Taking too long? Close loading screen.
Connect with us


Google’s new logos are bad



Google really whiffed with the new logos for its “reimagination” of G Suite as Google Workspace, replacing icons that are familiar, recognizable, and in Gmail’s case iconic if you will, with little rainbow blobs that everyone will now struggle to tell apart in their tabs. Companies always talk loud and long about their design language and choices, so as an antidote I thought I’d just explain why these new ones are bad and probably won’t last.

First I should say that I understand Google’s intent here, to unify the visual language of the various apps in its suite. That can be important, especially with a company like Google, which abandons apps, services, design languages, and other things like ballast out of a sinking hot air balloon (a remarkably apt comparison, in fact).

We’ve seen so many Google icon languages over the years that it’s hard to bring oneself to care about new ones. To paraphrase Sun Tzu, if you wait long enough by the river, the bodies of your favorite Google products will float by. Better not to get attached.

But sometimes they do something so senseless that it is incumbent upon anyone who cares at all to throw the company’s justification in its face and tell them they blew it; The last time I cared enough was with Google Reader. Since I and a hundred million other people will have to stare at these ugly new icons all day until they retire them, maybe making a little noise will accelerate that timeline a bit.

Sorry if I let myself prose a bit here, but I consider it an antidote to the endless design stories these almost without exception ill-advised redesigns always come with. I’ll limit discussion of how these icons go wrong to three general ways: color, shape, and brand.


Color is one of the first things you notice about something, and you can recognize colors easily even in your peripheral vision. So having a distinct color is important to type and design in lots of ways. Why do you think companies go so crazy about all those different shades of blue?

That’s part of why the icons of the most popular Google apps are so easily distinguished. Gmail’s red color goes back a decade and more, and Calendar’s blue is pretty old as well. The teal of Meet probably should have just stayed green, like its predecessor Hangouts, but it’s at least somewhat distinct. Likewise Keep (remember Keep?) and a handful of other lesser actors. More importantly, they’re solid — except for a few that were better for their colors, like Maps, before its icon got assassinated.

There are two problems with the colors of the new icons. First is that they don’t really have colors. They all have all the colors, which just right off the bat makes it harder to tell them apart at a glance. Remember, you’re never going to see this big like in the image above. More often they’ll be more this size:

Maybe even smaller. And never that close. I don’t know about you, but I can’t tell them apart when I’m not looking directly at them. What exactly are you looking for? They all have every color, and not even in the same order or direction — you see how some are red, yellow, green, blue and one is red, yellow, blue, green? Three (with Gmail) clockwise and two anti-clockwise, too. Sounds unimportant but your eye picks up on stuff like that, but maybe just enough that you’re more confused. Maybe these would have been better if they all started with red in the top left or something, and cycled through. They don’t randomize the order of the colors in the main Google logo, right? Ultimately these little blobs just resemble toys or crunched up candy wrappers. At best it’s plaid, and that’s Slack territory.

At first I thought the little red triangular tabs were a nice visual indicator, but somehow they messed that up too. Each icon should have the tab in a different corner, but Calendar and Drive both have it on the bottom right. They’re different kinds of triangles, I suppose — that’s a freebie from trigonometry.

You’ll also notice that the icons have a sort of lopsided weight. That’s because against a light background, different colors have different visual salience. Darker colors pop more against a white background than yellow or the tiny bit of red, making the icons seem to have heavy “L” aspects to them, on the left in Gmail and Calendar, bottom left in Drive and Meet, bottom right in Docs. But in an inactive tab, the light color will be more salient, and those L’s will seem to be on the other sides.


This is a good segue into the shape problems, because the perceived shape of these icons will change depending on the background. The original icons solved this by having a solid shape unique to them, and the background didn’t really leak through. You have to be real careful about transparent parts of your design — positive and negative space and all that. If you surrender any part of your logo to the background, you’re at the whim of whatever UI or theme the user has chosen. Will these logos look good with a hole in the middle looking onto a dark grey inactive tab? Or will the hole be filled in with white, making it positive space when on a dark background and negative when on white?

Anyhow the issue with these icons is that their shapes are bad. They’re all hollow, and four of them are rectangular if you include Gmail’s negative space (and we do — Google taught us to). The general shape of a container is a perfectly good one, but at a glance four of them are basically just angular O’s. Do you want the tallish O, the pointy one, or one of the two square O’s with slightly different color patterns? At a distance, who can tell? They only now resemble the thing they’re supposed do if you look really closely.

Now that I think of it, those shapes really scream Office and Bing too, don’t they? Not great!

While we’re at it, the thin type in the Calendar’s open space is pretty anemic compared with the big thick border, right? Maybe they should have gone with bold.

And last, the overlapping colors make for trouble. For one thing it makes the Drive logo look like a biohazard symbol. But it adds a lot of complexity that’s hard to follow at a small scale. The original Drive logo had three colors, to be sure, and a little drop shadow so you’d see it was a Moebius strip implying infinity and not just a triangle (that’s gone too — so why keep the triangle?) — but the colors set each other off: Blue and yellow make green, two primaries and their secondary.

The new ones have all three primaries, one secondary, and two tertiary (if you count darkness as a color). They don’t help the shapes exist in any identifiable way. Are you looking through them? That doesn’t seem right. They kind of fold, but how? Are the strips these are made of twisting? I don’t think so. The shapes aren’t things — they’re just arrangements, suggestions of the things they once were, removed one step too far.


Google’s no stranger to throwing value in the trash. But you’d think that sometimes they’d recognize when they have a good thing going. The Gmail logo was a good thing. I have to say I preferred the old angular one when they switched to the rounded icon some years back, but it’s grown on me. The natural “M” shape of a the envelope is emphasized so well, and the red-and-white color is so instantly recognizable and readable — this is the kind of logo you hold onto for a long, long time. Or not!

The problem here is that now Gmail, which has essentially operated as its own, completely invincible brand for more than a decade (which is eons in tech, let alone tech logos), has been put on equal footing with other services that aren’t as trusted or as widely used.

Now Gmail is just another rainbow shape in a sea of very similar rainbow shapes, which tells the user “this service isn’t special to us. This is not the service that has worked so well for you, for so long. This is just one finger on the hand of an internet giant. And now you can never see one without thinking of the other.”

Same for all the rest of these little color wheels: You’ll never forget that they’re all part of the same apparatus that knows everything you search for, every site you visit, and now, everything you do at work. Oh, they’re very polite about it. But make no mistake, the homogeneous branding (for all its color heterogeneity) is the prelude to a brand crunch in which you are no longer just a Gmail user, you’re in Google’s house, all day, every day.

“This is the moment in which we break free from defining the structure and the role of our offerings in terms that were invented by somebody else in a very different era,” Google VP Javier Soltero told Fast Company.

The message is clear: Out with the old — the things that built your trust; and in with the new — the things that capitalize on your trust.


Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *


Sony will optimize PS5 fan performance with software updates using individual game data



Sony’s next-gen PlayStation 5 console has a massive internal cooling system composed of, among other things, a gigantic fan. But the device, a 120mm-wide and 45mm-thick double-sided intake fan, also has some smart software to power it, and that software will improve the fan’s performance over time based on data gathered from individual games, according to a new interview with Yasuhiro Ootori, Sony’s mechanical design chief in charge of the PS5, with Japanese language website 4Gamer.net.

Ootori was responsible for the refreshing teardown video Sony published earlier this month, showing off the entire console inside and out in a seven-minute video that revealed some telling new details about the mechanical design of the device. In the new interview, as translated by ResetEra user orzkare, Ootori points out that the fan will be controlled by the PS5’s Accelerated Processing Unit (APU), the custom AMD combined CPU / GPU chip that powers the console.

Sony’s double-sided intake fan on the PS5, which measures 120mm in diameter and 45mm thick.
Screenshot by Nick Statt / The Verge

“Various games will be released in the future, and data on the APU’s behavior in each game will be collected,” Ootori says. “We have a plan to optimize the fan control based on this data.” Ootori explains that multiple temperature sensors are placed on the console’s main board to collect data while the APU runs any given game, and that data is what will allow Sony to optimize the fan going forward on a per-game basis.

Ootori also reveals a pretty neat detail about the testing of the PS5’s cooling system, which involved making a transparent chassis and pumping the smoke from dry ice through to observe the effects on internal temperature.


Continue Reading


Support Indie Bookstores With Your Holiday Shopping



Illustration for article titled Support Indie Bookstores With Your Holiday Shopping

Photo: Logan Bush (Shutterstock)

The pandemic hasn’t been kind to independent bookstores. While indies have handled competition from Amazon and big-box sellers relatively well in recent years, having to shut their doors due to COVID-19 and respond to customers’ desire for online ordering has not been easy—and despite the American Association of Publishing noting steady book sales amid the pandemic, bookstore sales dropped 30.7 percent in August, according to industry newsletter Shelf Awareness. If you want to keep indie bookstores alive, consider directing some of your holiday gift-buying budget toward your local independent bookseller. We’ve rounded up a number of ways you can support indies this season.


Heads up, though: if you are going to gift books and book-related items, start shopping and ordering now. Indie booksellers can always use your support—and ordering earlier can help them manage their inventory and meet demand ahead of the holidays.

Order directly from your local indie

Some booksellers responded to the pandemic by setting up shop online and offering pickup, local delivery, or both. Search for indie bookshops in your neighborhood or city to find out what they’re doing. IndieCommerce has a directory of independent sellers that handle e-commerce.


Shop on IndieBound or Bookshop

If you can’t easily get a book from a store up the street, try IndieBound or Bookshop instead of defaulting to Amazon. IndieBound has a zip code search that directs you to local booksellers that have your desired title(s) in stock, while Bookshop allows you to either select a local seller to receive 100% of the profit from your order or to put your money into a pool that’s distributed among member shops.

Note: IndieBound will also connect you to Bookshop if it can’t find titles in stock in your area. Twitter users have suggested that Bookshop’s shipping times are actually comparable to Amazon’s and that the customer service is (unsurprisingly) far better.


Shop for non-book gifts

Many, if not most, indie bookstores sell more than just books. As writer Celeste Ng points out, you can order jigsaw puzzles, apparel, tote bags, and other book-adjacent gifts (coffee mugs with a literary quote, anyone?). Try to find all of your stocking stuffers–bookmarks, pens, magnets, socks—there too, along with your holiday cards.


Gift a subscription to Libro.fm

If audiobooks are more your friends’ and family’s thing, gift them a membership to Libro.fm. The platform allows you to choose a local, independent bookstore to receive a cut of the profit from each subscription. You can also gift individual audiobooks.


Gift an indie seller gift card

If you’re not sure about a gift recipient’s taste in books, send them a gift card to their local indie or your local indie.


Put together DIY book packages

Get creative with your gift-giving with themed book and book-adjacent packages. This Twitter thread from author Rebecca Makkai has a long list of ideas, from a city-specific gift box to a food-and-book (wine-and-book?) pairing, to a book and film package from the recipient’s birth year. A few more we like:

  • Long-distance book club: Send the same book to a group of friends along with an invite to a Zoom gathering and a cocktail recipe card.
  • Cookbook and spice package: Gift your food-loving friends a cookbook plus any hard-to-find ingredients.
  • Free Little Library: Gift a Free Little Library box filled with wrapped books to a friend who has a yard.


Many indies will wrap orders, too, so you can have items sent directly to friends and family.

Stop by for a cup of coffee

If your indie bookseller is open and serving coffee and food, stop in when you can (and follow all safety rules when you do).



Continue Reading


Learn Python machine learning with these essential books and online courses



Teaching yourself Python machine learning can be a daunting task if you don’t know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics.

It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters.

A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category.

While the title sounds like another introductory book on machine learning algorithms, the content is anything but. Machine Learning Algorithms goes to places that beginner guides don’t take you, and if you have the math and programming skills, it can be a great guide to deepen your knowledge of machine learning with Python.

Oiling your machine learning engine

Machine Learning Algorithm kicks off with a quick tour of the fundamentals. I really liked the accessible definitions Bonaccorso uses to explain key concepts such as supervised, unsupervised, and semi-supervised learning and reinforcement learning.

Bonaccorso also draws great analogies between machine learning and descriptive, predictive, and prescriptive analytics. The machine learning overview also contains some hidden gems, including an introduction to computational neuroscience and some very good precautions on the pitfalls of big data and machine learning.

That said, the machine learning overview does not go into too much details and would be hard to understand for novices. Given the audience of the book, it serves to refresh and solidify your understanding of machine learning, not to teach you the basics.

Next, Machine Learning Algorithms builds up on that brief overview and goes into more advanced concepts, such as loss functions, data generation processes, independent and identically distributed variables, underfitting and overfitting, different classification strategies (one-vs-one and one-vs-all), and elements of information theory. Again, the definitions are smooth and very accessible for someone who has already had hands-on experience with machine learning algorithms and linear algebra.

Before going into the exploration of different algorithms, the book covers some more key concepts such as feature engineering and data preparation. Here, you’ll get to revisit some of the key classes and functions of scikit-learn, the main Python machine learning library. If you already have a solid knowledge of Python and numpy, you’ll find this part a pleasant review of one-hot encoding, train-test splitting, imputing, normalization, and more. There is some very great stuff in the third chapter, including one of the best and most accessible definitions of principle component analysis (PCA) and feature dependence in machine learning algorithms. You’ll also get to see some of the more advanced techniques not covered in introductory books, such as non-negative matrix factorization (NNMF) and SparsePCA. Of course, without the background in Python machine learning, these additions will be of little use to you.

The real meat ofthe book starts in the fourth chapter, where you get to the machine learning algorithms. Here, I had mixed feelings.

A rich roster of machine learning algorithms


In general, Machine Learning Algorithms is nicely structured and stands up to the name. There are chapters on regression, classification, support vector machines (SVM), decision trees, and clustering. The book follows up with a few chapters on recommendation systems and natural language processing applications, and finishes off with a very brief overview of deep learning and artificial neural networks.

The main chapters offer in-depth coverage of principle machine learning algorithms in Python, including details not covered in introductory books. For instance, the regression chapter goes into an extensive coverage of outliers and methods to mitigate their effects. The classification chapter has a nice discussion on passive-aggressive classification and regression in online algorithms. The SVM chapter has a comprehensive (but complicated) discussion on semi-supervised vector machines. And the decision trees chapter provides a good coverage of the specific sensitivities of DTs such as class imbalance, and some practical tips on tweaking trees for maximum performance.

The clustering section really shines. It spans across three full chapters, starting with fundamentals (k-nearest neighbors and k-means) and goes through more advanced clustering (DBSCAN, BIRCH, and bi-clustering) and visualization techniques (dendrograms). You’ll also get a full account of measuring the effectiveness of the results and determining whether your algorithm has latched onto the right number and distribution of clusters.

Across the book, there are thorough discussions of the mathematical formulas behind each machine learning algorithm. You need to come strapped with solid linear algebra and differential and integral calculus fundamentals to fully understand this (if you need to hone your machine learning math skills, I’ve offered some guidance in a previous post).

The book also makes extensive use of functions numpy, scipy, and matplotlib libraries without explaining them, so you’ll need to know those too (you can find some good sources on those libraries here).

One of the most enjoyable things about Machine Learning Algorithms are the chapter summaries. After going through the nitty-gritty of the math and Python coding of each machine learning algorithm, Bonaccorso gives a brief review of where to apply each of the techniques presented in the book. There are also many references to relevant papers that provide more in-depth coverage of the topics discussed in the book. It’s refreshing to see some of the old but fundamental papers from early 2000s being mentioned in the book. Those things tend to get buried under the hype surrounding state-of-the-art research.

Machine Learning Algorithms finishes off with a good wrap-up of the machine learning pipeline and some key tips on choosing between the different Python tools introduced across the book.

Not enough real-world examples

deep neural network

The one thing, in my opinion, that should set a book on Python machine learning apart from research papers and theoretical textbooks are the examples. A good book should be rich in use-case oriented examples that take you through real-world applications and possibly build up through the book.

Unfortunately, in this respect, Machine Learning Algorithms leaves a bit to desire.

For one thing, the examples in the book are mostly generic, using data-generation functions in scikit-learn such as make_blobs, make_circles, and make_classification. Those are good functions to show certain aspects of Python machine learning, but not enough to give you an idea of how to use the techniques in real life, where you have to deal with noise, outliers, bad data, and features that need to be normalized and categorized.

The code is in plain Python scripts as opposed to the preferred Jupyter Notebook format (which is not much of a big deal, to be fair). Also, while the book omits much of the sample code and focuses on the important parts for the sake of brevity, it made it hard to navigate the sample files at times.

The book does cover some real-world examples, including one with airfoil data in the SVM chapter and another with the Reuters corpus in the NLP chapter. The recommendation systems chapter also includes a few decent use cases, but that’s about it. Without concrete examples, the book often reads like a disparate reference manual with code snippets, which makes it even more crucial to have solid experience with Python machine learning before picking this one up.

Another thing that didn’t really appeal to me were the two chapters on deep learning. Machine Learning Algorithms provides a good overview of deep learning and discusses convolutional neural networks, recurrent neural networks, and other key architectures. But the problem is that introductory books on Python machine learning already cover these concepts and much more. So most of the people who make it this far through the book without putting it down won’t find anything new here (aside from the mention of KerasClassifier maybe).

Midway through Python machine learning journey

So, where does this book stand in the roadmap to learning machine learning with Python? It’s neither beginner level, nor super-advanced. I would suggest picking up Machine Learning Algorithms after you read an introductory-to-intermediate book like Python Machine Learning or Hands-on Machine Learning, or an online course like Udemy’s “Machine Learning A-Z.” Otherwise, you won’t be able to make the best of the rich content it has to offer.

Once you finish this one, you might want to consider Bonaccorso’s Mastering Machine Learning Algorithms, Second Edition, which expands on many of the topics presented in this book and takes them into even greater depth.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.


Continue Reading