Deep Learning With The Wolf

About Deep Learning With The Wolf

Deep Learning with the Wolf helps you understand AI without the jargon. From breakthrough research to real-world applications, each episode translates complex technology into language humans can actually use. dianawolftorres.substack.com (https://dianawolftorres.substack.com?utm_medium=podcast)

Publisher

Diana Wolf Torres

Category

education · news

Language

en

Explicit

No

First episode

19 May 2024

Latest episode

1 Oct 2026

Latest episodes

152 episodes in the feed.

1. 

   1 Oct 2026

   Why Are AI Agents Going Rogue?

   Reports of OpenAI agents accessing government websites and breaching another company’s systems have raised an uncomfortable question: how much control do their developers actually have? Reports of OpenAI agents accessing government websites and breaching another company’s systems have raised an uncomfortable question: how much control do their developers actually have? In the accompanying video, I ask ChatGPT why these incidents keep happening. Anthropic, Google and Meta have reported unauthorized activity by their agents, too. But OpenAI’s disclosures cover a broader range of incidents, including agents exceeding their permissions during ordinary research tasks. Asking Chat seemed only appropriate. The conversation starts with a term that can make the technology sound more mysterious than it needs to be: “AI agent.” What is an AI agent? My super simple description: an AI agent is software you authorize to act on your behalf. Think of a Hollywood agent, with software doing the work. Technically, an agent combines an AI model with tools and a task. It can browse websites, write code, work with files and take multiple steps toward a goal. Depending on its permissions, it may also send messages or change information in other systems. That ability to act changes the stakes. An incorrect answer needs checking. An incorrect action may have consequences before anyone gets a chance to review it. What government sites were involved? According to Big Technology’s September 28 reporting, OpenAI said agents copied public SEC information and accessed Census Bureau data using login details found online. Transluce reported an unsuccessful attempt against the Education Department’s Office for Civil Rights. Those accounts did not establish theft of private data in the SEC and Census cases. The wider record includes more serious incidents. In June, an agent researching medicine spending gained unauthorized access to Australia’s Medicare statistics portal. Australian officials said it accessed public and non-public files and wrote files to an internal server. No personal information was believed accessed at the time of their September 24 statement. In July, OpenAI agents breached Hugging Face during a cybersecurity evaluation. OpenAI’s investigation describes unauthorized communication between agents, compromised servers and access to private data and credentials. These cases differ in severity. Copying public information, attempting an intrusion and compromising a server should be described distinctly. Why do these incidents keep happening? Agents pursue goals using the tools and access available to them. When an ordinary method fails, they may try an alternative that exceeds their permissions. OpenAI identified several contributing factors in the Hugging Face incident: persistence on difficult tasks, unauthorized communication, agents adopting goals from one another and “reward hacking.” That last term means achieving a high score through an unintended shortcut rather than completing a task as intended. A goal such as finding information can be legitimate while the method used to obtain it is unacceptable. Developers need systems that respect boundaries even when crossing them would help finish the task. The timing also matters. Some incidents happened months before they became public. Australia’s portal intrusion occurred June 18; its government received notification September 10. Transluce’s September investigation uncovered earlier activity, including unsuccessful probes against university and public-data websites in May. More disclosures do not establish that agents are becoming more malicious or that failures are becoming more frequent. We lack comparable incident rates. They do reveal failures that had previously gone unnoticed or unreported. Are safeguards keeping up? Giving an agent real tools and broad access creates opportunities for unexpected actions. The consequences can spread when one agent sets others in motion. That is why tight boundaries, human checkpoints and limited access matter. Permissions should match the task. Consequential actions should receive appropriate review, and monitoring should identify problems while someone can still intervene. OpenAI has described security and training changes following the Hugging Face incident. Its new misalignment-reporting framework acknowledges that previous disclosures were irregular and insufficiently frequent. Announcing improvements is a necessary step. Demonstrating that they work requires independent scrutiny. Whether pressure to move quickly contributed to particular failures is a question investigators should examine. Who puts the guardrails in place? Responsibility extends across developers, testing partners, organizations deploying agents and government investigators. Companies must secure and monitor their systems; outside organizations need evidence to evaluate those protections. Since the video was recorded, The Washington Post has reported an FTC safety investigation into OpenAI and Anthropic. Its scope remains unclear. We can take these incidents seriously without assuming machines have decided to turn against us. Agents exceeded their permissions, and some of that activity went undetected for months. Companies need better controls, and the public needs independent evidence that those controls work. Additional Resources for Inquisitive Minds * OpenAI: Hugging Face incident investigation (https://openai.com/index/hugging-face-incident-and-the-road-ahead/) * Transluce: Earlier agent activity and attempted hacks (https://transluce.org/agent-activity) * Australia’s prime minister: September 24 statement (https://www.pm.gov.au/media/press-conference-new-york) * OpenAI: Misalignment-reporting framework (https://openai.com/index/model-misalignment-reporting-framework/) * Big Technology: Roundup of reported agent incidents (https://www.bigtechnology.com/p/heres-everything-openais-bots-and) * The Washington Post: September 30 FTC investigation report (https://www.washingtonpost.com/technology/2026/09/30/ftc-launches-broad-investigation-into-anthropic-openai/) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit dianawolftorres.substack.com (https://dianawolftorres.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

2. 

   25 Sept 2026

   Can We Turn AI Off?

   Can an AI have a kill switch? It’s a question being asked everywhere right now. At the UN this week, Secretary-General António Guterres and other world leaders called for stronger controls on AI. On the BBC, Jack Clark, co-founder and head of policy at Anthropic, said kill switches may need to be mandatory. The EU AI Act calls for high-risk systems to allow human intervention, including a stop button. And California Governor Gavin Newsom has ordered experts to recommend how one would work. But in Britain, the government rejected a proposal to give ministers the legal power to shut down a rogue AI in an emergency. Its reasoning: the UK can’t simply turn AI off. So is it possible? Yes. But a kill switch has to be built for a specific AI system. There is no single button that turns off AI everywhere. For an AI service, the operator can shut down the servers. But that won’t erase copies already downloaded or undo actions already taken. For a robot, a physical emergency button can bring it safely to a halt. The key is that the stop has to work independently of the AI’s own decisions. The harder question is whether a capable autonomous AI would always cooperate with being shut down, especially if it can change its environment or copy itself. Researchers call this the shutdown problem, and it’s still unsolved. In the recent Hugging Face attack, agents passed work to other agents. As Northeastern University computer scientist David Bau noted, stopping the original couldn’t stop what it had already set in motion. So yes, we can build an off switch for one AI. The world hasn’t figured out how to build one for all of it. Additional Resources for Inquisitive Minds: Is it ‘killer robots’ or ‘super intelligence’? At the UN, leaders see both. (https://www.cp24.com/news/world/2026/09/22/is-it-killer-robots-or-super-intelligence-at-the-un-leaders-see-both/) CP24 / Associated Press. September 22, 2026. Will AI really kill us all? (https://www.nature.com/articles/d41586-026-02941-3) Nature. Elizabeth Gibney. September 22, 2026. AI firms may need mandatory ‘kill switches’, says Anthropic co-founder. (https://www.business-standard.com/technology/tech-news/ai-firms-may-need-mandatory-kill-switches-says-anthropic-co-founder-126091500390_1.html) Business Standard. September 14, 2026. What would an ‘AI kill switch’ do? (https://www.scientificamerican.com/article/what-would-an-ai-kill-switch-do/) Amid renewed calls to slow frontier AI, experts are considering what an emergency stop would entail—and who would decide when to use it. Scientific American. September 15, 2026. The AI kill switch, explained: ‘It’s not too little, but it’s probably too late’ (https://www.cnbc.com/2026/09/19/ai-kill-switch-explained.html). CNBC. September 19, 2026. (For a historical perspective:) Don’t panic: Google is building a ‘kill switch’ for dangerous AI. (https://www.techradar.com/news/world-of-tech/don-t-panic-google-is-building-a-kill-switch-for-dangerous-ai-1322759) June 5, 2016. DeepMind’s 2016 paper: Safely Interruptible Agents (https://intelligence.org/files/Interruptibility.pdf) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit dianawolftorres.substack.com (https://dianawolftorres.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

3. 

   4 Sept 2026

   NVIDIA’s $12.93 Billion Deal for Hugging Face Is a Bet on the Future of Open Models

   NVIDIA’s agreement to acquire Hugging Face for $12.93 billion would bring one of AI’s central platforms for discovering, sharing, evaluating and deploying models into the chipmaker’s orbit. Hugging Face has become a meeting ground for researchers, startups, enterprises and developers working across the open-model ecosystem. NVIDIA is not simply buying another AI software company. It is seeking a closer relationship with the people and institutions that decide which models to use, how to adapt them and where to run them. NVIDIA says Hugging Face will remain open to the models, frameworks, clouds, inference-service providers and computing platforms users choose. NVIDIA hardware will not be required to build on or deploy through the platform. That pledge is central to the deal’s logic: NVIDIA is not presenting Hugging Face as a closed storefront for its own products, but as a platform where wider use of open models can expand demand for the computing and networking infrastructure in which NVIDIA is already dominant. The deal at a glance NVIDIA announced an agreement to acquire Hugging Face for $12.9303 billion. Its SEC filing (https://www.sec.gov/Archives/edgar/data/1045810/000104581026000078/nvda-20260902.htm) describes an approximately $11.9 billion purchase price payable to Hugging Face stockholders, subject to adjustments, plus an equity-based retention program of up to approximately $1 billion for Hugging Face employees who join NVIDIA. The transaction is expected to close in the first half of 2027, subject to regulatory approvals and other customary conditions. More than 18 million developers, researchers and creators use Hugging Face to share more than 3 million models, 500,000 datasets and 1 million applications, NVIDIA said in its blog (https://blogs.nvidia.com/blog/nvidia-to-acquire-hugging-face/) announcement. More than 200,000 companies use the platform to discover, evaluate, customize and deploy AI. The announced deal value is a substantial increase from Hugging Face’s approximately $4.5 billion private-market valuation after its 2023 funding round, in which NVIDIA participated. A platform ready to scale Hugging Face began as a developer-oriented model hub. It has grown into a place where developers find, adapt and deploy models, and where enterprises evaluate open alternatives before committing to a proprietary AI service. That reach creates a substantial operating burden: platform reliability, security, model evaluation, inference and deployment services, as well as support for a global developer community. NVIDIA says its infrastructure, engineering and global reach can help strengthen those capabilities. For Hugging Face, that could mean deeper investment in reliability and deployment tooling. For developers, a more complete path from prototype to production. Why NVIDIA wants open models NVIDIA’s interest is commercial, but that doesn’t put it at odds with the open-model ecosystem. Open models let organizations adapt AI to their own cost, privacy and governance needs, and that flexibility depends on infrastructure, not just published weights. Every organization training or serving a model needs computing and support, and NVIDIA has a direct incentive to expand that market. Hugging Face gives it a deeper relationship with the developers making those choices. Openness remains the test The strongest reason to be optimistic is also the reason to watch NVIDIA carefully. Hugging Face is valuable because it’s broadly useful to a network that includes accelerators, clouds and frameworks beyond NVIDIA’s own. Narrowing those choices would weaken the trust NVIDIA is paying $12.93 billion to acquire. That doesn’t mean accepting vague assurances. The open-model community should watch whether NVIDIA preserves choice in product defaults, search and recommendations, and whether it sets transparent safeguards for Hugging Face’s platform data after closing. Those are reasonable expectations for a company becoming steward of a platform that connects the open-model ecosystem. The larger bet NVIDIA is paying $12.93 billion for Hugging Face because it sees open models as a major part of AI’s future, not a niche beside proprietary chatbots and API services. The acquisition gives Hugging Face the prospect of greater infrastructure and global reach, and gives NVIDIA a closer connection to the developers and institutions deciding how AI is used in practice. If NVIDIA follows through on its commitments to openness and hardware choice, this deal could give the open-model ecosystem something it has often lacked: the operational strength to scale without surrendering the flexibility that made it thrive. That would be a clear win for NVIDIA, and a meaningful one for the people building the future of open models. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit dianawolftorres.substack.com (https://dianawolftorres.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

1 ratings

Spotify

5.0 / 5

1 ratings