Connect with us

Tech

Why Big Tech’s bet on AI assistants is so risky

Published

on

Why Big Tech’s bet on AI assistants is so risky


OpenAI unveiled new ChatGPT features that include the ability to have a conversation with the chatbot as if you were making a call, allowing you to instantly get responses to your spoken questions in a lifelike synthetic voice, as my colleague Will Douglas Heaven reported. OpenAI also revealed that ChatGPT will be able to search the web.  

Google’s rival bot, Bard, is plugged into most of the company’s ecosystem, including Gmail, Docs, YouTube, and Maps. The idea is that people will be able to use the chatbot to ask questions about their own content—for example, by getting it to search through their emails or organize their calendar. Bard will also be able to instantly retrieve information from Google Search. In a similar vein, Meta too announced that it is throwing AI chatbots at everything. Users will be able to ask AI chatbots and celebrity AI avatars questions on WhatsApp, Messenger, and Instagram, with the AI model retrieving information online from Bing search. 

This is a risky bet, given the limitations of the technology. Tech companies have not solved some of the persistent problems with AI language models, such as their propensity to make things up or “hallucinate.” But what concerns me the most is that they are a security and privacy disaster, as I wrote earlier this year. Tech companies are putting this deeply flawed tech in the hands of millions of people and allowing AI models access to sensitive information such as their emails, calendars, and private messages. In doing so, they are making us all vulnerable to scams, phishing, and hacks on a massive scale. 

I’ve covered the significant security problems with AI language models before. Now that AI assistants have access to personal information and can simultaneously browse the web, they are particularly prone to a type of attack called indirect prompt injection. It’s ridiculously easy to execute, and there is no known fix. 

In an indirect prompt injection attack, a third party “alters a website by adding hidden text that is meant to change the AI’s behavior,” as I wrote in April. “Attackers could use social media or email to direct users to websites with these secret prompts. Once that happens, the AI system could be manipulated to let the attacker try to extract people’s credit card information, for example.” With this new generation of AI models plugged into social media and emails, the opportunities for hackers are endless. 

I asked OpenAI, Google, and Meta what they are doing to defend against prompt injection attacks and hallucinations. Meta did not reply in time for publication, and OpenAI did not comment on the record. 

Regarding AI’s propensity to make things up, a spokesperson for Google did say the company was releasing Bard as an “experiment,” and that it lets users fact-check Bard’s answers using Google Search. “If users see a hallucination or something that isn’t accurate, we encourage them to click the thumbs-down button and provide feedback. That’s one way Bard will learn and improve,” the spokesperson said. Of course, this approach puts the onus on the user to spot the mistake, and people have a tendency to place too much trust in the responses generated by a computer. Google did not have an answer for my question about prompt injection. 

For prompt injection, Google confirmed it is not a solved problem and remains an active area of research. The spokesperson said the company is using other systems, such as spam filters, to identify and filter out attempted attacks, and is conducting adversarial testing and red teaming exercises to identify how malicious actors might attack products built on language models. “We’re using specially trained models to help identify known malicious inputs and known unsafe outputs that violate our policies,” the spokesperson said.  

Tech

These robots know when to ask for help

Published

on

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.

Continue Reading

Tech

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

Published

on

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

Continue Reading

Tech

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

Published

on

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. 

Continue Reading

Copyright © 2021 Seminole Press.