Emoji are now part of our language. If you’re like most people, you pepper your texts, Instagram posts, and TikTok videos with various little images to augment your words—maybe the syringe with a bit of blood dripping from it when you got your vaccination, the prayer (or high-fiving?) hands as a shortcut to “thank you,” a rosy-cheeked smiley face with jazz hands for a covid-safe hug from afar. Today’s emoji catalogue includes nearly 3,000 illustrations representing everything from emotions to food, natural phenomena, flags, and people at various stages of life.
Behind all those symbols is the Unicode Consortium, a nonprofit group of hardware and software companies aiming to make text and emoji readable and accessible to everyone. Part of their goal is to make languages look the same on all devices; a Japanese character should be typographically consistent across all media, for example. But Unicode is probably best known for being the gatekeeper of emoji: releasing them, standardizing them, and approving or rejecting new ones.
Jennifer Daniel is the first woman at the helm of the Emoji Subcommittee for the Unicode Consortium and a fierce advocate for inclusive, thoughtful emoji. She initially rose to prominence for introducing Mx. Claus, a gender-inclusive alternative to Santa and Mrs. Claus; a non-gendered person breastfeeding a non-gendered baby; and a masculine face wearing a bridal veil.
Now she’s on a mission to bring emoji to a post-pandemic future in which they are as broadly representative as possible. That means taking on an increasingly public role, whether it’s with her popular and delightfully nerdy Substack newsletter, What Would Jennifer Do? (in which she analyzes the design process for upcoming emoji), or inviting the general public to submit concerns about emoji and speak up if they aren’t representative or accurate.
“There isn’t a precedent here,” Daniel says of her job. And to Daniel, that’s exciting not just for her but for the future of human communication.
I spoke to her about how she sees her role and the future of emoji. The interview has been lightly edited and condensed.
What does it mean to chair the subcommittee on emoji? What do you do?
It’s not sexy. [laughs] A lot of it is managing volunteers [the committee is composed of volunteers who review applications and help in approval and design]. There’s a lot of paperwork. A lot of meetings. We meet twice a week.
I read a lot and talk to a lot of people. I recently talked to a gesture linguist to learn how people use their hands in different cultures. How do we make better hand-gesture emoji? If the image is no good or isn’t clear, it’s a dealbreaker. I’m constantly doing lots of research and consulting with different experts. I’ll be on the phone with a botanical garden about flowers, or a whale expert to get the whale emoji right, or a cardiovascular surgeon so we have the anatomy of the heart down.
There’s an old essay by Beatrice Warde about typography. She asked if a good typeface is a bedazzled crystal goblet or a transparent one. Some would say the ornate one because it’s so fancy, and others would say the crystal goblet because you can see and appreciate the wine. With emoji, I lend myself more to the “transparent crystal goblet” philosophy.
Why should we care about how our emoji are designed?
My understanding is that 80% of communication is nonverbal. There’s a parallel in how we communicate. We text how we talk. It’s informal, it’s loose. You’re pausing to take a breath. Emoji are shared alongside words.
When emoji first came around, we had the misconception that they were ruining language. Learning a new language is really hard, and emoji is kind of like a new language. It works with how you already communicate. It evolves as you evolve. How you communicate and present yourself evolves, just like yourself. You can look at the nearly 3,000 emoji and it [their interpretation] changes by age or gender or geographic area. When we talk to someone and are making eye contact, you shift your body language, and that’s an emotional contagion. It builds empathy and connection. It gives you permission to reveal that about yourself. Emoji can do that, all in an image.
This AI could predict 10 years of scientific priorities—if we let it
The survey committee, which receives input from a host of smaller panels, takes into account a gargantuan amount of information to create research strategies. Although the Academies won’t release the committee’s final recommendation to NASA for a few more weeks, scientists are itching to know which of their questions will make it in, and which will be left out.
“The Decadal Survey really helps NASA decide how they’re going to lead the future of human discovery in space, so it’s really important that they’re well informed,” says Brant Robertson, a professor of astronomy and astrophysics at UC Santa Cruz.
One team of researchers wants to use artificial intelligence to make this process easier. Their proposal isn’t for a specific mission or line of questioning; rather, they say, their AI can help scientists make tough decisions about which other proposals to prioritize.
The idea is that by training an AI to spot research areas that are either growing or declining rapidly, the tool could make it easier for survey committees and panels to decide what should make the list.
“What we wanted was to have a system that would do a lot of the work that the Decadal Survey does, and let the scientists working on the Decadal Survey do what they will do best,” says Harley Thronson, a retired senior scientist at NASA’s Goddard Space Flight Center and lead author of the proposal.
Although members of each committee are chosen for their expertise in their respective fields, it’s impossible for every member to grasp the nuance of every scientific theme. The number of astrophysics publications increases by 5% every year, according to the authors. That’s a lot for anyone to process.
That’s where Thronson’s AI comes in.
It took just over a year to build, but eventually, Thronson’s team was able to train it on more than 400,000 pieces of research published in the decade leading up to the Astro2010 survey. They were also able to teach the AI to sift through thousands of abstracts to identify both low- and high-impact areas from two- and three-word topic phrases like “planetary system” or “extrasolar planet.”
According to the researchers’ white paper, the AI successfully “backcasted” six popular research themes of the last 10 years, including a meteoric rise in exoplanet research and observation of galaxies.
“One of the challenging aspects of artificial intelligence is that they sometimes will predict, or come up with, or analyze things that are completely surprising to the humans,” says Thronson. “And we saw this a lot.”
Thronson and his collaborators think the steering committee should use their AI to help review and summarize the vast amounts of text the panel must sift through, leaving human experts to make the final call.
Their research isn’t the first to try to use AI to analyze and shape scientific literature. Other AIs have already been used to help scientists peer-review their colleagues’ work.
But could it be trusted with a task as important and influential as the Decadal Survey?
Securing the energy revolution and IoT future
In early 2021, Americans living on the East Coast got a sharp lesson on the growing importance of cybersecurity in the energy industry. A ransomware attack hit the company that operates the Colonial Pipeline—the major infrastructure artery that carries almost half of all liquid fuels from the Gulf Coast to the eastern United States. Knowing that at least some of their computer systems had been compromised, and unable to be certain about the extent of their problems, the company was forced to resort to a brute-force solution: shut down the whole pipeline.
The interruption of fuel delivery had huge consequences. Fuel prices immediately spiked. The President of the United States got involved, trying to assure panicked consumers and businesses that fuel would become available soon. Five days and untold millions of dollars in economic damage later, the company paid a $4.4 million ransom and restored its operations.
It would be a mistake to see this incident as the story of a single pipeline. Across the energy sector, more and more of the physical equipment that makes and moves fuel and electricity across the country and around the world relies on digitally controlled, networked equipment. Systems designed and engineered for analogue operations have been retrofitted. The new wave of low-emissions technologies—from solar to wind to combined-cycle turbines—are inherently digital tech, using automated controls to squeeze every efficiency from their respective energy sources.
Meanwhile, the covid-19 crisis has accelerated a separate trend toward remote operation and ever more sophisticated automation. A huge number of workers have moved from reading dials at a plant to reading screens from their couch. Powerful tools to change how power is made and routed can now be altered by anyone who knows how to log in.
These changes are great news—the world gets more energy, lower emissions, and lower prices. But these changes also highlight the kinds of vulnerabilities that brought the Colonial Pipeline to an abrupt halt. The same tools that make legitimate energy-sector workers more powerful become dangerous when hijacked by hackers. For example, hard-to-replace equipment can be given commands to shake itself to bits, putting chunks of a national grid out of commission for months at a stretch.
For many nation-states, the ability to push a button and sow chaos in a rival state’s economy is highly desirable. And the more energy infrastructure becomes hyperconnected and digitally managed, the more targets offer exactly that opportunity. It’s not surprising, then, that an increasing share of cyberattacks seen in the energy sector have shifted from targeting information technologies (IT) to targeting operating technologies (OT)—the equipment that directly controls physical plant operations.
To stay on top of the challenge, chief information security officers (CISOs) and their security operations centers (SOCs) will have to update their approaches. Defending operating technologies calls for different strategies—and a distinct knowledge base—than defending information technologies. For starters, defenders need to understand the operating status and tolerances of their assets—a command to push steam through a turbine works well when the turbine is warm, but can break it when the turbine is cold. Identical commands could be legitimate or malicious, depending on context.
Even collecting the contextual data needed for threat monitoring and detection is a logistical and technical nightmare. Typical energy systems are composed of equipment from several manufacturers, installed and retrofitted over decades. Only the most modern layers were built with cybersecurity as a design constraint, and almost none of the machine languages used were ever meant to be compatible.
For most companies, the current state of cybersecurity maturity leaves much to be desired. Near-omniscient views into IT systems are paired with big OT blind spots. Data lakes swell with carefully collected outputs that can’t be combined into a coherent, comprehensive picture of operational status. Analysts burn out under alert fatigue while trying to manually sort benign alerts from consequential events. Many companies can’t even produce a comprehensive list of all the digital assets legitimately connected to their networks.
In other words, the ongoing energy revolution is a dream for efficiency—and a nightmare for security.
Securing the energy revolution calls for new solutions equally capable of identifying and acting on threats from both physical and digital worlds. Security operations centers will need to bring together IT and OT information flows, creating a unified threat stream. Given the scale of data flows, automation will need to play a role in applying operational knowledge to alert generation—is this command consistent with business as usual, or does context show it’s suspicious? Analysts will need broad, deep access to contextual information. And defenses will need to grow and adapt as threats evolve and businesses add or retire assets.
This month, Siemens Energy unveiled a monitoring and detection platform aimed at resolving the core technical and capability challenges for CISOs tasked with defending critical infrastructure. Siemens Energy engineers have done the legwork needed to automate a unified threat stream, allowing their offering, Eos.ii, to serve as a fusion SOC that’s capable of unleashing the power of artificial intelligence on the challenge of monitoring energy infrastructure.
AI-based solutions answer the dual need for adaptability and persistent vigilance. Machine learning algorithms trawling huge volumes of operational data can learn the expected relationships between variables, recognizing patterns invisible to human eyes and highlighting anomalies for human investigation. Because machine learning can be trained on real-world data, it can learn the unique characteristics of each production site, and can be iteratively trained to distinguish benign and consequential anomalies. Analysts can then tune alerts to watch for specific threats or ignore known sources of noise.
Extending monitoring and detection into the OT space makes it harder for attackers to hide—even when unique, zero-day attacks are deployed. In addition to examining traditional signals like signature-based detection or network traffic spikes, analysts can now observe the effects that new inputs have on real-world equipment. Cleverly disguised malware would still raise red flags by creating operational anomalies. In practice, analysts using the AI-based systems have found that their Eos.ii detection engine was sensitive enough to predictively identify maintenance needs—for example, when a bearing begins to wear out and the ratio of steam in to power out begins to drift.
Done right, monitoring and detection that spans both IT and OT should leave intruders exposed. Analysts investigating alerts can trace user histories to determine the source of anomalies, and then roll forward to see what else was changed in a similar timeframe or by the same user. For energy companies, increased precision translates to dramatically reduced risk – if they can determine the scope of an intrusion, and identify which specific systems were compromised, they gain options for surgical responses that fix the problem with minimal collateral damage—say, shutting down a single branch office and two pumping stations instead of a whole pipeline.
As energy systems continue their trend toward hyperconnectivity and pervasive digital controls, one thing is clear: a given company’s ability to provide reliable service will depend more and more on their ability to create and sustain strong, precise cyber defenses. AI-based monitoring and detection offers a promising start.
To learn more about Siemens Energy’s new AI-based monitoring and detection platform, check out their recent white paper on Eos.ii.
Learn more about Siemens Energy cybersecurity at Siemens Energy Cybersecurity.
This content was produced by Siemens Energy. It was not written by MIT Technology Review’s editorial staff.
The US is about to kick-start its controversial covid booster campaign
Disagreements: Booster shots have been controversial. A group of top scientists, including experts at the FDA and WHO, published a review in The Lancet on Monday arguing that booster shots are unnecessary since vaccines are still very effective at preventing severe disease and death. Furthermore, they say, vaccine supplies could save more lives if they’re used for unvaccinated people rather than as boosters for the vaccinated. That’s why the WHO has been pleading with rich countries to stop handing them out until more of the world is vaccinated.
Unequal distribution: The US joins the UK, the UAE, France, Germany, and Israel, which have also launched booster programs. In the UK, for example, a rollout of booster shots to all over-50s is about to begin after officials gave the green light last week. Meanwhile, less than 4% of Africa’s population is fully vaccinated, compared with 70% of adults in the EU. In the US, it’s 55%, a figure that has stubbornly failed to significantly budge in recent weeks. Earlier this week, President Biden announced that the US would buy a further 500 million doses of vaccine to distribute to other parts of the world, bringing its total commitment to more than 1 billion.
Scramble: Millions of Americans are likely to try to get a third shot. A YouGov poll this summer found that three in five vaccinated Americans will get one if it’s available. Given the chaotic nature of the US vaccine rollout, it will be hard to prevent people from gaming the system to get a third shot even if they aren’t technically eligible.