Long gone are the days when you’d have to dial your friend from a landline to coordinate plans. Nowadays reaching your friends and family can happen anytime, anywhere quite literally in an instant…

According to the Encyclopedia Britannica, Instant Messaging (IM) was invented in 1971 as a computer-based messaging system. That’s barely younger than the Internet itself! It took a while for this to become popular, but AOL made that happen in the late 1980s. Social media companies and gaming platforms expanded on the idea, and telecommunications companies developed their own.

Sure, IM is a powerful communication tool, but not all messaging services are created equal. 

Beware! 

IM and SMS—Don’t Get it Twisted

First of all, SMS uses an entirely different infrastructure than IM, and yet the end result is the same: text-based communication between multiple people in real-time. Still, IM was more successful at the start because there was a proper keyboard involved. Despite also being developed in the late 1980s, SMS didn’t take off until telecommunications companies allowed for cross-company communication AND people had something like a real keyboard to type on.

SMS has some surprising limitations. There is a character limit of 70-160 characters for a true SMS message, and precisely the limit depends on how the telecommunications company you’re using implemented it. Still, thanks to a hard limit in the standard that defines SMS, it cannot go over 160 characters. Instead, the SMS app will break a message into parts to ensure it all goes across the airwaves as needed.

Another limitation is that true SMS _requires_ cell service. 

If all you have is wi-fi, then your mobile device’s native app has to do some behind-the-scenes shenanigans to convert it to a different type of message for it to send. That’s where services like Signal, WhatsApp, and Facebook Messenger come in handy.

One side note about SMS and security: For those who have turned on multi-factor authentication (MFA) on your accounts, GO YOU! However, it turns out that receiving your second factor via an SMS message is both common and not actually ideal. Where possible, using a third-party app like Authy is a better choice.

We know, annoying, right??? What gives?!

IM and Encryption

Back to actual IM services, the most important differentiator between IM apps is not whether it has the best emojis (though emojis are pretty cool). It’s whether the messages are encrypted the entire way from one phone to the next. Apps like Signal and WhatsApp support end-to-end encryption by default, but Telegram only supports end-to-end encryption between two parties. Even with that, you have to turn it on; as soon as you invite others to the chat, the message is not encrypted the whole way through.

That’s because as with everything on the Internet, the devil is in the details…

Some apps that claim encryption don’t encrypt what’s stored in backups, don’t encrypt by default, or don’t securely delete old messages wherever they are. Some apps like iMessage are a great option, but there’s a notable difference if everyone is using an iPhone (all the encryption! woohoo!) versus messaging across different operating systems (which will put you right back in SMS land).

Looking at all the trade-offs, IFM’s go-to is Signal. Signal offers top-notch encryption, is not owned by any big companies that thrive on collecting data about you, and is easy to use.

IM and the World

Most messaging apps work best when messaging other people who use the same service. That means that depending on where you are in the world, you’ll find different apps to be most useful for communicating with the natives. 

The all-around most globally popular is WhatsApp. Facebook Messenger is a pretty strong runner-up, though by how much depends on the country. But if you’re in China, you’re more likely to use WeChat.

Some messaging apps, like Telegram and Messenger, are very much part of a social network that feeds how and where they are used. Telegram, in particular, was in the news for its use in Ukraine as part of communicating about the war. 

With so many messaging options, it’s worse than trying to choose an email client! But that’s a different post.

In Closing…

Whether you call it texting, IMing, DMing, or just messaging, the desire to have a quick and easy way to send a message to a friend or colleague is necessary for how we function in the modern world. It only takes a little more thought to make sure that your communications are secure from snooping, and then you won’t have to think about whether any particular message needs to be secured before you send or receive it.

Did you know there are around 3,460 official emojis? 😮 That’s the count for ones officially blessed by the Unicode Consortium, arbiter for the representation of text in all modern software products and standards. They provide the code a computer will use to refer to the right emoji. They provide the description. They provide the keywords. They even provide a visual sample. And then every software vendor out there that supports emojis can do their own artistic take on what the emojis should look like on their platform. (Some software vendors may also offer emojis unique to their platforms; the dancing parrot on Slack comes to mind.)

Before There’s an Emoji

OK, so lots of official emojis, more being added all the time…but there has to be more to it than that, right? Oh yes, yes indeed. It’s like a breakdancing contest 🕺where people are introducing new moves all the time, hoping it becomes a Thing.

Analogy aside, though, there is an official process for how an emoji becomes an emoji. The short, short version is that it starts with an Emoji Proposal to the Unicode Emoji Subcommittee. Anyone can submit a proposal, but there are rules. Lots of rules. For example:

• You can only submit something between 📅 April 4 through July 31 📅 of any given year.

• You need to verify 🔎that someone else didn’t propose the same thing.

• You need to understand 🤓 the gory details regarding the limitations of what can be an emoji. 

• You need to follow a specific format 📜 for submission.

• You need to include a sample image in a specific file format, of a specific size, with specific variations.

And so on and so on and so on. 

Emojis and Diversity

Emojis reflect how we communicate via writing. The very first emoji came about in 1997 and was a graphical representation of emoticons (think: creative use of punctuation to create smiles, frowns, shrugs… ¯\_(ツ)_/¯), which were themselves first used (or so the story goes) in 1982. Hundreds and thousands of emojis later, skin tones that reflect the diversity of the world were added as an official modifier in 2015. There are still debates as to whether that’s enough, and it’s really bizarre where the skin tones came from in the first place. They came from work done in the 1970’s by a gent named Thomas Fitzpatrick, who was studying how skin burns in the sun and came up with a list. Now, why there are five skin types defined by the Unicode Consortium and six skin types defined by this Fitzpatrick fellow, I have no idea.  

Getting Geeky with Emojis

I’ll be honest: I generally can crank out a blog post in an hour or so. But not this series! It was way too much fun to dive into the rabbit hole to see all the different emojis, read the descriptions written by very geeky people, and learn about how a seemingly-innocuous emoji that I use every day does not always mean what I think it means! (‘OK’ 👌 symbol, I’m looking at you!). I’m sure you want see what is in this 🐇 hole with me, so here are a few links for you to follow:

• “How Different Cultures Perceive Emojis”
• The Emojipedia
• The Emoji List, v14.0 (or, as I call it, the Source of All Emoji!)

Wrap Up

While there are over 3000 official emojis available (not including all the skin tone variations), not all platforms support all emojis. “Pregnant Person” , for example, is really hard to find. “Pregnant Woman” and “Pregnant Man” , no problem. The decision on which emojis to include is entirely up to the software vendor, and it is a combination of user demand, software brand, and cultural expectations that drive the decisions. There are probably still MORE than enough emojis out there for you to tell your story, but stay tuned…more will almost certainly become available over time.

Photo by Domingo Alvarez E on Unsplash

March is Women’s History Month in the United States. March 8 is International Women’s Day. March has been a great time for an overwhelming number of #womenintech posts and memes!

IFM is run by two women who, between them, have over 50 years of experience in tech, but we don’t really want to focus on tooting our own ♀ horn. Instead, we want to examine some important aspects of technology that particularly (but not exclusively!) impact people who identify as female. Earlier this month, we wrote about digital personas and how they influence what you see (and what people learn about you) online. In this post, we’re going to take a different look at how a pair of specific and immensely powerful technologies influence all humans, but with particular implications for women and minorities, online: Artificial Intelligence (AI) and Machine Learning (ML).

AI and ML

But first, what are AI and ML? AI is short for artificial intelligence. It’s another way of saying “super-smart algorithms.” If I asked you to look at two sets of data that describe two personas, you will be able to compare them in your head and draw conclusions. (That’s because you are a smart human, and that kind of analysis is literally what humans have been designed to do over eons of evolution.) However, if I took 3 million of those personas and put them in front of you and asked for real-time analysis… Yeah, not so much. That’s what computers are for! And that is where AI shines. AI can index and analyze all that data at lightning speeds and then make decisions, based on the options in their code, as to what to do next.  

But AI by its lonesome has limitations. If it finds some personas that don’t match anything in its algorithm, then we have a game of “stump the chump” with a computer. Which is fun, granted, but people spending Big Money on Big Data generally aren’t amused. That’s where Machine Learning comes in. Machine Learning takes the options in the code ‘under advisement’ and instead looks at the data that doesn’t match and says “awesomesauce! Let’s just adapt this code a bit, shall we?”

It sounds like magic, but honestly, it’s just really complicated decision trees. Does the data fit in A? No? Then go to B. Does it fit in B? Yes! Great, go to B+1. Say that 10 million times fast. 

AI, ML, and Other People – the Downsides

With the sheer amount of data online, we wouldn’t have a particularly functioning Internet if it weren’t for the ongoing evolution of AI and ML. Think about it: how can search engines do their thing? How do big web commerce sites know to offer me particular recommendations? How do voice-recognition systems recognize so many different voices and accents? How can systems do facial recognition with so many faces out there?

AI and ML are absolutely critical, no two ways about it, but they share a particular problem: AI and ML and all their children start with a human. Statistically speaking, that human is probably male. Someone(s) had to write that code, and in writing that code, they cannot help but introduce some bias into the system they are designing. For example, an audit by Harvard University showed that facial-recognition algorithms consistently had the lowest accuracy for dark-skinned women and the highest accuracy for light-skinned men.   In an ideal world, the developer is a member of a highly diverse team where biases are quickly identified and dealt with in the code before anything goes live. I like that world. I want to live there. 

Unfortunately, that’s not the world we currently live in. I can point to a gazillion articles that say women and minorities are not well represented in tech (keeping the focus on AI and ML, I really like this article from Wired). If biases aren’t dealt with in the development phase, we are going to see all sorts of problems. In fact, we DO see all sorts of problems, in healthcare, in criminal justice, in hiring systems… the list goes on. I can even point to how the algorithm can drive individuals towards extremist views. 

Mitigating the Bias in AI/ML

All that sounds pretty dire. Is the value of having speedy search engines enough to justify the societal costs of biased AI? That’s the question, isn’t it. If there was no way to address bias in AI and ML systems, then I’d probably say no, it’s not worth it. But, if it all worked as designed… Medical treatments could happen at the very earliest moments where interventions would be most useful. People could get the support they need before they make life choices that will get them arrested. Hiring systems would actually be neutral and fair, and not solely dependent on human judgment (still a little dependent, though). Heck, AI could even start generating its own datasets!

It’s not like we don’t know that bias in these systems is a problem. There is quite a bit of literature out there on how to handle it, too. Do a web search on “how to prevent bias in AI”. I personally can’t decide which article to read first: the one that promises three ways, four ways, six ways, or seven ways to reduce or mitigate bias in AI systems. And this is just the popular content out there. Going to a site like Google Scholar will net you properly researched studies from real data scientists on the topic. 

Even the Organisation for Economic Cooperation and Development (more commonly known as the OECD; think high-powered, treaty-based, international organization) has guidance for how AI should be designed. No one is required to follow those guidelines, but they’re a good place to start. Microsoft, a company that does quite a bit with AI, has some pretty extensive guidelines and governance as well. So there is hope and some established guidelines out there. 

What it Means for You

So, what does this mean for women (and everyone else)? Well, since I’m not Queen of the Universe (if I were, I would wave my magic wand and get equal representation in the fields of automation, AI/ML, and all of tech for that matter) I’ll just say: be aware this is a thing. Know that the data you put into the system, be it a streaming service, search engine, or social media site, will automatically influence what you’re going to see in ways that might be really hard to stop. You (or your doctor) are going to be shown curated material based on what the supersmart algorithm and its buddy, machine learning, think you want (or need) to see. Maybe it’s right, maybe it’s wrong, but what you’re being shown is only a small slice of the information pie, and it’s based on what a computer thinks you want to see. Your medical care provider might not even realize that they are also relying on AI to help them be more efficient in how they handle patients, and that information might be right (or not) as well. 

Just because the computer told you so, doesn’t mean it will always be correct. Feel free to question what you’re seeing, or feel free to feed it more data so that music selection on your streaming service is exactly what you want.