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Thủy Singer On Coachella And Chasing Your Dreams

With Pride Month right around the corner, Acrisure Arena is making an effort to show their support to the LGBTQ+ arena. Their most popular crosswalk near the Main Plaza has been painted in rainbow colors, they are calling it the “Pathway to Pride.” This comes just ahead of game one of the ...

Acrisure Arena paves the way for Pride Month in the Coachella Valley - KESQ

Desert Sun readers sound off about current events in today&#x27;s letters to the editor.

Coachella Valley needs train expansion, fast

Palm Springs Life Magazine in Palm Springs California - is the complete online source for everything Palm Springs California

Where to Find Vinyl Records in the Coachella Valley

Coachello

coachello

I get often asked how to start with Machine Learning. But then I consider myself a maker. I truly believe experience is key and solving actual real world applications are the key to unlock the mysteries. Once you have your first success at building and understanding a solution that solved your problem you can dig deeper and refine the building blocks. The problem that we faced, (see ), was that we have a multitude of data vendors providing us with event information. All of these vendors use different classifications and classification systems for their event data. Data quality is not that good and in some cases there are even different classifications for the same event coming from the same vendor. I believe many company world-wide experience the same problem in one way or the other. The problem is, how can we maintain a high quality data-set to ensure a good experience for our app users? Part 1 Manually and daily refining mapping tables for new categories is not feasible without hiring. Since we are still a small team and can’t manually maintain global mapping tables, we choose to apply a combination of data science, statistics, and algorithms commonly known as machine learning to solve the problem for us globally, reliably, swiftly, and automatically. In this article we will be covering the following steps that guided us through our journey. Understand your data What is a Vector Space Model (VSM)? Prepare the data for the TFIDF vectorizer Working with the TFDIF Vectorizer Applying the Multinomial Native Bayes Store the model for production usage Understand your data Our data is stored in a common MySQL database. Might be boring, but MySQL is a proven technology that scales well. The tables in the DB are populated once a drop!in app user requests data for a given city. If we can not find an dataset in the cached table or the dataset is expired we re-query the data vendors for an update. The data we source usually includes only the next 48 hours to minimize the search volume and thus the request time. Since there are many data vendors we are getting a variety of data formats from these vendors. For this analysis we are focusing on the event descriptions and classifications that we receive from the data vendors and how we can make sense from it. We will extract the data from our MySQL server using the Python MySQL connector. First we have to connect to the DB and setup a cursor. import MySQLdbimport MySQLdb as Databaseimport MySQLdb.cursors db = MySQLdb.connect(host=”localhost”, port=3306, user=”***”, passwd=”*****”, db=”****”) try:cursor = db.cursor( ) MySQLdb.cursors.DictCursor Defining the cursor as a DictCursor returns a dictionary. This is important since we will not be using tuples for the data preparation. Further, we will be working with UTF-8 encoded strings in Japanese, Chinese, German, French, Spanish, English and several other languages. That’s why we need to ensure the connection and character set matches the data. cursor.execute(‘SET NAMES utf8;’)cursor.execute(“SET CHARACTER SET utf8;”)cursor.execute(“SET character_set_connection=utf8;”) You don’t want that the algorithm breaks on irregularly encodedinformation. Both of these seem minor and unimportant but will throw violent error messages stealing likely hours of your development time when you run into them. Next you need to define how large your sample size should be helps you test the loose ends before you start running your algorithm over the full data set likely taking hours of your time. sampleSize = 10000000 cursor.execute(“””SELECT distinct id, title,description,    category_id, category_name FROM databasename.tblnamelimit %s “”” , ) (sampleSize,) result =cursor.fetchall() except MySQLdb.Error, e:print “MySQL Error: %s” % str(e)db.rollback() Here again, the little comma after the sample size is easy overlooked. Why the tuple you might ask? . That’s why. Further you should always use a try/except block when working with MySQL cursors to make sure you are handling the MySQL errors in the right way. Because the DB API requires it Now that we can receive the data from the DB we can have a more detailed look at the classification data we get from the vendors and we soon notice a beautiful chaos of words and categories. In our sample we observed roughly 500 distinct event categories. Since drop!in is used by thousands of people daily everywhere on the planet, how can we now ensure that all of them get a consistent and easy to use event mapping ? word cloud from all global events in the sample (500K+ events) Therefore we need to map the 500 event categories to a predefined number of categories. We chose 12 categories because based on our data research in information density 12 is the optimal number of categories to present in a mobile app. So for the app and the initial expert mapping we had defined 12 meaningful categories and one ‘others’ category that was supposed to catch the fall-backs. In data, similar to Accounting standards ‘others’ is never a good thing because it has no real meaning. Still we can observe a large population ending up in ‘other’. Not a good thing. For creating the predictive model, we will be excluding this ‘Other’ segment from the analysis. In the end, we don’t want the ‘Other’ segment to exist. So the predictive model should be able to bin it into a real segment. In addition, we will exclude all events where the description is less than 15 characters to make sure that the algorithm has enough words to choose from. Since we now have a useful sample how can we get from the text to the predictive model? The missing link between machine learning models and our classification problem is that the data is not numerical. So the statistical methods can not work with them. Therefore, the next problem we have to solve is how can we convert our text into numbers that have meaning and are able to differentiate? The Vector Space Model is the solution to this problem. What is a Vector Space Model (VSM) ? VSM is an algebraic model representing textual information as a vector. A simplistic explanation could be this. Assume you have a given dictionary of classifications (“Arts”, “Entertainment”, “Sports”,”Technology”) then you could encode these 4 events as the vectors : Arts          = (0,0,0,1)Entertainment = (0,0,1,0)Sports        = (0,1,0,0)Technology    = (1,0,0,0) However, this assignment of vectors does not help much for the classification problem since neither of these vectors represent the importance of a term. In addition, working off the already existing classifications will not help as well since we can not extract sufficient differentiation from these short words. This is where the event descriptions come in. Every single event in the data set has a descriptive element to it with up to 2048 characters in length. From this document corpus we get sentences. These sentences can be converted into the vectors we need. One random example we get from the sample could look like this : With her US live show debut at Coachella, her debut album Run, debuting at #1 on the US Electronic Billboard charts, a US tour taking in stops in NYC, Miami and Chicago with EDC Las Vegas, Lollapalooza and a HUGE sold out Australian Warehouse tour to come, 2015 is a year to remember for Alison Wonderland.The way you hear her tell it, Alison Wonderland has wanted to name her first album Run since before she knew how to play an instrument. We are using the already introduced Term Frequency — Inverse Document Frequency (TF-IDF) method. As already outlined in , TF-IDF is a well known method to evaluate the relative importance of a word in a document corpus. Part 1 In Python, a great TF-IDF vectorizer comes handily as part of the sklearn package. So, for our Python implementation we import first the relevant packages, plus the TF-IFD vectorizer. In addition to the sklearn package we will import which will help us store the completed model back in the db (LongBlob), and to split our original dataset into a training and a testing dataset. NumPy is a fundamental package for scientific computing in Python. cPickle model_selection import sklearnimport cPickleimport numpy as npfrom sklearn import model_selection from sklearn.feature_extraction.text import TfidfVectorizer Prepare the data for the TF-IDF vectorizer Before we can start working with the TF-IDF Vectorizer we need to prepare the data so the algorithm can function correctly. TF-IDF expects to get a list as input. Since we are in the domain of supervised learning we need to provide the algorithm with X (The dataset with the features) and Y (the dataset with the labels). Each list has to be the exact length of the observations found in the sample dataset from the database. So the first thing after we pushed the entire sample into a dictionary result-set is initialize the X and Y list variables. In addition, we keep track of the cursor state with the counter variable ‘i’. result =cursor.fetchall()i=0X = [None] * cursor.rowcountY = [None] * cursor.rowcount Once this is done we simply loop over the result set and pass the data from the variables in the lists. A few things to note here. It took us some tries to get the right mix of descriptive data together. In the end we kept ‘description’ as it has the relevant terms ‘Coachella’ or ‘EDM’. Secondly, we pass the vendors classification at it’s highest granularity level. X[i]=row[‘ ’] +” “ +row[‘ ’].replace(“/”, “ “)Y[i]=row[‘ ’]i=i+1 for row in result: description category_name cat If you take ‘Recreation/Arts and Crafts/Leather works from the above example. This step creates three distinct items from this list ‘Recreation’, ‘Arts and Crafts’, and ‘Leather works’. The column ‘cat’ holds our previously confirmed correctly working mapping. In the next step we randomly split the feature set into a training and a verification (testing) sample. test_size = 0.5seed = 7X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed) Test_size should be a number between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. The seed variable is used for the random sampling from the dataset. In the first shot we set the test_size to 0.5 so we can evenly split the dataset. The train_test_split function returns 4 datasets X_train (the training features data), X_test (the testing features data), Y_train (the training labels), and Y_test (the testing labels) Working with the TF-DIF Vectorizer Now we have prepared the datasets to train our first model. First we create a TF-IDF Vectorizer object. The overall task of this object will be to convert our list of event descriptions to a matrix of TF-IDF features. We are not passing an encoding since the default is ‘UTF-8’, fortunately we had prepared above our data to be in that encoding. The strip_accents removes all accents from the event descriptions so ‘Montréal’ becomes ‘Montreal’. vectorizer = TfidfVectorizer(strip_accents=’unicode’) X_vectors = vectorizer.fit_transform(X_train) The second line here uses the defined TF-IDF vectorizer and fit_transforms the training feature data-set. I.e., the algorithm learns the vocabulary and idf’s and then returns a term-document matrix. The term document matrix describes which words exist in which document in the sample and has as value the calculated IF-IDF score. source : http://web.eecs.utk.edu/~mberry/sc95/gif/berry_table402.gif Now one major portion of our project is done. We have successfully created numerical expressions from our text data. As the next step we have to use these numerical expressions of our texts to build our forecasting model. In this example we will use the Multinomial Naive Bayes (MNB) classifier. The MNB is a simple probabilistic classifiers which applies Bayes’ theorem. Bayes Theorem helps us to predict the sequence of words that have led to the given classification. The benefit of using Bayes’ is that this method assumes a strong independence assumptions between the features. = ().fit(X_vectors, Y_train)train_score = classifier.score(X_vectors, Y_train) classifier MultinomialNB print “Scored an accuracy of %s “ % (train_score)> Scored an accuracy of 0.93206467363 The training function uses the previously created vectors plus the related labels to create the classification. Once this is done we can score the result. The scoring function returns the mean accuracy on the given test data and labels. In our example the accuracy reaches 93.2% which is better than what we had hoped for. pickledModel = cPickle.dumps(classifier)filename = ‘finalized_model.sav’cPickle.dump(classifier, open(filename, ‘wb’)) Since we are satisfied with the results we can store the model to save it for later. Thank you for reading all the way down to here. In part 3 (coming soon) we will show how to bring this into real world production. This article was brought to you by tenqyu , a startups making urban living more fun, healthy, inclusive, and thriving using big data, machine learning, and LOTs of creativity.

How to Better Classify Coachella With Machine Learning (Part 2)

This is the time when the cold winter month are ending. Temperatures are slowly rising, flowers are blossoming, the faint scent of BBQ in the air, and we soon start dancing outside to pulsating beats surrounded by a group of sweaty people. It’s time for Summer Festivals. It’s time for the Coachella Festival to kick off the season of large outdoor live music weekend events around the United States. For the few uninitiated, t is an annual event held at the Empire Polo Club in Indio, California. It attracts hundred- thousands of visitors each year since 1999 to enjoy an increasingly broad lineup of artists previously considered off the main stream. Some critics might say Coachella tries to be everything for every one, thus losing character. Other’s would say more people attended Lady Gaga’s Coachella concert than Donald Trump’s inauguration. But who am I to judge? he Coachella Music and Arts Festival But what exactly is Coachella? Is it a music concert, an art exhibition, or just a sea of rich hot people swaying? Even we humans struggle with classifying such events. Machines even more so. For machines ‘music’ , ‘arts’ , or ‘ swaying humans’ are just series of 1’s and zeroes’ and the infinite amount of rational numbers in between. Categories matter. People interpret what they see quickly and automatically thus recognising familiar objects as members of categories. That helps us to make decisions fast. Categories allows humans and machines to effectively find the information they need. On the other hand, I personally would be very interested to see what would happen if we store items in the supermarket by alphabet. Aisle A has Apples, Aspirin, and Avocados. Aisle B has Bananas, Bread, Butter, and Baking Powder. But then alphabets in itself are again a categorization. Let’s just distribute the goods randomly and change it every day. But who would do such a thing ? I guess we need to save the day. Jane’s Addiction’s Perry Farrell Categorizations are closely related to our experiences and preferences. Therefore, correct classification is a crucial part of the overall experience of a service or good we purchase. For machines, categorization is a hard task and teaching machines to support our human categorization needs is difficult. Radiohead surely had a story to tell this year when technical difficulties blighted their performance. Here, I admit publicly to being a fan since ‘ ’. Great album, btw. Pablo Honey So how can you teach machines to categorize better? Our popular has daily active users worldwide. There are people in every corner of this planet from Belize to Marakkesh and Melbourne to Tokyo who are daily looking for nearby events happening now. And of course is the best app for this. So many events actually, that already in the first year of operations our users have generated 601,923 events on our platform with hundreds of millions of interactions between them. drop!in app drop!in Event volume by region 2016 The problem that we have operationally, is that we can not afford to have people in each country manually or system supported categorize the events correctly. Which leads to unhappy users which rely on looking for sports events and not food events. This is where machine learning comes in. Here is the battle plan: Since we have already classified events in the past correctly we use our event data as the source for supervised learning and exclude the data where we know the classification was not correct. In more detail, we want to use the description of the specific events to give us the to classify the event correctly. bag of words The Yuma tent is still the festival’s best-kept secret It always starts with a problem, right ? The problem is that the raw data we have is variable size text which cannot be fed directly to ML algorithms because they require fixed size numerical feature vectors. The solution to our problem is combining TF-IDF vectorization with multinomial Naive Bayes classification. We use Multinomial Naive Bayes classification is a a probabilistic learning method because it calculates the likelihood of given event description to be of a given event category. We will assume that the words in an event description are conditionally independent for a given category. For instance, if for a given event description cat= 1 means ‘Arts’ “Coachella” is word 420 and “sweaty” is word 1036; then we are assuming that if I tell you that particular event is ‘Arts’, then the knowledge of word 420 (if “Coachella” appears in the event description) will have no effect on whether “sweaty” appears 1036. The second component is T vectorization which follows two main concepts. f-idf The of a term that occurs in a document is simply proportional to the term frequency. (The Luhn Assumption 1957) weight The of a term can be quantified as an inverse function of the number of documents in which it occurs. specificity So when Radiohead plays Coachella and many people tweet (or instagram) about it, and these words jointly show up in each description then the weight of the terms increases. Of course if people are complaining about the long lines and sweaty people at Coachella in a separate thread then the specificity of Radiohead increases and the specificity of Coachella decreases. Sounds straightforward, right ? Oh Alice! Down the rabbit hole we go A wordcloud made from 50,000 events in New York in 2016 We will calculate tf–idf as the product of term frequency (tf) and inverse document frequency (idf). But before we can start calculating we need to generate ‘same length’ vectors based on the description fields of our events. The vectorization of a body of text assigns each word in the event description a number. Fortunately, Python has a TfidfVectorizer in the fabulous sklearn package. For the algorithm to work as intended, very common words, such as “a” or “the”, are like Madonna on MTV. They are awesome but are so common that they receive heavily discounted tf-idf scores. The result of the calculation is a matrix of tf-idf scores with one row per document and as many columns as there are different words in the dataset. If the text is too long we need to find ways to reduce dimensionality. Fortunately in our dataset the max length of observations was only 102 words (average 673.1 characters) based on the notion that the text needed to be displayed on the mobile phone. Thank you for reading all the way down to here. In we will show the actual implementation in Python. part 2 This article was brought to you by tenqyu , a startups making urban living more fun, healthy, inclusive, and thriving using big data, machine learning, and LOTs of creativity. [1] https://www.coachella.com/ [2] http://cs229.stanford.edu/notes/cs229-notes2.pdf

How to Better Classify Coachella With Machine Learning (Part 1)

Here's a new problem, and a new set of jobs for humans, that will be caused as more and more Robotaxis show up (think of an Uber that works without a human in the car). What is the problem? Transportation behavior will change as we get more and more vehicles on the road with no humans inside. Something that's already happening in San Francisco, where you see many autonomous vehicles driving around with no humans inside from companies like Waymo, Zoox, and others. Expected are many others, including Tesla, and GM's Cruise which we hear will be back on the road later this year. The problem? Football stadiums, concerts, basketball stadiums, conferences, events like the Olympics, all will see radically different usage patterns. For instance, today, if you attend a football game, you park in one of dozens of parking lots around the stadium and you walk in, or take a shuttle to one of the entrances. This adds some "randomness" to the flow of crowds in and out and lets city officials who plan out traffic flows bring traffic to those parking lots with a few different routes. Even today it's often a mess with traffic taking an hour to get into many venues with large numbers of people but officials, and others, who work in these industries tell me autonomous vehicles will make traffic far worse. Why? Well, let's assume in five years you are in a Cybertruck taking your family to a football game and that by then the Cybertruck can drive with no humans in the car. Will you want your Cybertruck to take you to a far off parking lot and make you walk to the stadium? No. You'll want it to drop you off near the football stadium. And here's the rub: so will everyone else. Which means you'll have thousands of Waymos, Cruises, Teslas, all trying to get to the same drop-off point. This new human expectation will lead to traffic nightmares. Worse, I've been talking with executives at these kinds of venues and they say the coordination between sports teams, city officials/police, and ride-sharing companies is nearly non-existent.One told me they tried to get Uber and Lyft to come meet with them to design new traffic flows for their vehicles and they just don't get very little help or interest from the ride-sharing companies. One, who works with the 49ers, tells me city officials often put up signs for Uber/Lyft drivers that lead them into dead-end roads where they need to waste time turning around and figuring out where they need to go. Signage, cones, road closures, all could be done in a much better way around venues. And that's before you consider the humans. Often crowds of humans getting out of venues are drunk, don't follow traffic rules, and, sometimes, even turn riotous. Has the autonomous vehicle industry worked with venues to understand how traffic systems change around event venues? No, is the answer I keep hearing, and getting Waymo or Tesla interested in studying the problem together and designing new traffic facilities around big-crowd venues is difficult. Oh, it can happen. At Coachella, for instance, Uber built a huge parking lot with waiting areas for humans that were covered (it's hot often waiting for your ride to show up). Uber also built contextual systems to work with drivers around the Western United States to convince drivers to come to Palm Springs for a month and drive people around (Coachella is two weekends, where 150,000 people visit each weekend, followed by a country music festival that has slightly smaller crowds, but Uber, alone, brings thousands of cars to handle the crowds of people who want to use it to get to and from the venue). Along with the parking lot city officials block off lanes and have clear signs for Uber/Lyft/Taxis to use and have police at every intersection directing traffic and making sure things flow smoothly. Even with all that work it often takes one to three hours of standing in line to get a Taxi or an Uber out of the venue. Now imagine if thousands of people were arriving in autonomous vehicles. If the vehicles weren't programmed into where to go, and if Tesla doesn't have its own drop off and pickup lot things could get nasty, and fast. Last week at NVIDIA's big event my Tesla was programmed to pick up a friend at the Marriott. Problem was it took me down a route that is usually just fine, but during that event NVIDIA and city officials had the street in front of the conference center closed due to the large crowds that were attending that event. My Tesla didn't know about the closed street so it tried to take me down that closed street, figured it out too late, and then got stuck in traffic going on a detour around, wasting 15 minutes. Now 15 minutes doesn't seem like a big deal, but it is if I was driving an Uber passenger. Why? Well, first of all now I have a passenger who isn't happy with the navigation choices the car made, maybe giving me a lower score than I otherwise would get, but, worse, if I was picking up a passenger that person might see that I'm now 15 minutes away instead of two and decide to hop in a cab or cancel my ride. Wasted time, which means if I'm driving an Uber I don't get paid. There also is a difference between permanent venues, like a football stadium, which have the same problems every weekend (while football isn't being played these stadiums are often used for other things, like concerts) and music festivals that only happen one or two weekends a year, like Coachella. There aren't enough vehicles to support these temporary crowds so vehicles have to be moved into that area. There are dozens of such festivals all over the world, and that's counting just the ones with 50,000 attendees or more, like EDC in Vegas, Glastonbury in UK, etc. So, a company, like a Tesla, which is expected to have humanless vehicles driving in most Western Countries, would need to work with dozens of venues to setup drop off and pick up zones, and with local police to understand how to work with them so that they understand how to work with autonomous vehicles. Once in a while one of those will get stuck, for instance, maybe with a blown tire or an equipment malfunction, or, worse, an accident. At SXSW a drunk driver plowed through crowds of people one year, killing many, and causing a major first-responder response. In such a situation all the constituencies would need to work together to change traffic flows to make sure more vehicles aren't drawn to the area, or interfere with first responders. Is there a relationship lead between all these constituencies? I hear there is not, except in the best of cases. So, this leads to a new job for humans. AI's can't attend city council or meetings with police. AI's can't often figure out why all traffic has stopped. Once, at Half Moon Bay's pumpkin festival it started to rain. Everyone headed for their cars at the same time, but both Waze and Apple Maps continued to give incorrect data for an hour, both on recommendations for what street to head down, and on time it will take to get out of the area. These systems are built for normal traffic flows, and often can't understand that police have even reversed traffic, so that cars are going down the wrong way on a road. Which leads to the AI. Uber's founder, Travis Kalanick, told me his goal is to have one car near someone who is calling for a ride. That way the ride will arrive in less than five minutes and everyone is happy. Uber built a complex "traffic control system" that hooks drivers and passengers up together. That system usually works great, but when high-crowd events show up these systems often mislead drivers and passengers alike. Waymo is, so far, punting, and refuses to go near these events, and has a very small number of passengers today. Even if you live in San Francisco you can't just get a Waymo, you have to wait to be added to the system and they are adding new passengers slowly so that the whole system remains healthy and doesn't hit these large-crowd issues yet. That will change as they scale up the system to be in many more cities over the next few years and open up the system to everyone, not just a limited number of beta testers. So, to wrap it up, soon entrepreneurs, like Elon Musk, who is making plans for bringing its millions of vehicles online to do human-less driving, will need to do new AI modeling work for how cars should behave around high-crowd events, leading to new traffic-control systems to bring thousands of cars in and out of these venues, and will need teams of humans working with venues and cities to build new drop-off-areas for autonomous vehicles to drop off and pick up people. That is a human job, not one that an AI or robot can do. And a company that has aspirations for building a global network of autonomous vehicles, will need many such workers to work with local customs/officials/venues/teams/events. I see very little modeling work, although I saw MIT Media Lab doing some early work to understand how cities would change due to autonomous vehicles, and I don't see anyone in the AI/Autonomous Vehicle industry talking about research/work in this areas. (And yes, I do have a list of those people here on X: https://x.com/scobleizer/lists) I'm doing this post to try to spur industry interest in working on these issues and to connect people who may already be working on them. Are you positioned for these new jobs? If you are, these are fun things to work on and you might even get free tickets to go see concerts or football games for you and your family. I got taken to a Taylor Swift concert by an entrepreneur who built technology systems for the local stadium, so I know it's possible. :-) Technology solves old problems and causes new problems. Which is why I'm still on team human. AI's aren't good at figuring this stuff out and I don't see them getting good at figuring them out, either, for years. Sounds like a good job to have for a while. Here's a new problem, and a new set of jobs for humans, that will be caused as more and more Robotaxis show up (think of an Uber that works without a human in the car). What is the problem? Transportation behavior will change as we get more and more vehicles on the road with no humans inside. Something that's already happening in San Francisco, where you see many autonomous vehicles driving around with no humans inside from companies like Waymo, Zoox, and others. Expected are many others, including Tesla, and GM's Cruise which we hear will be back on the road later this year. The problem? Football stadiums, concerts, basketball stadiums, conferences, events like the Olympics, all will see radically different usage patterns. For instance, today, if you attend a football game, you park in one of dozens of parking lots around the stadium and you walk in, or take a shuttle to one of the entrances. This adds some "randomness" to the flow of crowds in and out and lets city officials who plan out traffic flows bring traffic to those parking lots with a few different routes. Even today it's often a mess with traffic taking an hour to get into many venues with large numbers of people but officials, and others, who work in these industries tell me autonomous vehicles will make traffic far worse. Why? Well, let's assume in five years you are in a Cybertruck taking your family to a football game and that by then the Cybertruck can drive with no humans in the car. Will you want your Cybertruck to take you to a far off parking lot and make you walk to the stadium? No. You'll want it to drop you off near the football stadium. And here's the rub: so will everyone else. Which means you'll have thousands of Waymos, Cruises, Teslas, all trying to get to the same drop-off point. This new human expectation will lead to traffic nightmares. Worse, I've been talking with executives at these kinds of venues and they say the coordination between sports teams, city officials/police, and ride-sharing companies is nearly non-existent.One told me they tried to get Uber and Lyft to come meet with them to design new traffic flows for their vehicles and they just don't get very little help or interest from the ride-sharing companies. One, who works with the 49ers, tells me city officials often put up signs for Uber/Lyft drivers that lead them into dead-end roads where they need to waste time turning around and figuring out where they need to go. Signage, cones, road closures, all could be done in a much better way around venues. And that's before you consider the humans. Often crowds of humans getting out of venues are drunk, don't follow traffic rules, and, sometimes, even turn riotous. Has the autonomous vehicle industry worked with venues to understand how traffic systems change around event venues? No, is the answer I keep hearing, and getting Waymo or Tesla interested in studying the problem together and designing new traffic facilities around big-crowd venues is difficult. Oh, it can happen. At Coachella, for instance, Uber built a huge parking lot with waiting areas for humans that were covered (it's hot often waiting for your ride to show up). Uber also built contextual systems to work with drivers around the Western United States to convince drivers to come to Palm Springs for a month and drive people around (Coachella is two weekends, where 150,000 people visit each weekend, followed by a country music festival that has slightly smaller crowds, but Uber, alone, brings thousands of cars to handle the crowds of people who want to use it to get to and from the venue). Along with the parking lot city officials block off lanes and have clear signs for Uber/Lyft/Taxis to use and have police at every intersection directing traffic and making sure things flow smoothly. Even with all that work it often takes one to three hours of standing in line to get a Taxi or an Uber out of the venue. Now imagine if thousands of people were arriving in autonomous vehicles. If the vehicles weren't programmed into where to go, and if Tesla doesn't have its own drop off and pickup lot things could get nasty, and fast. Last week at NVIDIA's big event my Tesla was programmed to pick up a friend at the Marriott. Problem was it took me down a route that is usually just fine, but during that event NVIDIA and city officials had the street in front of the conference center closed due to the large crowds that were attending that event. My Tesla didn't know about the closed street so it tried to take me down that closed street, figured it out too late, and then got stuck in traffic going on a detour around, wasting 15 minutes. Now 15 minutes doesn't seem like a big deal, but it is if I was driving an Uber passenger. Why? Well, first of all now I have a passenger who isn't happy with the navigation choices the car made, maybe giving me a lower score than I otherwise would get, but, worse, if I was picking up a passenger that person might see that I'm now 15 minutes away instead of two and decide to hop in a cab or cancel my ride. Wasted time, which means if I'm driving an Uber I don't get paid. There also is a difference between permanent venues, like a football stadium, which have the same problems every weekend (while football isn't being played these stadiums are often used for other things, like concerts) and music festivals that only happen one or two weekends a year, like Coachella. There aren't enough vehicles to support these temporary crowds so vehicles have to be moved into that area. There are dozens of such festivals all over the world, and that's counting just the ones with 50,000 attendees or more, like EDC in Vegas, Glastonbury in UK, etc. So, a company, like a Tesla, which is expected to have humanless vehicles driving in most Western Countries, would need to work with dozens of venues to setup drop off and pick up zones, and with local police to understand how to work with them so that they understand how to work with autonomous vehicles. Once in a while one of those will get stuck, for instance, maybe with a blown tire or an equipment malfunction, or, worse, an accident. At SXSW a drunk driver plowed through crowds of people one year, killing many, and causing a major first-responder response. In such a situation all the constituencies would need to work together to change traffic flows to make sure more vehicles aren't drawn to the area, or interfere with first responders. Is there a relationship lead between all these constituencies? I hear there is not, except in the best of cases. So, this leads to a new job for humans. AI's can't attend city council or meetings with police. AI's can't often figure out why all traffic has stopped. Once, at Half Moon Bay's pumpkin festival it started to rain. Everyone headed for their cars at the same time, but both Waze and Apple Maps continued to give incorrect data for an hour, both on recommendations for what street to head down, and on time it will take to get out of the area. These systems are built for normal traffic flows, and often can't understand that police have even reversed traffic, so that cars are going down the wrong way on a road. Which leads to the AI. Uber's founder, Travis Kalanick, told me his goal is to have one car near someone who is calling for a ride. That way the ride will arrive in less than five minutes and everyone is happy. Uber built a complex "traffic control system" that hooks drivers and passengers up together. That system usually works great, but when high-crowd events show up these systems often mislead drivers and passengers alike. Waymo is, so far, punting, and refuses to go near these events, and has a very small number of passengers today. Even if you live in San Francisco you can't just get a Waymo, you have to wait to be added to the system and they are adding new passengers slowly so that the whole system remains healthy and doesn't hit these large-crowd issues yet. That will change as they scale up the system to be in many more cities over the next few years and open up the system to everyone, not just a limited number of beta testers. So, to wrap it up, soon entrepreneurs, like Elon Musk, who is making plans for bringing its millions of vehicles online to do human-less driving, will need to do new AI modeling work for how cars should behave around high-crowd events, leading to new traffic-control systems to bring thousands of cars in and out of these venues, and will need teams of humans working with venues and cities to build new drop-off-areas for autonomous vehicles to drop off and pick up people. That is a human job, not one that an AI or robot can do. And a company that has aspirations for building a global network of autonomous vehicles, will need many such workers to work with local customs/officials/venues/teams/events. I see very little modeling work, although I saw MIT Media Lab doing some early work to understand how cities would change due to autonomous vehicles, and I don't see anyone in the AI/Autonomous Vehicle industry talking about research/work in this areas. (And yes, I do have a list of those people here on X: https://x.com/scobleizer/lists ) https://x.com/scobleizer/lists I'm doing this post to try to spur industry interest in working on these issues and to connect people who may already be working on them. Are you positioned for these new jobs? If you are, these are fun things to work on and you might even get free tickets to go see concerts or football games for you and your family. I got taken to a Taylor Swift concert by an entrepreneur who built technology systems for the local stadium, so I know it's possible. :-) Technology solves old problems and causes new problems. Which is why I'm still on team human. AI's aren't good at figuring this stuff out and I don't see them getting good at figuring them out, either, for years. Sounds like a good job to have for a while.

On The Drop Off and Pick Up Problem for Robotaxis at Crowded Events like Football Games and Concerts

ফুটবল গেমস এবং কনসার্টের মতো ভিড়পূর্ণ ইভেন্টগুলিতে রোবোটক্সির জন্য ড্রপ অফ এবং পিক আপ সমস্যা

Zum Bring- und Abholproblem für Robotertaxis bei überfüllten Veranstaltungen wie Fußballspielen und Konzerten

Πρόβλημα κατά την παράδοση και την παραλαβή για ρομποταξία σε πολυσύχναστες εκδηλώσεις όπως αγώνες ποδοσφαίρου και συναυλίες

Sobre el problema de dejar y recoger a los robotaxis en eventos multitudinarios como partidos de fútbol y conciertos

Բազմամարդ իրադարձությունների ժամանակ, ինչպիսիք են ֆուտբոլային խաղերը և համերգները, ռոբոտաշինգի խնդիրը թողնելու և վերցնելու մասին

Sul problema del ritiro e della consegna dei robotaxi durante eventi affollati come partite di calcio e concerti

サッカーの試合やコンサートなどの混雑したイベントでのロボタクシーの降車・乗車問題について

Robotaxis izlaišanas un uzņemšanas problēma pārpildītos pasākumos, piemēram, futbola spēlēs un koncertos

Problemă de livrare și ridicare pentru Robotaxis la evenimente aglomerate precum jocuri de fotbal și concerte

Dejinta oo u soo qaado dhibta Robotaxis ee Munaasabadaha dadka badan sida Ciyaaraha Kubadda Cagta iyo Riwaayadaha

Kwenye Kuacha na Kuchukua Tatizo kwa Roboteksi kwenye Matukio yenye Watu wengi kama vile Michezo ya Kandanda na Tamasha

Eka The Drop Off and Pick Up Problem for Robotaxis eka Swiendlakalo leswi Taleke ku fana na Mintlangu ya Bolo ya Milenge na Tikhonsati

Role of AI in Immunization: The Shots that Your Body Needs

টিকাদানে এআই-এর ভূমিকা: আপনার শরীরের প্রয়োজনীয় শট

Rolle der KI bei der Immunisierung: Die Impfungen, die Ihr Körper braucht

El papel de la IA en la inmunización: las vacunas que tu cuerpo necesita

Le rôle de l’IA dans la vaccination : les vaccins dont votre corps a besoin

टीकाकरण में एआई की भूमिका: आपके शरीर को किन टीकों की ज़रूरत है

予防接種におけるAIの役割: 体に必要な注射

면역 접종에서 AI의 역할: 신체에 필요한 주사

Papel da IA na imunização: as vacinas que seu corpo precisa

Роль ИИ в иммунизации: прививки, необходимые вашему организму

Yapay Zekanın Aşılamadaki Rolü: Vücudunuzun İhtiyaç Duyduğu Aşılar

Vai trò của AI trong tiêm chủng: Các mũi tiêm mà cơ thể bạn cần

人工智能在免疫中的作用：你的身体需要注射哪些疫苗

So, I saw this article about deepfakes causing a stir, and let’s just say, the stakes are spicier than a mutton bunnychow on a Durban summer day. Here’s the pressing question: are deepfakes really the boogeyman everyone’s making them out to be? Don’t get me wrong, the potential for misuse is undeniable. Okay, let’s just take a minute to imagine; malicious actors manipulating political campaigns, spreading misinformation, or even worse, impersonating loved ones for nefarious purposes. Yeah! Scary stuff. Netizens have been talking about deepfakes for years, the forerunner of the deepfake has existed since 1997. However, last year there was an explosion in the popularity of deepfakes due to the boom in interest in artificial intelligence. 2024, & Big Tech is insane right now Google Gemini + Apple Vision Pro + Open AI’s Sora, talk about setting trends: a new era of technological alchemy, imagine the possibilities, reshaping perception and engagement… …is the Creative industry ready to navigate through this wizardry… But! there’s a flip side: deepfakes are built on powerful , and like any tool, it can be used for good or evil. We need to keep talking about how synthetic media can revolutionize education, healthcare, and even entertainment. I wish immersive learning experiences were around during my time, personalized medical simulations, or even hyper-realistic movies that blur the line between fiction and reality. AI technology Of course, with great power comes great responsibility. Yes, we need ethical frameworks, transparency, and user education to ensure deepfakes are used for good. But, why always fear the future, can’t we embrace the potential: I would have loved being in a classroom where you could feel the weight of Martin Luther King’s words through a speech while he walked through our aisle. Just think, after exploring the Amazon rainforest led by a virtual David Attenborough, or dissecting a virtual frog with a holographic Marie Curie guiding their scalpel (we all can relate to this life-defining moment when we decide to become a doctor or “pass out“) but can you picture your kids forgetting that kind of learning experience. They would be talking about it for days. Education : deepfakes could even be powerful empathy machines. The doctor could use a patient’s avatar to explain a complex diagnosis in a lot easier way to understand. Or, therapists can connect with those patients who otherwise hesitate to ask for help. I know I would appreciate the gap being bridged between medical jargon and my real-life experiences. Healthcare: Sign language interpreters could seamlessly appear in the news, Edu-learning materials can be translated into local dialects, and inclusivity for people with disabilities ~ hyper-realistic prosthetics that don’t just look real but react and move naturally. A world where anyone can connect and understand each other, regardless of background or ability. Accessibility: I remember when the Tupac hologram was used at Coachella, for those few moments, Tupac wasn’t just a memory on a screen. And yet, despite the initial shock, while some might argue it blurred the lines of reality, others saw it as a groundbreaking moment in entertainment. Entertainment: Deepfakes are already being used in these ways, and the potential is endless. The key? Responsible use. So, what I’m trying to say is, instead of panicking and throwing the AI baby out with the bathwater, let’s just take a breath and continue focusing on building safeguards. We robust detection methods, stricter regulations, and a culture of digital literacy that empowers everyone to be discerning consumers of information. Think of it like learning to spot a Photoshop fake in the age of social media. “deep” need Just my two cents: : Let’s be upfront about when deepfakes are used, especially for creative purposes. No more hidden agendas or blurred lines. Transparency is ALWAYs key : Equip people with the skills to critically evaluate online content. Media literacy needs to be a core education pillar. Empower the users : Work with tech companies, researchers, and policymakers to develop effective detection and prevention methods, not just knee-jerk regulations that stifle innovation. Collaborate, don’t regulate We all need to remember, that just like the internet was, now deepfakes are just tools. It’s still up to us to ensure it’s used responsibly and ethically. So, embrace the potential for good while mitigating the risks. And hey, if anyone needs help navigating this wild world of emerging tech, hit me up! I’m always down for a chat ( ). and maybe even a braai, if you’re in Durban Peace out, P.S. Don’t forget to fact-check everything, even this article!

Deepfakes — A Looming Threat or Misplaced Fear?

Your network is your net worth is the most bullshit stuff I've ever heard and a terrible piece of advice to give anyone early in their career. Crypto Twitter feels like a deserted island these days. No one seems interested in crypto anymore, and the app itself has quickly become useless. Our feeds are full of junk, packed with endless AI tools threads, and overflowing with people like me shouting into the void for 3 likes. On this deserted island, you have two choices to save yourself. Choice 1: The Waving Approach The first choice you can make is to wave your arms frantically and shout for help to any passing ship in the distance, desperately seeking rescue. This is the choice I made in the past. When I started my career and was looking for new opportunities, everyone kept throwing the same worn-out advice at me: "Hey, Eliot, focus on growing your network, man." So during my first internship in Amsterdam, I went to networking events. Now, for context, I've always been scared shitless to talk to new people, and in real life, I'm more of an introvert and a private person. So believe me, the idea of going into a sea of unfamiliar faces and forced conversations was like being thrown into a pool with sharks, except the sharks were aspiring Web3 bros. Quickly, those so-called networking events turned into me spending the first 30 minutes of the event analyzing the room, then going to the mini-bar every 10 minutes to pretend I was actually doing something, and eventually ending up talking to a dude during the remaining 30 minutes. Obviously, it didn't help me land new opportunities. Half the fault lies with me - I'll admit it. But the other half? Those events were filled to the brim with people who had nothing to offer and no idea why they were there in the first place. But with no other experiences, I thought it was just the way it was, and I needed to get used to it— keep trying to grow my network. Fast forward a few years, leading us to today, where I want to drop some truth bombs: Your network is your net worth is the most bullshit stuff I've ever heard and a terrible piece of advice to give anyone early in their career. Because it makes ambitious young people believe that, in order to accomplish anything, they first need to grow their network. It makes the person on the desert island believe that to be saved and get new opportunities, they must wait for someone to come and help them. That they have to keep shouting into the void instead of using the few resources they have to start taking their future into their own hands. But here's a piece of advice you probably won't hear often: You should probably stop looking for help by going to these networking events or sending those cold DMS on Twitter. I know. This is the opposite of what all your favorite influencers are telling you. And maybe this is too late, you've already spent many hours circling the buffet of those events, or hitting that send button. But let me tell you something. There's one thing your favorite creator is not telling you. People answer DMs only when the person sending it has something to bring to the table. Not to say that you don't, but if you're rocking a whopping five Twitter followers with no profile picture, your chances of getting an answer are close to zero. It's a harsh reality, but it's the truth. And this truth is even more tangible in crypto, where anyone with a somewhat "high profile" gets their DMs bombarded by airdrop farmers or scammers. In those tough times, there's a much better option to grab someone's attention, and this is doing cool shit and sharing them online. This leads us to our second option. Choice 2: The Bonfire Approach Your second ticket off the "Crypto Twitter" island is what I call the blazing bonfire approach. Here, you build a massive bonfire, shooting flames high into the sky, signaling your presence and capturing the attention of the passing ship. You decide to showcase your worth and use your skills and talents to create something impossible to ignore. Let me share a personal story here. Two years ago, when my pleas for any help to find a job in Amsterdam fell on deaf ears, I decided to start writing online. And if you think I knew how to write, that I had some kind of special talent, and that you can't do it, trust me, I was far from being a Shakespeare. Coming from France, my ability to speak English was questionable at best, and my writing skills were, let's say, a work in progress. But I kicked myself in the ass and started forcing myself to write online daily. I started slowly, anonymously, writing comments under other people's articles until I gained enough confidence to write my own tweets and articles. And after a couple of months of sharing my Web3 learnings with the world, one of the editors at Bankless—a crypto publication I could only dream of being spotlighted in—reached out asking if I wanted to write for them. I was ecstatic. But do you know they gave me this opportunity? why Hint: it's not because I sent them a DM asking to “pick their brain” or see if there were “synergies” (in this desolate island, no one sees you or cares that you're in dire need of help anyway). . No, it was because I had proved my worth Because I had proof of my work online. Because I was constantly publishing online, and because over time if you're consistent, it becomes inevitable that the right people will discover you. Was it an obvious decision for them? Well, . maybe not But at least they didn't have to take a leap of faith in me because they already had access to an extensive portfolio of work to dig into. After that, the opportunities started to compound. After publishing , I got the opportunity to write for . Then with all my articles, I decided to turn my work into a book. The book's release got me to talk with the person who manages Coachella's Web3 strategy. my article on Bankless Business Insider And still today, despite the bear market, I keep getting new opportunities to ghostwrite for leaders on Twitter or advise Web3 communities. Side note; One of my latest articles just got featured in JUMP, a community for Web3 marketers —You can . read it here The crazy thing is that, by doing something I truly enjoy daily, I talk with far more people than in any networking events I attended. Which made me realize: A great piece of content about a cool thing you’ve done will reach 100x more people than forcing luck in Twitter DMs or at networking events. And if you continue to be consistent and share online, the ship will come closer, ready to take you away from this abandoned island and towards new horizons. Closing Thoughts More than ever, Web3 companies and creators need help with their content or growth. And if there's one good thing about the current bear market, it's that it allows those who persist to get noticed more quickly. Not by sending DMs. But by keeping doing cool shit online. By keeping showing their worth. By keeping doing stuff no one can ignore. Your content and personal brand are your job insurance. And if you keep shipping, you will inevitably attract opportunities your way, even in this bear market. Speak soon, - Eliot PS: What did you think of this article? If you enjoyed today's edition, - It'll help me understand what you want me to write about (and it only takes you 10sec). let me know here Also published here

Advice: Keep Doing Cool Shit Online

উপদেশ: অনলাইনে কুল শিট করতে থাকুন

Tipp: Machen Sie weiterhin coole Sachen online

Consejo: sigue haciendo cosas geniales en línea

Conseil : Continuez à faire de la merde cool en ligne

सलाह: ऑनलाइन बढ़िया काम करते रहें

アドバイス: オンラインでクールなことをやり続けよう

조언: 온라인에서 계속 멋진 일을 하세요

Conselho: continue fazendo coisas legais online

Tavsiye: İnternette Harika Şeyler Yapmaya Devam Edin

Lời khuyên: Tiếp tục làm những điều thú vị trên mạng

建议：继续在网上做一些很酷的事情

In this day and age, almost everyone has some kind of side hustle. Whether it’s driving for a rideshare program, or reselling. For some, this is a means to an end, allowing them to pay rent and put food on the table. For others, this enables them to build a savings account and live a life full of traveling, shopping, etc. Whatever the reason may be, reselling has always been a relatively easy field to get into, with people all over the world collecting items and listing them on the internet. freelancing, We’ve seen everything from sneakers to baseball cards to vintage cards sold online. And we don’t foresee this side hustle going anywhere anytime soon. In fact, we’re only scratching the surface. With new companies like Hoken popping up, you now have the chance to turn your hotel rooms into an asset that puts money into your pocket. What is the Assetization of Hotels? Once upon a time, if you booked a non-refundable hotel, you were stuck with the reservation, no matter what circumstances arose. This meant that if your vacation fell through and you opted out of travel insurance, you were out hundreds if not thousands. It also meant that there was no way to make a profit on a hotel room. There was no such thing as buying a sought-after reservation only to turn around and sell it to someone else. That is, until now. We know what you may be thinking, is it even worth trying to profit from a hotel room? You’d be surprised at just how lucrative it can be–especially in larger cities near some of the hottest events around the U.S. Why Resell Hotels? If you’re not convinced that reselling hotels is the right side hustle for you, we understand. Because it’s such a new concept, it’s easy to be wary. But there are quite a few benefits that you won’t get trying to resell that pair of Jordans you spent hours trying to get. No Extra Inventory Let’s face it, reselling physical products such as sneakers, trading cards, or clothing takes up a lot of space. And one of the riskiest aspects of reselling is holding onto inventory that doesn’t seem to be selling how you had hoped. This can lead to having to get a storage unit to store the products, which can cost a small fortune. It can also lead to donating thousands of dollars of merchandise or drastically lowering the price, causing you to break even or lose profits. When reselling hotels, there are no physical items that you have to hold onto. Sure, you do run the same risk of not being able to resell, but if you’re carefully selecting the hotels based on large events such as festivals or concerts, you dramatically decrease this chance. No Shipping Costs When reselling, you want to maximize your profits. After all, the entire point of a side hustle is to give you more cash. By selling hotel rooms, you eliminate the need for shipping costs. This means not scaring off potential customers with shipping costs and more money for you. Once the purchase goes through, all you have to do is transfer the reservation into their name. This can be done with a quick phone call–it’s that simple. More Reliable If you’ve ever followed the reselling industry, you’re aware of just how much it fluctuates. One minute you could be making thousands of dollars, and the next, the demand has fallen off, leaving you unsure of how you’re going to afford dinner. Because hotel reselling is such a brand-new concept, now’s the time to get into it. It’s only going to get bigger from here. Less Competition Speaking of it being a new idea, when reselling hotels you are going to face a lot less competition. The market may be saturated everywhere else, but not here. There’s not too much inventory out there yet, which means the likelihood of you selling is exceptionally high. If you’re someone afraid of risk, this is huge. How Hoken is Set to Change Event Accommodations Music festivals, concerts, sports games, conferences–there’s no shortage of events in the world. With these events comes out the demand for accommodations. Coachella attracts over 200,000 people every year. Lollapalooza brings in over 350,000. Needless to say, being ahead of the curve is essential if you’re attending one of these events and need a place to stay. That’s where Hoken comes in. With Hoken’s , you can buy and sell hotel rooms for some of the hottest events in the United States. Buying using the marketplace can save you up to $350 a night. And it really is as simple as buying the room and showing up on your reserved date. Hotel Marketplace If plans change, or you’re just looking to make a little extra cash, you can resell the room using Hoken. It allows you to track what you paid and what you could earn by tracking market demand. This means you make money, and someone else gets that extremely hot hotel room. It’s a win-win for everyone involved!

Exploring the Rise of Hotel Assetization with Hoken 

Non-fungible tokens (NFTs) have taken the world by storm. Most people who know about NFTs think of art and collections. These are the NFTs that garner media attention due to eye-catching headlines. NFTs have many use cases that will change various industries. Here are 5 industries that NFTs will transform. 1. Authenticity of Luxury Goods Luxury goods have been a fast-growing market. Some examples of these products are Louis Vuitton purses and Rolex watches. These accessories can sell for tens of thousands of dollars each. People love to show off their “status” in-person and on social media. The luxury goods market is full of knockoffs and scams. This is a huge issue. People who are paying $10,000 for a Rolex want to ensure that the watch is real. By tying an NFT to the watch, buyers and sellers can verify that the watch is real and not counterfeited. When the watch gets sold, the NFT would be transferred to the new owner. Anybody can verify who the previous owners of the watch were. Shark Tank host Kevin O’Leary has been advocating for authentication NFTs: This can be applied to luxury purses, shoes and even baseball cards. This market needs a way to prove authenticity. NFTs can prove that physical items are authentic. The fake goods market tops $500 billion a year. 2. College Textbooks Every college student knows the struggle of buying textbooks. Students pay hundreds of dollars a semester for textbooks that they will only use for a specific class for one semester. Many of these textbooks are digital, so they can’t sell them to other students after the class is over. What if students could own and trade digital textbooks? An NFT can be linked to a textbook that allows access to the owner. Students would be able to buy and sell textbooks with each other to save money. Not only that, but the author would be able to set a royalty that gets paid to them whenever a textbook is sold to another student. This saves money for the students while also creating a passive income stream for the author. Billionaire entrepreneur Mark Cuban has a great on this that I highly recommend. Twitter thread This can be applied to any type of publication. NFT ownership benefits both the reader and the publisher. https://twitter.com/mcuban/status/1481301870882787328?embedable=true 3. Tickets The ticketing industry is ripe for disruption. People buy tickets off of marketplaces like that are full of unnecessary fees. Ticket buyers get stuck in the ecosystem. If they want to sell their ticket, they have to do it through Ticketmaster because that’s where they bought it from. Tickets will eventually be sold as NFTs through the event directly instead of ticket marketplaces. Ticketmaster What are the benefits for the ticket holder? The ticket can be embedded with cool artwork that can change after the event The ticket serves as memorabilia, that can potentially be valuable (see ) Collectible Ticket Stubs NFTs can act as an event “stock”, where profits from the event can be distributed to ticket holders VIP tickets can have additional benefits such as access to exclusive digital content after the event What are the benefits for the event holder? Revenue from tickets goes to the artist, venue, and event staff instead of ticket marketplaces The artist can receive royalty revenue from ticket resales As a real-world example, music festival with benefits such as unique on-site experiences and physical items. Coachella decided that they will be selling NFT collectibles We will see all types of events have NFTs for tickets and collectibles. 4. Music Music artists are notoriously underpaid by streaming platforms such as Spotify and Apple Music. Artists must get at least 100 streams to reach a payout of $1.00. Artists need to be paid more for having people stream their music. If the artist sold their music as NFTs, then they would be able to receive the full payment from the NFT. Why would fans buy NFTs of their artists music instead of streaming them? The money would support the artist directly The NFT may have to become a passive income source for the owner profit-sharing elements NFTs can become rare collectibles that are part of album memorabilia Music NFTs benefit both the creators and fans. I expect NFTs to drastically change the music industry. 5. Gaming The gaming industry is growing rapidly. In a popular game called , players play games to unlock cosmetic items and skins. Players can also spend money to unlock items. Fortnite The issue is their items are lost when they switch games or if the game shuts down. There is no way to transfer your items from game to game. Their progress is lost. Example of Fortnite skins NFTs solve this issue. Items in games can be represented as NFTs. Players can own their items in their wallet instead of them being siloed in that specific game. When players own their items as NFTs, then they can use their items in other games that support it. They also don’t lose the item when the game shuts down. They can transfer it to another game that supports that item. is a project that is trying to realize this vision. Enjin gaming GameStop also recently announced that they will be creating a game item marketplace based around NFTs. Most games will have in-game items that are NFTs in the future. It makes sense for both the players and developers. Other Industries will Change NFTs will change other industries such as real estate, supply chain, intellectual property and digital identification. Any physical item that is unique and valuable will eventually be tied to an NFT. There are too many benefits to ignore this technology. Most people think of NFTs as digital artwork. It’s more than that. It’s the tokenization of the physical world. The implications of NFTs are astronomical. We are in the early stages of a Watch this space closely because you can build a new idea to change your life forever. digital renaissance. Disclaimer: Any views or opinions expressed in this post are personal and belong solely to me and do not represent the views or opinions of my employer. You should not treat any opinion expressed by Josh as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of his opinion. This article is for informational purposes only. Also published here.

How NFTs Will Disrupt These 5 Major Industries

🦧 Yuga Labs acquires WENEW WENEW, which was co-founded by the famous artist “Beeple”, has collaborated with a number of brands including Gucci, Playboy, and Wimbledon. . More 👟 Nike launches new web3 platform .Swoosh Swoosh is the home of Nike’s web3 efforts. In collaboration with Polygon, the platform allows Nike’s community to ‘co-create the future of Nike’, access virtual events, purchase Nike’s digital wearables, and unlock real-world benefits such as physical apparel and access to pro athletes. Nike plans for its creations to be interoperable among metaverses and blockchain games. More. 🧢 Adidas’s World Cup teaser features a Bored Ape Adidas’s Indigo Herz Bored Ape NFT, which the brand purchased last September, featured in the advert alongside Messi, Benzema, and Stormzy. . More 💆‍♀️ Dove Pakistan launches NFTs in the campaign to help damaged hair As part of the campaign, 10 winners will be chosen to win NFTs that grant their owners real-world benefits including a year’s supply of Dove shampoo. . More 🍏 Job posting suggests Apple plans to build its own metaverse The job post suggests that the technology giant is looking for engineers who can develop a 3D mixed-reality world for its coming headset. . More 🚘 Skoda enters the metaverse In the Skodaverse, users can drive the newest ENYAQ Coupé RS iV model, create custom avatars, & interact with Skoda’s NFT collections. . More 👑 Harry and Megan Markle to launch metaverse experience The Duke and Duchess of Cambridge have partnered with the metaverse platform, pax.world, for the launch. . More ⚽️ Manchester United to gift first ever official NFTs to fans In partnership with their official sponsor, Tezos, Manchester United plan to give fans free NFTs to kick-start their move into web3. . More 🏈 Rarity league has released a set of officially licensed NFL helmet NFTs The collection will have utility on NFL Rivals, an arcade-style game where collectors can gain access to special events, in-game rewards, and more. . More 💄L’Oreal & Maybelline partners with Ready Player Me The partnerships with the leading interoperable metaverse avatar platform enable brand-customized hair and makeup styles for avatar characters. . More 🟡 Cristiano Ronaldo launches NFT collection in collaboration with Binance The CR7 NFT collection will be Ronaldo’s first official NFT drop. . More 👚Adidas Originals reveals new 16-piece NFT collection of interoperable virtual wearables The wearables were airdropped to Phase 2 Capsule NFT holders. The wearables can be worn by virtual avatars and have been designed to be interoperable with other identity-based projects and metaverses. . More 🥤PepsiCo India to launch NFTs for Indian customers celebrating Pepsi Black The Pepsi Black Zero Sugar collection will feature 20 NFTs. The NFTs will be given away to the winners of the #PepsiBlackeffect challenge. . More 🪞Charlotte Tilbury unveils 3D virtual store Charlotte’s Beauty Realm is the brand’s first virtual store and comes with a gaming experience. The store is launching in the experiential e-commerce platform, Odyssey. Visitors will be able to buy products in the store. . More 💇🏾‍♀️ TRESemme enters the metaverse The TRESverse will feature a virtual salon, where users can style their avatars. . More 🎾 ATP Tour announces upcoming data-driven NFT collection The LOVE collection comprises 300 NFTs based on data from the Nitto ATP Finals and was created in collaboration with Art Blocks Engine & artist, Martin Grasser. . More 👗 Givenchy and Bstroy launch NFTs alongside a new physical collection LVHMs Givenchy has partnered with the streetwear brand to offer free NFTs to purchasers of the new clothing line. . More 🥣 Kelloggs’ Poptarts creates in-game NFT characters for Blankos Block Party Poptarts’ NFT characters will be playable within the NFT-based game. . More 🏎️ Azuki featured on Red Bull F1 cars for the last race of the 2022 season Azuki #8494 could be found between Tezos and Bybit. . More 🥃 Johnnie Walker lets NFT holders vote on the design of a new bottle The leading whisky brand has partnered with artist VANDYTHEPINK to submit designs for the bottle on which NFT holders will vote. The design with the most votes will be used for a new exclusive range of Johnnie Walker Blue Label. . More 💔 A number of NFT collections are stuck on the currently disabled FTX NFT marketplace Brands including Tomorrowland, Coachella, Steph Curry, Mercedes & the Golden State Warriors all have their NFTs stuck on insolvent FTX, leaving fans unable to interact with their collectibles. The brands are actively looking for solutions. . More

Weekly Web3 Brand Tracker: Nike Web3 Platform, Manchester United NFTs, and More 🔭

Rastreador semanal de la marca Web3: plataforma Nike Web3, NFT del Manchester United y más 🔭

Suivi hebdomadaire de la marque Web3 : plate-forme Nike Web3, NFT de Manchester United et plus 🔭

साप्ताहिक वेब3 ब्रांड ट्रैकर: नाइके वेब3 प्लेटफॉर्म, मैनचेस्टर यूनाइटेड एनएफटी, और अन्य 🔭

ウィークリー Web3 ブランド トラッカー: Nike Web3 プラットフォーム、マンチェスター ユナイテッド NFT など 🔭

Rastreador semanal da marca Web3: Plataforma Nike Web3, NFTs do Manchester United e muito mais 🔭

Công cụ theo dõi thương hiệu Web3 hàng tuần: Nền tảng Nike Web3, Manchester United NFT, v.v. 🔭

每周 Web3 品牌追踪器：Nike Web3 平台、曼联 NFT 等 🔭

GM. It's Crypto Player One, the first and only daily newsletter on crypto gaming. Today, we're bringing you the heat with: Daily Trivia. A web3-native game is coming on Steam. In Other News. Daily Trivia What's the name of the first Warcraft game? Scroll down to find out the answer. Superior is Launching on Steam Today Superior, , is launching on Steam today. a third-person web3 game Its blockchain version is available on Gala Games. Superior will cost $25, but players can enjoy a 15% discount during launch week. The company behind the game, Drifter, has made popular VR games like: Gunheart. Lies Beneath. Robo Recall: Unplugged. Now, they're focusing on PC titles like Superior. In Superior, players hunt down superheroes to steal their powers. Players can upgrade the characters as they complete more missions, making them stronger. Drifter's devs have a ton of gaming experience, having worked on AAA games like: Gears of War. Halo. Doom. Apex Legends. Drifter hasn't taken VC money but has partnered with Gala Games in a publishing deal. Gala financed the studio and the game, and in exchange, Drifter made a blockchain version of Superior. The Steam version doesn't use blockchain tech, but it allows the team to access larger distribution. But Gala Games' partnership with Drifter paves the wave for gaming partnerships between web2 game makers and web3 distributors. In Other News Footballing superstars join The Sandbox Metaverse. Coachella NFTs are stuck on FTX. Answer Orcs & Humans New to the newsletter? Sign up to get the first and only daily newsletter on web3 gaming — it’s free!

🎮 A Web3-Native Game Is Making Its Debut on Steam

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