Amazon’s new feature could make it easier to get into the latest release in a series, especially if it’s been some time since you’ve read the previous books. The new Recaps feature is part of the latest software update for the Kindle, and the company compares it to “Previously on…” segments you can watch for TV shows. Amazon announced Recaps in a blog post, where it said that you can get access to it once you receive the software update over the air or after you download and install it from Amazon’s website. Amazon didn’t talk about the technology behind the feature in its post, but a spokesperson has confirmed to TechCrunch that the recaps will be AI generated. 

Shortly after the feature rolled out, users talked about it on social media, wondering if Amazon is using generative AI to write series summaries. They expressed concerns about the use of generative AI, especially about the possibility of the technology hallucinating plot elements that aren’t actually in the books. “We use technology, including GenAI and Amazon moderators, to create short recaps of books that accurately reflect book content,” Amazon spokesperson Ale Iraheta told the publication. Iraheta assured TechCrunch that Amazon’s recaps are accurate, but of course, use it at your own risk. 

At the moment, the Recaps feature is available for best-selling English-language book series on all Kindle devices in the US. To know if your favorite series has one, look for the “View Recaps” button within the series page in your Kindle library. It will soon be available for the Kindle app on iOS, as well. 

This article originally appeared on Engadget at https://www.engadget.com/ai/amazon-will-use-ai-to-generate-recaps-for-book-series-on-the-kindle-170018503.html?src=rss

The advent of LLMs and machine learning-based applications “opened the door to a new wave of security threats,” argues Google’s security blog. (Including model and data poisoning, prompt injection, prompt leaking and prompt evasion.)

So as part of the Linux Foundation’s nonprofit Open Source Security Foundation, and in partnership with NVIDIA and HiddenLayer, Google’s Open Source Security Team on Friday announced the first stable model-signing library (hosted at PyPI.org), with digital signatures letting users verify that the model used by their application “is exactly the model that was created by the developers,” according to a post on Google’s security blog.

[S]ince models are an uninspectable collection of weights (sometimes also with arbitrary code), an attacker can tamper with them and achieve significant impact to those using the models. Users, developers, and practitioners need to examine an important question during their risk assessment process: “can I trust this model?”
Since its launch, Google’s Secure AI Framework (SAIF) has created guidance and technical solutions for creating AI applications that users can trust. A first step in achieving trust in the model is to permit users to verify its integrity and provenance, to prevent tampering across all processes from training to usage, via cryptographic signing… [T]he signature would have to be verified when the model gets uploaded to a model hub, when the model gets selected to be deployed into an application (embedded or via remote APIs) and when the model is used as an intermediary during another training run. Assuming the training infrastructure is trustworthy and not compromised, this approach guarantees that each model user can trust the model…

The average developer, however, would not want to manage keys and rotate them on compromise. These challenges are addressed by using Sigstore, a collection of tools and services that make code signing secure and easy. By binding an OpenID Connect token to a workload or developer identity, Sigstore alleviates the need to manage or rotate long-lived secrets. Furthermore, signing is made transparent so signatures over malicious artifacts could be audited in a public transparency log, by anyone. This ensures that split-view attacks are not possible, so any user would get the exact same model. These features are why we recommend Sigstore’s signing mechanism as the default approach for signing ML models.

Today the OSS community is releasing the v1.0 stable version of our model signing library as a Python package supporting Sigstore and traditional signing methods. This model signing library is specialized to handle the sheer scale of ML models (which are usually much larger than traditional software components), and handles signing models represented as a directory tree. The package provides CLI utilities so that users can sign and verify model signatures for individual models. The package can also be used as a library which we plan to incorporate directly into model hub upload flows as well as into ML frameworks.
“We can view model signing as establishing the foundation of trust in the ML ecosystem…” the post concludes (adding “We envision extending this approach to also include datasets and other ML-related artifacts.”)

Then, we plan to build on top of signatures, towards fully tamper-proof metadata records, that can be read by both humans and machines. This has the potential to automate a significant fraction of the work needed to perform incident response in case of a compromise in the ML world…

To shape the future of building tamper-proof ML, join the Coalition for Secure AI, where we are planning to work on building the entire trust ecosystem together with the open source community. In collaboration with multiple industry partners, we are starting up a special interest group under CoSAI for defining the future of ML signing and including tamper-proof ML metadata, such as model cards and evaluation results.

Read more of this story at Slashdot.

UK-based Jaguar Land Rover says it’s pausing shipments to the US after President Donald Trump imposed a 25 percent tariff on passenger vehicles and other auto imports. The pause will be in effect this month, the Associated Press reports. While the full impact of the tariffs remains to be seen, analysts have said the move could ultimately drive up the cost of new and even used cars.

“The USA is an important market for JLR’s luxury brands,” Jaguar Land Rover said in a statement to AP. “As we work to address the new trading terms with our business partners, we are taking some short-term actions including a shipment pause in April, as we develop our mid- to longer-term plans.”

Trump’s tariffs go well beyond the auto industry, and we’re only seeing the beginning of how the US’ trading partners will respond. The president announced a 10 percent baseline tariff on “all countries” this week, and some will face even higher “reciprocal” tariffs. Among the immediate effects, Nintendo has delayed pre-orders of the new Switch 2 in the US.

This article originally appeared on Engadget at https://www.engadget.com/transportation/jaguar-land-rover-pauses-us-shipments-while-it-figures-out-a-plan-for-trumps-tariffs-172512506.html?src=rss

Indonesian President Prabowo Subianto’s ambitious plan to create 1 million hectares of new rice farms in eastern Merauke Regency faces strong criticism from scientists who have warned it will fail due to unsuitable soils and climate. Military “food brigades” are currently guarding bulldozers clearing swampy forests in Indonesian New Guinea for the project, which aims to boost food self-sufficiency for the nation’s 281 million people.

Soil scientists warn that Merauke’s conditions could lead to acidic soils unable to support economically viable rice farming, potentially resulting in abandoned fields vulnerable to wildfires. “Farmers will get no profit at all,” said Dwi Andreas, a soil scientist at Bogor Agricultural University who tested 12 rice varieties in similar soils with poor results.

The initiative mirrors past failed megaprojects, including a 1990s attempt to convert 1 million hectares of Borneo peatlands to rice paddies and a 2020 onion and potato farming expansion in North Sumatra that saw 90% of fields abandoned. A previous 2010 attempt to expand rice farming in Merauke also failed, destroying forests that Indigenous Papuans relied on and increasing childhood malnutrition, according to anthropologist Laksmi Adriani.

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The US Space Force announced Friday it selected SpaceX, United Launch Alliance, and Blue Origin for $13.7 billion in contracts to deliver the Pentagon’s most critical military to orbit into the early 2030s.

These missions will launch the government’s heaviest national security satellites, like the National Reconnaissance Office’s large bus-sized spy platforms, and deploy them into bespoke orbits. These types of launches often demand heavy-lift rockets with long-duration upper stages that can cruise through space for six or more hours.

The contracts awarded Friday are part of the next phase of the military’s space launch program once dominated by United Launch Alliance, the 50-50 joint venture between legacy defense contractors Boeing and Lockheed Martin.

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An anonymous reader quotes a report from TechCrunch: In a world filled with “vibe coding,” Zach Yadegari, teen founder of Cal AI, stands in ironic, old-fashioned contrast. Ironic because Yadegari and his co-founder, Henry Langmack, are both just 18 years old and still in high school. Yet their story, so far, is a classic. Launched in May, Cal AI has generated over 5 million downloads in eight months, Yadegari says. Better still, he tells TechCrunch that the customer retention rate is over 30% and that the app generated over $2 million in revenue last month. […]

The concept is simple: Take a picture of the food you are about to consume, and let the app log calories and macros for you. It’s not a unique idea. For instance, the big dog in calorie counting, MyFitnessPal, has its Meal Scan feature. Then there are apps like SnapCalorie, which was released in 2023 and created by the founder of Google Lens. Cal AI’s advantage, perhaps, is that it was built wholly in the age of large image models. It uses models from Anthropic and OpenAI and RAG to improve accuracy and is trained on open source food calorie and image databases from sites like GitHub.

“We have found that different models are better with different foods,” Yadegari tells TechCrunch. Along the way, the founders coded through technical problems like recognizing ingredients from food packages or in jumbled bowls. The result is an app that the creators say is 90% accurate, which appears to be good enough for many dieters. The report says Yadegari began mastering Python and C# in middle school and went on to build his first business in ninth grade — a website called Totally Science that gave students access to unblocked games (cleverly named to evade school filters). He sold the company at age 16 to FreezeNova for $100,000.
Following the sale, Yadegari immersed himself in the startup scene, watching Y Combinator videos and networking on X, where he met co-founder Blake Anderson, known for creating ChatGPT-powered apps like RizzGPT. Together, they launched Cal AI and moved to a hacker house in San Francisco to develop their prototype.

Read more of this story at Slashdot.