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USA Today
3 hours ago
- USA Today
MCP Connects, SDP Delivers: The Missing Half of AI Memory is Here
Prescott, Arizona / Syndication Cloud / July 22, 2025 / David Bynon Key Takeaways Model Context Protocol (MCP) creates AI connections to external tools but doesn't define structured memory content Semantic Digest Protocol (SDP) provides trust-scored, fragment-level memory objects for reliable AI operations Multi-agent systems typically fail due to missing shared, verifiable context rather than communication issues MCP and SDP together form a complete memory architecture that stops hallucinations and contextual drift MedicareWire will implement SDP in 2025 as the first major deployment of AI-readable, trust-verified memory in a regulated domain AI's Memory Crisis: Why Today's Systems Can't Remember What Matters Today's AI systems face a critical problem: they process vast information but struggle with reliable memory. This isn't merely a technical issue — it's what causes hallucinations, inconsistency, and unreliability in advanced AI deployments. This problem becomes obvious in multi-agent systems. When specialized AI agents work together, they don't typically fail from poor communication. They fail because they lack shared, scoped, and verifiable context. Without standardized memory architecture, agents lose alignment, reference inconsistent information, and produce unreliable results. David Bynon, founder at MedicareWire, identified this issue early on. In regulated areas like Medicare, incorrect information can seriously impact consumers making healthcare decisions. The solution needs two protocols working together to create a complete memory system for AI. The first protocol, Model Context Protocol (MCP), addresses the connection problem. But it's just half of what's needed for truly reliable AI memory. Understanding Model Context Protocol (MCP) IBM recently recognized the Model Context Protocol (MCP) as core infrastructure for AI systems, describing it as 'USB-C for AI' — a universal connector standard allowing AI models to connect with external tools, data sources, and memory systems. This recognition confirmed what many AI engineers already understood: standardized connections between AI models and external resources build reliable systems at scale. IBM's Recognition: The 'USB-C for AI' Breakthrough The USB-C comparison makes sense. Before USB standardization, connecting devices to computers required numerous proprietary ports and cables. Before MCP, every AI tool integration needed custom code, fragile connections, and ongoing maintenance. IBM's official support of MCP acknowledged that AI's future requires standardized interfaces. Just as USB-C connects any compatible device to any compatible port, MCP creates a standard protocol for AI systems to interact with external tools and data sources. What MCP Solves: The Transport Problem MCP handles the transport problem in AI systems. It standardizes how an AI agent: Negotiates with external systems about needed information Creates secure, reliable connections to tools and data sources Exchanges information in predictable, consistent formats Maintains state across interactions with various resources This standardization allows developers to build tools once for use with any MCP-compliant AI system. Custom integrations for each new model or tool become unnecessary — just consistent connectivity across platforms. The Critical Gap: Missing Content Definition Despite its value, MCP has a major limitation: it defines how AI systems connect, but not what the content should look like. This resembles standardizing a USB port without defining the data format flowing through it. This creates a significant gap in AI memory architecture. While MCP handles connections, it doesn't address: How to structure memory for machine understanding How to encode and verify trust and provenance How to scope and contextualize content How information fragments should relate to each other This explains why AI systems with excellent tool integration still struggle with reliable memory — they have connections but lack content structure for trustworthy recall. Semantic Digest Protocol: The Memory Layer MCP Needs This is where the Semantic Digest Protocol (SDP) fits — built to work with MCP while solving what it leaves unaddressed: defining what memory should actually look like. Trust-Scored Fragment-Level Memory Architecture SDP organizes memory at the fragment level, instead of treating entire documents as single information units. Each fragment — a fact, definition, statistic, or constraint — exists as an independent memory object with its own metadata. These memory objects contain: The actual information content A trust score based on source credibility Complete provenance data showing information origin Scope parameters showing where and when the information applies Contextual relationships to other memory fragments This detailed approach fixes a basic problem: AI systems must know not just what a fact is, but how much to trust it, where it came from, when it applies, and how it connects to other information. Using the 'USB-C for AI' analogy, SDP is a universal, USB-C thumb drive for the Model Context Protocol. It provides data, across multiple surfaces, in a format MCP recognizes and understands Machine-Ingestible Templates in Multiple Formats SDP creates a complete trust payload system with templates in multiple formats: JSON-LD for structured data interchange TTL (Turtle) for RDF graph representations Markdown for lightweight documentation HTML templates for web publication Invented by David Bynon as a solution for MedicareWire, the format flexibility makes SDP work immediately with existing systems while adding the necessary trust layer. For regulated sectors like healthcare, where MedicareWire operates, this trust layer changes AI interactions from educated guesses to verified responses. The Complete AI Memory Loop: MCP + SDP in Action When MCP and SDP work together, they form a complete memory architecture for AI systems. Here's the workflow: From User Query to Trust-Verified Response The process starts with a user query. Example: 'What's the Maximum Out-of-Pocket limit for this Medicare Advantage plan in Los Angeles?' The AI model uses MCP to negotiate context with external resources. It identifies what specific plan information it needs and establishes connections to retrieve that data. The external resource sends back an SDP-formatted response with the requested information. This includes the MOOP value, geographic scope (Los Angeles County), temporal validity (2025), and provenance (directly from CMS data), all with appropriate trust scores. With trust-verified information, the model answers accurately: 'The 2025 Maximum Out-of-Pocket limit for this plan in Los Angeles County is $4,200, according to CMS data.' No hallucination. No vague references. No outdated information. Just verified, scoped, trust-scored memory through standardized connections. Eliminating Hallucinations Through Verified Memory This method addresses what causes hallucinations in AI systems. Rather than relying on statistical patterns from training, the AI retrieves specific, verified information with full context about reliability and applicability. When information changes, there's no need to retrain the model. The external memory layer updates, and the AI immediately accesses new information—complete with trust scoring and provenance tracking. Real-World Implementation: MedicareWire 2025 This isn't theoretical — SDP launches on in August 2025, marking the first major implementation of AI-readable, trust-scored memory in a regulated domain. 1. First Large-Scale Deployment in a Regulated Domain The healthcare industry, especially Medicare, offers an ideal testing ground for trust-verified AI memory. Incorrect information has serious consequences, regulations are complex, and consumers need reliable guidance through a confusing system. MedicareWire's implementation will give AI systems unprecedented accuracy when accessing Medicare plan information. Instead of using potentially outdated training data, AI systems can query MedicareWire's SDP-enabled content for current, verified information about Medicare plans, benefits, and regulations. 2. Solving Healthcare's Critical Information Accuracy Problem Consumers using AI assistants for Medicare options will get consistent, accurate information regardless of which system they use. The SDP implementation ensures any AI agent can retrieve precise details about: Plan coverage specifications Geographic availability Cost structures and limitations Enrollment periods and deadlines Regulatory requirements and exceptions All come with proper attribution, scope, and trust scoring. 3. Creating the Foundation for Multi-Agent Trust Infrastructure Beyond immediate benefits for Medicare consumers, this implementation creates a blueprint for trust infrastructure in other regulated fields. Multi-agent systems will have shared, verifiable context — eliminating drift and hallucination problems that affect complex AI deployments. The combination of MCP's standardized connections and SDP's trust-verified memory builds the foundation for reliable AI systems that can safely operate in highly regulated environments. From Connection to Memory: The Future of Reliable AI Is Here David Bynon, founder of Trust Publishing and architect of SDP, states: 'We didn't just create a format. We created the trust language AI systems can finally understand — and remember.' As AI shapes important decisions in healthcare, finance, legal, and other critical fields, reliable, verifiable memory becomes essential. The MCP+SDP combination shifts from probabilistic guessing to trust-verified information retrieval — defining the next generation of AI applications. SDP will be available as an open protocol for non-directory systems, supporting broad adoption and continued development across the AI ecosystem. As the first major implementation, MedicareWire's deployment marks the beginning of a new phase in trustworthy artificial intelligence. MedicareWire is leading development of trustworthy AI memory systems that help consumers access accurate healthcare information when they need it most. David Bynon 101 W Goodwin St # 2487 Prescott Arizona 86303 United States
UPI
2 days ago
- UPI
Education Department releases $7 billion held from schools nationwide
July 25 (UPI) -- The U.S. Department of Education finished releasing more than $7 billion in funds for school programs nationwide after a pause at the start of July, an agency spokeswoman said Friday. Last week, $1.3 billion was released with more than $6 billion remaining. The U.S. Office of Management and Budget was reviewing the rest. "OMB has completed its review of Title I-C, Title II-A, Title III-A, and Title IV-A ESEA funds and Title II WIOA funds, and has directed the department to release all formula funds," said Madi Biedermann, deputy assistant secretary for communications for the Education Department, said in an email to media, including The Hill and ABC News. "The agency will begin dispersing funds to states next week." Earlier, the Education Department didn't disperse routine payments for schools that include money for after-school and summer activities, classes for non-English learners and adults, and teacher preparation. The funding was authorized by Congress and was due July 1, before the start of the school year. The school districts were notified of the pause one day before. U.S. Sen. Shelley Moore Capito, a Republican serving West Virginia, had pushed for the funds' release. She and nine colleagues had written a letter to OMB. "This supports critical programs so many West Virginians rely on and I made that clear to OMB Director Vought," Capito posted on X. In a news release Friday, she said: "The programs are ones that enjoy longstanding, bipartisan support like after-school and summer programs that provide learning and enrichment opportunities for school aged children, which also enables their parents to work and contribute to local economies, and programs to support adult learners working to gain employment skills, earn workforce certifications, or transition into postsecondary education." Also, 24 Democratic-led states and the District of Columbia filed suit July 14 seeking the funds' release. A coalition of school districts, teachers' unions, nonprofits and parents sued Monday in Rhode Island. Originally, the White House said the pause was because money was going to the "radical left-wing agenda." Secretary of Education Linda McMahon told ABC News on Thursday: "We want to make sure that we have the right focus on what we're trying to do with our students." She said it could be released by the end of the year. An administration official told The Washington Post that unspecified "guardrails" were put on the money so they align with the policy. More than 200 superintendents went to senators' offices to seek an end to the freeze. David Schuler, executive director of the School Superintendents Association, applauded the change. "On the heels of our survey released Tuesday, detailing how disruptive withholding these funds would be for our nation's students, we thank our members and allies on the Hill," Schuler said in a statement. "We appreciate their tireless advocacy, communication and outreach to the Administration about the importance of releasing these critical funds." The Education Department's proposed fiscal year 2026 budget is $66.7 billion, which is a 15.3% reduction , or $12 billion, from the previous year. President Donald Trump wants to dismantle the Education Department, with states and other federal agencies taking over the dispersal of funds, including student loans and other programs. On July 14, the U.S. Supreme Court allowed for mass firings by lifting an injunction while litigation proceeds. In March, the agency's workforce was slashed in half, with 1,378 terminated. The high court didn't rule on abolishing the agency, which must be approved by Congress.
Yahoo
2 days ago
- Yahoo
Chubb net income soars 33% to $2.97bn in Q2
Chubb has reported net income of $2.97bn for the second quarter of 2025 (Q2 2025), a surge of 33% from $2.23bn recorded last year. The company achieved core operating income of $2.48bn, or $6.14 per share, reflecting a 12.9% rise. For the quarter ended 30 June, consolidated net premiums written totalled $14.2bn, representing a 6.3% increase, or 7.1% when adjusted for constant currency. Property and casualty (P&C) premiums accounted for $12.39bn, up by 5.2%, while life insurance premiums reached $1.8bn, a 14.1% increase. In North America, P&C premiums grew by 5.3%, with personal lines increasing by 9.1% and commercial lines by 4.1%. The middle market and small commercial segments saw an 8.5% increase, bolstered by a 10.2% rise in P&C lines and a 2.7% growth in financial lines. Major accounts and specialty businesses experienced a 1.5% increase. Overseas general premiums rose by 8.5%, or 10.2% in constant dollars, driven by a 15.3% increase in consumer insurance and a 6.8% rise in commercial. Increases were also noted in Latin America (17.3%), Asia (12.7%) and Europe (8.2%). Conversely, agricultural insurance in North America reported a 3.3% decline in net premiums written, attributed to lower commodity prices. Underwriting income in the P&C segment reached $1.63bn, a 15% increase, resulting in a combined ratio of 85.6%. Excluding catastrophe losses, underwriting income for the current accident year was $2.01bn, with a combined ratio of 82.3%. Pre-tax net investment income was reported at $1.57bn, while adjusted net investment income increased by 7.9% to $1.69bn. Operating cash flow was $3.55bn, with adjusted cash flow at $3.23bn. Total catastrophe losses before tax amounted to $630m, compared to $580m in the previous year. The company also reported favourable prior period reserve development of $249m pre-tax. Chubb noted a 6.1% increase in book value per share to $174.07, while tangible book value per share rose by 8% to $112.64, influenced by gains in investment assets and foreign exchange. Annualised return on equity was 17.6%, with a core operating return on tangible equity of 21% and a core operating return on equity of 13.9%. The company returned $1.06bn to shareholders, comprising $676m in share buybacks and $388m in dividends. Chubb CEO and chairman Evan Greenberg said: "We had a great second quarter. Most all of our businesses and regions of the world contributed to record quarterly results, illustrating the distinctive, diversified nature of our company. Our balance of business, geographically by customer segment and product, is a distinguishing feature of our company. 'As I observed at the beginning of the year, about 80% of our businesses globally have good growth prospects, and we are capitalising on a wide range of opportunities. I have great confidence in our ability to grow revenue and operating income at a superior rate, CATs [catastrophe bonds] and FX [foreign exchange trade] notwithstanding." On the flip side, the company's half-year net income saw a marginal decline of 1.7%, totalling $4.29bn, down from the previous year's $4.37bn. Chubb reported Q1 2025 net income of $1.33bn, down 37.9% year-over-year. "Chubb net income soars 33% to $2.97bn in Q2 " was originally created and published by Life Insurance International, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
