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The pandemic has changed how criminals hide their cash—and AI tools are trying to sniff it out

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When economies across the world shut down earlier this year, it wasn’t only business owners and consumers who had to adapt. Criminals suddenly had a problem on their hands. How to move their money?

Profits from organized crime are typically passed through legitimate businesses, often exchanging hands several times and crossing borders, until there is no clear trail back to its source—a process known as money laundering.

But with many businesses closed, or seeing smaller revenue streams than usual, hiding money in plain sight by mimicking everyday financial activity became harder. “The money is still coming in but there’s nowhere to put it,” says Isabella Chase, who works on financial crime at RUSI, a UK-based defense and security think tank.

The pandemic has forced criminal gangs to come up with new ways to move money around. In turn, this has upped the stakes for anti-money laundering (AML) teams tasked with detecting suspicious financial transactions and following them back to their source.

Key to their strategies are new AI tools. While some larger, older financial institutions have been slower to adapt their rule-based legacy systems, smaller, newer firms are using machine learning to look out for anomalous activity, whatever it might be.

It is hard to assess the exact scale of the problem. But according to the United Nations Office on Drugs and Crime, between 2% and 5% of global GDP—between $800 billion and $2 trillion at current figures—is laundered every year. Most goes undetected. Estimates suggest that only around 1% of profits earned by criminals is seized.

And that was before covid-19 hit. Fraud is up, with fears around covid-19 creating a lucrative market for counterfeit protective gear or medication. More people spending time online also creates a bigger pool for phishing attacks and other scams. And, of course, drugs are still being bought and sold.

Lockdown made it harder to hide the proceeds—at least to begin with. The problem for criminals is that many of the best businesses for laundering money were also those hit hardest by the pandemic. Small shops, restaurants, bars, and clubs are favored because they are cash-heavy, which makes it easier to mix up ill-gotten gains with legal income.

With bank branches closed, it has been harder to make large cash deposits. Wire transfer services like Western Union—which usually allow anyone to walk in off the street and send money overseas—shut their premises, too.

But criminals are nothing if not opportunistic. As the normal channels for money laundering closed, new ones opened up. Vast sums of money have started flowing into small businesses again thanks to government bailouts. This creates a flurry of financial activity that provides cover for money laundering.

Breaking the rules

The upshot is that there are more demands being placed on AML tech. Older systems rely on hand-crafted rules, such as that transactions over a certain amount should raise an alert. But these rules lead to many false flags and real criminal transactions get lost in the noise. More recently, machine-learning based approaches try to identify patterns of normal activity and raise flags only when outliers are detected. These are then assessed by humans, who reject or approve the alert.

This feedback can be used to tweak the AI model so that it adjusts itself over time. Some firms, including Featurespace, a firm based in the US and UK that uses machine learning to detect suspicious financial activity, and Napier, another firm that builds machine learning tools for AML, are developing hybrid approaches in which correct alerts generated by an AI can be turned into new rules that shape the overall model.  

The rapid shifts in behavior in recent months have made the advantages of more adaptable systems clear. Financial regulators around the world have released new guidance on what sort of activity AML teams should look out for but for many it was too late, says Araliya Sammé, head of financial crime at Featurespace. “When something like covid happens, where everybody’s payment patterns change suddenly, you don’t have time to put new rules in place.”

You need tech that can catch it as it is happening, she says: “Otherwise by the time you’ve detected something and alerted the people who need to know, the money is gone.” 

For Dave Burns, chief revenue officer for Napier, covid-19 caused long-simmering problems to boil over. “This pandemic was the tipping point in many ways,” he says. “It’s a bit of a wake-up call that we really need to think differently.” And, he adds, “some of the larger players in the industry have been caught flat-footed.”

But that doesn’t simply mean adopting the latest tech. “You can’t just do AI for AI’s sake because that will spew out garbage,” says Burns. What’s needed, he says, is a bespoke approach for each bank or payment provider.

AML technology still has a long way to go. The pandemic has revealed cracks in existing systems that have people worried, says Burns. And that means that things could change faster than they were going to. “We’re seeing a greater degree of urgency,” he says. “What is traditionally very long, bureaucratic decision-making is being accelerated dramatically.”



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Facebook wants to make AI better by asking people to break it

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The explosive successes of AI in the last decade or so are typically chalked up to lots of data and lots of computing power. But benchmarks also play a crucial role in driving progress—tests that researchers can pit their AI against to see how advanced it is. For example, ImageNet, a public data set of 14 million images, sets a target for image recognition. MNIST did the same for handwriting recognition and GLUE (General Language Understanding Evaluation) for natural-language processing, leading to breakthrough language models like GPT-3.

A fixed target soon gets overtaken. ImageNet is being updated and GLUE has been replaced by SuperGLUE, a set of harder linguistic tasks. Still, sooner or later researchers will report that their AI has reached superhuman levels, outperforming people in this or that challenge. And that’s a problem if we want benchmarks to keep driving progress.

So Facebook is releasing a new kind of test that pits AIs against humans who do their best to trip them up. Called Dynabench, the test will be as hard as people choose to make it.

Benchmarks can be very misleading, says Douwe Kiela at Facebook AI Research, who led the team behind the tool. Focusing too much on benchmarks can mean losing sight of wider goals. The test can become the task.

“You end up with a system that is better at the test than humans are but not better at the overall task,” he says. “It’s very deceiving, because it makes it look like we’re much further than we actually are.”

Kiela thinks that’s a particular problem with NLP right now. A language model like GPT-3 appears intelligent because it is so good at mimicking language. But it is hard to say how much these systems actually understand.

Think about trying to measure human intelligence, he says. You can give people IQ tests, but that doesn’t tell you if they really grasp a subject. To do that you need to talk to them, ask questions.

Dynabench does something similar, using people to interrogate AIs. Released online today, it invites people to go to the website and quiz the models behind it. For example, you could give a language model a Wikipedia page and then ask it questions, scoring its answers.

In some ways, the idea is similar to the way people are playing with GPT-3 already, testing its limits, or the way chatbots are evaluated for the Loebner Prize, a contest where bots try to pass as human. But with Dynabench, failures that surface during testing will automatically be fed back into future models, making them better all the time.

For now Dynabench will focus on language models because they are one of the easiest kinds of AI for humans to interact with. “Everybody speaks a language,” says Kiela. “You don’t need any real knowledge of how to break these models.”

But the approach should work for other types of neural network too, such as speech or image recognition systems. You’d just need a way for people to upload their own images—or have them draw things—to test it, says Kiela: “The long-term vision for this is to open it up so that anyone can spin up their own model and start collecting their own data.”

“We want to convince the AI community that there’s a better way to measure progress,” he adds. “Hopefully, it will result in faster progress and a better understanding of why machine-learning models still fail.” 



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In Praise Of The DT830, The Phenomenal Instrument You Probably Don’t Recognise For What It Is

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If we had to make a guess at the single piece of electronic bench equipment owned by the highest proportion of Hackaday readers, it would not be a budget oscilloscope from Rigol, nor would it be a popular portable soldering iron like the TS100. Instead we’re guessing that it’s a multimeter, and not even the most accomplished one.

The DT830 is a genericised Chinese-manufactured 3.5 digit digital multimeter that can be had for an astonishingly low price. Less than a decent hamburger gets you an instantly recognisable plastic case with a chunky rotary range selector switch, and maybe a socket for some kind of transistor or component tester. Make sure that there is a 9 volt battery installed, plug in the pair of test leads, and you’re in business for almost any day-to-day electrical or electronic measurement. They’ve been available in one form or another for decades and have been the subject of innumerable give-aways and loss-leader offers, so it’s a reasonsble guess that you’ll have one somewhere. I have three as far as I know, they make great on-the-go instruments and have proved themselves surprisingly reliable for what they are.

Persuading You Is Going To Be A Tough Sell

An undervalued instrument, by my estimation.

If you talk about the DT830 in polite company, you might be greeted with snorts of derision. It’s not difficult to find reviews that tear one down and compare it to a more expensive meter, and not surprisingly find the pricey meter to be of higher quality.

And it’s certainly true that for a couple of dollars, you get a switch that won’t last forever and high voltage isolation that maybe isn’t quite up to spec. But I’m going to advance a different take on the DT830 that may surprise some of you: to me it’s a modern classic, an instrument that provides performance for its price that is nothing short of phenomenal. Because that pocket-money meter not only measures voltage, current, and resistance, it does so accurately and repeatably, and to compare that with what might have gone before is to show just much better a device it is.

Thirty years ago, a digital multimeter was an expensive item, and most multimeters were still analogue. A cheap multimeter was therefore invariably a small pocket analogue device, and the very cheap ones could be astoundingly awful. Accuracy and repeatability in reading wasn’t their strong point, and while I am a great fan of analogue multimeters when it comes to spotting dips and trends in tweaking analogue circuitry, even I can’t find reason to praise the inexpensive ones. By comparison the DT830 delivers reliable and accurate readings with a high-impedance input, something I would have given a lot for in 1985.

That Performance Is No Fluke

An ICM7106 epoxy blob on a 40-pin DIP-shaped PCB
An ICM7106 epoxy blob on a 40-pin DIP-shaped PCB in this roughly 18-year-old DT830

So given that it costs considerably less than a pint of beer in a British pub, how does such a cheap instrument do it? The answer is, by standing on the shoulders of giants. My colleague Anool Mahidharia supplied the answer here back in 2017 when he took a look at the Intersil 71XX series of integrated circuits; the archetypal DT830 contains an ICM 7106 3.5 digit digital panel meter chip, whose roots lie in a much more exclusive stratum of the industry.

(Despite there being a load of newer and more accomplished multimeter chips on the market I was surprised to find that none of them had found their way into the meters I’d opened.)

The ICM 7106 was based on work Intersil did in 1977 to produce the part in Fluke’s first portable DMM, the model 8020A.

Google hasn't found any ICM7106 conterfeiters!
Google hasn’t found any ICM7106 conterfeiters!

So you’re not getting anywhere near the physical design or component quality of that expensive meter, but you are benefiting from the tech that made its ancestor a very good instrument for the 1970s. The dual-slope integrating ADC and precision reference are the same as the ones in many far more expensive meters, which is what makes the reading from your few-dollar DT830 one you can trust. Not bad for something you might dismiss as a piece of junk!

If there is something to be gleaned from this story, it is a very real demonstration of the power of semiconductor manufacturing. Assuming it has passed acceptable factory QA, every 7106 is as good as any other 7016, from the first one made by Intersil in the 1970s through to the unknown-origin chip hiding under an epoxy blob in my cheap meter. The manufacturer can skimp on every other component in the meter, but assuming that there’s no money in counterfeiting a 43-year-old chip that long ago left its premium product phase behind and has been manufactured by many sources over the years, they can’t skimp on the chip that powers it. To be an ICM7106, it must have the same features as the original from the 1970s, thus my bargain-basement meter still shares something that matters with one of far higher quality.

The DT830 multimeter, then. It may be a heap of junk, but it’s an astonishingly good heap of junk. I for one, salute it.



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How To Play Call Of Duty Mobile With A Controller?

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Call of Duty Mobile is a mammoth battle royale game with millions of players worldwide. Like every other mobile game, most Call of Duty: Mobile players enjoy the game using the touch controls. However, what most players don’t know is that they can also play Call of Duty Mobile with a controller on both Android and iOS devices.

Activision announced the controller support for Call of Duty Mobile back in November 2019. However, most players are still unaware of this feature. So, if you want to play Call of Duty Mobile with a controller but don’t know how to connect it with the game, then you’re in luck. In this article, we’re going to guide you through the entire process to connect a controller with Call of Duty Mobile.

Supported Call of Duty Mobile Controllers

As of now, the official Dualshock 4 PS4 controller, except the first generation, and the official Xbox One controllers can be used to play Call of Duty Mobile. However, the good thing is that over time, Call of Duty: Mobile will bring support for new controllers as well.

If, somehow, you manage to connect a non-supported controller with Call of Duty Mobile, you might still run into problems while playing the game. So, we highly recommend that you only use one of the supported controllers.

How To Connect A Controller With Call Of Duty Mobile?

Follow these easy steps to connect a supported controller with Call of Duty Mobile on both Android and iOS:

  1. Enable Pairing On Controller

    On a PS4 controller, hold the PS and SHARE button simultaneously, whereas, in case of Xbox One controller, hold the Xbox and Sync button to enable pairing.

  2. Turn On Bluetooth On Mobile

    Navigate to your mobile device's settings and turn on the Bluetooth.

  3. Find The Controller

    Find the 'Wireless Controller' in the device list and tap on it to connect. The game will automatically detect the controller.

  4. Enable Controller in Call of Duty Mobile

    In Call of Duty: Mobile settings, go to Controller and enable the 'Enable Controller Support' option.

  5. Customize Controls

    Finally, customize the sensitivity for both Battle Royale and Multiplayer mode and get going.Change controller sensitivity in Call of Duty Mobile

That’s it; now you’ll be able to play Call of Duty Mobile with a controller in both Battle Royale and Multiplayer.

Note: If you leave your phone idle for a long time, then the controller might get disconnected from your phone, so you’ll have to connect it again.

COD Mobile Controller Support: Everything You Need To Know

When you play Call of Duty Mobile with a controller, you will only get matched against players using the controllers. Moreover, if you’re playing in a squad where one of your squad members is playing with a controller, even then, you’ll have to go against players using the controller support.

It’s important to note that you can’t use COD Mobile controllers to explore the in-game menu. So, if you want to change the weapons loadout and customize your character before entering the match, then you’ll have to do that using game’s native controls.

Also, make sure you enable the controller before entering the match. If you forget to do that before entering the pre-game lobby, the controller won’t work in the entire game.

The post How To Play Call Of Duty Mobile With A Controller? appeared first on Fossbytes.



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