Algorithms With No Chill: A Netflix Original Series

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Stealth Venture Update: We Are Launching Our Alpha Product

Today, we will break down this whole AI hype and make it accessible for everyone, even your sixth-grade cousin who's still learning long division.

As an AI founder, I'm knee-deep in the machine learning game. My team and I are working on an AI that helps people connect with the world and combat the mental health struggles that have come with Covid-19.

It's a big project, but we believe that everyone deserves a little help finding their dream job or meeting their soulmate, especially if they're feeling isolated and alone.

We're launching a closed Alpha test in April, and if you want in, just let me know in the comments below. But first, let's get down to the nitty-gritty of machine learning.

How Machine Learning Works

Now, you've probably heard a lot about AI in the media, but some of it is just plain fake news. So let's break it down in a way that even your grandma can understand.

We'll start by introducing a genuine machine learning genius, assistant professor Hamsa Bastani from 美国宾夕法尼亚大学 - 沃顿商学院 at the 美国宾夕法尼亚大学 . She's been exploring ways to maximize AI in her courses and Executive Education lectures.

In a recent article in Wharton Magazine, she said:

"A lot of businesses haven't seen value coming out of machine-learning predictions...only the ones that inform specific decisions."

Basically, not all predictions are helpful, and the ones that are needed to be updated regularly. Bastani gives an example of Netflix recommendations:

"if they don't recommend a movie you like, they'll never know you wanted to watch it."

How Netflix Uses Data

Netflix is a master of data. They collect more data about you than your ex did during your last relationship. They track every single show and movie you watch, every pause and every "Like" you give. They use this data to create a personalized algorithm that predicts what you might want to watch next. And let's be real, we all need some good predictive powers in our lives, especially when it comes to deciding what to watch on a Friday night.

But it's not just about the quantity of data, it's also about the quality. Machine learning algorithms rely on clean, consistent, and relevant data to make accurate predictions. If the #data is full of errors or biases, then it's like trying to solve a Rubik's cube blindfolded while riding a unicycle. It's not going to end well.

And speaking of algorithms, choosing the right one is key. It's like choosing the right tool for the job. You wouldn't use a screwdriver to hammer a nail, unless you want to end up with a wonky bookshelf. Similarly, you wouldn't use a regression algorithm for image recognition unless you want your computer to think that your cat is a toaster.

So, to sum it up, if you want to be a machine learning master like Netflix, you need to have good data, choose the right algorithm, and consider the bigger picture. And who knows, maybe one day you'll be able to predict what your ex will do next based on their previous behavior. Hey, a human can dream, right?

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Responsible for administering an annual budget of $87M across 17 country offices, strategically managing donor relations consisting of bilateral/multilateral donors and private #foundations, and cultivating team morale through her hands-on approach in corporate meetings and field visits. As Country Director of Kenya, she successfully raised $5M in cash and in-kind monthly donations to bring relief during the 2011 drought and led the writing/application efforts to secure a $50M #grant from #USAID.

Eeny Meeny Model Moe - How to Pick An Algorithm

Let's dive into the world of different types of machine #learning algorithms. Don't worry, we won't get too technical. Think of it like choosing your favorite flavor of ice cream - there are many options, but they all have their unique qualities that make them delicious in their own way.

Supervised Learning

First up, we have supervised learning, which is like having a teacher guide you through a lesson. The #algorithm is given a set of labeled data, meaning the desired outcome is already known, and it learns to make #predictions based on that data. For example, an algorithm could be trained on a set of labeled images of cats and dogs, and then used to predict whether a new image is a cat or a dog.

Unsupervised Learning

Next, we have unsupervised learning, which is like exploring a new city without a map or guide. The #algorithm is given a set of unlabeled data, meaning there is no desired outcome, and it must identify patterns and relationships on its own. This type of learning is useful for tasks like clustering, where the #algorithm groups similar data points together based on their characteristics.

Reinforcement Learning

Finally, we have reinforcement learning, which is like training a dog with treats and punishments. The algorithm learns to make decisions based on a reward system, where it receives positive feedback for making correct decisions and negative feedback for making incorrect ones. This type of learning is commonly used in applications like game-playing and robotics.

Let's Bake An ML Cake

#machinelearning #algorithms are like recipes for machines. Just like how you need a recipe to bake a cake, machines need a recipe to learn and make predictions. Now, let's talk about some popular machine learning #algorithms - Decision trees, Naive Bayes, K-Nearest Neighbors, and Linear Regression.

• A Decision tree is like a game of 20 questions. Imagine Netflix is trying to predict what kind of movies you like. It starts by asking a question like "Do you like romantic movies?" If you say "No," it will move on to the next question until it finds a movie that you might like. It's like a process of elimination! Netflix could use #decisiontrees to recommend movies or TV shows to viewers based on their previous viewing history. For example, if a viewer has watched several romantic comedies in the past, a decision tree could recommend similar movies or TV shows that are likely to appeal to that viewer.
• Naive Bayes is like a detective trying to solve a mystery. It looks at the evidence and tries to figure out the probability of something happening. For example, if Netflix knows that you like action movies and that a new action movie is coming out, it might predict that you will watch it based on your past behavior. Netflix could use #NaiveBayes to analyze viewer feedback and determine what types of content are most popular among their audience. For example, if viewers consistently rate action movies highly, Naive Bayes could identify that trend and recommend more action movies to viewers.
• K-Nearest Neighbors is like asking your friends for advice. Netflix might look at what other people who like the same kind of movies as you are watching and recommend those movies to you. It's like asking your friends who have similar tastes for recommendations. Netflix could use K-Nearest Neighbors to help viewers discover new content that is similar to their favorite movies or TV shows. For example, if a viewer loves the TV show Stranger Things, #KNearest Neighbors could recommend similar shows like The OA or Black Mirror.
• Lastly, Linear Regression is like drawing a line on a graph. It helps Netflix make predictions based on a relationship between two things. For example, it might use Linear Regression to predict how many hours you will spend watching Netflix based on how many hours you watched last week. Netflix could use linear regression to predict how successful a new TV show or movie will be based on factors like its genre, director, and cast. For example, if a new sci-fi movie is coming out, linear regression could use past data to predict how well it will perform at the box office.

Culture: What's On Netflix

"Zero" on Netflix is a thought-provoking series that masterfully blends superhero tropes with a poignant commentary on the socioeconomic struggles of modern-day Milan. In a refreshing departure from the usual superhero formula, the show's protagonist, Zero, possesses no supernatural powers. Instead, he relies on his wit, courage, and street smarts to navigate the treacherous world of organized crime and corruption.

At its core, "Zero" is a story about the power of community and the resilience of the human spirit in the face of adversity. The show takes a nuanced approach to the issues of poverty, racism, and gentrification, which are all too familiar in many urban areas worldwide. It shows how these issues affect real people, particularly young people of color, who often bear the brunt of the social and economic inequities in their communities.

The show's creators are to be commended for their commitment to authenticity and inclusivity. The cast is predominantly made up of Black and brown actors, and the characters speak a mix of Italian and various immigrant languages, reflecting the multicultural reality of Milan. This diversity is not just a superficial nod to political correctness but an integral part of the show's message of solidarity and unity.

Moreover, the show highlights the importance of social entrepreneurship as a means of creating positive change in underserved communities. Zero and his friends start a food truck business, which not only provides them with a source of income but also serves as a way of bringing healthy, affordable food to their neighborhood. This is just one example of how entrepreneurship can be a powerful tool for social and economic empowerment.

According to recent data from the Global Entrepreneurship Monitor (GEM), social entrepreneurship is rising worldwide, particularly in low-income countries where traditional job opportunities are scarce. GEM's latest report states that "social entrepreneurs are driven by a mission to create social or environmental impact, and they are more likely to employ vulnerable groups, such as women, youth, and refugees, than mainstream businesses." This aligns perfectly with the ethos of "Zero," which shows how ordinary people can make a difference in their communities by taking action and working together.

"Zero" is a must-watch for anyone who loves a good superhero story but cares about social justice and community empowerment. The show's themes of resilience, solidarity, and social entrepreneurship are more relevant than ever in a world that is increasingly divided and unequal. As we continue to grapple with the fallout from the pandemic and other global crises, we need stories like "Zero" to remind us of the power of collective action and the potential for positive change.

Money, Jobs, Fellowships

• Funding | MIT Global Challenges |Deadline: May 9th, 2023
• Funding | The Camelback Fellowship | Deadline: March 30th, 2023
• Job | Global Citizen Year | Finance and Operations Manager