AI Giants Monitor Elections; Chatbots Unreliable for Voting Info

Key Takeaways

• <ul><li>OpenAI, Anthropic, and Meta have set up Election Day monitoring operations to combat AI-driven misinformation</li><li>ChatGPT and Claude remain unreliable for voting information due to hallucinations and partisan bias</li><li>UK data center demand of 72 gigawatts threatens net zero carbon goals by potentially increasing gas power use</li><li>NATO's Crossed Swords 2026 exercise in Estonia trained 250+ participants from 40+ countries using AI agents in cyber operations</li><li>MIT researchers found GPT-6 Astra improved a smaller model's math scores from 7.9% to 48.1% by changing only 0.016% of weights</li><li>NVIDIA boosted Ling-3.0-flash speed by 42.5% on DGX Spark using Humming kernels, FP8 LM head, and MTP speculative decoding</li><li>Geoffrey Hinton warns AI could cause major infrastructure failures within years and calls for independent oversight</li><li>Dartmouth's AI at 70 conference drew over 1,000 attendees and included an Anthropic-sponsored student hackathon</li><li>70% of Dartmouth students believe AI has made take-home assignments increasingly unfair</li><li>AI is showing promise in healthcare, helping doctors write more accurate hospital discharge summaries</li></ul>

AI firms set up Election Day war rooms to combat misinformation

Major AI companies like OpenAI and Anthropic are preparing for Election Day by creating war rooms to monitor information. They are partnering with Democracy Works and The Associated Press to provide voters with accurate election data. Lawmakers have urged these firms to ensure platform accuracy and neutrality. AI's search functions now prioritize real-time sources over baseline training for political questions. Meta has also reactivated its Elections Operations Center to respond to threats.

Why AI chatbots are not ready to help people vote

AI chatbots like ChatGPT and Claude should not be used for voting because they are prone to hallucinations and partisan bias. These systems can mislead voters about candidates and ballot issues. They also struggle to understand the nuances of human language and behavior. Experts warn that relying on these chatbots for election information can have serious consequences. AI technology has improved but is still not ready for Election Day tasks.

AI data centers threaten UK net zero carbon goals

Planned hyperscale data centers in the UK may use gas-fired power, undermining the nation's 2050 net zero goal. Data center developers are seeking connections totaling 72 gigawatts, while the country's peak demand last year was 45 gigawatts. Some developers face waits of up to 10 years for grid connections. The government says data centers are essential and its Data Centre Covenant sets fair cost expectations. Officials stress that increased natural gas use cannot be the future.

NATO trains with AI agents in cyber operations exercise

NATO's Crossed Swords 2026 exercise in Tallinn, Estonia, introduced AI agents into cyber operations training. Over 250 participants from more than 40 countries practiced synchronizing cyber effects with kinetic military action. This year featured a dedicated track where participants build and deploy AI agents alongside human operators. The exercise environment runs on locally hosted AI with human oversight maintained. Companies like OpenAI, Bitdefender, and ESET supported the event with specialized tooling.

Dartmouth marks 70 years of AI research with conference

Dartmouth hosted an AI at 70 conference marking the 70th anniversary of the 1956 Dartmouth Summer Research Project on Artificial Intelligence. Over 1,000 attendees joined 36 events, with livestreams viewed more than 23,000 times. Topics included creative expression, medicine, national security, and federal AI regulation. A student survey found 70% believe AI has made take-home assignments increasingly unfair. The conference concluded with an Anthropic-sponsored student hackathon on October 31.

How AI slowly takes over human decisions

Wharton researcher Cornelia Walther warns that AI can shift from helping us to making decisions for us. Small uses of AI in daily work can gradually replace independent thinking. Asking AI for options differs from letting AI choose those options. Four simple questions can help people stay in control of their own judgment.

ISO-NE staff share AI and energy expertise at October events

ISO New England staff presented at multiple industry events in October. Topics included AI in electricity systems, long-duration energy storage, market reforms, and generator interconnection. Speakers included Chris Geissler, Steven Otto, Matt Kakley, and several policy advisors. The presentations highlighted collaboration and innovation in energy planning across the region.

MIT research shows AI improving AI without training

MIT researchers found that GPT-6 Astra improved a smaller AI model's math scores without training. Astra changed only 0.016% of the model's weights and raised scores from 7.9% to 48.1%. The approach also worked on identifying games and writing code from learned patterns. This research points toward future AI systems that could upgrade or fix other models on request.

AI godfather Geoffrey Hinton warns of serious risks

Geoffrey Hinton, a Nobel-winning AI pioneer, warned that AI could cause major incidents like power or banking system failures within years. He said humans might have been better off never developing AI. Hinton called for independent oversight similar to how drugs are regulated. He believes the next decade will reveal whether society can manage AI's risks safely.

AI makes hospital discharge summaries more accurate

Using artificial intelligence can help doctors write hospital discharge summaries with greater accuracy and completeness. The technology aims to make the process more efficient for physicians. AI assists in preparing patient summaries when they leave the hospital. This could improve the quality of medical records and communication between care teams.

NVIDIA boosts Ling-3.0-flash speed by 42.5 percent on DGX Spark

NVIDIA found three ways to make the Ling-3.0-flash model run faster on the DGX Spark desktop system. By using new Humming kernels, an online FP8 LM head, and MTP speculative decoding, the system processed tokens much quicker. These changes increased throughput from 37.81 to 53.89 tokens per second while keeping accuracy nearly the same. The improvements worked best when all three methods were used together without technical conflicts. This setup helps developers run large 124 billion parameter models efficiently on a single desktop machine.

Sources

• Inside AI giants’ Election Day ‘war rooms’
• Here's Why You Shouldn't Use AI to Vote
• AI data centres could undermine UK's net zero goals
• NATO’s Crossed Swords Exercise Brings AI Agents Into Cyber Operations Training
• Dartmouth hosts ‘AI at 70’ conference
• When AI Makes Unacceptable Decisions Feel Normal
• AI, energy storage, and more: ISO-NE staff share expertise at October events
• MIT shows AI improving AI, no training required
• AI ‘godfather’ Geoffrey Hinton says it might turn out humans would have been better off without AI
• AI improves accuracy and completeness of hospital discharge summaries
• NVIDIA Shows 3 Ways to Speed Up MoE Inference on DGX Spark, Lifting Ling-3.0-flash Throughput 42.5%