I Spent a Week Recording Myself Doing Chores for Money. Who's the Robot Now?

In this article, we cover three topics: what to visualize during training, the tools that provide those visualizations, and the methods to capture model computations directly using hooks and breakpoints.

AI agents look brilliant in a demo because demos are friendly worlds. The data is curated, the tools behave, and nothing important changes while the agent is in mid-thought. Production is the opposite: data arrives late, facts conflict, permissions bite, APIs time out, and the underlying state changes constantly. That gap is why early “agents in production” often get scoped down to something safer: read-only assistants, human-in-the-loop workflows, or narrow domains with heavily curated data. Several high-profile deployments have also been scaled back after meeting messy real-world constraints. Rather than being a verdict on autonomy, these stumbles are a reminder that autonomy is unforgiving. Small cracks in your data stack become large cracks in agent behavior. The same pattern shows up whenever agents move from toy workflows to systems with real state. As scope increases, weak guarantees create predictable symptoms: overconfident actions on stale data, brittle reasoning when meaning

The 245-paragraph document was presented alongside Anthropic co-founder Christopher Olah, whose company is actively suing the Trump administration over military AI use.

Microsoft and EY will spend $1 billion on helping their customers adopt AI over the next five years. The billion will support assisting clients with pioneering AI projects and capability building, said EY’s global Microsoft alliance leader, Paul Clark. Clients will be able to access those resources based on their specific needs, he said. “We’re intentionally building the EY forward deployed engineer (FDE) capability through close collaboration and training with Microsoft, while maintaining integrated EY-Microsoft teams in the field,” he said in an email. “Clients will continue to experience this as one combined team, bringing together engineering depth and transformation expertise.” EY has acted as “client zero” in this initiative, embedding AI in all facets of its organization while it validated ways of working with Microsoft’s technologies. After an initial trial of Microsoft Copilot with 150,000 users, it is now rolling it out through Microsoft 365 E7 to all 400,000 staff. Its combi

企業・組織がデータをまとめることに躍起になっている間に、本質を見失っていることが多く見受けられます。 データは手段であり、目的ではありません。 「データ統合」という言葉を、皆さんは何度耳にされているでしょうか。 経営会議で、ITロードマップで、ベンダー提案書で。まるで呪文のように繰り返されるこの言葉に、私はずっと違和感を覚え意義をとなえ続けています。 結論を先に言います。「データ統合」は、そのままでは意味をなさない概念です。 本当に企業が取り組むべきは「意思決定の統合」であり、データはその手段に過ぎません。 １．「データ統合」はそもそも何を指しているのか 実は私はそこをあまりわかっていません。なぜなら私の中にその概念がないためです。ただ世の中での「データ統合」という言葉の使われ方から推察すると、実際には二つの異なる意味で使われているように見受けられます。 💡キーポイント： 「①置く統合」はメッシュアーキテクチャで不要になりつつある。 「②加工の統合」はそれ自体が目的ではなく、意思決定改善という目的への手段に過ぎない。 どちらの解釈においても、「データ統合」は価値あるものとしては定義できない。 ２．データは組織を動かさない Data doesn't drive your organization. Decisions do. こちらのブログで詳しくお話していますが、データは組織を動かしません。意思決定が組織を動かします。 これは私が日頃お伝えしているメッセージですが、「データ統合」という言葉が流行するとき、この順序が逆転していることが多いです。 自律型AIエージェント時代の意思決定～ROI創出とリスク管理を「技術」ではなく「意思決定」で整理する ３．「統合データベース」という幻想 仮にデータ統合という意味が正確に定義できなくても明らかに手段であるものではなく、目的志向で、「すべての意思決定モデルを格納した統合データベース」を作ろうとすると、どうなるでしょうか。 企業の中で生まれる意思決定は無数にあります。価格設定、在庫補充、顧客対応、採用、設備投資、与信判断、リスク管理……それぞれの意思決定は、固有のコンテキスト・時間軸・責任者を持ち、互いに複雑に干渉し合っています。 それらすべてを網羅する「統合データベース」などは、私が知る限り現時点では現実的に存在しないですし、

As stewards of vast quantities of data, the sector could play a critical role in fuelling the digital economy

The post AI and Web3: How the Two Narratives Are Converging appeared on BitcoinEthereumNews.com. Artificial intelligence and Web3 used to feel like separate technology stories. AI was about models, automation, data, and productivity. Web3 was about ownership, crypto assets, open networks, wallets, and programmable money. Today, that separation is becoming harder to maintain. The reason is simple: AI systems are becoming more agentic, while blockchains are becoming better at handling identity, settlement, coordination, and programmable financial activity. If AI agents can search, compare, negotiate, and act on behalf of users, they may also need wallets, permissions, spending limits, reputation systems, data access, and audit trails. For crypto investors and Web3 users, the convergence of AI and Web3 is both an opportunity and a risk zone. It could create demand for decentralized compute, smart wallets, stablecoin payments, oracle infrastructure, data networks, and agent marketplaces. B

Explore how AI and Web3 are converging through agents, wallets, data, compute, payments and crypto infrastructure - plus key risks to watch.