Quote for the day:
“Let no feeling of discouragement prey upon you, and in the end you are sure to succeed.” -- Abraham Lincoln
Lean4: How the theorem prover works and why it's the new competitive edge in AI
Lean4 is both a programming language and a proof assistant designed for formal
verification. Every theorem or program written in Lean4 must pass a strict
type-checking by Lean’s trusted kernel, yielding a binary verdict: A statement
either checks out as correct or it doesn’t. This all-or-nothing verification
means there’s no room for ambiguity – a property or result is proven true or
it fails. ... Lean4’s value isn’t confined to pure reasoning tasks; it’s also
poised to revolutionize software security and reliability in the age of AI.
Bugs and vulnerabilities in software are essentially small logic errors that
slip through human testing. What if AI-assisted programming could eliminate
those by using Lean4 to verify code correctness? ... Beyond software bugs,
Lean4 can encode and verify domain-specific safety rules. For instance,
consider AI systems that design engineering projects. A LessWrong forum
discussion on AI safety gives the example of bridge design: An AI could
propose a bridge structure, and formal systems like Lean can certify that the
design obeys all the mechanical engineering safety criteria. ... For
enterprise decision-makers, the message is clear: It’s time to watch this
space closely. Incorporating formal verification via Lean4 could become a
competitive advantage in delivering AI products that customers and regulators
trust. We are witnessing the early steps of AI’s evolution from an intuitive
apprentice to a formally validated expert. How pairing SAST with AI dramatically reduces false positives in code security
In our opinion, the path to next-generation code security is not choosing one
over the other, but integrating their strengths. So, along with Kiarash Ahi,
founder, Virelya Intelligence Research Labs and the co-author of the
framework, I decided to do exactly that. Our novel hybrid framework combines
the deterministic rigor and the speed of traditional SAST with the contextual
reasoning of a fine-tuned LLM to deliver a system that doesn’t just find
vulnerabilities, but also validates them. ... The framework embeds the
relevant code snippet, the data flow path and surrounding contextual
information into a structured JSON prompt for a fine-tuned LLM. We fine-tuned
Llama 3 8B on a high-quality dataset of vetted false positives and true
vulnerabilities, specifically covering major flaw categories like those in the
OWASP Top 10 to form the core of the intelligent triage layer. Based on the
relevant security issue flagged, the prompt then asks a clear, focused
question, such as, “Does this user input lead to an exploitable SQL
injection?” ... A SAST and LLM synergy marks a necessary evolution in static
code security. By integrating deterministic analysis with intelligent,
context-aware reasoning, we can finally move past the false positive crisis
and equip developers with a tool that provides high signal security feedback
at the pace of modern development with LLMs.Quantum Progress Demands Manufacturing Revolution, Martinis Says
Quantum computing’s next breakthroughs will come from factories, not physics
labs, according to John Martinis ... He argued that a general-purpose quantum
computer will require at least a million physical qubits, a number that is far
beyond today’s devices and out of reach without a fundamental shift in how the
hardware is built. ... Current machines rely on dense tangles of wires,
components and cooling structures that dwarf the tiny chip at the bottom of the
machine. He writes that “The complexity of the plumbing completely overwhelms
the quantum device itself.” Martinis said the solution is to abandon today’s
hand-built, research-lab approach and move to fully integrated chips similar to
the transformation that turned 1960s mainframes into the microchips inside
smartphones. The field, he argued, must invest in cryogenic integrated circuits
that can operate at the ultra-low temperatures required for superconducting
qubits. Using that approach, Martinis suggests that engineers could place about
20,000 qubits on a single wafer and reach the million-qubit scale by linking
wafers together. That level of integration would also require abandoning
manufacturing methods that date back more than half a century. He singled out
the “lift-off” process still used in many quantum labs as too dirty and too
limited for industrial-scale production.
Dream of quantum internet inches closer after breakthrough helps beam information over fiber-optic networks
"By demonstrating the versatility of these erbium molecular qubits, we're taking
another step toward scalable quantum networks that can plug directly into
today's optical infrastructure,” David Awschalom, the study's principal
investigator and a professor of molecular engineering and physics at the
University of Chicago, said in the statement. ... That's largely where the
comparison ends, though. Whereas classical bits compute in binary 1s and 0s,
qubits behave according to the weird rules of quantum physics, allowing them to
exist in multiple states at once — a property known as superposition. A pair of
qubits could, therefore, be 0-0, 0-1, 1-0 and 1-1 simultaneously. Qubits
typically come in three forms: superconducting qubits, which are made from tiny
electrical circuits; trapped ion qubits, which store information in charged
atoms held in place by electromagnetic fields; and photonic qubits, which encode
quantum states in particles of light. ... Operating at telecom wavelengths
provides two key advantages, the first being that signals can travel long
distances with minimal loss — vital for transmitting quantum data across fiber
networks. The second is that light at fiber-optic wavelengths passes easily
through silicon. If it didn't, any data encoded in the optical signal would be
absorbed and lost. Because the optical signal can pass through silicon to
detectors or other photonic components embedded beneath, the erbium-based qubit
is ideal for chip-based hardware, the researchers said.AWS Outage Fallout: Lessons In Resilience
The impact of the AWS outage has led to multiple warnings about the issues when relying on one cloud provider. But experts warn it’s important to keep in mind that moving to multi-cloud can also cause problems. Multi-cloud is “not the default answer,” says Ryan Gracey, partner and technology lawyer at law firm Gordons. “For a few crown jewel services, splitting across providers can reduce single-supplier risk and satisfy regulators, but it also raises cost and complexity, and opens new ways to fail. Chasing a lowest common denominator setup often means giving up the very features that make cloud attractive.” ... The takeaway from the latest outage is not just to buy more redundancy, says Gracey. “It’s about designing systems that bend, not break. They should slow down gracefully, drop non-essential features and protect the most important customer tasks when things go wrong. A part of this is running drills so teams know who decides what actions to take, what to say to customers and what to do first.” For the cloud service provider, it’s important to recognise where a potential single point of failure – or “race condition” in the case of AWS – may exist, says Jones. “AWS will be looking at its architecture to ensure single points of failure are eliminated and the potential blast radius of any incident is dramatically reduced.” Maintaining operations during outages requires “architectural and operational preparation,” says Nazir.AI Is Not Just a Tool
At some point in every panel, someone leans into the microphone and says it: “AI
is just a tool, like a camera.” It’s meant to end the argument, a warm blanket
for anxious minds. Art survived photography; we’ll survive this. But it is
wrong. A camera points at the world and harvests what’s already there. A modern
AI system points at us and proposes a world — filling gaps, making claims,
deciding what should come next. That difference is not semantics. It’s
jurisdiction. ... A photo is protectable because a human author made it. Purely
AI-generated material, absent sufficient human control, isn’t. The law refuses
to pretend the prompt is the picture. That alone should retire the analogy. That
doesn’t mean the output is “authorless”; it means the law refuses to pretend the
user’s prompt equals human creative control. Cameras yield photographs authored
by people; models yield artifacts whose legal status relies on the extent to
which a human actually contributed. Different authorship rules = different
things. ... The model is not a person, but it isn’t an empty pipe. It embodies
choices that will be made (over and over) at human scale, with the same
confidence we misread as competence. That’s why generative AI feels creative
without being human. It performs composition: not presence, but pattern. It
produces objects that look like testimony. Cameras can lie (through framing),
but models conjecture. They create the very thing we then argue about.Are Small Businesses at Risk by Outsourcing Parts of Their Operations?
When you outsource a function or department, you're doing more than simply delegating tasks. Every third-party vendor, managed service provider, virtual assistant, or consultant who requires access to your critical systems carries an element of risk; they're ostensibly a potential entry point into your business. ... Some organizations are bound by specific, stringent regulatory frameworks and standards, depending on their sector(s) of operation. Some remote-working IT or marketing contractors may not be subject to the same data privacy laws that govern your organization, for example. Similarly, an HR outsourcing provider may store employee information in cloud servers that are deemed security-compliant in some jurisdictions but not in others. These compliance gaps create additional security vulnerabilities that threat actors would actively exploit without hesitation if the opportunity arose. ... As AI becomes more ingrained into business operations, the process of outsourcing becomes increasingly gray. According to recent statistics, more than half of businesses have experienced AI-related security vulnerabilities. What's more, cybercriminals are harnessing generative AI technology to escalate and amplify their attacks. ... The biggest danger that SMBs face when outsourcing is the assumption that someone else is now responsible for upholding security standards.Why AI Integration in DevOps is so Important
Traditional DevOps pipelines rely heavily on a high degree of automated testing
and monitoring. The drawback is that they often lack the machine intelligence
needed to recognize new or evolving threats. AI addresses this gap by
introducing learning-based security systems capable of real-time behavioral
analysis. Instead of waiting for known vulnerabilities to appear or be actively
exploited, these systems recognize the predicate behavior and code activity.
Once detected, engineers are alerted before an incident occurs. Within DevOps,
AI is able to fortify each stage of the process: Reviewing commits for
suspicious or vulnerable code, monitoring container environment integrity and
evaluating system logs for anomalies that may have escaped real-time
recognition. Insights like these help teams locate weak spots and reduce the
impact of human error over time. ... AI integration with existing CI/CD
workflows gives DevOps teams real-time visibility into security risks.
AI-powered automated scanners analyze components automatically. Source code,
dependencies and container images are all scanned for hidden vulnerabilities
before the build phase is complete. This helps identify issues that could
otherwise slip through manual reviews. AI-driven monitoring tools also track
activity across the entire delivery pipeline, identifying potential attacks such
as credential theft, code injection or dependency poisoning. As these tools
learn over time, they adapt to new threat behaviors that traditional scanners
might overlook.
NTT: How Japan Leads in Cybersecurity Amid Rising Threats
The Active Cyber Defense Law passed in May 2025 is intended to minimise the
damage caused by substantive cyberattacks that can compromise national security,
while Japan has also established new requirements for critical infrastructure
companies to enhance their cybersecurity practices under the revised Economic
Security Promotion Act. ... Gen AI has lowered the bar for adversaries to launch
cyberattacks, meaning defenders have no choice but to empower themselves to
automate at least partially their tasks including log or phishing analysis,
threat detection, behavioural analysis and incident report drafting. This is
crucial for defenders who are overwhelmed by ever increasing work around the
clock to minimize burnout risks. ... As Japanese companies are increasingly
expanding their businesses globally, multiple firms have reported their overseas
subsidiaries being hit by ransomware attacks in the United States, Vietnam,
Thailand, Singapore and Taiwan. To manage supply chain risks and ensure business
continuity, it is becoming more crucial than ever to ensure global governance in
cybersecurity and keep proper data backups, the principle of least privilege and
network segmentation. Surprisingly, Japan is the country where ransomware
infection ratio is lowest amongst 15 major countries such as the United States,
the United Kingdom, France and Germany.
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