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Recovering from the SolarWinds hack could take 18 months

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Recovering from the SolarWinds hack could take 18 months


SolarWinds Orion, the network management product that was targeted, is used in tens of thousands of corporations and government agencies. Over 17,000 organizations downloaded the infected back door. The hackers were extraordinarily stealthy and specific in targeting, which is why it took so long to catch them—and why it’s taking so long to understand their full impact.

The difficulty of uncovering the extent of the damage was summarized by Brad Smith, the president of Microsoft, in a congressional hearing last week. 

“Who knows the entirety of what happened here?” he said. “Right now, the attacker is the only one who knows the entirety of what they did.”

Kevin Mandia, CEO of the security company FireEye, which raised the first alerts about the attack, told Congress that the hackers prioritized stealth above all else.

“Disruption would have been easier than what they did,” he said. “They had focused, disciplined data theft. It’s easier to just delete everything in blunt-force trauma and see what happens. They actually did more work than what it would have taken to go destructive.”

“This has a silver lining”

CISA first heard about a problem when FireEye discovered that it had been hacked and notified the agency. The company regularly works closely with the US government, and although it wasn’t legally obligated to tell anyone about the hack, it quickly shared news of the compromise with sensitive corporate networks.

It was Microsoft that told the US government federal networks had been compromised. The company shared that information with Wales on December 11, he said in an interview. Microsoft observed the hackers breaking into the Microsoft 365 cloud that is used by many government agencies. A day later, FireEye informed CISA of the back door in SolarWinds, a little-known but extremely widespread and powerful tool. 

This signaled that the scale of the hack could be enormous. CISA’s investigators ended up working straight through the holidays to help agencies hunt for the hackers in their networks.

These efforts were made even more complicated because Wales had only just taken over at the agency: days earlier, former director Chris Krebs had been fired by Donald Trump for repeatedly debunking White House disinformation about a stolen election. 

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These robots know when to ask for help

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These robots know when to ask for help


A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.

Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.

For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.

That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng. 

Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.

The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.

KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels. 

When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.

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The Download: inside the first CRISPR treatment, and smarter robots

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The Download: inside the first CRISPR treatment, and smarter robots


The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.

Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.

—June Kim

Medical microrobots that travel inside the body are (still) on their way

The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.

We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.

—Cassandra Willyard

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5 things we didn’t put on our 2024 list of 10 Breakthrough Technologies

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5 things we didn’t put on our 2024 list of 10 Breakthrough Technologies


We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).  

Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now. 

New drugs for Alzheimer’s disease

Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause. 

Sustainable aviation fuel 

Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.  

Solar geoengineering

One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now. 

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