Category: Uncategorized

Theres autonomous tractors and “smart spraying” systems that use AI-powered cameras to identify weeds, just for starters. “Among the critical components of precision agriculture: Internet- and GPS connected agricultural equipment, highly accurate remote sensors, ‘big data’ analytics and cloud computing…”

As with any technological revolution, however, there are both “winners” and “losers” in the emerging age of precision agriculture… Precision agriculture, once broadly adopted, promises to further reduce the need for human labor to run farms. (Autonomous equipment means you no longer even need drivers!) However, the risks it poses go well beyond a reduction in the agricultural work force. First, as the USDA notes on its website: the scale and high capital costs of precision agriculture technology tend to favor large, corporate producers over smaller farms. Then there are the systemic risks to U.S. agriculture of an increasingly connected and consolidated agriculture sector, with a few major OEMs having the ability to remotely control and manage vital equipment on millions of U.S. farms… (Listen to my podcast interview with the hacker Sick Codes, who reverse engineered a John Deere display to run the Doom video game for insights into the company’s internal struggles with cybersecurity.)

Finally, there are the reams of valuable and proprietary environmental and operational data that farmers collect, store and leverage to squeeze the maximum productivity out of their land. For centuries, such information resided in farmers’ heads, or on written or (more recently) digital records that they owned and controlled exclusively, typically passing that knowledge and data down to succeeding generation of farm owners. Precision agriculture technology greatly expands the scope, and granularity, of that data. But in doing so, it also wrests it from the farmer’s control and shares it with equipment manufacturers and service providers — often without the explicit understanding of the farmers themselves, and almost always without monetary compensation to the farmer for the data itself. In fact, the Federal Government is so concerned about farm data they included a section (1619) on “information gathering” into the latest farm bill.
Over time, this massive transfer of knowledge from individual farmers or collectives to multinational corporations risks beggaring farmers by robbing them of one of their most vital assets: data, and turning them into little more than passive caretakers of automated equipment managed, controlled and accountable to distant corporate masters.

Weighing in is Kevin Kenney, a vocal advocate for the “right to repair” agricultural equipment (and also an alternative fuel systems engineer at Grassroots Energy LLC). In the interview, he warns about the dangers of tying repairs to factory-installed firmware, and argues that its the long-time farmer’s “trade secrets” that are really being harvested today. The ultimate beneficiary could end up being the current “cabal” of tractor manufacturers.

“While we can all agree that it’s coming…the question is who will own these robots?”

First, we need to acknowledge that there are existing laws on the books which for whatever reason, are not being enforced. The FTC should immediately start an investigation into John Deere and the rest of the ‘Tractor Cabal’ to see to what extent farmers’ farm data security and privacy are being compromised. This directly affects national food security because if thousands- or tens of thousands of tractors’ are hacked and disabled or their data is lost, crops left to rot in the fields would lead to bare shelves at the grocery store… I think our universities have also been delinquent in grasping and warning farmers about the data-theft being perpetrated on farmers’ operations throughout the United States and other countries by makers of precision agricultural equipment.

Thanks to long-time Slashdot reader chicksdaddy for sharing the article.

The tech giant, Bloomberg has learned, has plans to integrate AI features into its next software updates for its iPhone and iPad known internally as Crystal. Glow, another internal AI project, is slated to be added to MacOS. The company is also building features that will generate Apple Music playlists and slideshows, according to the outlet. An AI-powered search feature titled Spotlight, currently limited to answering questions around launching apps, is in the works as well, Bloomberg reported.

Meanwhile, on the trade front, the Commerce Department has continued to step up restrictions, limiting the performance of accelerators sold to China. But, as we’ve previously reported, while these efforts have made it harder for countries like China to get their hands on American chips, they are far from perfect. To address these limitations, the researchers have proposed implementing a global registry for AI chip sales that would track them over the course of their lifecycle, even after they’ve left their country of origin. Such a registry, they suggest, could incorporate a unique identifier into each chip, which could help to combat smuggling of components.

At the more extreme end of the spectrum, researchers have suggested that kill switches could be baked into the silicon to prevent their use in malicious applications. […] The academics are clearer elsewhere in their study, proposing that processor functionality could be switched off or dialed down by regulators remotely using digital licensing: “Specialized co-processors that sit on the chip could hold a cryptographically signed digital “certificate,” and updates to the use-case policy could be delivered remotely via firmware updates. The authorization for the on-chip license could be periodically renewed by the regulator, while the chip producer could administer it. An expired or illegitimate license would cause the chip to not work, or reduce its performance.” In theory, this could allow watchdogs to respond faster to abuses of sensitive technologies by cutting off access to chips remotely, but the authors warn that doing so isn’t without risk. The implication being, if implemented incorrectly, that such a kill switch could become a target for cybercriminals to exploit.

Another proposal would require multiple parties to sign off on potentially risky AI training tasks before they can be deployed at scale. “Nuclear weapons use similar mechanisms called permissive action links,” they wrote. For nuclear weapons, these security locks are designed to prevent one person from going rogue and launching a first strike. For AI however, the idea is that if an individual or company wanted to train a model over a certain threshold in the cloud, they’d first need to get authorization to do so. Though a potent tool, the researchers observe that this could backfire by preventing the development of desirable AI. The argument seems to be that while the use of nuclear weapons has a pretty clear-cut outcome, AI isn’t always so black and white. But if this feels a little too dystopian for your tastes, the paper dedicates an entire section to reallocating AI resources for the betterment of society as a whole. The idea being that policymakers could come together to make AI compute more accessible to groups unlikely to use it for evil, a concept described as “allocation.”

The new model is called Big Adaptive Streamable TTS with Emergent abilities, which they have contorted into the abbreviation BASE TTS. The largest version of the model uses 100,000 hours of public domain speech, 90% of which is in English, the remainder in German, Dutch and Spanish. At 980 million parameters, BASE-large appears to be the biggest model in this category. They also trained 400M- and 150M-parameter models based on 10,000 and 1,000 hours of audio respectively, for comparison — the idea being, if one of these models shows emergent behaviors but another doesn’t, you have a range for where those behaviors begin to emerge. As it turns out, the medium-sized model showed the jump in capability the team was looking for, not necessarily in ordinary speech quality (it is reviewed better but only by a couple points) but in the set of emergent abilities they observed and measured. Here are examples of tricky text mentioned in the paper:

– Compound nouns: The Beckhams decided to rent a charming stone-built quaint countryside holiday cottage.
– Emotions: “Oh my gosh! Are we really going to the Maldives? That’s unbelievable!” Jennie squealed, bouncing on her toes with uncontained glee.
– Foreign words: “Mr. Henry, renowned for his mise en place, orchestrated a seven-course meal, each dish a piece de resistance.
– Paralinguistics (i.e. readable non-words): “Shh, Lucy, shhh, we mustn’t wake your baby brother,” Tom whispered, as they tiptoed past the nursery.
– Punctuations: She received an odd text from her brother: ‘Emergency @ home; call ASAP! Mom & Dad are worried… #familymatters.’
– Questions: But the Brexit question remains: After all the trials and tribulations, will the ministers find the answers in time?
-Syntactic complexities: The movie that De Moya who was recently awarded the lifetime achievement award starred in 2022 was a box-office hit, despite the mixed reviews. You can read more examples of these difficult texts being spoken naturally here.