society

Social contagion is the process by which ideas, emotions, and behaviors spread through networks, influencing individual actions and mass movements. 	“We are more connected than we think—our decisions ripple through our networks.”

Concept of Social Contagion

Social media has proven to be a key player in political campaigns, influencing election outcomes through targeted ads and viral political content. 	“Elections are no longer won in debates or town halls, but in newsfeeds and social threads.”

Influence of Social Media on Elections

The vast amount of personal data collected by social platforms raises concerns about privacy and how it is used for profit, often at the cost of user trust. 	“The digital world offers convenience at the price of our privacy.”

Balancing Privacy & Data in the Digital World

Social platforms thrive on advertising dollars, using user engagement and data as their currency, which drives them to prioritize content that maximizes attention. 	“In the attention economy, our time online is being monetized with every click.”

Economics Behind Social Platforms

There’s growing debate on how to regulate social platforms to minimize harm, ensure transparency, and protect public interests without stifling innovation. 	“The rules for the digital age are still being written, but regulation is inevitable.”

Regulating Social Media for Public Good

As social media continues to evolve, its influence will only grow, presenting both opportunities and challenges on a global scale. 	“The future of social media will be defined by how we choose to wield its power.”

The Future of Social Media

1.	Algorithms shape what we see and do online.	2.	Social media polarizes public opinion.	3.	Viral content has the power to shape society.	4.	Misinformation is a growing threat.	5.	Ideas spread like a virus through social networks.	6.	Social media influences election outcomes.	7.	Privacy concerns dominate the digital landscape.	8.	Social platforms thrive on user data.	9.	Regulation will be key to mitigating harm.	10.	The future of social media holds immense global implications.

Discover how social media shapes our lives, influences opinions, and impacts society in The Hype Machine.

A landmark insider’s tour of how social media affects our decision-making and shapes our world in ways both useful and dangerous, with critical insights into the social media trends of the 2020 election and beyond  “The book might be described as prophetic. . . . At least two of Aral’s three predictions have come to fruition.”—New York NAMED ONE OF THE BEST BOOKS OF THE YEAR BY WIRED • LONGLISTED FOR THE PORCHLIGHT BUSINESS BOOK AWARD Social media connected the world—and gave rise to fake news and increasing polarization. It is paramount, MIT professor Sinan Aral says, that we recognize the outsize effect social media has on us—on our politics, our economy, and even our personal health—in order to steer today’s social technology toward its great promise while avoiding the ways it can pull us apart. Drawing on decades of his own research and business experience, Aral goes under the hood of the most powerful social networks to tackle the critical question of just how much social media actually shapes our choices, for better or worse. He shows how the tech behind social media offers the same set of behavior influencing levers to everyone who hopes to change the way we think and act—from Russian hackers to brand marketers—which is why its consequences affect everything from elections to business, dating to health. Along the way, he covers a wide array of topics, including how network effects fuel Twitter’s and Facebook’s massive growth, the neuroscience of how social media affects our brains, the real consequences of fake news, the power of social ratings, and the impact of social media on our kids. In mapping out strategies for being more thoughtful consumers of social media, The Hype Machine offers the definitive guide to understanding and harnessing for good the technology that has redefined our world overnight.

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When you hear Arcium, think of “using encrypted data without decryption.” Full stop.

This is basically what Arcium has come to do in the web3 space. A little story: I and my 4 friends want to create a secret recipe for an amazing soup. We all want to work together, but we don't want to reveal our personal ingredients for the soup. Thinking of how to solve the problem? We get a magical box. We all put our ingredients in the box, and the box produces the soup for us. This way, we made an amazing soup, using our top-notch secret ingredients without revealing what those ingredients are.

Story As Usual

Analogically, my friends and I are institutions that may want to collaborate together. The soup could be any useful result for which we are working together, e.g., training an AI model for diagnostic purposes. The secret ingredients represent sensitive information, like personal information from patients' medical history and stuff. Last but not least, the magic box is ARCIUM.

Critiquing The Analogy

Definitively, Arcium is the first parallelized confidential computing network empowering developers and applications to flexibly access trustless, verifiable, and performant computing power. Think of it as a worldwide supercomputer using cutting-edge cryptography to allow encrypted data to be processed securely without exposing (decrypting) that data. Fundamentally, Arcium aims to solve the problem of “how sensitive data is processed in the web3 space.”

Definite Definition

<a href="https://x.com/yrschrade">Yannik Schrade</a> <a href="https://www.linkedin.com/in/yannik-schrade?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app">LinkedIn</a> <a href="https://x.com/manzgoeggel">Lukas Steiner</a> <a href="https://www.linkedin.com/in/lukas-ste?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app">LinkedIn</a> <a href="https://x.com/julian_arcium">Julian Deschler </a> <a href="https://www.linkedin.com/in/juliandeschler?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app">LinkedIn</a> founded Arcium in the year 2022 in Baar, Switzerland. PS : embedded link is to X profile

Founders Connect And Year Founded

Data is at the core of everything we do online. From browsing the web to making online purchases or sharing files, our data can exist in three states: At Rest: Stored on a device, like photos or videos on your phone. In Transit: Moving from one place to another, like when you send an email. In Use: Being processed, like when a computer calculates your taxes. Some people already know how to protect data when it’s at rest (using passwords) or in transit (using SSL encryption), but data in use has traditionally been hard to secure.

LET'S SEE THE WORKINGS OF ARCIUM.

Conventionally, when you send data—say an email, a photo, or a text—it has to be decrypted before being processed, and this leaves it vulnerable to hacks. Arcium ensures that data remains encrypted even while being processed, making it highly secure.

Where Arcium Steps In.

Multi-Party Computation (MPC), which is like having multiple people work on different pieces of a puzzle without anyone seeing the full picture, is one of the technologies employed by Arcium to ensure this protection. Using this analogical explanation for MPC, when the pieces are brought together, the puzzle is solved without anyone knowing what the entire puzzle looks like. Other technologies used include Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKPs).

I have already explained analogically how MPC works, but here is another analogy according to Whiteboard Crypto: Let’s say five healthcare companies want to work together to build an AI model for cancer diagnosis. They all have patient data, but sharing it directly is illegal or risky due to privacy laws. Instead, they break the data into smaller, meaningless pieces and distribute these pieces among each other. None of the companies have access to full data, but by combining their pieces, they can train the AI without compromising any patient’s privacy.

WBC's Analogy

As of the time of documentation, Arcium has no native token. Some notable investors in Arcium include:<a href="https://www.x.com/greenfield_cap">Greenfield Capital</a><a href="https://www.x.com/cbventures">Coinbase Ventures</a><a href="https://www.x.com/HeartcoreCap">Heartcore Capital</a><a href="https://www.x.com/LongHashVC">Longhash VC</a>L2 Iterative Ventures, Staking Facilities, Smape Capital, and Everstake. Check out this <a href="https://x.com/Echoeweb/status/1788806946893509076">tweet by Echoesweb</a> for more on the investors

Investors

It can be used for secret voting in DAOs (Decentralized Autonomous Organizations), where no one can see how you voted, but the system remains verifiable and trustworthy. With Arcium, players can play on-chain games like poker, where players’ hands are kept confidential without relying on a central server. In situations where DeFi users want to keep their trades private, Arcium makes provision for things like encrypted order books—a way to keep trade details secure, preventing market manipulation.

Some use cases for Arcium:

Arcium provides opportunities for companies to train AI models using sensitive data like patient information or financial data without ever revealing that data to anyone else, revolutionizing the healthcare and finance landscape, where privacy is critical.

Although Arcium is based on the Solana blockchain, it aims to expand to other chains, bringing massive innovation to sectors like AI, finance, gaming, and even government services in the future. In summary, Arcium is revolutionizing how we think of data security, making it feasible and seamless for businesses, developers, and even regular users to use and share data safely in ways that were previously impossible. <a href="https://youtu.be/mj24WOShyC8?si=wBK7YKf_c5q-awZv">DYOR, do your own research</a> <a href="https://docs.arcium.com/">Arcium docs</a>

A Look Ahead

YANNIK SCHRADE

LUKAS STEINER

JULIAN DESCHLER

I watched the YT video by Whiteboard crypto and decided to write.

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Cryptocurrency

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Arcium (BC9)

1.	The Evolution of Generative AI	2.	Key Technologies Powering Generative AI	3.	Ethical Challenges in AI Development	4.	AI’s Impact on Creativity and Innovation	5.	The Role of Data in Training AI Models	6.	Real-World Applications of Generative AI	7.	The Future of AI and Human Collaboration	8.	Limitations of Generative AI Systems	9.	The Importance of Understanding AI for the Future Workforce

Topics Explained

Generative AI has come a long way from early algorithms to powerful models like GPT-3. The history of AI development reflects the rapid growth and increasing complexity of these systems. 	“Generative AI today is an amalgamation of decades of technological advancement.”

The Evolution of Generative AI

Generative AI systems rely on neural networks, machine learning, and deep learning technologies to create content. These core technologies enable AI to mimic human creativity and learning. 	“Deep learning forms the foundation of how generative AI creates and innovates.”

Key Technologies Powering Generative AI

With power comes responsibility, and generative AI presents significant ethical concerns, from data privacy to misinformation. Developers must carefully navigate these issues to build trust. 	“The ethics of AI will determine whether it is embraced or feared.”

Ethical Challenges in AI Development

Generative AI is transforming industries by automating creative processes. From art to design and content creation, AI helps humans push creative boundaries. 	“AI doesn’t replace creativity—it enhances and expands human imagination.”

AI’s Impact on Creativity & Innovation

Data is the fuel that powers AI models. Generative AI requires vast amounts of high-quality data to learn and generate accurate outputs, raising questions about data sourcing and integrity. 	“The quality of AI’s output is only as good as the data it’s trained on.”

The Role of Data in Training AI Models

From generating art to writing code, generative AI is already being used in various fields. Its application in healthcare, entertainment, and business is shaping the future of industries. 	“The practical applications of generative AI are as diverse as the problems it can solve.”

Real-world Applications of Generative AI

Generative AI and humans will work side by side, complementing each other’s strengths. While AI takes over repetitive tasks, humans will focus on strategy, creativity, and ethical oversight. 	“The future of work will be a partnership between humans and AI.”

The Future of AI & Human Collaboration

Despite its power, generative AI has limitations, including biases in data, errors in output, and the inability to understand context in the same way humans do. 	“AI is brilliant at replication, but it lacks the nuanced understanding that defines human intelligence.”

Limitations of Generative AI Systems

As AI continues to evolve, it’s crucial for the workforce to develop a strong understanding of these systems. Learning to work with and leverage AI will be essential for future success. 	“In a world shaped by AI, those who understand it will lead.”

The Importance of Understanding AI for the Future Workspace

1.	AI Evolution: Generative AI is rapidly advancing.	2.	Core Technologies: Neural networks and deep learning drive AI.	3.	Ethical Considerations: AI presents new ethical dilemmas.	4.	Creativity Enhancement: AI boosts creative industries.	5.	Data-Driven: High-quality data is essential for AI.﻿6.	Real-World Use: AI’s applications span many industries.	7.	Collaboration: AI and humans will work together.	8.	AI Limitations: AI still has significant shortcomings.	9.	AI Literacy: Understanding AI is vital for future careers.

Explore how generative AI transforms creativity, ethics, and innovation in the modern world!

The last book on AI you’ll ever need. We swear! AI technology moves so fast that this book is probably already out of date! But don’t worry—The Complete Obsolete Guide to Generative AI is still an essential read for anyone who wants to make generative AI into a tool rather than a toy. It shows you how to get the best out of AI no matter what changes come in the future. You’ll be able to use common automation and scripting tools to take AI to a new level, and access raw (and powerful) GPT models via API. Inside The Complete Obsolete Guide to Generative AI you will find: • Just enough background info on AI! What an AI model is how it works • Ways to create text, code, and images for your organization's needs • Training AI models on your local data stores or on the internet • Business intelligence and analytics uses for AI • Building your own custom AI models • Looking ahead to the future of generative AI Where to get started? How about creating exciting images, video, and even audio with AI. Need more? Learn to harness AI to speed up any everyday work task, including writing boilerplate code, creating specialized documents, and analyzing your own data. Push beyond simple ChatGPT prompts! Discover ways to double your productivity and take on projects you never thought were possible! AI—and this book—are here to show you how. About the technology Everything you learn about Generative AI tools like Chat-GPT, Copilot, and Claude becomes obsolete almost immediately. So how do you decide where to spend your time—and your company’s money? This entertaining and unbelievably practical book shows you what you can (and should!) do with AI now and how to roll with the changes as they happen. About the book The Complete Obsolete Guide to Generative AI is a lighthearted introduction to Generative AI written for technology professionals and motivated AI enthusiasts. In it, you’ll get a quick-paced survey of AI techniques for creating code, text, images, and presentations, working with data, and much more. As you explore the hands-on exercises, you’ll build an intuition for how Generative AI can transform your daily work and communication—and maybe even learn how to make peace with your new robot overlords. What's inside • The big picture of Generative AI tools and tech • Creating useful text, code, and images • Writing effective prompts • AI-driven data analytics About the reader Written for developers, admins, and other IT pros. Some examples use simple Python code. About the author David Clinton is an AWS Solutions Architect, a Linux server administrator and a world-renowned expert on obsolescence. The technical editor on this book was Maris Sekar. Table of Contents 1 Understanding generative AI basics 2 Managing generative AI 3 Creating text and code 4 Creating with media resources 5 Feeding data to your generative AI models 6 Prompt engineering: Optimizing your experience 7 Outperforming legacy research and learning tools 8 Understanding stuff better 9 Building and running your own large language model 10 How I learned to stop worrying and love the chaos 11 Experts weigh in on putting AI to work A Important definitions and a brief history B Generative AI resources C Installing Python

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Artificial Intelligence

David Clinton

The Complete Obsolete Guide to Generative AI

Merge Project and Task Management the easiest way possible.

Endless Tasks or Vague Goals?

Traditional methods like long to-do lists or annual planning don't work in today's fast-paced world.

Forget Traditional Methods

It's the workflow we use at the <a href="https://paperlessmovement.com/">Paperless Movement®</a> that helps manage goals, projects, and tasks effectively.

The Execution Beast

Focus on them.They are aligned with your business outcomes, helping to stay nimble and focused.

Quarterly Goals

It stands for Plan, Execute, Align.Ensures each task moves you closer to your goals and gives answer to three critical questions for any busy professional:1. When am I going to do this? (Plan)2. What do I do now? (Execute)3. Am I doing what I should be doing? (Align)

The PEA Workflow

To do so, you need to pay attention to four key variables:<ol><li class="ql-align-justify">Energy Level</li><li class="ql-align-justify">Urgency</li><li class="ql-align-justify">Speedies</li><li class="ql-align-justify">Unexpected Events</li></ol>

Always execute correctly

Schedule Deep Work when your energy is highest, typically in the morning.Tackle your most important task (the Highlight of the Day) when you’re at your peak.

Energy Level

Not everything is Urgent.Focus on what truly impacts your business, and avoid distractions that don’t align with your Weekly Goals or Highlight of the Day.

Urgency

Capture quickly Speedies (under-15-minute tasks) to handle later in scheduled batches.This will keep you focused on high-priority tasks without letting small tasks derail your day.

Speedies

When the unexpected happens, prioritize based on what moves you closer to your goals (the task at hand or the unexpected one?).This simple “binary” decision-making process will help you stay on track.

Unexpected Events

Take our FREE quiz to uncover what’s working and how to optimize it!

Is your productivity system truly working, or are you constantly struggling?

Simplify Project Management and achieve seamless execution

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Transform How You Work: The Power of Execution for Busy Professionals

Salesforce is a SaaS (Software as a Service) company. It's an IT firm that helps businesses manage their relationships with customers. They have acquired numerous companies and don’t plan to stop anytime soon as they continue expanding the cloud technology landscape. Yeeeehhhhh!!! Today, it's Salesforce! <a href="https://www.linkedin.com/company/salesforce">Salesforce LinkedIn</a> | <a href="https://x.com/salesforce">Salesforce on X (Twitter)</a> But what is Salesforce? Is it to sell by force? Or maybe the forces that drive sales? Or is it just another term for digital marketers? Let's demystify this bogglesome word.

KEY FACTS

Think of Salesforce as a company that helps businesses keep track of their customers. That’s its core. Salesforce fundamentally helps businesses to: Keep track of important customer details like contact info, past purchases, and communication history. Sell products. Market new offerings. Assist customers with their problems. Analyze data to make better business decisions—and more.The best part? Everything is done online. Businesses just need to log in and do whatever they need without installing software or managing servers or hardware.

Wen Asked...

Remember when we discussed cloud computing? Remember SaaS?Yes!!! Salesforce is exactly that—a software as a service. Salesforce is an American IT, cloud-based company specializing in helping businesses manage customer relationships through their Customer Relationship Management (CRM) software. It also offers tools to assist businesses with: Sales E-commerce Marketing Customer service App development Analytics

With about 72,682 employees as of 2024, Salesforce was founded by Marc Benioff (former Oracle executive) <a href="https://x.com/Benioff">Marc Benioff on</a> X | <a href="https://www.linkedin.com/in/marcbenioff?utm_source=share&utm_medium=member_mweb&utm_campaign=share_via&utm_content=profile">LinkedIn</a>, Parker Harris <a href="https://x.com/parkerharris">Parker Harris on X </a>| <a href="https://www.linkedin.com/in/parker-harris-451040211?utm_source=share&utm_medium=member_mweb&utm_campaign=share_via&utm_content=profile">LinkedIn</a>, Dave Moellenhoff <a href="https://www.linkedin.com/in/dave-moellenhoff-b139?utm_source=share&utm_medium=member_mweb&utm_campaign=share_via&utm_content=profile">LinkedIn</a>, and Frank Dominguez on February 3, 1999. The company's headquarters are in Salesforce Tower, San Francisco, California, with Marc Benioff serving as Chairman and CEO. In 2024, Salesforce realized $34.86 billion in revenue and an accruing net income of $4.136 billion<a href="https://en.m.wikipedia.org/wiki/Salesforce"> (Wikipedia).</a> Salesforce’s corporate culture is rooted in the Hawaiian concept of “Ohana,” meaning family.

Overview

Through its cloud platform, Salesforce provides a wide array of services, including: E-commerce solutions for online businesses. Customer support software to enhance customer service. Marketing automation tools to streamline marketing efforts. Data analytics and software creation tools. Sales automation tools to help businesses manage and improve their sales processes.

Value they offer?

Quip (collaborative productivity software) Heroku (cloud application platform) Acumen Solutions (business consulting) Tableau (data visualization software) MuleSoft (data integration software) Slack Technologies (business communication), and more.

Notable subsidiaries

Globally, Salesforce operates in 110 offices, including prominent cities like Paris, Sydney, Tokyo, Hong Kong, London, and Tel Aviv. After its establishment in 1999, Larry Ellison, the co-founder of Oracle, and Halsey Minor, the founder of CNET, invested early in the company.Salesforce experienced rapid growth despite coinciding with the challenges of the dot-com bubble. With its notable "end of software" marketing, Salesforce gained traction, increasing its revenue from $5.4 million in 2001 to over $100 million by 2003. That same year, Salesforce held its first Dreamforce conference.

Let’s take a look at how far Salesforce has come ⤵️

In 2004, Salesforce went public on the NYSE under the ticker CRM, raising $110 million. In 2006, it launched IdeaExchange, an innovative platform for customer feedback. During the same period, it acquired Sendia, a mobile web service, for $15 million, and Keiden, an online advertising service. In 2007, Salesforce acquired Moral, a software for content management. By 2008, Salesforce made significant strides by acquiring Intranet for $31.5 million, getting added to the S&P 500 index, and switching its servers from Sun Microsystems to Dell servers running on Linux.

⤵️⤵️⤵️

Upon reaching $1 billion in annual revenue in 2009, Salesforce introduced Service Cloud, designed for customer service management. This was also when the company began investing in startups, which eventually led to the creation of Salesforce Ventures (SFV), headed by John Somorjai. Salesforce Ventures supports startups, especially those focused on cloud computing. In 2010, Salesforce acquired Jigsaw, a cloud data service, for $142 million, Heroku for a whopping $212 million, and Activa Live Chat.

🖇️🖇️🖇️

In 2011, Salesforce acquired Dimdim, a web conferencing tool, for $31 million, along with Radian6, a social media tracking software for $340 million, and RYPPLE, a performance management tool. In 2012, Salesforce continued expanding, acquiring Buddy Media (social media marketing) for $689 million and GoInstant (browser collaboration) for $70 million. Despite these accomplishments, the following years saw even more mind-boggling expansion. In 2013, Salesforce partnered with Oracle and acquired ExactTarget (email marketing) for $2.5 billion.

⛓️⛓️⛓️,

In 2014, Salesforce launched the Salesforce1 fund via Salesforce Ventures to support startups building mobile applications. That same year, it acquired RelateIQ (data) for $390 million and launched Trailhead, a free learning platform. The Salesforce UK data center was also established that year to comply with European data regulations. In 2015, Salesforce acquired SteelBrick (software) for $360 million.

🎙️🎙️

Salesforce's acquisition spree continued into 2016. It acquired Demandware (e-commerce) for $2.8 billion and Quip (word processing) for $750 million. Salesforce also integrated an AI model called Einstein into its services. To meet data residency requirements in certain countries, it began using Amazon Web Services.

↪️↩️

In 2017, Salesforce acquired Sequence (user experience design), followed by MuleSoft (cloud services) for $6.5 billion in 2018, as well as Rebel (email services) and Datorama (AI marketing). As of August 2018, Salesforce Ventures had invested $1 billion in 275 companies, including notable names like: CloudCraze (e-commerce) Figure Eight (artificial intelligence) Forter (online fraud prevention) FinancialForce (automation software)

💎💎

That same year, Salesforce expanded its support for startups by launching the Japanese Trailblazer Fund, investing in Japanese startups. In 2019, Salesforce made a massive acquisition, purchasing Tableau (data analytics) for $15.7 billion. Salesforce also made significant investments in companies like: Domo (data visualization) SurveyMonkey (online surveys) Twilio (cloud communications) Dropbox (cloud storage) DocuSign (e-signature services)

⬜⬜

In 2020, Salesforce joined the prestigious Dow Jones Industrial Average, replacing ExxonMobil and remaining there to date. This year also saw the stepping down of Co-CEO Keith Block, leaving Marc Benioff as the sole CEO.

🍃🍃

A Hallmark Year: Salesforce made its largest acquisition ever by acquiring Slack (office messaging) for $27.7 billion. During the same period, Salesforce appointed Bret Taylor as co-CEO. However, the company also laid off about 8,000 employees (10% of its workforce) due to over-hiring during the pandemic. Meanwhile, Salesforce Ventures saw investments generating a $2.17 billion annual gain.

2021

In 2022, Salesforce acquired Troops.ai (Slack-bot maker) and Airkit.ai (AI customer service apps). The company ranked 126th on the Fortune 500 list, reporting $26.49 billion in revenue, marking a 25% increase from the previous year. In September, Salesforce also became the world’s largest enterprise software company by ranking 61st globally in terms of market value at $153 billion.

⚓⚓

In 2023, Salesforce doubled the size of its Generative AI Fund from $250 million to $500 million to support AI startups. During the same period, Co-CEO Bret Taylor stepped down. Salesforce also laid off an additional 700 employees and acquired Spiff (commission management). Talks to acquire Informatica were held but later abandoned. Salesforce ranked 491st on the 2023 Fortune 500 list.

🚿🚿

In 2024, Salesforce continued its acquisition streak by acquiring Own (backup solutions) for $1.9 billion, along with PredictSpring and Tenyx. They also launched Einstein Copilot: Health Actions, an AI assistant for healthcare tasks like managing appointments and patient data.

🧊🧊

Salesforce has come a veeeeery loooooooong way! Salesforce’s acquisitions have played a critical role in keeping the company ahead in the cloud technology space. Ultimately, Salesforce remains pumped for more acquisitions, innovative solutions, and pioneering revolutions in the tech industry, especially in cloud computing and cloud technologies.Thank you for reading 🫰You're visionary

🗻🗻

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Software Engineering

Salesforce (TC6)

DYOR

Imagine a situation where machines can learn like humans, solve problems like them, and make predictions… It's wild, but guess what? It's already happening. Welcome to the world of Artificial Intelligence (AI)—a powerhouse for data analysis, aggregation, sequencing, and any process you can think of that requires data. This is the world where computer programs, or algorithms, are trained to process data, provide solutions, and make predictions.

Jus' Imagine

Okay, put it this way: An AI model is an algorithm trained to perform tasks by processing data. Making predictions and providing solutions are examples of these tasks. Pretty simple, right? So, the next time you hear "AI models," think of: task-performing, trained computer programs. You should also know that AI models are the backbone of modern tech. From your smartphone’s voice assistant to the recommendations you get on YouTube, TikTok, or Netflix, everything is powered by an AI model.

Deep Learning Models (DLM) Machine Learning Models (MLM) Supervised Learning Models (SLM) Unsupervised Learning Models (ULM) Reinforcement Learning Models (RLM) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Transformers

Types of AI Models

Let’s dive into each of these, one by one. I’ll use initials; it’s not conventional, but it adds a bit of flair (smile). DLMInspired by the human brain, Deep Learning Models use neural networks to process large amounts of data. They are especially efficient in tasks like image recognition and understanding sounds and languages. MLMThink of them as students learning from examples. Machine Learning Models are designed to learn from data and improve their performance over time.

DLM & MLM

SLMSupervised Learning Models learn from labeled data. For example, if you give them pictures labeled as Bitcoin and Ethereum, they will learn to distinguish between the two and identify them correctly. ULMUnsupervised Learning Models, on the other hand, don’t rely on labeled data. They find patterns in unorganized data, grouping similar objects together—like organizing pictures based on color or shape. RLMReinforcement Learning Models learn by interacting with their environment and receiving rewards or penalties, similar to how a game AI might learn strategies through trial and error.

SLM x ULM x RLM

CNNConvolutional Neural Networks are excellent at recognizing objects in images. They are frequently used in image recognition tasks. RNNRecurrent Neural Networks are used for tasks that involve sequences, like analyzing time-series data or predicting the next word in a sentence. TransformersThese are the latest breakthrough in Natural Language Processing (NLP), seen in models like GPT (used in chatbots) and BERT (used for understanding the context of sentences).

CNN  RNN • T

AI models are trained using data. The more data you provide, the more efficient and accurate they become. AI models rely on data and features. For the training to be effective, it must include key features of the data. For example, if you're training an AI to recognize houses, the features might include dimensions like the size of windows and doors. Training and testing are critical steps. Models are trained by letting them analyze data, and afterward, they are tested on new, unseen data to evaluate how well they make predictions.

The Workings of AI Models

AI models also adjust themselves through a process known as “learning.” They compare their predictions with actual outcomes and make corrections to minimize errors.

LEARNING

GPT (Generative Pretrained Transformer)Used for generating text, answering questions, and engaging in conversations. Examples include AI-powered customer service, Meta AI in WhatsApp, Grok on X, and the well-known ChatGPT. BERT (Bidirectional Encoder Representations from Transformers)A powerful AI model used for understanding the meaning of sentences in context. Google Search uses BERT to enhance search results. GANs (Generative Adversarial Networks)These are used to create realistic images and videos, like deepfake videos that mimic people's faces and voices.

AI Models in Use Today

Autoencoders: These models are used to reduce the complexity of data and are commonly found in image compression applications.

In Finance Predicting stock prices. Detecting fraudulent transactions. Powering automated trading systems. In Healthcare Predicting patient health outcomes using historical data. Assisting medical practitioners by analyzing medical images for diagnosis. In Autonomous Vehicles Helping self-driving cars identify objects and make driving decisions. In Natural Language Processing (NLP) Virtual assistants like Alexa, Siri, and Cortana are a direct result of AI models in NLP. They are also used in language translation applications and play a major role in understanding human language.

AI Use Cases

Resource-intensiveTraining AI models like GPT requires substantial computing power and time. BiasAI models can become biased if trained with biased data, leading to skewed predictions. InaccuracyIncomplete or inaccurate training data can lead to incorrect predictions. Black Box ModelsSome AI models, like deep learning models, can be difficult to understand in terms of how they make decisions.

Challenges of AI Models

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