Intel’s AI division is one of the unsung heroes of the modern machine-learning movement. It’s talented researchers have advanced the state of AI chips , neuromorphic computing , and deep learning . And now they’re turning their sights on the unholy grail of AI: the hive mind.

Okay, that might be a tad dramatic. But every great science fiction horror story has to start somewhere.

And Intel’s amazing advances in the area of multiagent evolutionary reinforcement learning (MERL) could make a great origin story for the Borg – a sentient AI that assimilates organic species into its hive mind, from Star Trek .

MERL, aside from being a great name for a fiddle player, is Intel’s new method for teaching machines how to collaborate.

Per an Intel press release :

The new system is complex and involves novel machine-learning techniques, but the basic ideas behind it are actually fairly intuitive.

AI systems don’t have what the French call une raison d’exister . In order for a machine to do something, it needs to be told what to do.

But, often, we want AI systems to do things without being told what to do. The whole point of a machine learning paradigm is to get the machine to figure things out for itself.

However, you still need to make the AI learn the stuff you want it to and forget everything else.

For example, if you’re trying to teach a robot to walk, you want it to remember how to move its legs in tandem and forget about trying to solve the problem by hopping on one foot.

This is accomplished through reinforcement learning , the RL in MERL. Researchers tweak the AI‘s training paradigm to ensure it’s rewarded whenever it accomplishes a goal, thus keeping the machine on task.

If you think about AI in the traditional sense, it works a lot like a single agent (basically, one robot brain) trying to solve a giant problem on its own.

So, for an AI brain responsible for making a robot walk, the AI has to figure out balance, kinetic energy, resistance, and what the exact limits of its physical parts are. This is not only time-consuming – often requiring hundreds of millions of iterative attempts – but it’s also expensive.

Intel’s MERL system allows multiple agents (more than one AI brain) to attack a larger problem by breaking it down into individual tasks that can then be handled by individual agents. The agents collaborate in order to speed up learning across each task. Once the individual agents train up on their tasks, a control agent utilizes the sum of training to organize a method by which the entire goal is accomplished – in our example, making a robot walk.

If this system was people instead of AI, it’d be like the hit 1980s cartoon Voltron , where individual pilots fly individual vehicles but they come together to form a giant robot that’s more powerful than the sum of its parts.

But since we’re talking about AI, it’s probably more helpful to view it more like the aforementioned Borg. Instead of a single AI brain controlling all the action, MERL gives AI the ability to form a sort of brain network.

One might even be tempted to call it a non-sentient hive mind.

Researchers used AI to crack Microsoft Outlook’s CAPTCHA

Not only are they super annoying, but it turns out text-based CAPTCHAs are also potentially a security risk.

Researchers from security firm F-Secure have figured out how to use AI to trick Microsoft Outlook‘s text-based CAPTCHA into thinking a human solved it. In a blog post , the security experts explained the biggest challenge wasn’t correctly labelling the text, but mimicking the keystrokes a human would make when submitting the answer.

Considering their past experience in cracking text-based CAPTCHAs, the researchers were confident the mechanism protecting Microsoft’s Outlook Web App portal wouldn’t stand a chance. It turns out they were wrong.

After manually labelling about 200 CAPTCHAs, Outlook’s system turned out surprisingly resilient to F-Secure’s convolutional neural network, which could only identify the characters with an accuracy of 22% . This is when the researchers decided to up their sample size and label a total of 1,400 CAPTCHAs.

In the process, they figured out that the noise in the CAPTCHAs made it difficult for their AI solution to identify the letters. To counteract this effect, they tweaked their tool to remove noise.

Even after all this work, the algorithm didn’t perform any better — in fact, its accuracy dropped to a little under 16%.

Upon further inspection, the researchers realized that they wrongly labelled roughly 50 out of every 300 manually entered CAPTCHAs. Some of the common mistakes included confusing “I” for “l” (lower-case L) and “Y” for “V.”

They noticed a pattern: Outlook‘s CAPTCHA mechanism never used “l” (lower-case L). With this in mind, the researchers narrowed down their crack alphabet, which helped them improve the accuracy of the algorithm from nearly 16% to 47%.

The next step was figuring out how to submit the cracked CAPTCHA to Outlook’s web portal. Since the CAPTCHA was designed to keylog each letter the user submits, the researchers had to mimic the keystrokes a human would make to fool the mechanism.

Here’s a video of F-Secure’s cracking tool in action . In the meantime, those interested in a closer look at F-Secure’s CAPTCHA cracking tool ought to head to this blog post .

Of course, breaking CAPTCHAs is nothing new.

Back in 2013, researchers developed software that cracked Google, Yahoo, and PayPal CAPTCHAs with an over 90% success rate.

“ We said it last year and we will say it again: text-based CAPTCHAs are just not cutting it anymore,” F-Secure said in its blog post. “There are some interesting new CAPTCHA samples on the market, but it is just a matter of time before these also buckle under the CAPTCHA Cracken. We are not saying that CAPTCHAs are useless, they should just not be seen as the silver bullet that stops automated attacks.”

Tinder’s new feature lets users chat before they match

For the first time, Tinder is letting users chat before they match.

The app has launched a game called Hot Takes that connects potential spouses in a speed dating-style game that’s now available every day from 6PM-midnight local time.

Users will first be shown some multiple-choice questions, such as: “Which of these is most pretentious? Bragging you don’t have TikTok, natural wine, saying you found something first , or or Oxford comma enthusiasts.”

Tinder will then drop them into a chat with another user who’s given similar answers.

The potential spouses can then talk before deciding whether to give one another a “Like” or a “Nope.” But users will need to make their first impression quickly — they only get 30 seconds to make their choice.

For those of us who prefer a more leisurely pace of courtship, Tinder is also introducing a new section called Explore that provides different ways of meeting people.

The tab, which launches later this summer, lets users search for matches with similar interests. They’ll also find other Tinder features, such as Festival Mode , which is designed to connect people attending the same festival.

If you don’t have a cute puppy, glamorous vacation, or flawless physique to show off in your profile, Explore and Hot Takes could at least let you show off your inner beauty. But if your personality sucks, you might have better luck flaunting your physical gifts in another new Tinder feature: videos in profiles.

The app now lets users add up to nine clips to their profiles, which they can trim, crop, and preview before uploading.

Tinder said the new feature would be particularly suitable for Gen Z, which makes up more than 50% of the app’s global members:

Tinder’s focus on younger users has a strong business case. When parent company Match fully acquired Hinge, a dating app with an older user base, it made sense for Tinder to concentrate on a different age group. But the new features could also make the online dating world a marginally less superficial place.