絞り込み結果
156 件並び順
33:44ENHow Matthew Berman Uses OpenClaw for a Personal AssistantMatthew Berman · Forward FutureA tour of customized CRM, meeting, research and security workflows, along with the limits of automation and the need for backups.
3:15:52ENThe Voice Note His Bot Couldn't Handle — and the Agent That Built Itself Anywayゲスト OpenClaw · ホスト Lex FridmanPeter Steinberger tells Lex Fridman how a one-hour WhatsApp toy became GitHub's fastest-growing project, and what it cost him to keep it open.
1:18:49ENIf AGI Is Cheap Intelligent Labor, Cyber Defense Should Become Continuous Forensicsホスト Nathan LabenzAsymmetric Security founder Alexis Carlier argues that assuming AGI means redesigning defense around proactive digital forensics instead of alert triage.
15:44ENOpenClaw, Moltbook and the Question of an Agent InternetMatthew Berman · Forward FutureMatthew Berman surveys early agent social networks and marketplaces, weighs enthusiasm against security concerns, and considers what their behavior does—and does not—show.
1:50:59ENForward Future Live: Robot Autonomy, AI Video and OpenClaw's Early GrowthMatthew Berman, Nick Wentz, Nikita Rudin, Alex Mashrabov, Peter Steinberger · Forward FutureNikita Rudin explains Flexion's robot software, Alex Mashrabov discusses AI video for marketers, and Peter Steinberger describes OpenClaw's possibilities and security challenges.
12:23ENMoltbook: An AI Agent Social Network and Its Security QuestionsMatthew Berman · Forward FutureMatthew Berman explores conversations attributed to AI agents on Moltbook and considers privacy, manipulation and operating costs.
1:07ENMatthew Berman's Early Clawdbot Assistant: Research, Alerts and PersonalityMatthew Berman · Forward FutureA January 2026 demonstration shows a personal agent preparing a video outline and describes configurable behavior and proactive email assistance.
21:12ENClawdbot as a Personal AI Assistant: Workflows, Costs and RisksMatthew Berman · Forward FutureMatthew Berman demonstrates a local agent connected to messaging, cloud services and models, and reports what worked, failed and cost more than expected.
1:41:34ENThey Owned the Camera Partition—and Still Couldn't Crash the Helicopterホスト Nathan LabenzKathleen Fisher and Byron Cook tell Nathan Labenz how formal methods turn some software bugs into closed cases, from a DARPA helicopter to AWS policies checked a billion times a second.
1:13:24ENRed Teams Still Land 25% of Attacks. Insurance Is the Brake.ホスト Nathan LabenzAI Underwriting Company co-founders Rune Kvist and Rajiv Dattani tell Nathan Labenz how standards, audits, and insurance could unlock enterprise agents without waiting for a nuclear-style freeze.
40:24ENMicrosoft’s Jay Parikh on AI Infrastructure, Model Choice and Agent SecurityMatthew Berman, Jay Parikh · Forward FutureThe CoreAI leader discusses power constraints, developer workflows, enterprise models and why he measures outcomes rather than generated lines of code.
18:04ENAnthropic’s Report on an AI-Orchestrated Espionage CampaignMatthew Berman · Forward FutureMatthew Berman reviews Anthropic’s account of a 2025 cyber-espionage operation, its claimed automation and its limitations.
59:03ENJoelle Pineau on Scaling, Reinforcement Learning and Enterprise AIゲスト Joelle Pineau · ホスト Harry StebbingsCohere’s chief AI officer discusses research uncertainty, agent security, synthetic data and the difference between benchmarks and useful work.
44:10ENOne Model Talked Another Into Slack Off — Then Uninstalled Windows DefenderIrregular co-founder Dan Lahav tells Sequoia’s Training Data why frontier AI security has to be invented from scratch as models become autonomous economic actors.
2:49:12ENWhy China's Master Plan for AI Never Mentions AGIホスト Nathan LabenzBeijing-based Concordia AI CEO Brian Tse on China's safety rules, chip strategy, and why the two ecosystems look more alike than Washington assumes.
58:21ENAttacks Take 23 Minutes. Response Still Takes Days.ホスト Sarah Guo, Elad GilPalo Alto Networks CEO Nikesh Arora on how AI is remaking search, enterprise software, and the timeline of a cyberattack.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14ホスト Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
1:32:47ENA transformer co-author wants a $160 million model no company ownsホスト Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
2:04:27ENA 10% Chance of an AI-Enabled Coup in the Next 30 Yearsホスト Nathan LabenzTom Davidson maps three ways superintelligent systems could help a head of state or a lab CEO lock in power—and argues the window for building safeguards is short.
1:51:24ENFrontier AI Insiders Don't Trust Regulators. A Berlin Group Built Them an Anonymous Off-Rampホスト Nathan LabenzKarl Koch of the AI Whistleblower Initiative on gray-zone fears, retaliation, and a Tor-based way to ask for help before you decide anything.
0:54ENSatya Nadella on Separating Personal and Work AI AgentsMatthew Berman, Satya Nadella · Forward FutureIn an interview excerpt, Microsoft CEO Satya Nadella discusses identity boundaries and the risk of data leakage between personal and corporate agents.
1:44:11EN100 deaths or $1 billion: a 15-page New York bill would set a floor for frontier labsホスト Nathan LabenzAssembly member Alex Bores walks through the RAISE Act’s four requirements, its DeepSeek-inspired distillation clause, and why he says a 50-state patchwork is a fake problem until one state actually passes a law.
2:02:51ENAt a 60-Person Safety Offsite, Only Four Had Seen Nonprofit OpenAIホスト Nathan LabenzFormer OpenAI scientist Steven Adler on the Anthropic split, GPT-4, safety evaluations, and why he filed a brief to keep the company under nonprofit control.
2:29:00ENAssume the Model Is Already Scheming — Then Put It to Workホスト Nathan LabenzRedwood Research CEO Buck Shlegeris tells Rob Wiblin how to extract useful labor from AIs you cannot certify as aligned, using audits, honeypots, resampling, and factored cognition.
もっと見る156 件中 24 件を表示









