Because working full-time is a recipe for disaster.

We’re living in a very interesting time. New inventions are being created. New jobs and lifestyles become possible. The very definition of the word “work” changes.

A month ago, I argued that the job of the future is content creation.

It’s true.

Something similar happened when we switched from physical labor employment to intellectual labor.

About one hundred years ago, we stopped going to factories and started working in offices. We didn’t need that much physical labor anymore because most of it was automated. Intellectual work and the post-industrial economy soared.

Now, we’re in a new transition. There are almost eight billion people on this planet — and while there is still a large percentage of those who don’t have access to the internet (about half, to be exact) — the western, developed world just doesn’t need that many employees.

This is what the 2008 real estate crisis showed us. There were many layoffs, but most of them didn’t recover. Why? The CEOs were secretly happy that they had to fire all those people. The brutal truth is that they just didn’t need them.

This COVID crisis also showed us just that.

Most employees were either forced to work from home or go on a three-month-long vacation. The world economy tanked for a bit (then recovered to all-time-highs), but not because all those people couldn’t work — but because most offline businesses had to shut down.

There was less demand for products and services.

That last point is crucial.

What many people still don’t realize is that we live in a “demand-driven” economy. In this economy, bitcoin grows not because it’s backed by some incredible asset (it’s not backed by anything), but simply because it grows. It grows because people buy it. And when people see that it grows, they buy some more of it. The rest of the economy is built in a similar way.

The whole world economy revolves around the demand of 1% of the global population.

And because the population grows (especially fast in the developed countries), you have demand that’s accelerating.

More demand = more opportunities for business. And less need for work. In the conventional sense, at least.

This is where global concepts clash with personal demands.

“But how do I feed myself then, if nobody needs my work?” you might ask.

I am glad you did.

You have three choices.

Choices 1–2: you can enter one of the two economies:

1. The ‘gig economy’. And sell your time working as a freelancer on UpWork or an Uber driver. So far, this is the easiest and fastest way to make cash.
2. The ‘passion economy’. And make money by being who you are. Unlike freelancers who sell their time, your main asset is not time — but your audience. This calls for another post, so I won’t go into much detail here. (This is also the subject of my weekly newsletter which you can join here.)

Choice 3: become an entrepreneur and distribute value in some other way — mainly by organizing people from the ‘gig economy’ to create something that has demand from consumers.

And while yes, most people are still working as full-time employees, the old concept of “career” is becoming quickly obsolete — much like the estates and ranks of the medieval ages.

In the next decade or two, we’ll see that these three choices are all you’ve got.

You’re probably not surprised to see the words “gig economy” — as it existed for quite some time. After the COVID pandemic ceases, we’ll see more of it booming, as ex-employees go freelance. You’re also probably not surprised to see entrepreneurship in this, as the media is full of stories of new-born small business owners. Both of these choices are the by-product of our demand-driven economy.

But the words ‘passion economy’ are probably new to you.

That’s because this economy is new, the youngest of the three. It’s nevertheless quite real.

There is a guy who lives on a farm and feeds his animals. Just twenty years ago, he’d be considered as a slacker and hermit. Today he’s a YouTuber, making more than $100K/yr from Patreon subscriptions.

There are writers on this platform without college degrees who work two hours per day and make the same amount of money as someone who went to an Ivy League school and busted their asses off climbing the career ladder.

This economy — whether you call it ‘creative economy’ or ‘passion economy’ or plain-old ‘influencers’ — existed for a decade. It started off with pioneers and legends like Casey Neistat, Joe Rogan, Tim Ferriss, the Kardashians, and others — but now it’s becoming mainstream.

Today we see more and more people making not millions — but solid full-time incomes by just being themselves.

In a way, we’re going back to the roots: some 80,000 years ago, our ancestors spent 99.9% of the time scratching their dicks, and at night they told stories by the campfire.

Now we’re circling back. As the world becomes safer, the population grows, demand grows, more people become redundant and conventional work becomes obsolete, we’ll see a whole new class of professions created, which will allow humans to tell stories for a living.

What else should they do?

Hans Moravec wrote, “In time, almost all humans may work to amuse other humans, while robots run competitive primary industries, like food production and manufacturing.”

We’re accustomed to thinking of such prophecies as sci-fi or things of the distant future, which we might not live to see.

But it’s already here.

Take me, for example. I am 22. If I was born just ten years earlier, I would have been miserable and without a job. Or I would have worked as a journalist for a shitty Russian newspaper, and that’s all. My skills, desires, ambitions would have been unnecessary.

Today though, because we have platforms like Medium, YouTube, Spotify — I can go straight to creating art for a living. I don’t have to take on a “day job” (a term coined in the twentieth century by struggling musicians), I can get paid the instant I create something valuable, interesting, new.

Not only careers become obsolete, but degrees themselves too. It used to be that a degree was a platform. It was something that gave people opportunities: to find like-minded people, to have credibility, to be seen, and hired. Today it’s nothing. When people ask me where I went to college, I usually reply, “Babson”, although I never finished school, and nobody cares.

But this doesn’t mean you don’t need a platform. It just changed form.

The platform of this day and age is the audience. Your people. Network. Its trust.

And just like a good college degree, it can give you a brand, credibility, and — what’s more important — community. You can have $0 in the bank, but if you have an engaged audience that trusts you — you’ll survive.

They say in Medium’s curation guidelines that every article should have a strong CTA (call-to-action) at the end. So here’s mine.

If there’s one thing you take away from this thought piece, let it be this: the world has changed.

You don’t have to live by the old rules. Don’t listen to your parents and don’t trust people over 35 — most of them don’t know what they’re talking about — they were born and raised in another world.

You have three economies to choose from. Working in an office full-time is not one of them — and is a recipe for disaster over the long-term (although now it might be a lucrative option).

But most importantly, what this new world has brought us is power — the ability to choose.

So choose yourself.

The Job of the Future Is Content Creation

Soon We’ll Be Creating Content For a Living was originally published in The Startup on Medium, where people are continuing the conversation by highlighting and responding to this story.

from The Startup – Medium https://medium.com/swlh/soon-well-be-creating-content-for-a-living-b7afdb942f39?source=rss—-f5af2b715248—4

As the United States experiences a coin shortage due to the pandemic, some brick-and-mortar retailers are forcing consumers to tell cashiers to “keep the change.”

Kroger is the largest chain so far to do so, temporarily refusing to give coins out as change. Customers can either round up their bills to the nearest dollar and give the excess to charity or get their change stored on loyalty cards.

“The Federal Reserve is experiencing a significant coin shortage that is impacting our store operations and ability to provide change,” the company wrote on Twitter. “As a result, the company is implementing a new process for providing change to customers. In all staffed lanes, coin change owed to the customer can be applied to your loyalty card and can be used on your next in-store, Pick-Up or Delivery purchase. Alternately, we can round your transaction up to the nearest dollar and donate it to your local foodbank.”

The Fed had said in a June 11 statement that the pandemic “has significantly disrupted the supply chain and normal circulation patterns for U.S. coin. In the past few months, coin deposits from depository institutions to the Federal Reserve have declined significantly, and the U.S. Mint’s production of coin also decreased due to measures put in place to protect its employees.”

Kroger isn’t alone in attempting to make customers do without coins amid the shortage. Some stores in the Giant Food chain are reportedly doing the same. And convenience store chain Wawa is reportedly asking shoppers to pay through exact change, debit cards, credit cards or the Wawa mobile app if they can, although the request isn’t mandatory.

Some cash-paying customers might dislike such moves, but putting change on loyalty cards could be a real boon for merchants. For openers, shoppers who don’t already have loyalty cards for a given chain might sign up just to keep from losing their change. Not only will those customers give the grocers some personal information in doing so, but they’ll presumably revisit the retailer in the future to spend their unused change.

Grocers are already tying loyalty programs to the contactless payment systems that many customers seem to favor in a post-pandemic world.

For instance, Kroger launched its Kroger Pay contactless payment option in April. The system allows consumers to link their payment information to their loyalty account at the supermarket chain through an app, then use a QR code for payment at checkouts or self-checkouts.

Similarly, southeast U.S. grocery chain Publix recently unveiled a new loyalty program called Club Publix. It brings together digital features, including a branded digital wallet, the choice to receive eReceipts and early notifications of promotions.

If the U.S. coin shortage grows, more and more retailers could force shoppers to “keep the change” on their loyalty cards. That might be a real positive for retailers.

from News – PYMNTS.com https://www.pymnts.com/news/retail/2020/how-retailers-are-cashing-in-on-a-us-coin-shortage/

Do you know them all?

JavaScript keeps adding new and neat features. Sometimes, it’s hard to keep up. In this article, I’ll share a couple of cool tips & tricks to keep you up to speed and deepen your JS knowledge.

1. Create an array with unique values using the “Set” object

Imagine having an array with some duplicate items and wanting to filter out only the unique ones.

You could try writing a map or filter to achieve this. Alternatively, ES6 introduces the Set object, which solves this problem in just 1 line of code.

const arrayWithUniqueItems = [...new Set([1, 2, 3, 3,])]
// [1, 2, 3]

Now, this example uses integers, but you can use strings and floating-point numbers as well!

For a little more in-depth knowledge about the Set object, check out this article by Claire-Parker Jones.

2. Shorten your “if” statements

Now this is a tricky one.

Shortening your “if” statements can be a great way to simplify your code.

However, if you need to write more complicated statements, you should definitely go for the first option.

// Instead of using this                                      
if (iAmHungry) {
   bakeAnEgg()
}

// You can use this
if (iAmHungry) bakeAnEgg()

// Or this
iAmHungry? bakeAnEgg() : 0

Remember, readability & ease-of-use are more important than a couple less lines of code.

3. Shorten an array using its length property

A great way of shortening an array is by redefining its length property.

let array = [0, 1, 2, 3, 4, 5, 6, 6, 8, 9]
array.length = 4

// Result: [0, 1, 2, 3]

Important to know though is that this is a destructive way of changing the array. This means you lose all the other values that used to be in the array.

4. Using the spread operator to combine objects

Let’s say you want to combine multiple objects into one object containing them all.

The spread operator ( … ) is a great way to achieve this!

const obj1 = {'a': 1, 'b': 2}
const obj2 = {'c': 3}
const obj3 = {'d': 4}

// Combine them using the spread operator            
const objCombined = {...obj1, ...obj2, ...obj3}

// Result: {'a': 1, 'b': 2, 'c': 3, 'd': 4}

Something to keep in mind while using this is that whenever you update one of the objects, it doesn’t reflect those changes in the combined object.

5. Using the window.location object

JavaScript can access the current URL using the window.location object. Pretty neat, but even cooler is that this object contains certain parts of the URL as well.

Get access to the protocol/host/pathname/search/and more!

// JavaScript can access the current URL in parts. For this URL:
`https://thatsanegg.com/example/index.html?s=article`

window.location.protocol == `https:`
window.location.host == `thatsanegg.com`
window.location.pathname == `/example/index.html`
window.location.search == `?s=article`

That’s all!

Thanks for reading, look at how much you’ve learned 😄

This article was originally posted on “That’s an Egg” 🍳

5 Must-know Javascript Tips & Tricks was originally published in Prototypr on Medium, where people are continuing the conversation by highlighting and responding to this story.

from Prototypr https://blog.prototypr.io/5-must-know-javascript-tips-tricks-d1a04e9014de?source=rss—-eb297ea1161a—4

from Big Data – VentureBeat https://venturebeat.com/2020/07/11/announcing-the-second-annual-venturebeat-ai-innovation-awards-at-transform-2020/

from I need a guide http://inagblog.com/2020/07/jesse-stone/?utm_source=rss&utm_medium=rss&utm_campaign=jesse-stone&utm_source=rss&utm_medium=rss&utm_campaign=jesse-stone

“Our intelligence is what makes us human, and AI is an extension of that quality”. -Yann LeCun, Professor at NYU

Introduction to Generative AI and Markov Chains

Generative AI is a popular topic in the field of Machine Learning and Artificial Intelligence, whose task, as the name suggests, is to generate new data.

There are quite a few ways in which such AI Models are trained , like using Recurrent Neural Networks, Generative Adversarial Networks, Markov Chains etc.

In this article, we are going to look at Markov Chains and understand how they work.We won’t dive deep into the mathematics behind it, as this article is simply meant to get you comfortable with the concept of Markov Chains

Markov Chains are models which describe a sequence of possible events in which probability of the next event occuring depends on the present state the working agent is in.

This may sound confusing, but it’ll become much clearer as we go along in this article. We will be covering the following topics:

• Concept of Markov Chains
• Application of Markov Chains in Generative AI
• Limitations of Markov Chains

Concept Of Markov Chains

A Markov Chain model predicts a sequence of datapoints after a given input data. This generated sequence is a combination of different elements based on the probability of each them occuring immediately after our test data. The length of the input and output data sequences depends on the order of the Markov Chain — which will be explained later in this article.

To explain it simply, lets take an example of a Text Generation AI. This AI can construct sentences if you pass a test word and specify the number of words the sentence must contain.

Before going further, lets first understand how a Markov Chain model for text generation is designed. Suppose you want to make an AI that generates stories in the style of a certain author. You would start by collecting a bunch of stories by this author. Your training code will read this text and form a vocabulary i.e list out the unique words used in the entire text.

After this, a key-value pair is created for each word, where the key is the word itself, and the value is a list of all words that have occured immediately after this key. This entire collection of key-value pairs is basically your Markov Chain model.

Now, lets get on with our example of a Text Generation AI. Here’s a snippet of an example model

This is just a snippet. For the sake of simplicity, I have shown key-value pairs for only 4 words.

Now, you pass it a test word, say “the”. As you can see from the image, the words that have appeared after “the” are “new”, “apple”, “dog”, “cat”,“chair” and “hair”. Since they all have occured exactly once, there is an equal chance of either of them appearing right after “the”.

The code will randomly pick a word from this list. Lets say it picked “apple”. So, now you’ve got a part of a sentence : “the apple”. Now the exact same process will be repeated on the word “apple” to get the next word. Lets say it is “is”.

Now the portion of sentence you have is : “the apple is”. Similarly, this process is run on the word “is” and so on until you get a sentence containing your desired number of words (which is the number of time you will run the program in a loop). Here’s a simplified chart of it all.

As you can see, our output from the test word “the” is “the apple is delicious”. It is also possible that a sentence like “the chair has juice” (assuming “has” is one of the values in the key-value list of the word “chair”) is formed.

The relevance of the generated sentences will directly depend on the amount of data you have used for training. The more data you have, the more vocabulary your model will develop.

One of the major things to note is that the more number of times a particular word occurs after a certain test word in your training data, the higher is the probability of it occuring in your final output.

For example, if in your training data , the phrase “the apple” has occured 100 times, and “the chair” has occured 50 times, in your final output, for the test word “the”, “apple” has a higher probability of occuring than “chair”.

This is based on the basic probability rules

Now, lets look at a term we came across earlier in this section : Order of a Markov Chain

Order Of A Markov Chain

The order of the Markov Chain is basically how much “memory” your model has. For example, in a Text Generation AI, your model could look at ,say,4 words and then predict the next word. This “4” is the “memory” of your model, or the “order of your Markov Chain”.

The design of your Markov Chain model depends on this order. Lets take a look at some snippets from models of different orders

This is the basic concept and working of Markov Chains.

Lets take a look at some ways you can apply Markov Chains for your Generative AI projects

Application of Markov Chains in Generative AI

“Talking to yourself afterwards is ‘The Road To Success’. Discussing the Challenges in the room makes you believe in them after a while”

—Generated by TweetMakersAI

Markov Chains are a great way to implement a ML code, as training is quite fast, and not too heavy on an average CPU.

Although you won’t be able to develop complex projects like face generation like that made by NVIDIA, there’s still a lot you can do with Markov Chains in Text Generation.

They work great with text generation as there isn’t much effort required to make the sentences make sense. The thumb rule (as is for most ML algorithms) is that the more relevant data you have, the higher accuracy you will achieve.

Here are a few applications of Text Generation AI with Markov Chains

• Chat Bot: With a huge dataset of conversations about a particular topic, you could develop your own chatbot using Markov Chains. Although they require a seed (test word) to begin the text generation, various NLP techniques can be used to get the seed from the client’s response. Neural Networks work the best when it comes to chat bots, no doubt, but using Markov Chains is a good way for a beginner to get familiar with both the concepts — Markov Chains, and Chat Bots.
• Story Writing: Say your language teacher asked you to write a story. Now, wouldn’t it be fun if you were able to come up with a story inspired by your favourite author? This is the easiest thing to do with Markov Chains. You can gather a large dataset of all stories/books written by an author (or more if you really want to mix different writing styles), and train a Markov Chain model on those. You will be surprised by the result it generates. It is a fun activity which I would highly recommend for Markov Chain Beginners.

There are countless things you can do in Text Generation with Markov Chains if you use your imagination.

A fun project that uses Generative AI, is TweetMakers. This site generates fake tweets in the style of certain Twitter users.

As an AI enthusiast, and a meme lover, I believe meme creation is going to be a major application for Generative AI. Check out my blog about the sites which have already started doing so.

Although there’s a lot you can do with Markov Chains , they do have certain limitations. Lets have a look a few of them.

Limitations Of Markov Chains

In text generation, Markov Chains can play a huge role. However, there are some minor restrictions to it:

• The seed should exist in the training data: The seed (test phrase or word) which you pass in order to generate a sentence, must exist in the key-value pairs collection of your Markov Model. This is because the way these Chains work is that they get the next word based on which words have occured after the seed and with what frequency. This is the reason most Text Generation AI bots don’t take any user input, instead select a seed from the existing data.
• Might Generate Incomplete Sentences: Markov Chains cannot understand whether a sentence is complete or not. It’ll simply generate words the number of time you run the code in a loop. For example, a sentence like “This is a new” can be generated. Very clearly, this sentence is incomplete. Although Markov Chains cannot tell you if the sentence is complete or not, various NLP techniques can be used to get a complete sentence as an output.

Markov Chains are a basic method for text generation. Although their output can directly be used for various purposes, you will inevitably have to do some post-processing on the output to achieve complex tasks

Conclusion

Markov Chains are a great way to get started with Generative AI, with a lot of potential to accomplish a wide variety of tasks.

Generative AI is a popular topic in ML/AI, so it is a good idea for anyone looking to make a career in this field to get into it, and for absolute beginners, Markov Chains is the way to go.

I hope this article was helpful and you enjoyed it 🙂

Machine Learning Algorithms: Markov Chains was originally published in The Startup on Medium, where people are continuing the conversation by highlighting and responding to this story.

from The Startup – Medium https://medium.com/swlh/machine-learning-algorithms-markov-chains-8e62290bfe12?source=rss—-f5af2b715248—4

from VentureBeat https://venturebeat.com/2020/07/11/announcing-the-second-annual-venturebeat-ai-innovation-awards-at-transform-2020/

Ebikes are hot right now. Coronavirus lockdown measures have spurred people to find socially distanced forms of transport and many are choosing bicycles. Sales are soaring, and in some places supply is hard to come by. So what do you do if you can’t get hold of one? Sabotage your washing machine to build your own ebike, of course! That’s what one enterprising home-engineer did recently, and yes, they’ve named it: “The Spin Cycle.” Nice. [Read: EV fans deplete Dutch gov’s €10M electric vehicle grant in just 8 days] The picture below tells you everything you need to know to…

This story continues at The Next Web

from The Next Web https://thenextweb.com/shift/2020/07/10/110kph-homemade-ebike-powered-washing-machines-parts-spin-cycle/

from The Appcues Blog https://www.appcues.com/blog/personalized-product-examples

from adage.com https://adage.com/article/news/advertisers-block-black-lives-matter-keywords-while-public-support-movement-skyrockets-thursday-wake/2261591