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Want to see what other books we’ve covered? Read our other reviews in the SEJ Book Club archive.

Outliers by Malcom Gladwell (affiliate link) challenges how we view successful people and how we look at ourselves. It asks the question “Is success a birth right or is it earned?” And not just small successes, like earning a really awesome link or getting a ton of traffic on a killer infographic, but extraordinary success like Bill Gates or the most elite athletes in the world.

What Does Success Look Like?

Often, we look at the extraordinarily successful with a sense of “otherness”. Sure, we may consider ourselves smart and quite capable, but not on level with, say, Warren Buffet. These personal perceptions may vary from person to person, but I suspect most of us do not consider ourselves on par with the richest men in the world. (Not for lack of dreaming on my part.)

So, how do these highly successful people look to many of us? And how did they get there? Demographically, they are likely to be white men. They tend to be from middle or upper class households. Often the parents are well educated, they usually go to better schools. They end up at the Harvards of the world, which just makes it more likely they will end up fabulously successful.

But are these preconceived notions accurate?

According to Gladwell, they aren’t.

He breaks down our conceptions of success and what it takes to be exceptionally successful. Lets look at a few of the factors Gladwell argues play a role in how successful we are.

Hard Work Matters More Than We Know

This aspect of success seems obvious. We all know that hard work matters, right?

But Gladwell breaks down how much it matters.

One example he gives is a study of violin players in Berlin, Germany. In the 1990’s a psychologist by the name of Ericsson studied the practice habits of elite violin players. They asked each player:

“Over the course of your entire career, ever since you first picked up the violin, how many hours have you practiced?”

Practice Makes Perfect

All the violists had picked up the violin at roughly the same age and practiced similar amounts as small children. Around eight years of age, scientist began to notice a difference. The very best violinist players, those likely to have a career as a world class violin player, practiced more than any other players. At age nine, they practiced six more hours a week than violists deemed to be “good” or “unlikely to play professionally”. By the age of 20, they practiced up to 30 hours a week more.

Those are incredible numbers. To be fair, most of us do not have the ability to do much other than work for 30 hours a week. For world class musicians, there are likely other factors at play – the ability to attend an elite music school (which all the violinists in the study did), a family who supports and encourages, and even an interest in playing for that long.

But what doesn’t play a part?

Pure, raw talent.

According to Malcom, the most talented violinists in the world aren’t innately better than you or I – they just practice more. This rings true for everyone from Mozart to Bill Gates.

Communication Can Make or Break You

As marketers, we understand the importance of communication. That is why we A/B test and run keyword audits. But could being a good communicator make the difference between a life of success and failure?

Turns out it can.

Consider the different life paths of two equally intelligent men: Chris Langan and Robert Oppenheimer.

Chris was born into a painfully poor family. His mother’s husband was a drunk who often deserted the family for weeks at a time. It was obvious to the school and his family that Chris was a gifted student, and he was offered two full scholarships upon graduating high school. That’s where things went down hill.

College was an extremely difficult transition for a boy from the wrong side of the tracks. Then his mother didn’t fill out the financial aid paper work correctly and he lost his scholarship. The school would do nothing to help. So he transferred to a school closer to home and worked odd jobs. Then his truck broke down and he need to switch to evening classes, and his teachers wouldn’t allow him. Today, Chris studies and writes complicated theories about physics, philosophy, and math. Work which will likely never be published or even read by the academic community.

Robert Oppenheimer, much like Chris, was a highly intelligent child and considered a genius by his parents. He went onto attend Harvard and then Cambridge. He had an extraordinary mind, but often struggled with depression. In one instance, he was caught trying to poison his teacher.

What was the end result? He was put on probation and later became the physicist who headed the American efforts to develop a nuclear bomb in WWII. That is correct – a man who apparently struggled with mental illness to the point where he tried to poison his teacher was later in charge of one of the most dangerous weapons ever developed in the United States.

Why the Difference?

What accounts for the difference in the trajectory of these two men’s lives? Money? Probably a bit. Intelligence? No, the two men seemed to have similar minds. The time period in which they were born? Perhaps.

Robert was better at communicating, plain and simple.

Are Opportunities The Real Secret To Success?

Call them opportunities, or call them dumb luck. Either way, these are factors no one can control or even predict.

As an example, Gladwell looks at Bill Joy, one of the programmers who wrote the UNIX code. At college Joy happened to live within walking distance to the Computer Center at University of Michigan, which happened to be one of the most cutting edge centers in the country, and he happened to find a bug in the software that allowed him to code for hours without being charged. These opportunities allowed him the opportunity to practice for hours on end.

Or, consider Canadian hockey players born on January 1st, which is the cut off age for joining hockey leagues. On January 1st, he has just had his ninth birthday. He is nearly a year older than a child born on December 15th. As a result, the older child starts out a bit larger, a bit stronger, and a bit more mature.

At that point, he is a slightly better player — which means he is more likely to be chosen for elite teams where he has access to better coaches, more play time, and more practice. In the end he is a much better player than the child born on December 15th, but he didn’t start that way.

Gladwell offers examples of these opportunists over and over again.  And they aren’t always so obviously positive. Some times those opportunities are being born Jewish and shunned from joining the top law firms. Or that your father was a garment worker, or a grocer.

Discussion Time

Gladwell’s book inspired me, partially because, for me at least, it destroyed the idea that success is based upon some innate skill or talent which cannot be earned or taught. It also left me feeling a touch indifferent because so much of success seems built upon luck, or opportunities. After reading Outliers, or my analysis of the book, what do you think of the following points?

1. Gladwell argues the idea of “rag to riches” self made man is a myth and highly successful people often succeed because of their circumstances, not in spite of them. Does this ring true to you?

2. Did the book change how you view highly successful people? If so, in what way? Does exceptional success seem more or less attainable for you?

3. Do you accept the idea of the 10,000 hour rule? Why or why not?

4. Do you believe in the idea of “innate talent”? If so, how do you define it?

Want to see what other books we’ve covered? Read our other reviews in the SEJ Book Club archive.

This post contains Amazon affiliate links.

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Apple Discussing Iphone Payments Service With High

Apple wants to replace yet another daily tool with your iPhone: your wallet.

Executives from the Cupertino-based technology company have begun discussions with directors from retail store chains about a mobile payments service, according to a source with direct knowledge of the talks. Previous reports indicated that Apple is exploring new payments services through discussions with executives from existing payments companies. These latest mobile payments-related discussions, which have occurred with retail store brands such as those that sell luxury clothing and premium goods, have taken place over the past couple of months, according to the source. The source declined to be named and requested that the identities of the companies talking to Apple not be published.

The Apple mobile payments service would be integrated into iOS Devices such as the iPhone and would be a comprehensive solution that would allow an iPhone user to leverage their device as a form of payment in retail stores. Based on information from various people briefed on the matter, the service would tie directly to iTunes accounts. Apple Senior Vice President Eddy Cue noted last night that Apple has 800 million iTunes accounts with credit cards, and that this arsenal opens up the door for many future products and services. Apple CEO Tim Cook previously hinted that the iPhone’s Touch ID fingerprint identity sensor could someday be leveraged for mobile payment purposes beyond the existing iTunes and App Stores…

The Apple discussions with retail chains included talk about the challenges of building a single payments service that could integrate with various retail stores. Every retail outlet has unique payment and transaction practices, so building a single mobile payments solution will require extensive research from Apple, and the company appears to be in that research and development phase. Apple has also been asking retailers to survey customers regarding potential interest in paying for items with their smartphones and other mobile devices. Apple also has been seeking general insight from retail store chains to see if the companies would be interested in utilizing an Apple payments service.

Apple has also been pushing retail companies to adopt iBeacon location technology, and it is possible that iBeacons could integrate into a future iPhone payments service. iBeacons are small pieces of hardware that could be placed around retail stores in order to pinpoint a customer’s location to a high degree of accuracy. The beacons connect to sensors inside of devices like the iPhone and iPad. Apple’s own retail stores are somewhat of a pilot for Apple’s future mobile payments and iBeacons ambitions as Apple Stores have had an iBeacon-integrated sales experience for a few months. New Apple retail leader Angela Ahrendts is seeking to revamp Apple’s own stores with a mobile payments services, sources said earlier this month.

The sources briefed on Apple’s talks with retailers said that the discussions are exploratory and it does not appear that such an Apple payments service is in the cards to launch in the near future. Apple is holding its Worldwide Developers Conference next week where it will unveil enhancements to its iOS and OS X operating systems, but mobile payments is unlikely to be a topic on the keynote schedule. Earlier this decade, Google tried to develop a similar retail-based mobile payments service via NFC technology, but Apple’s approach is likely to use technologies revolving around iBeacons, low-energy Bluetooth, and Passbook-like scanning, based on Apple’s reluctance to adopt Near-Field-Communication chips in its devices.

While Apple’s discussions with retailers appear preliminary, separate sources confirm that Apple has begun work on the iTunes-based iPhone payments service internally. The project is said to be led by former Apple Online Store chief Jennifer Bailey, and Bailey has formed a team around former managers from various iTunes and mobile hardware projects. Bailey has also hired multiple executives from the payments world to work on the future service. Tommy Elliot, a former senior director for Visa (and the Visa-acquired Cybersource payments company), joined Apple earlier this year to work on the project. Andrew McCarthy, a former top mobile payments executive for J.P. Morgan Chase Bank, and various engineering managers from payments companies such as eBay have also joined Apple.

With a history of seamlessly integrated hardware, software, and services, combined with several hundred million customers with credit cards on file with Apple, Tim Cook and Eddy Cue have the opportunity to expand Apple’s product portfolio with a service that could dramatically shake up the mobile payments and retail world.

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Python Treatment For Outliers In Data Science

What is Feature Engineering?

When we have a LOT OF FEATURES in the given dataset, feature engineering can become quite a challenging and interesting module.

The number of features could significantly impact the model considerably, So that feature engineering is an important task in the Data Science life cycle.

Feature Improvements

In the Feature Engineering family, we are having many key factors are there, let’s discuss Outlier here. This is one of the interesting topics and easy to understand in Layman’s terms.


An outlier is an observation of a data point that lies an abnormal distance from other values in a given population. (odd man out)

Like in the following data point (Age)


An outlier is an object(s) that deviates significantly from the rest of the object collection.

List of Cities

New York, Las Angles, London, France, Delhi, Chennai

It is an abnormal observation during the Data Analysis stage, that data point lies far away from other values.

List of Animals

cat, fox, rabbit, fish

An outlier is an observation that diverges from well-structured data.

The root cause for the Outlier can be an error in measurement or data collection error.

Quick ways to handling Outliers.

Outliers can either be a mistake or just variance. (As mentioned, examples)

If we found this is due to a mistake, then we can ignore them.

If we found this is due to variance, in the data, we can work on this.

In the picture of the Apples, we can find the out man out?? Is it? Hope can Yes!

But the huge list of a given feature/column from the .csv file could be a really challenging one for naked eyes.

First and foremost, the best way to find the Outliers are in the feature is the visualization method.

What are the Possibilities for an Outlier? 

Of course! It would be below quick reasons.

Missing values in a dataset.

Data did not come from the intended sample.

Errors occur during experiments.

Not an errored, it would be unusual from the original.

Extreme distribution than normal.

That’s fine, but you might have questions about Outlier if you’re a real lover of Data Analytics, Data mining, and Data Science point of view.

Let’s have a quick discussion on those.

Understand more about Outlier

Outliers tell us that the observations of the given data set, how the 

data point(s) differ significantly from the overall perspective. Simply saying 

odd one/many. this would be an 

error during 

data collection. 




 statistical results while doing the EDA process, we could say a quick example is the MEAN and MODE of a given set of data set, which will be misleading that the 


values would be higher than they really are.

Positive Relationship 

When the correlation coefficient is closer to value 1

 Negative Relationship

When the correlation coefficient is closer to value -1


When X and Y are independent

, then the

correlation coefficient

is close to

 zero (0)

We could understand the data collection process from the Outliers and its observations. An analysis of how it occurs and how to minimize and set the process in future data collection guidelines.

Even though the Outliers increase the inconsistent results in your dataset during analysis and the power of statistical impacts significant, there would challenge and roadblocks to remove them in few situations.

DO or DO NOT (Drop Outlier)

Before dropping the Outliers, we must analyze the dataset with and without outliers and understand better the impact of the results.

If you observed that it is obvious due to incorrectly entered or measured, certainly you can drop the outlier. No issues on that case.

If you find that your assumptions are getting affected, you may drop the outlier straight away, provided that no changes in the results.

If the outlier affects your assumptions and results. No questions simply drop the outlier and proceed with your further steps.

Finding Outliers

So far we have discussed what is Outliers, how it affects the given dataset, and Either can we drop them or NOT. Let see now how to find from the given dataset. Are you ready!

We will look at simple methods first, Univariate and Multivariate analysis.

Univariate method: I believe you’re familiar with Univariate analysis, playing around one variable/feature from the given data set. Here to look at the Outlier we’re going to apply the BOX plot to understand the nature of the Outlier and where it is exactly.

Let see some sample code. Just I am taking chúng tôi as a sample for my analysis, here I am considering age for my analysis.

plt.figure(figsize=(5,5)) sns.boxplot(y='age',data=df_titanic)

You can see the outliers on the top portion of the box plot visually in the form of dots.

Multivariate method: Just I am taking titanic.csv as a sample for my analysis, here I am considering age and passenger class for my analysis.

plt.figure(figsize=(8,5)) sns.boxplot(x='pclass',y='age',data=df_titanic)

We can very well use Histogram and Scatter Plot visualization technique to identify the outliers.

mathematically to find the Outliers as follows Z-Score and Inter Quartile Range (IQR) Score methods

Z-Score method: In which the distribution of data in the form mean is 0 and the standard deviation (SD) is 1 as Normal Distribution format.

Let’s consider below the age group of kids, which was collected during data science life cycle stage one, and proceed for analysis, before going into further analysis, Data scientist wants to remove outliers. Look at code and output, we could understand the essence of finding outliers using the Z-score method.

import numpy as np kids_age = [1, 2, 4, 8, 3, 8, 11, 15, 12, 6, 6, 3, 6, 7, 12,9,5,5,7,10,10,11,13,14,14] mean = np.mean(voting_age) std = np.std(voting_age) print('Mean of the kid''s age in the given series :', mean) print('STD Deviation of kid''s age in the given series :', std) threshold = 3 outlier = [] for i in voting_age: z = (i-mean)/std outlier.append(i) print('Outlier in the dataset is (Teen agers):', outlier) Output

The outlier in the dataset is (Teenagers): [15]

(IQR) Score method: In which data has been divided into quartiles (Q1, Q2, and Q3). Please refer to the picture Outliers Scaling above.  Ranges as below.

25th percentile of the data – Q1

50th percentile of the data – Q2

75th percentile of the data – Q3

Let’s have the junior boxing weight category series from the given data set and will figure out the outliers.

import numpy as np import seaborn as sns # jr_boxing_weight_categories jr_boxing_weight_categories = [25,30,35,40,45,50,45,35,50,60,120,150]  Q1 = np.percentile(jr_boxing_weight_categories, 25, interpolation = 'midpoint') Q2 = np.percentile(jr_boxing_weight_categories, 50, interpolation = 'midpoint') Q3 = np.percentile(jr_boxing_weight_categories, 75, interpolation = 'midpoint') IQR = Q3 - Q1 print('Interquartile range is', IQR) low_lim = Q1 - 1.5 * IQR up_lim = Q3 + 1.5 * IQR print('low_limit is', low_lim) print('up_limit is', up_lim) outlier =[] for x in jr_boxing_weight_categories: outlier.append(x) print(' outlier in the dataset is', outlier) Output

the outlier in the dataset is [120, 150]


Loot at the boxplot we could understand where the outliers are sitting in the plot.

So far, we have discussed what is Outliers, how it looks like, Outliers are good or bad for data set, how to visualize using matplotlib /seaborn and stats methods.

Now, will conclude correcting or removing the outliers and taking appropriate decision. we can use the same Z- score and (IQR) Score with the condition we can correct or remove the outliers on-demand basis. because as mentioned earlier Outliers are not errors, it would be unusual from the original.

Hope this article helps you to understand the Outliers in the zoomed view in all aspects. let’s come up with another topic shortly. until then bye for now! Thanks for reading! Cheers!!

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#Appyweek With Ak: Testing The Official 7 Minute Workout App By J&J

Welcome to Appy Week with AK, aka Arshmeet Kaur, where I choose one app every week and put it through its paces. I will be using the app each day for a week, test it, explore all its features, pinpoint pros & cons, and more. And to kickstart the series, I have chosen to review J&J Official 7 Minute Workout.

January can officially be called the resolution-making month, and the most common goals at the start of the year are fitness related. So, the 7 Minute Workout app by Jhonson & Jhonson is an obvious pick.

Quoted as one of the best free iOS workout apps, but is it as good as it sounds on paper? Does it really give the user what it promises? Should you download or pass? Well, I can help answer your queries and save your time and efforts. Keep scrolling to know how well it performed on the AK Meter.

J&J Official 7 Minute Workout: Introduce fitness to your life

The Johnson & Johnson home workout app is as relevant an app in 2023 as it was in 2014 during its launch. Yes! It is an old app. Nothing much has changed, but then science never grows old, does it?

The app uses science-based methods to help you reach your fitness goal and maintain it, even if you are short on time. Designed to make every minute count, it uses a mix of circuit and high-intensity training for more significant impact in less time.

Let’s understand it from the horse’s mouth itself, the program developer and fitness expert at Jhonson & Jhonson, Chris Jordan.

First impression

The app is designed for the layman; there are no pompous claims or promises of transformation. It takes you straight to the point, or shall I say, exercises, and I love that about this app.

Is the regime really just 7 minutes?

Yes & no! The built-in, default exercise regime comes around seven minutes. If you add the warm-up and cool-down timing, it’s around 10 – 15 minutes, which is manageable even in our busy schedules.

Beginner-friendly or for pros? 

For the benefit of beginners, Chris Jordan’s digital avatar and voice guide you through the circuits. It even prompts when to breathe in, hold, and release a pose. Plus, you can even check out and learn each exercise individually.

As for intermediate and pros, they can choose a Smart Workout designed according to their fitness and motivation level. You can level up by selecting a mod or hard regime from the workout library.

What’s more?

You can even construct your custom workouts.

Features to look forward

Fits your style – A major plus for me as you are not bound to a specific preset. The app offers 72 exercises and 22 preset workouts with varying intensity and duration. Whether you have seven minutes, half-hour, or more to devote, there is something for everyone.

 Thumbs up or down – You are free to like and dislike an exercise, which will be taken into account in your Smart Workout.

Reminder – If toggled On, you can receive workout and inactivity reminders set according to your preference.

iOS-friendly – Like every good iOS app, the 7 Minute Workout app also syncs with your Health app to track your workout.

Pumping Music – While the app does not incorporate a workout music station, you can blast the beats of your choice from Apple music or your favorite music app in the background.

Socialize –  You can also share your progress with your friends and family via Facebook and Twitter.

My feelings after one week

J&J Official 7 Minute Workout is an almost perfect app for beginners testing the waters for home workouts. It just demands your time and dedication to gift you a fitter body. The tutorials are effective, and the app is super easy to maneuver.

However, the app is a bit monotonous, stagnant, and I felt that it lacks a motivational quotient. Overall, it does not inspire you to continue your workout or come back again. Moreover, no new exercises or other studies are being added, so the app is good to start with, but you will have to switch to something better later.

AK Meter (points out of 5)

User interface – 4

Ease of Use – 4.5

Enrichment – 4

Value for Money – 5 (it’s free)

Fun Quotient – 3.5

Motivating – 3.5

Exercise – 4

What is Appy Week with AK?

Being a through and through enthu-cutlet for all things tech & design, I love exploring new apps and understanding their uses, the developer’s perspective, and how well they work.

Through Appy Week with AK, I try to bring all those observations to the table to help you discover new apps and decide whether the app is worth your time and energy. I plan to test and review a new app every week. And I invite you all to join me on this journey.

You may also like to read: 

Author Profile


A self-professed Geek who loves to explore all things Apple. I thoroughly enjoy discovering new hacks, troubleshooting issues, and finding and reviewing the best products and apps currently available. My expertise also includes curating opinionated and honest editorials. If not this, you might find me surfing the web or listening to audiobooks.

15 Incredible Photos Taken By The Rosetta Spacecraft

This image shows that Philae landed safely on the surface of the comet. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

During the final two months Rosetta traveled 1.2 miles (2km) from the comet and was able to capture detailed images of the surface. This image was taken on Sept., 17 2023, during the craft’s 14th ellipse. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

The European Space Agency uploaded the final images from the Rosetta mission to comet 67P earlier this week and the results are quite dazzling. Rosetta was the first spacecraft to orbit a comet and over the course of its 12 year mission, the craft captured nearly 100,000 images from space using both a narrow and wide angle camera.

During the final months of Rosetta’s journey the spacecraft shot images from approximately a mile away from the surface of the comet—documenting previously unseen details from its surface.

“The final set of images supplements the rich treasure chest of data that the scientific community are already delving into in order to really understand this comet from all perspectives – not just from images but also from the gas, dust and plasma angle – and to explore the role of comets in general in our ideas of Solar System formation,” says Matt Taylor, ESA’s Rosetta project scientist. “There are certainly plenty of mysteries, and plenty still to discover.

All images from Rosetta’s mission are all available for free through a Creative Commons lisence and are available for download through Archive Image Browser or Planetary Science Archive .

These are some of our favorite shots captured by Rosetta during its time in space.

Look for the thin vertical line with a broad top on the left hand edge of this image. (Don’t see it? How about now?) It’s one of the legs of the Philae lander, which was lost soon after touching down in 2014. Rosetta finally located Philae’s final resting place in September 2023. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

During the two years that Rosetta flew alongside comet 67P it was bombarded with dust grains coming from the comet’s surface. The streaks in this image are the dust grains passing by Rosetta’s camera and was captured with a 146 second exposure. Studying this beautiful dust will hopefully give scientists a better understanding of how comets develop. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

A plume of dust from comet 67P captured on July, 3 2023. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

Captured in May 2023 this image was taken the same month that Rosetta first detected organohalogen methyl chloride. It was the first time the substance had been found in space. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

Taken during a three week period when Rosetta flew close to the nucleus of the comet searching for xenon. The xenon found on comet 67P closely resembled the mixture that is believed to have been delivered to Earth during the formation of our solar system. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

67P captured during Rosetta’s final descent on Sept., 30 2023. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

67P photographed 59.5 miles from the comet’s nucleus. Captured Dec., 18, 2023. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

This image was taken about half an hour before the Philae spacecraft touched down on the comet’s surface. Captured November 2014. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

This image shows that Philae landed safely on the surface of the comet. SA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

Rosetta captures the moon on Nov. 13, 2007, approximately nine hours after its closest approach to Earth. A neutral density filter was used to reduce the sensitivity of the camera on board. ESA ©2007 MPS for OSIRIS Team MPS/UPD/LAM/IAA/RSSD/INTA/UPM/DASP/IDA

An impact crater detected on the surface of comet 67P. Taken Sept. 2014. ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

This image documents the diversity of the comet’s active regions. Captured Sept. 20, 2014 from a distance of 16.1 miles. ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

Captured on Valentine’s Day 2023, Rosetta’s shadow can be seen on the surface of the comet because of the sun, the spacecraft and the comet being perfectly aligned. ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA

Comet captured on March 27, 2023 when Rosetta was 204.4 miles from the nucleus of the comet. ESA/Rosetta/NavCam

3 Ways To Use Chatgpt With Google Search Side By Side

The first extension you can use to get ChatGPT responses next to your search result is called ChatGPT for Google, there are currently 2,000,000+ users. Here’s how to use this extension.

2. Once the extension is added to your browser, you will be redirected to this customization page, here you can customize the following and save the changes:

Change the Trigger Mode – Choose between Always, Question Mark, or Manually, as when you want to see the ChatGPT extension results.

Theme – Choose a Light or Dark theme for the extension settings.

Language – Choose the language you want to see the ChatGPT results in, the best is to leave it on Auto.

AI Provider – You can either use ChatGPT’s web app API or for stable results, provide the API from your OpenAI account, which will be counted from your daily quota, if you are a ChatGPT Plus user.

To use the OpenAI API key from your account, go to this link.

Copy the link of your API key.

Now, paste this link into the OpenAI API tab.

5. Once you are logged in to your Open AI account. Based on your trigger preference, you will be greeted with the search results from ChatGPT next to Google search.

3. On the extension’s setting page, you can customize the following things as per your preference:

Trigger Mode – Choose between Always, Question Mark, or Manually, as when you want to see the ChatGPT extension results.

Theme – Choose a Light or Dark theme for the extension settings.

API Option – You can either use ChatGPT’s web app API or for stable results, provide the API from your OpenAI account, which will be counted from your daily quota, if you are a ChatGPT Plus user.

6. Once you are logged in to your Open AI account. Based on your trigger preference, you will see the search results from ChatGPT next to Google search.

Lastly, you can use the Merlin extension to get ChatGPT responses next to your search result. It doe not require an OpenAI account, as all you need to do is log in using Merlin. Even with 5,00,000+ users, I found Merlin to be the fastest when it comes to generating a result for a search query. Here’s how you can use it.

4. Once logged in, you will see the search results from ChatGPT next to Google search.

Though Merlin comes with a quota of 51 requests per day, the queries generated using search do not count under this quota.

Both fetch data from the internet based on your query, while Google gives a brief synopsis of the result, which might not be complete, on the other hand, ChatGPT generates concise results from all the data. You can use ChatGPT with Google search to judge it yourself, follow the above-mentioned methods to use ChatGPT side by side with Google to compare them.

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