Author: Franz Malten Buemann

Open source AI platforms are helping humanity move toward a futuristic world faster than most anticipated.

OpenAI, Microsoft, and Google have had an outrageous month in the artificial intelligence (AI) space, and this field keeps accelerating.

What is open source AI?
Defining this term requires understanding a few others.
Open source is a software development term that means any programmer can jump in and work with it, the goal being to develop robust software in a shorter amount of time.
This is a great way to leverage the novel ideas of the best minds to fuel progress in technology. Think of open source as a group project where humanity benefits from A+ work.
Artificial Intelligence is a branch of computer science that develops programs and algorithms (step-by-step processes designed to solve a problem or answer a question) that help make various machines operate in more human-like ways.
There are several subfields of this science, including:

Natural language processing (NLP), which focuses on developing natural interactions between humans and computers. Specialized software helps machines process human language, create understandable words, and interact with humans through language.
Machine learning (ML), which prioritizes a machine’s ability to analyze information and use it to make recommendations or decisions based on the data sets it has provided.
Computer vision, which is all about creating machines that can understand and then interpret visual information.
Robotics that can physically perform tasks without human micromanagement, including interaction with humans.

Right now, companies verify we are human by having us choose photos from a set with one thing in common, such as cars or volcanoes. And if we were to see a car at the base of an active volcano, we can extrapolate that the car will be damaged. Machines are still developing these abilities.
Open source AI, then, can be defined as software engineers collaborating on various artificial intelligence projects that are open to the public to develop. The goal is to better integrate computing with humanity.
We need one last bonus keyword that helps us tie open source AI to marketing: Industry 4.0.
Industry 4.0 is the idea that advanced computing and AI have unlocked a new era in human productivity.

The first industrial revolution was about creating machines to do work via steam or water power.
The second industrial revolution was when we converted machines to electric power and embraced mass production. Products were built by human assembly lines, assisted by electric conveyor belts that brought the work to their hands.
The third industrial revolution was when we plugged computers into the machinery to boost efficiency and automation. Car factories now have machines programmed to quickly and precisely build cars without human assembly lines.
And now the fourth industrial revolution — dubbed Industry 4.0 — is about how the industry is changing now that humanity and computing are so closely interconnected. Business doesn’t just happen in boardrooms and on factory floors anymore. We carry it in our pockets.

We can sell and reinvest stocks from our kitchens at the touch of a button. Without touching anything, a voice-activated computer can order groceries, add appointments to our digital calendars, and tell us jokes as we work from home instead of commuting to an office.
It’s this new landscape, this new era in production via interconnected technologies, where open source AI for marketing comes into play.

How can marketers use AI?
AI offers a huge range of functionality to marketers who want to take the plunge, from small assists all the way up to running campaigns for you.
We’ll share some use cases to give you an idea of what’s out there.
Automated Social Posts
One of the smallest ways to leverage smart technology in marketing is to use a program that schedules and posts your pre-loaded social content.
You set the frequency (several times a day to once a month or more) and then load up all of your prepared content. It does the work for you on your own custom schedule.
Content Creation
This is an area where AI is booming. Marketers charged with creating written content have similar struggles across the industry. How do they keep coming up with ideas that will resonate with their audience? How can they produce content in less time to boost conversion?
Companies count on AI content to save the time it takes to create the body of such work, spend less on writers, and call on their experienced wordsmiths to then dial in on quality.
Personalized Emails and Data Capture
Most of us have experienced follow-up emails to the effect of, “Hey, you left an item in your cart!” or, “There’s an item on your wish list that just dropped in price!”People cannot possibly write these billions of daily emails customized to each consumer’s shopping habits — but AI can.
Algorithms have been devised to pull user data, analyze how each customer interacts with a brand, and create personalized email content. Then, AI schedules and sends that content, all without any human interaction after it’s been set up.
Saying “Send a thank you note to Savannah” initiates an algorithm that pulls Savannah’s email address from your contact list, creates a thank you email, and splices Savannah’s name into it. The program can send it then or read the note to you, allowing you to make changes before sending.
Ad Targeting and Pay-Per-Click Campaigns
If you advertise on Google or Facebook, programs like AdWords give you deep insight and scalpel-minute details to help you gauge how your advertising campaigns are playing out. They also facilitate pay-per-click (PPC) bidding so you can efficiently allocate your ad budget.
AI can analyze who has been engaging with your ads, then redirect ad spend to groups that market research may not have anticipated. You may be delightfully surprised by how many leads you discover or conversions you gain.

Ethical Considerations Before Using Open Source AI
Nothing new comes easy. Even the highest level of technology development has important human elements that must be addressed sooner rather than later.
Biases
People write algorithms and datasets, and people have biases — whether they know the unique lenses through which they see the world or not. Those influences can and do change what a program does, especially if the AI’s output is designed to change based on human behavior in virtual spaces.
These problems become apparent when searching for bias-charged words. You’ll want to build programs that avoid stereotypes and false information.
So how can programming be less biased? This is one of the hottest topics in AI right now, and the solutions (and laws) are still being forged.
Incorrect or Incomplete Information 
Just because it’s on the internet and AI finds it doesn’t mean it’s true. And just because something’s popular doesn’t mean it’s right.
Likewise, just because you have true information doesn’t mean you have the whole picture, no matter how hard you push your search engine to find the truth.

10 Top Open Source AI Platforms and Tools
Now to the main event: We’ve compiled a list of open source AI tools to introduce you to some of the best options as you wade through this topic, decide if micro AI could help boost your ROIs, or if larger open source AI projects are what you need to meet your company’s goals.
1. TensorFlow

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TensorFlow is an entire support structure for programmers who want to help each other create something novel while reaping the benefits of other experts’ existing models.
TensorFlow is one of the most robust AI platforms and offers training videos to help jumpstart your success.
What we like: This platform supports several programming languages, including Swift, Python, and JavaScript — the most common programming language used on Earth.
Price: Free.
2. PyTorch

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PyTorch, like TensorFlow, is a one-stop shop for transforming ideas into functional applications. It’s an entire framework created to support various aspects of open source AI project development, including vast libraries and datasets to pull from.
This platform is easy to use for developers who already code with Python. Its object-oriented approach helps bundle up usable chunks of code that do just one job.
This known and reliable “object” can then be plugged into a more extensive sequence to do a more complicated job, helping programmers help each other.
Pro tip: Programmers fluent in Python flourish here, but it also has a C++ interface for those who don’t code with Python.
Price: Free.
3. Keras

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Billed as being designed for humans, Keras is an application programming interface (API) that allows you to quickly and easily share the front end of your deep learning models.
You can export your models from Keras and run them in browsers, iOS, and Android. Their Python libraries tend to focus on artificial neural networks.
Best for: Programmers who prefer a more streamlined user interface while working with the newest versions of TensorFlow, simplifying interaction with the software as it’s being built.
Price: Free.
4. OpenAI

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OpenAI is all over the news, and for good reason — it’s changing the game of natural language processing (NLP) AI programs. They offer a model called Codex that changes natural language into code in the programming language you specify.
What’s more, like other open source AI projects, you can access their models and customize the code yourself.
OpenAI is mastering what Alexa/Siri does and taking the next step in Industry 4.0. This AI can synthesize its own natural language answers from the information it finds instead of just pointing to a website and reading it. Incredible stuff, and you can work with it!
Price: Free $18 credit to experiment for three months, then prices are token-based and depend on what you use as you go.
5. OpenCV

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OpenCV is well-known for its open source AI platform for computer vision. If TensorFlow has an undergrad degree in general AI, OpenCV holds a master’s in AI vision. And it works pretty much everywhere because its library was written in C, which it claims can be ported to everything from “PowerPC Macs to robotic dogs.” It includes a new C++ interface, and wrappers have been developed for Java, Python, and other languages to encourage cross-language development.
Best for: Developing AI specifically for computer vision applications.
Price: Free, including for commercial use.
6. H20.ai

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H2O.ai’s AI Cloud Platform copy claims that it is “the fastest, most accurate AI platform on the planet” and appears to be aware of ethical issues in AI.
They strive to democratize AI by making it available to anyone, empowering humanity to use it to impact the world positively.
A solid choice for: Companies that prioritize development speed and also plan to use AI to enhance their offerings, working toward streamlined AI management across the board.
Price: Free to develop open source software and to use their H20 Wave API.
7. Rasa

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Rasa is great for building conversational AI (chatbots) and deploying it via the cloud for free. It’s flexible and touted as “future proof” because it’s been designed so you can plug any NLP or ML model into Rasa to give you increasingly accurate results as technology improves with time.
Best for: Branded conversational AI for enterprises that comes with built-in integrations for social messaging like Slack and Facebook.
Price: Free. There are also paid pro options for enterprises.
8. Amazon Web Services (AWS)

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If you have code to run or want a familiar place to start building, you can do it for free on AWS/ The platform also stores the results/output of your programs.
In addition, AWS offers numerous value-added features for business marketing, such as customizing your code for their content delivery network and managing task coordination for your various cloud applications, all for free.
Best for: If you have a handle on coding but could use some support services adjacent to development — including business features to help you level up toward Industry 4.0.
Price: Costs vary. There are short-term offers, 12 months free, and always free options.
9. GitHub

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Regardless of your chosen platform, the GitHub platform keeps collaborative work orderly.
GitHub is the biggest name in programming cooperation. The platform helps organize projects when many hands touch the same code, keeping track of version histories, notes, and Wikis.
Best for: Individuals or teams that don’t know each other but want to work productively on a project.
Price: Basic $0, Team $44, Enterprise $231.
GitHub AI Projects: Instagram Spam Protection & Fake Product Review Identification

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We’re including these open source AI projects in development on GitHub because so much of marketing involves moderating your social networks once content is live.
These projects allow you to pull pre-existing datasets for training your programming models to do the work more thoroughly — better protecting your brand to keep those leads rolling in.
Pro tip: Google open-sourced their ALBERT model for you to emulate. The program excels at natural language processing and is agile with language-specific issues like interpreting meanings in context.
Price: Free
The Future Unfolding Around Us
Be sure to keep your eye on the developing topic of AI. It’s happening quickly all around us.
It’s sure to be a wild ride! The cutting edge of technology always is, and AI gets sharper, smarter, faster, more enmeshed by the day.

You may not necessarily think of technology when you think of the nonprofit sector. Typically restricted due to the model in which they receive funding, nonprofits have to remain nimble and focus on cost-effective solutions. Traditionally, technology hasn’t always made the cut.   However, nonprofits operate in a dynamic environment that requires them to be efficient,…
The post The Role Technology Plays in Nonprofit Work appeared first on Benchmark Email.

Welcome to HubSpot Marketing News! Tap in for campaign deep dives, the latest marketing industry news, and tried-and-true insights from HubSpot’s media team.
Coachella didn’t start off as the Met Gala for influencers.
The festival was first held in October 1999 and was intended to be an accessible event for alternative music fans. Held just three months after the infamous Woodstock ‘99, the first Coachella had an audience of just 25,000 people and failed to make a profit, costing organizers nearly $1 million.
After taking a year off, Coachella made its comeback in April 2001. While Coachella began picking up popularity in its first decade, the 2010s ushered in a distinctly new era for the festival and it became a profitable and style-defining event.
So, what changed?
In its first few years, Coachella featured predominantly alternative artists, with headliners like Beck and Rage Against the Machine. By the 2010s, mainstream artists including Jay-Z, Lady Gaga, and Beyonce started drawing bigger crowds.
What started as a single-day event evolved into a six-day festival spanning over consecutive weekends.  By 2016, there were over 99,000 attendees at Coachella each weekend — combined to be nearly 10x the attendance of the first event.
How Influencer Marketing Changed Coachella
The rise of social media also had a major impact on Coachella’s growth. Influencer culture and “festival fashion” became nearly synonymous with the event.
As content creators and celebrities began attending Coachella in droves, what they wore nearly overshadowed what was happening on stage. Brands, particularly brands that relied on influencer marketing, began leveraging Coachella as a pivotal part of their business strategies.
In 2015 and 2016 H&M partnered with Coachella organizers to launch #HMLovesCoachella, a clothing collection that captures the boho aesthetic the festival is known for. H&M also hosted a pop-up shop at the 2016 festival where attendees could purchase the clothes on-site.
Perhaps no company has used Coachella as an influencer marketing tool as heavily as the LA-based clothing company Revolve.
How Revolve Uses Influencer Marketing at Coachella to Drive Revenue
It’s reported that nearly 70% of the company’s sales come from influencers, and experiential marketing with content creators at events like Coachella is a core revenue driver.
Since 2015, Revolve has hosted Revolve Festival, an invite-only party for celebrities and influencers.

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Over the years Revolve Festival has made headlines for partnering with celebrity brands like Kendall Jenner’s 818 Tequila and Hailey Bieber’s Rhode Beauty, and for last year’s transportation issues that left influencers comparing the party to 2017’s disastrous Fyre Festival.  
Despite the controversy, Revolve Festival, combined with content distributed by influencers dressed in Revolve’s clothes, has helped the brand generate an astounding five billion social and media impressions.
Between sponsors throwing money at the opportunity to have their brands seen at the event, and influencers turning their experiences into content for their followers, Coachella has gone from a modest music festival to a $1+ billion marketing machine.
Elsewhere in Marketing
The latest marketing news and strategy insights.
Instagram is now letting users put up to five links in their bio.
YouTube is ending its in-video shopping feature.
Twitter gives Twitter Blue subscribers the ability to monetize their popular tweets.
Google is reportedly working on an AI-powered search engine to compete with Bing and OpenAI.
AI in content marketing: the HubSpot blog recently surveyed a group of marketers to learn how they’re using AI in their processes.

I created this amazing website that’s basically a chatGPT for kids. It works great and I even made custom quiz generator and writing assistance tools. My own kids are using it and their grades are improving but i’ve gotten no signups. Are there tools to automate the marketing for this? I’ve tried tiktok videos but didn’t get any views their either. Any tips you can provide on how to get this out there, I’m all ears. I was a non-profit manager for years so this is out of my wheelhouse. submitted by /u/aitutorgenie [link] [comments]

We are a team of graduate students working with an industry client to understand the needs and perspectives of business/technical users involved with managing a customer loyalty program. If you or anyone that you know has been involved in any part of the customer loyalty management process, please consider filling this survey out and sharing it with people in your network. In addition, if you would be willing to share your perspective with us over a 30-to-45-min call, please schedule a time to chat with our team members using this link. Thank you very much! submitted by /u/constantly-evolving [link] [comments]

Consumers’ use of augmented reality (AR) and virtual reality (VR) technology is a novel trend that seems to be scaling upward lately — such a trend, what some may view as a fleeting fad, may be the next big, innovative opportunity for marketers across the globe to engage customers, both new and current. In the last few years, AR and VR technologies have been continually, and at an increasingly rapid pace, transforming the way consumers choose to spend their hard-earned dollars. In an omnichannel marketing strategy, AR and VR technologies ultimately provide customers with a digital experience in place of a traditional, physical one, offering brands a new space to market their products and services.
The money-making power of the internet
With such ongoing innovations in e-commerce, forecasters at Forbes predict that the global e-commerce market will total $6.3 trillion in 2023, and by 2026, the e-commerce market is expected to total over $8.1 trillion. If brands wish to get a slice of this pie, it may be worthwhile to note that AR and VR trends are expected to continue growing in the 2023 – 2024 years and accelerate over the 2023 – 2027 period, giving retailers the chance to enhance online shopping experiences in an exciting way that may bring in a fresh, young audience. 
As the money-making power of the internet radically changes the world economy, the economy of virtual goods generates more than a modest portion of overall global gaming revenue. With the gaming industry expected to maintain its recent growth, possibly becoming worth more than $321 billion by 2026, the market seems to be dripping with opportunities for brands to generate more direct sales. Indeed, in a VR world with billions of users, these goods aren’t simply gaming products — they are the same products brands are marketing — trying to drive revenue with — in real life. While the rise of cryptocurrency continues to find a place in the global economy for the long term, the world of VR is already seeing innovation and development from leading brands, in both virtual-to-physical and physical-to-virtual transactions.
How brands are driving revenue in virtual reality
By creating virtual experiences for shoppers such as product trials and tutorials as well as virtual store experiences like in-store navigation apps and games for shoppers, brands are both enhancing their image and yielding an impressive ROI. Notable examples of brands driving revenue in VR come from companies like Estée Lauder, MAC, Gucci, and Dior, to name only a few. These brands, and others, allegedly created AR “try-on” advertisements that successfully generated direct sales. These “try-on” ads allow app users to use their smartphone cameras to superimpose 3D digital replicas of products onto their bodies. According to The Coin Republic, “Dior’s digital sneakers had 2.3 million views and a sixfold return on advertising investment.” 
As a savvy marketer looking for new ways to drive revenue, you may be thinking this sounds like an excellent brand-enhancing opportunity, but how do transactions in VR work? Depending on which platform consumers are engaging on, where brands have set up shop, and whether users are making real-to-virtual or virtual-to-real transactions — will all determine how money moves across wires. The short explanation is that in some VRs, consumers can link their payment info into the app. In other instances, users are making purchases with cryptocurrencies; however, rest assured that whichever way brands are making cash in VR now — they are actually seeing those dollars in real life. 
Think first — Don’t jump into Decentraland just yet
While the opportunity may seem golden, don’t jump into Decentraland and set up shop just yet. It’s important to remember that consumers have typically used AR and VR for gaming only, so there is a lot to consider when thinking about VR as an interactive consumer experience, in which users actively engage with brands online in real time. 
Only recently, mostly with Mark Zuckerberg’s company’s rebrand to Meta, has VR become more of a social engagement platform, allowing users to participate in VR for reasons other than gaming. Users are shopping, dining, socializing, etc. There is no doubt that money is being spent, and investors are reaping the profits. Domino’s is taking pizza orders in the Metaverse, to deliver actual pizza to customers’ doorsteps in real life, and Gucci, using an NFT method, is engaging shoppers with lower-cost virtual replicas of its products to adorn users’ avatars. 
In a recent article, Reuters reports the Investment bank Morgan Stanley forecasts that by 2030, the digital fashion industry may rise by $50 billion with consideration to new VR purchasing trends. Yet, this is only a prediction — like many social platforms we have seen over the last two decades, they rise and fall. Those individuals deeply invested in VR, in the Metaverse, in incorporating it into their lifestyle, are fortelling that VR will be the next iteration of the internet — a platform that will change society for generations. 
However, if you casually browse chat boards engaged in by VR users and creators, the overall attitude is that the VR platform is stalling — it’s not growing, and companies’ demands of it will likely outbid its abilities. The start-build-stall pattern driving the engineering behind VR may entirely be its downfall, and as consumers desire more and more from a platform that can simply not deliver, the novelty may quickly dissipate. 
The questions all marketers must ask
Undoubtedly, offering an omnichannel experience is a great way to include and engage a multitude of customers; however, deciding whether to make AR and VR a part of your omnichannel strategy takes major consideration. Some key factors to keep in mind might include questions such as who are my customers and what type of experience would drive their engagement? How can my brand create a differentiated experience? And, does AR and VR offer a unique opportunity to showcase my value proposition?
There are countless opportunities for most industries to try AR and VR and win a high ROI — especially now since consumers are being drawn to it for lifestyle experiences. Will it catch on, will it truly deliver the ROI you desire, or will it be a failed endeavor? — these are the first, essential questions every marketer must remember to ask when taking on a very new channel that still sits only as a possibility. 
The post Should You Add Virtual Reality to Your Omnichannel Marketing Strategy? appeared first on Campaign Monitor.