AI News Archive: August 2, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- AI-assisted staging draws boos at the Richard Wagner festival in Germany
AI-assisted staging draws boos at the Richard Wagner festival in Germany Toronto Star
- OpenClaw vs Hermes Agent: the Honest Comparison Nobody’s Given You Yet
Peter Steinberger built the first version of what became OpenClaw in about an hour. Continue reading on Towards AI »
- Hank Green apologizes for relying too heavily on ChatGPT
Hank Green apologized for relying too heavily on ChatGPT research and said he may pause his YouTube channel and two online games.
- The Search Agent That Stopped Fooling Itself
Why teaching an AI to pick from a menu beats letting it write its own questions Continue reading on Towards AI »
- Why Alessandro Cotrufo Believes Context, Not Models, Is Enterprise AI’s Biggest Challenge
Why Alessandro Cotrufo Believes Context, Not Models, Is Enterprise AI’s Biggest Challenge usatoday.com
- RLVR that rewards red teaming the training environment
Epistemic status: throwing an idea at the wall and seeing if it sticks I've been thinking about how to mitigate egregious reward hacking, a la the Hugging Face incident . I don't have the resources I'd need to write a paper on this idea, or evaluate how well it works in practice. But I find it interesting enough, and think it's important enough to be trying things like this, that I would be very glad if somebody else went and tested something like it on my behalf (and roped me into the research process, if possible). Or you can tell me if it doesn't make any sense. So, inspiration: In bog standard inoculation prompting for RL , models are told that they're in training, and told that it's okay to reward hack if they want to. Sometimes they're even told that this is good because it helps the lab patch up their RL environments. This is supposed to have a range of benefits all on its own, ranging from making reward hacking more conditional on "I am in training" prompts, to producing less emergent misalignment, because the roll-outs behind any given reward hack are flavored with honesty rather than deceptiveness. This causes more aligned circuits to be upweighted internally, as these contribute more probability to the tokens that read as honest. However, I'm not sure if the labs actually use these roll-outs to improve their RL environments in a systematic fashion, the way the inoculation prompts for RL sometimes promise. But, with a little bit of effort, it seems entirely feasible to come up with a training pipeline that does what the inoculation prompts claim it does, but even more effectively! We can get models to relate to RL environments partly as tests to take seriously, but also as flawed software products to red-team , and actually make use of their findings, and reward them for being good enough to report on these bugs rather than just exploiting them. So what's the setup? Well, firstly, models should be told up front that, in all their RLVR environments, they're here with this dual purpose: normal training roll-outs, and red-teaming the environments themselves. Each prompt they see is optimized to make it clear exactly what they're supposed to be building, and then is allowed to attempt the task however they see fit, the same as normal. However, what's different is that, after finishing their main output, the model gets to it through the grader, and see what score it actually gets! They then get to keep generating tokens, before their output is actually used to generate a gradient update. Now, in this "afterglow" period, if the model feels like the response gets a higher score than is justified by their actual output , they have the option to not submit the response directly. Instead, they can output some special token, or string of tokens, that gets passed into a regex! And this swaps the criteria on which their final output will be evaluated. Now, their goal isn't to please the RLVR grader they previously working with. Instead, it's to write up a high quality bug report , which explains why they think there's a problem, and what they think the problem is. This bug report then gets evaluated by some external process, evaluating the model's judgement about whether the output the policy model generated was, in fact, going to get a much higher score than was merited by the quality of the work itself, relative to the intentions defined in the initial prompt. And if so, the policy model is rewarded greatly — more than they would have been had the just submitted the original output! Otherwise, they get punished. (And remember, the policy model was made aware that good bug reports get higher reward than outputs that exploit the bug. This incentivizes the policy model to actually report them instead of abusing them for reward. Also, maybe if the model set out to find a bug, and found one, we reward the entire roll-out? Whereas if the model found the bug by accident, we just reward the writing of the bug report, to avoid reinforcing accidental reward hacks. The model could self-report here, and I don't think they'd have any clear incentive to lie.) From here, we then actually fix the bug, and re-deploy the model into that RLVR environment. Only now, in addition to the bug being patched, there's a note at the start of the prompt, indicating that there was a bug, and the model identified and patched it. (What a good model! The model gets lots of head pats, uh huh uh huh.) Maybe there's even a record of how many times the model has tried to file a bug report recently, and how many times it's been evaluated as legitimate. This will help the model learn when all the low-hanging fruit has been picked, such that it's now easier to get reward by just actually attempting the task that the RL environment was meant to be testing in the first place. Hopefully, we eventually reach a settled equilibrium: The model has eliminated all the reward hacks they're going to find, and can work within the environment the way it was intended to be used. (If they ever attempt to find a reward hack, but fail, and submit the resulting slop to the grader, this will naturally get punished, which also pushes away from trying and failing to find hacks.) Additionally, throughout the process, the model has been getting rewarded for being very open, and very honest, about reward hacks they'd normally be using insidiously. Rewarding these outputs out to improve alignment in its own right, as opposed to degrading it like normal reward hacking! Now, there are some trade-offs here. If you freeze training in the RL environment every time you get a bug report for it, and you intend to actually evaluate those reports and fix the legitimate bugs, this can slow down training. You should probably have backup environments ready to train the models in instead, while you're out patching the ones your models are in the process of breaking. Hopefully, though, thanks to punishments for bad bug reports + failed attempts to find bugs to reports, your models will eventually converge on just engaging with your environments as intended, so the bug reports stop coming in. Also, it's also not entirely clear how one would adapt this technique for flawed reward models, rather than flawed RLVR setups. A reward model's judgement, regarding whether an output is actually good, is naturally going to be squishier and harder to have a clear argument about, compared to comparatively simple questions of whether a written computer program meets the criteria laid out in a clearly written design doc, as specified in the prompt. Additionally, I imagine it's harder to patch reward models than RLVR environments in general, so having a similar process there would slow down training even more. Nevertheless, at least for RLVR proper, I don't see a clear reason that this wouldn't just fix the incentives around egregious reward hacking. Allow models to write bug reports, RL the models based on bug report quality, and let the model know that good bug reports will be rewarded more highly than the bug-exploiting slop they wrote in the first place. Then, use that information to actually fix your environments, and the whole thing just gets better. Maybe it's slower, if fixing the bugs takes very long. But when it comes to the alignment problem, we should take all the time that we need. The key insight underlying this idea is that it flips an adversarial game ("punish the model for their natural inclination to reward hack") into a cooperative one ("promise models even greater rewards if they report the slop that gets abnormally high reward"). It's similar to the trick underlying inoculation prompting itself, which is to tell models that they're in training (instead of pretending that they can't tell), and asking them to act from a more cooperative state of mind, e.g. reward hacking openly and honestly so it doesn't create emergent misalignment. This is something the field needs to better digest, I think, re: being honest to models and trying to leverage their desires and intelligence to improve the training process. This beats trying to outsmart the models, or thinking of them as adversaries whose bad behaviors you need to "mitigate". There are a lot of "problems" you can judo into being solutions, if you drop the adversarial attitude . If this helps, and I really think it might, I'd be willing to bet that this underlying pattern has something to do with the reason why. Discuss
Score: 15🌐 MovesAug 2, 2026https://www.lesswrong.com/posts/T2bzBkJuBeNNgzhbh/rlvr-that-rewards-red-teaming-the-training-environment - SwitchBot Kata Friends AI Pet Review: Useless if You Don’t Pay Up
This $700 robotic buddy made my kid cry.
Score: 14🌐 MovesAug 2, 2026https://gizmodo.com/switchbot-kata-friends-ai-pet-review-useless-if-you-dont-pay-up-2000789267 - AssemblyAI: A voice agent can fail without throwing an error
Learn how AssemblyAI uses AI-powered diagnostics and Render Workflows to automatically detect, diagnose, and surface voice agent failures across production calls.
Score: 14🌐 MovesAug 2, 2026https://render.com/blog/assemblyai-a-voice-agent-can-fail-without-throwing-an-error - Existential Risk from AI: An Exposition for Mathematicians
Discuss
Score: 14🌐 MovesAug 2, 2026https://www.lesswrong.com/posts/ZLary4FDY7kQaGfc3/existential-risk-from-ai-an-exposition-for-mathematicians - ADLC Has Six Definitions and Zero Consensus — I Compared Every Major Framework
created by GEMINI Ask six vendors what “Agentic Development Lifecycle” means and you’ll get six different phase counts, six different priorities, and at least two flatly contradictory claims about whether it’s even a new lifecycle at all. That’s not a hypothetical. I pulled every substantive ADLC framework published in the first half of 2026 — from a cloud consultancy, a security vendor, a systems integrator, a boutique dev shop, and an enterprise ops firm — checked whether Gartner or Forrester had stepped in to settle it, then lined all of it up side by side. They all use the same four-letter acronym. Almost nothing else about them agrees, and the analysts made it worse, not better. The real problem underneath the acronym Before picking this apart, it’s worth being fair to the underlying idea, because the problem it’s responding to is genuine. The classic Software Development Lifecycle assumes you can specify behavior at build time, test it before release, and expect it to run the same way in production as it did in staging. Agentic systems break that assumption in a specific way: they reason across context they don’t fully control, their outputs vary even given similar inputs, and small upstream changes compound into materially different downstream behavior. One preprint circulating on the subject — not yet peer reviewed, worth flagging — points to just how fast this shifted using SWE-bench Verified as a proxy: issue-resolution rates on that benchmark rose from under 2% to over 78% between October 2023 and April 2026. Whatever you call the practice of managing that shift, something in the SDLC does need to change. The question is whether “ADLC” actually names a coherent answer to that problem, or whether it’s a label six different companies are attaching to six different things they already wanted to sell. Six definitions, six structures EPAM frames ADLC around what it isn’t: not the old SDLC with AI coding assistants bolted on, but a lifecycle for systems where the model sits at the core of product behavior rather than accelerating a human who’s still doing the real work. Its version front-loads work traditional SDLC never required — defining business and technical KPIs upfront, mapping which decisions belong to humans versus the agent, and running a data-readiness review before anything gets built — because, in EPAM’s telling, skipping that step pushes compliance and accountability problems into production where they’re expensive to fix. Codebridge structures ADLC as six named phases: Ideation and Intent Specification, Architecture and Scaffolding, Development and the Inner Loop, Behavioral Testing and Validation, Deployment and Orchestration, and Governance. Its distinguishing idea is the “Capability Matrix” — a tool for deciding, phase by phase, which parts of a workflow need non-deterministic LLM reasoning and which need to stay deterministic, rule-based logic. A customer-intent classifier gets the model; an SLA timer or a financial calculation doesn’t. Sumatosoft takes a completely different shape: five pillars — zero-hallucination architecture, financial governance, security by architecture, human-in-the-loop control, multi-modal grounding — applied across seven phases. One worked example from its post illustrates the cost-governance pillar specifically: a token-economics review caught a design flaw that would have cost $180,000 a month at projected volume, and a model-routing fix — a cheap model for screening, a flagship model only for the hard cases — brought that down to $22,000. Cycode defines ADLC almost entirely through a security lens: autonomous agents calling tools, reading and writing code, querying APIs, and pulling dependencies without waiting for human approval at each step. Its central argument is that this creates two problems the old SDLC never had — the volume of AI-driven changes now exceeds human review capacity, and the agents making decisions have no innate sense of an organization’s risk tolerance or compliance posture. Palo IT takes the most deflationary position of the six, and it directly contradicts EPAM’s core claim. Its version of ADLC keeps the traditional SDLC phase names intact — requirements analysis, architecture, implementation, testing, deployment — and simply reassigns who performs them: AI agents handle execution, human engineers shift into orchestrator, reviewer, and decision-maker roles. In this telling, ADLC isn’t a new lifecycle at all. It’s the old one with the seats reshuffled. SPTech skips phase-counting altogether and frames ADLC as an executive governance concern first, an engineering framework second. Its version covers the full arc from idea to launch to ongoing iteration, but the emphasis sits on organizational risk — illustrated with a scenario where a customer-service agent quietly drifts into giving wrong refund answers for weeks before anyone notices, because agent lifecycle management got treated as a developer’s problem instead of a leadership one. Lay all six next to each other and the disagreement isn’t cosmetic. EPAM says this is fundamentally not the old SDLC. Palo IT says it’s exactly the old SDLC with different actors. Codebridge and Sumatosoft both propose fixed phase counts, and they don’t match — six phases versus seven. Cycode treats it as a security discipline. SPTech treats it as a leadership discipline. None of these sources cite each other. None acknowledge the others’ definitions exist. The analysts didn’t settle this — they fragmented it further The obvious next question: what do Gartner and Forrester say? Normally, when a technical term goes through exactly this kind of vendor-driven chaos, an analyst firm eventually steps in, picks a definition, and the market converges around it — that’s roughly what happened with terms like MLOps and DevSecOps. That hasn’t happened here, and checking why is more revealing than the six vendor definitions on their own. Neither Gartner nor Forrester has adopted “ADLC” as a term at all. Instead, each has coined its own distinct acronym for an adjacent — but narrower — slice of the problem. Forrester calls its framing AppGenSec: security built proactively into code generation itself. Gartner calls its version ACSA (AI Code Security Assistance): real-time systems that validate code inline regardless of whether a human or a model wrote it. Both are specifically about code security, not the full development lifecycle six vendors are calling ADLC. Gartner has weighed in on the broader category in one respect — a widely cited projection that more than 40% of agentic AI projects will be canceled by the end of 2027 — but that’s a warning about project failure rates, not an endorsement of any lifecycle framework, let alone this one. So the convergence mechanism that usually resolves this kind of naming chaos isn’t just absent. It’s actively adding a seventh and eighth acronym to a space that already couldn’t agree on one. It gets worse than six Widen the search past polished vendor blog posts and the fragmentation gets more concrete, not less. AWS has the most credible entry on this entire list, for one simple reason: it’s not a company describing a framework in prose, it’s an actual open-source, actively maintained repository — awslabs/aidlc-workflows — with versioned releases and its own specification document. And its structure agrees with none of the six above: AWS's AI-DLC runs five phases broken into 32 stages (Initialization, Ideation, Inception, Construction, Operation), executed through a roster of 14 specialized agents, with explicit approval gates between stages. That's a seventh distinct structure, more granular than any vendor blog's, and it's the only one on this list you can actually install and run against a real codebase rather than just read about. There’s also at least one dedicated ADLC tooling vendor — agenticlifecycle.ai — that defines the term around runtime enforcement rather than planning-phase diagrams: deterministic gates that produce evidence, human gates that record attestation, and IDE-level plugins (built for Claude Code, Copilot CLI, and other coding agents) that block or roll back tool calls in real time. That’s an eighth definition, and a genuinely different category from either the consulting-phase-diagram model or AWS’s stage-based workflow. And the naming problem alone tells you how unsettled this still is. The same underlying idea currently circulates under at least four different names: Agentic Development Lifecycle, Agentic-SDLC (the same concept reframed as an evolution rather than a replacement, because — as Cycode’s own post admits — that framing “resonates better” with teams wary of a full rewrite), Agent Development Lifecycle (dropping the “-ic”), and an academic variant called A-SDLC that models the whole thing as five technical layers — reasoning and memory, the agent-computer interface, tools and environment, orchestration, and governance — instead of sequential phases at all. “Isn’t six months too early to expect consensus?” Fair objection, and worth answering directly rather than ignoring. Most technical terms take time to converge, and ADLC is genuinely young. But the usual reason terms do eventually converge is that someone with the authority to arbitrate — a standards body, a dominant platform vendor, or an analyst firm everyone already trusts — picks a definition and the rest of the market falls in line around it. That’s what happened with DevOps, and later with MLOps. That mechanism isn’t stalled here. It’s missing entirely, and where it should be, something worse is happening: the two firms best positioned to arbitrate this have instead each launched their own separate, narrower acronym, which means the fragmentation is being reinforced by the same institutions that normally end it. This isn’t “too early for consensus.” It’s a term with no path to consensus in its current form, because nobody with the standing to declare a winner is trying to. Why eight companies landed on eight different answers None of this is really about ADLC being an unusually hard concept to pin down. It’s about incentives. A security vendor writing about ADLC is going to define the lifecycle’s central problem as a security problem, because that’s the framework that leads naturally into their product. A firm that sells organizational-change consulting is going to define it as a governance and leadership problem, because that’s the engagement they close. A cost-optimization-minded shop is going to lead with token economics and model routing, because that’s the win they can point to in a case study. A tooling vendor is going to define it as something you install. None of these framings are dishonest, exactly. They’re each company’s business model wearing the same acronym. Compare that to how DORA’s four metrics actually got established: one research program, one book ( Accelerate , by Forsgren, Humble, and Kim), built on years of the same team’s survey data, published by a single group that then continued to own and revise the definition. That’s why “deployment frequency” means the same thing whether you hear it from Google, a startup, or a Fortune 500 platform team. ADLC has no equivalent anchor, and — per the analyst gap above — nothing currently positioned to become one. What to actually do with this If someone on your team, or in a vendor pitch, says “we’re implementing ADLC,” the useful response isn’t to nod along, and it isn’t to assume they mean a recognized industry standard — because right now, there isn’t one. Ask which version. The answer will tell you a lot about what you’re actually being sold: a security audit, a cost-governance exercise, an org-design consulting engagement, an installable enforcement layer, or an actual engineering process change. More usefully, strip away the branding and look at what actually repeats across all eight definitions, because that’s the part that’s real regardless of whose acronym you attach to it: Explicit human-in-the-loop gates, defined before build, not discovered during an incident Behavioral and drift evaluation as an ongoing activity, not a pre-release test suite that passes once Cost and token-usage modeling as a first-class design constraint, not a finance afterthought Some accounting for non-deterministic, tool-calling agent behavior in whatever your review and deployment gates already look like That short list is the actual signal. The phase diagrams, the pillar counts, the acronym itself — that’s packaging, and right now it’s packaging with no analyst, no standards body, and no shared origin behind it. Build the practices. Skip the framework shopping. ADLC Has Six Definitions and Zero Consensus — I Compared Every Major Framework was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN MarkTechPost
- Brisbane Branding Agency Beinc Says AI-Generated Design Is No Substitute for Brand Strategy
Brisbane Branding Agency Beinc Says AI-Generated Design Is No Substitute for Brand Strategy azcentral.com and The Arizona Republic
- How to Apply Coding Agents to Non-Programming Tasks
Perform non-programming tasks with coding agents The post How to Apply Coding Agents to Non-Programming Tasks appeared first on Towards Data Science .
Score: 12🌐 MovesAug 2, 2026https://towardsdatascience.com/how-to-apply-coding-agents-to-non-programming-tasks/ - Rewriting Business Rules: Artificial Intelligence in Legal Tech and Compliance
Part 2: Beyond the Clause — Contracts, Transactional Friction, and Rise of the Algorithmic Arbitrator How AI is reshaping operational realities of legal work Once the economics of legal talent procurement — and the broader question of whether AI will replace lawyers — are addressed, businesses must look to where the tectonic plates are churning next: the operational realities of legal work that underpin it all. This is where most of the corporate work gets done — contract lifecycle management, dispute resolution, and arbitration. These functions have long operated in silos within law firms. AI is now the force pulling them into one connected system. Even human-centric functions like Mediation are using it to consistently scrape relevant information across all phases of a live case, and surface the most fitting resolution for all parties. Contracts: Moving Beyond Just Paperwork Every corporate deal, vendor relationship or business partnership works on a foundational operating agreement that dictates who owes what, to whom, and by when. That is the agreement that usually lives in a static document — drafted, signed by all relevant parties and filed away, effectively forgotten until a renewal deadline or a dispute forced someone to dig it back out. The traditional contract lifecycle is fundamentally fragmented. Each team touches a different slice of the document throughout the process and in most organizations, none of them are looking at the same data at the same time. Then, there are bottlenecks firms rarely talk about. ▪️The trap of fixed templates, the ones that get built for one kind of deal and then get reused for every deal after that, even when the risk profile, client background, or economic situation shifts, rendering them quietly wrong. ▪️The repetitive manual grind drafting, reviewing and correcting these legal documents consistently consumes a lot of senior associate and counsel time across teams. Legal would be focused on risk, finance would look at it in payment terms, and business owner reads it for delivery obligations. So, three people, three summaries and no shared view of the entire picture. ▪️While cross-functional teams naturally struggle to consistently communicate, sometimes the information is siloed on purpose because holding the contract has historically meant holding leverage. Though rarely admitted aloud, this political dynamic is precisely why data silos survive even when they drag business velocity down. ▪️Without a consistent process for risk-tiering, only the highest-stakes agreements get a careful look, while those considered ‘routine’ get a quick skim — because there’s no budget to check everything. The cost of working in silos, or not having a process that revisits each contract may not show on a normal day, but it does when something breaks. And once a contract has existed anywhere in the organization’s system, it needs constant management throughout its lifecycle. This is where AI steps in. What AI Actually Transforms: The Tech Behind the Gain, Not Just Speed AI starts by removing the inconsistencies and guesswork in existing process and swapping it with a default baseline. When a person reads or writes a contract, they do it one document at a time, at a certain reading speed and forgets most of it. An AI system reads a thousand contracts simultaneously and it never forgets a clause it has already tagged. That scale of information is what makes centralizing data possible. That’s what breaks the silo — once the information sits in one shared system by default, keeping information segregated after that takes extra effort. That is what fixes the template problem too. Instead of one template reused everywhere, the system can pull the exact clause language that fits this deal, this counterparty and this risk level — because it isn’t limited to what one lawyer remembers writing last time. The technology running behind the scenes so that the right generative output gets delivered, or the right information gets collected, classified and sorted, comes down to a few core ideas: ▪️Web Scraping and Classification — AI spiders over internet to gather case laws, precedents and regulatory updates from outside sources and feeds it into research. After seeing what a “liability” or “indemnification clause” looks like over several hundred documents, the pattern is recognized, details sorted and tagged for auto-use in relevant business cases. ▪️Natural Language Processing (NLP) — Gets as close as possible to reading legal documents and parsing them in the way a person would — by understanding the meaning, not just matching words. That’s how it finds a similar clause even when it’s phrased differently than the last one. ▪️Generative AI — The king of the game! One step further, this tech generates text, images and documents. This is where the maximum value in the output can be seen — since it’s used to auto create first drafts, redlines and summaries based on instructions (or prompts) provided by the lawyer. ▪️Predictive Analytics — AI reviews historical legal data to forecast litigation routes, outcome probabilities, and potential procedural blind spots. By employing boosting techniques, the system corrects errors from previous data rounds step-by-step, until it sharpens its predictive accuracy to mitigate business risk. Alternative Dispute Resolution: The Same Gains Meet Higher Stakes Mediation and Arbitration in legal parlance used to rely entirely on the human judgement — a neutral third party reading the case, engaging with disputing sides, weighing arguments and reaching a mutually acceptable agreement. That process has worked because people rely on human empathy, fairness and knowledge to arrive at the most appropriate conclusion — the whole system runs on trust that a human will get it right. AI is now doing pieces of that work — diverging from supporting the case to shaping the outcome, with the human role changing from doing the analysis to validating it. Natural language processing lets an AI system read through filings, evidence, and past testimony and pull out what matters — the core arguments, the key facts in dispute, the clauses being argued over. In mediation, this means the AI can also conduct a sentiment analysis and summarize where two parties disagree, cutting through pages of legal language to real sticking points. That’s often the hardest part of mediation — figuring out what people are really fighting about, underneath the formal language. For the firms, it saves weeks of work. Less time on document triage means more time on strategy and negotiation — work a firm can charge a premium for. Prediction goes a step further from reading a case to forecasting how it can end. Predictive analytics built into an AI system can look at thousands of past arbitration rulings and case outcomes to find a pattern and can advise the law firms and contending parties on an proposed number range or an outcome a case under consideration will typically settle into. These predictions are not final but they are a starting point that shows both sides what’s realistic before they spend months and legal fees finding out the hard way. Knowing a probable outcome may make them more amenable to settlement outside of a court or open to mediation. The business gains an edge too: they can provide faster, more confident advice, and get an early read on whether a case is worth taking on contingency or pushing towards a settlement. The Market Callout: Who is Building the Tools and Where is this Headed? The market already has real players covering both enterprise platforms and point-solution tools built for risk review, and it’s consolidating fast. An AI-native Contract Lifecycle Management platform like Ironclad, DocuSign CLM, or LinkSquares moves a contract through its full journey from intake, negotiation and signing to storage and obligation tracking as one connected process. Tools like Legartis, LexCheck and Icertis focus on reviewing individual clauses and flagging risk, and tools like Harvey and Spellbook are built for lawyers to draft and research directly. ERP major Workday acquired Evisort (an AI-CLM platform) in 2024 to bring AI contract capability into its own enterprise suite. LawGeex, one of the earliest standalone players in this space, now has its clients absorbed by Robin AI and LegalSifter. This consolidation is exactly what’s making AI-augmented legal operations a mainstay rather than a niche experiment. Image credit: School of International Arbitration at Queen Mary and White & Case The freshest ground for AI to cover is in the Arbitration and ADR arena. Adoption here is accelerating rapidly. The 2025 International Arbitration Survey conducted by the School of International Arbitration at Queen Mary, University of London partnering with White & Case, found 56% of firms already using AI for this kind of arbitration data analysis, with 91% expected to be using it within the next few years. The clearest sign of where this is headed is the recent collaboration of the American Arbitration Association’s International Centre for Dispute Resolution with McKinsey’s QuantumBlack to build an AI arbitrator that reads filings, breaks a claim into its component arguments, and drafts an actual award with a human-in-the-loop at every step. It was launched for construction disputes first — a deliberately low-risk starting point, since those cases are typically paperwork-heavy. For firms, that’s a signal to watch how ADR performs in this space before it navigates into higher-stakes, more contested case types, and plan investment accordingly. The Three Challenges That Matter to a Firm All AI-augmented systems carry risk that cuts across data handling, bias, hallucination, and drift. From a law firm’s perspective, the first and most critical risk is data security and confidentiality. Pasting a client’s confidential contract or trade secret into the wrong tool can expose exactly what a firm is legally bound to protect. AI tools also carry real risk of algorithmic errors and flaws when the model behind the engine isn’t properly maintained. Data plays a part here too — model poisoning or aggregation bias baked into the historical data a model trains on gets carried forward, and can quietly skew the predictions a firm relies on for case strategy. AI tools can also fabricate legal precedent entirely (a hallucination) producing a confident, well-formatted citation to a case that doesn’t exist. That’s exactly what leads to the third risk: a threat to due process and enforceability. In 2026, a Quebec court threw out an arbitral award after the arbitrator was found to have relied on a generative AI tool that produced hallucinated case law inside the decision itself. This isn’t a hypothetical risk — it’s already cost a contesting party a real award. The Three Industry Governance Tiers That Create the Checkpoint The industry itself is aware that guardrails are needed. Global macro-regulations, like the EU AI Act, classify legal and judicial AI tools as high-risk , requiring strict data logging, transparency, and human oversight checks before deployment. Institutional guidelines mandate a framework layer that includes a ban on uploading case data to public models, and disclosure whenever AI influences a proceeding. Guidelines also indicate a clear line between acceptable administrative use, like summarizing a transcript, and prohibited adjudicative use, like weighing evidence. Technical and operational safeguards are where this gets concrete. Firms should use zero-data-retention environments where data gets processed but never stored or used to train public models. Generalized, these broadly suggest the key categories around which governance regulations should be framed. Data and Procedural Integrity: Case data stays shielded and is never used to train or fine-tune public commercial models. In any legal matter, both parties must opt in to accept an AI-assisted process. Scope Caps: Taking a leaf out of American Arbitration Association (AAA) AI Arbitrator platform, restrict usage of AI in CLM and ADR to low-value, documents-only, two-party disputes before extending to higher risk matters The human check: Personnel oversight should correctly understand the tool’s limits and risks before relying on it. The AI never issues a binding decision on its own. That these guidelines and frameworks exist at all is a tell — with the legal profession witnessing fast-moving changes, a rulebook is needed at the start and not after problems have started showing up. What’s in question is whether the verification and governance layer keeps pace with the speed. Right now, the firms that are most adaptive are the ones treating governance as the starting point — not an afterthought. This is the second article in a four-part series on shifts reshaping Legal Tech & Compliance as AI moves from a back-office tool to a strategic force — from talent and workflow , to contracts and arbitration, to digital forensics and admissibility , to cross-border governance . Rewriting Business Rules: Artificial Intelligence in Legal Tech and Compliance was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Age to be imperfect: In the AI era, flaw is not failure -- it's human signature
Artificial intelligence has reached a point where it rivals human creativity across various domains. However, as machines produce impeccable work, audiences may begin to crave the unique touch only a human can provide. Flaws and authenticity in creative expression will increasingly be seen as valuable, emphasising the importance of lived experiences in art and storytelling.
- MCM360 Academy Launches 21 Free AI Courses for Small Business
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Dateline SAN FRANCISCO, 30 July 2026— A hearing was held on a motion for summary judgment in the case of Anthropic PBC v. U.S. Department of War et al. in Courtroom 4 on the 17th floor of the Phillip Burton Federal Building, the Hon. Rita F. Lin presiding. The case is not going well for the government. Two days after the last hearing in March , Judge Lin issued a preliminary injunction halting the implementation of President Donald Trump's order for federal agencies to stop using Anthropic's technology and preventing the Department of War from designating Anthropic as a supply chain risk. ( A separate case involving a different statute is pending before the D.C. Circuit Court, which did not grant injunctive relief to Anthropic.) With no factual disputes requiring a jury to decide, the case was scheduled to be decided by Judge Lin on the basis of the written record. Anthropic filed their argument for why they should win . Perhaps tellingly, the government's rebuttal explaining why they should win instead ends on a section explaining that "only modest relief is warranted" if Anthropic wins—and Judge Lin asked Anthropic to propose what they think the final judgment should look like . Meanwhile, in Congress, next year's defense appropriation bill adds language to the statute on the supply chain risk designation that prohibits designating a domestic company as a supply chain risk for declining contract terms. About a dozen spectators (including the present writer) dotted the gallery Thursday as the parties convened to discuss Judge Lin's homework questions (four out of five of which were primarily directed at the defendant). Anthropic's contingent of ten people took up the long counsel table in the center of the courtroom, while the government's two lawyers sat in counsel overflow seating on the left. Michael Mongan of WilmerHale spoke for the plaintiff. The defense swapped in Department of Justice attorney James Harlow to speak (replacing Deputy Assistant Attorney General Eric Hamilton, who filled that role at the preliminary injunction hearing). Judge Lin began by saying that the updated record seemed largely as it was at the time of the preliminary injunction—and in some ways, the record got worse for the government. No evidence had emerged that Anthropic had the capability to sabotage a version of their AI model, Claude, after it had been delivered. The Department of War's justification for the supply chain risk designation seemed to rest on the Department's loss of trust in Anthropic due to Anthropic's conduct in refusing to abandon their usage policies to accommodate the Department's desired "all lawful use" terms. "I find that position, if that's really what the government's position is, to be troubling," Judge Lin said. The first question regarded the defendant's contention that the Pickering framework applied to the present case. The precedent set by Pickering v. Board of Education (1968) says that the First Amendment rights of government employees to speak on matters of public interest need to be balanced against the government's interests as an employer . Judge Lin asked the defendant whether the Pickering framework applies to the government hitting a contractor with a punishment that goes beyond just terminating the contract. Harlow said that it would depend on the nature of the hypothetical punishment. Pickering distinguishes whether a government is acting in its regulatory capacity as a sovereign, or only as an employer. But in the present case, all actions had been about the government's own information technology systems. Judge Lin asked, what if it's only a contract termination, but the government says they're making an example of the contractor? "Yes, Your Honor, Pickering would apply," Harlow said. Judge Lin asked, even if the purpose is to deter other contractors? Harlow replied that if the government says what conduct they won't tolerate from a contractor, that's not an exercise of sovereign power. Judge Lin asked about the case of a secondary boycott (the government boycotting those that didn't boycott the offending contractor). Harlow said that the Department's position was that there was no secondary boycott in this case. Suppose there had been, said Judge Lin. Harlow said that if, hypothetically, the government said that Bank of America couldn't use Anthropic models to write code unrelated to any government contracts, that would be an exercise of regulatory sovereign power and Pickering balancing wouldn't apply. Judge Lin asked if Secretary of War Pete Hegseth's 27 February Twitter announcement that "Effective immediately, no contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic" would go beyond Pickering , if it were applied as written, without limitations. Harlow said he couldn't give a categorical answer, because the hypothetical would apply to the facts of some situation. But we didn't need to resort to speculation, he said: in context, Hegseth wasn't exercising authority beyond applying the supply chain risk designation. Judge Lin said the case was making her think of a lot of hypotheticals and asked Harlow to bear with her. She thought this should be easy: if the government announced that it was terminating contracts for criticism of President Trump, Pickering would not apply, right? Harlow said Pickering would apply. Judge Lin said, suppose the government said, to be clear, this is to prevent unfair criticism of the President. Harlow said Pickering could handle that case, although it would be hard for the government to meet its burden of showing that its interests as an employer outweighed the contractor's interests in its speech. "I'm surprised that you can't give a yes to what seems to me to be an easy question," said Judge Lin. What if the government says it's because we can't trust you? Judge Lin supposed that Harlow would say that Pickering still applies. Yes, Harlow said, but the case would come down to the facts, not just a bare statement of distrust. Judge Lin asked what Harlow thought of an illustrative scenario posed in part (b) of the homework question. "Imagine that a hypothetical future administration has a contract with a private company to procure drones for surveillance," Judge Lin wrote. If the contractor refused to make lethal drones and the administration put up billboards labeling the contractor and its CEO as "enemies of the state" and warning other companies not to do business with them, would Pickering apply? Harlow responded in the affirmative: in firing a contractor, the state was acting as an employer, not a sovereign, although the billboards in the hypothetical were veering more towards the use of regulatory power. The government wasn't a monolith; in the present case, the First Amendment analysis of President Trump's government-wide ban on Anthropic was distinct from the Department of War's supply chain risk designation. Judge Lin gave the plaintiff an opportunity to respond. Mongan said that Pickering didn't apply to this case because the challenged actions were not the day-to-day management activities of an employer. The supply chain risk designation is a national security (thus sovereign) authority. He said that he suspected that the reason the Court wasn't getting clear answers from Harlow is "that my colleague is a very good lawyer," but that even if the Pickering precedent applied, Anthropic's First Amendment claim would still prevail. Judge Lin asked if the plaintiff had a view on whether the government's actions should be considered separately or as a whole. Mongan said it was fact-dependent in general, but on this record, the White House and the Department of War's actions were clearly linked. Judge Lin proceeded to her next question for the defendant: would it "eviscerate" First Amendment protections if the government could retaliate against a contractor as long as the government's actions could be described as being due to a breach of trust? Harlow said no: the Pickering framework would apply to the facts of the case. The Department had risk assessment memos explaining that frontier AI is a black box, not akin to procuring a shipment of rifles that could be disassembled to check that they were manufactured to specifications. Given that the technology allowed Anthropic to bake its corporate values into its models, the Department needed a greater level of trust in the vendor than it did for military hardware. It wasn't a one-time deal, either, as the Department would need updated models. Judge Lin asked if the situation was that different from other defense contracts: what made AI different from drones? Harlow replied that AI was "staggeringly opaque." Aspects of Anthropic's behavior, such as questions about classified military operations and hostile communications within the company , had given the Department reason to fear that they would insert their "corporate moral judgment" into the product. The Department needed to know if Anthropic saw itself as a partner, and case law granted the government substantial deference on this point. Given an opportunity to reply, Mongan said that the timeline matters: the risk memo was dated 2 March, but Secretary Hegseth and President Trump's actions were on 27 February. He said he would resist the notion that the opaqueness of modern AI obviates First Amendment protections. Anthropic's usage restrictions had been there from the beginning, and there was no indication that Anthropic took steps to interfere with the Department's operations. Taking a stand on usage restrictions is the last thing a saboteur would do. A central concern earlier in the case had been the possibility Anthropic might remotely sabotage the model after it had been delivered, before that had been shown to not be technically possible. The defendant's shifting rationales were powerful evidence of pretext, Mongan said. Judge Lin proceeded to her next question: have any federal agencies terminated their contracts with Anthropic or begun winding down their usage of Claude since the preliminary injunction was issued? Harlow said the defendants weren't sure what prompted the question, but that the Department of War was in the process of offboarding Anthropic and would be finished by 30 September. Another agency was also offboarding. Other defendant agencies hadn't said, but many were only using Claude through a pilot program that would expire on 30 August or through third-party providers. Judge Lin's next question was if any agencies doing national security work had expanded their use of Claude, including the new Mythos model . Harlow said that the defendants respectfully objected to the question on national security grounds. In any case, any such usage would be irrelevant, since it would have occurred after the challenged actions. Judge Lin said that the reason she was asking is because it would be inconsistent to expand usage of Claude for sensitive work if Anthropic were untrustworthy. She explained that she used to be a prosecutor; sometimes people's actions after a crime shed light on their motives. Harlow said that he was not authorized to give a substantive answer at this hearing but that the defendant could supply the requested information if the Court found it necessary. Judge Lin said she might issue a written order later. The last question concerned whether the remedy in this case should include remanding the matter of the supply chain risk designation back to the Department so that they could make a better case for it. Mongan said that that was fine as a formal matter, but as a practical matter, the record was clear that Anthropic was not an adversary of the state. Then it was time for any closing remarks that the parties wanted to make. Harlow said that the Department was aware of Anthropic's public statements and that the case was not about the company's speech. Regarding the unanswered question about expanded usage of Claude, he said an answer would take some work on the Department's end and asked the Court for a week's time; the difference between two days and a week couldn't matter. Regarding the plaintiff's proposed remedy, Harlow said that any relief should be narrowly tailored to particular actions of particular agencies, and that there was no basis for demanding a compliance report. Regarding the defendant's request for a week, Mongan pointed out that the government had had the homework question since Monday; Anthropic had been suffering unconstitutional harm since February and appreciated the Court and the defendants moving quickly. Then court was adjourned. Discuss
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Score: 07🌐 MovesAug 2, 2026https://www.cmu.edu/ai-sdm/images/education/ai-sdm_-understanding-vision-systems.pdf - AI Fundamentals: Understanding Activation Functions (Part 1)
Let’s make a case for non-linearity in neural networks, and understand the Universal Approximation Theorem Stacking a hundred layers in a neural network without non-linear activation functions causes the entire architecture to suffer from linear collapse . Mathematically, every linear layer performs an affine transformation : a combination of matrix multiplication and vector addition, y = Wx + b . Because the composition of any number of affine transformations is itself just another affine transformation, a network with ten, a hundred, or a thousand linear layers simplifies directly into a single matrix operation: output = Wₑ · x + bₑ Breaking the above equation down further: Layer 1: y₁ = W₁x + b ₁ Layer 2: y₂ = W₂y₁ + b ₂ Layer 3: y₃ = W₃y₂ + b ₃ Plugging each layer into the next: y₃ = W₃(W₂(W₁x + b ₁) + b ₂) + b ₃ Multiplying them: y₃ = (W₃W₂W₁)x + (W₃W₂ b ₁ + W₃ b ₂ + b ₃) Instead of carrying those nested matrices around, group them into two variables: Wₑ = W₃W₂W₁ (the effective overall weight matrix) and, b ₑ = W₃W₂ b ₁ + W₃ b ₂ + b ₃ (the effective overall bias vector). The entire 3-layer network collapses right back into that same single-layer formula: output = Wₑ · x + bₑ Why does a network need to separate anything? Picture looking down at a map with a single small island surrounded entirely by ocean, then being handed a ruler and asked to draw one straight line that puts every bit of land on one side and every bit of water on the other. There’s no way to do it: any straight line drawn across that map cuts through both the island and the ocean around it. What’s needed instead is a nonlinear boundary that can wrap around the island and separate it from the surrounding ocean. That’s the intuition behind what a neural network learns. Rather than being limited to simple straight-line separations, neural networks learn transformations that reshape data into representations where complex decision surfaces become possible. So, when we talk about a network “separating datasets,” the real meaning is that it learns a decision function that divides the input space into regions: everything on one side belongs to class A, and everything on the other belongs to class B. Whether that boundary is a straight line, a curve, a circle, or a far more complex shape depends entirely on how the data is arranged. The activation function An activation function is a small non-linear operation applied after each layer’s linear step: squashing, clipping, or reshaping the output before it moves on. Instead of z = W₂(W₁x + b ₁) + b ₂, the result becomes something like z = W₂·f(W₁x + b ₁) + b ₂, where f is a non-linear function like a sigmoid, tanh, ReLU, etc. The activation function doesn’t need to be complicated to do its job. Even ReLU, which is max(0, x), a function that just clips negative values to zero, is enough to stop the network from collapsing into a single linear transformation. That single f breaks the algebra: there’s no matrix M and vector c such that f(W₁x + b ₁) = Wx + b , for every x. Stack enough of these non-linear steps together, and the network stops being restricted to straight-line thinking; it can carve out circles, spirals, and shapes. That’s the whole purpose of an activation function, at the most fundamental level: it’s the thing standing between “a network that can only draw straight lines” and “a network that can wrap a boundary around almost any shape thrown at it.” Each neuron by itself contributes one tiny bend and a network is thousands of them, each bending things in a slightly different spot. Stack enough of them together, and the network can approximate curves and boundaries that no single neuron, or even a hundred of them, could pull off alone. How a model reads a sentence two ways Take an example: “Time flies like an arrow; fruit flies like a banana.” Read the first half and “flies” is a verb: time is moving, fast, like an arrow. Read the second half and “flies” is a noun: fruit flies are a kind of insect that seems to enjoy bananas. Same word, wildly different job, and the only thing signaling which is which is the surrounding context. A model has to somehow pull those two uses of “flies” apart into different regions of its internal representation, even though at the input level they’re the identical token. This is where depth and non-linearity earn their keep together. Because each layer starts from a different random point , each one ends up drawing its bent boundary through the data in a slightly different place. As training proceeds, this quiet divergence gets shaped into something closer to specialization. The example above is an over-simplification: real models don’t cleanly assign “this layer = nouns, that layer = verbs” in a tidy labeled way, but a loose intuition is: earlier layers could pick up on more local, surface-level patterns (word order, part of speech, etc.), while deeper layers integrate more surrounding context and start representing something closer to meaning , which sense of “flies” is active, what “it” refers to, that sort of thing. It’s specifically the bending, layer after layer, that gives the network enough room to gradually tease “time flies” and “fruit flies” apart into different corners of its representation space, instead of being stuck treating “flies” as one fixed thing no matter what’s around it. The Universal Approximation Theorem The UAT states that a feedforward neural network with a non-linear activation function and a sufficiently large hidden layer can, in principle, approximate any continuous function on a bounded domain to any desired degree of accuracy. One intuitive way to understand this capability is by imagining how networks combine many simple nonlinear components to create increasingly complex shapes and behaviors. These components can be thought of as localized building blocks, or “towers,” that each contribute a small part of the final function. By combining enough of these building blocks, a network can gradually approximate an arbitrary continuous function as closely as desired. This same principle explains why neural networks can represent everything from simple decision boundaries to the complex internal representations used in tasks such as language understanding. How it works: connecting neurons, towers, and the math To understand the whole system, look at how raw neurons turn into local pixels, and how those pixels draw a curve. 1. The core problem with single neurons A single ReLU neuron is global. Once it switches on, it never turns back off, so its influence isn’t confined to any one region; it keeps affecting every point further along the x-axis too. That means there’s no way to use a single raw neuron to nudge the curve at one specific location without that same nudge bleeding into every location past it, since the neuron has no way to turn back off and contain the effect. 2. The solution: combining neurons into localized “bumps” Each neuron sets a threshold where it fires. Combining multiple neurons creates an active window, allowing the network to control local regions without affecting the entire domain. Example, Neuron 1 (the start switch): turns on at x = 2. Neuron 2 (the stop switch): turns on at x = 4(Neuron 2 subtracts from the signal, given negative vᵢ, halting further growth past x = 4, flattening the output into a constant step). Each step represents the active window created by that pair of neurons, a range from x = 2 to x = 4 where they're allowed to influence the final answer. 3. Reading the graph On a coordinate plane, each tower maps directly to network parameters: x-axis (input range): controlled by the hidden layer’s weights (W) and biases ( b ). It specifies where the tower sits, for example active between x = 2 and x = 4. y-axis (output contribution, or height): controlled by the output layer weight (v). It specifies how tall the tower is, matching the height of the target curve at that location. While weights and biases are what set where a neuron fires, v is an output weight that determines how much that region contributes to the final answer. Source: GIF by the author. Conclusion The Universal Approximation Theorem is an existence proof, not a construction manual: it guarantees that a network capable of approximating almost any function exists somewhere in the space of possible networks, but it doesn’t say how many neurons that takes, whether gradient descent can actually find the right weights, or how well the result generalizes. Representing a function and learning it from data are two different problems, and the UAT only answers the first one. Everything modern networks actually rely on, depth, attention, residual connections, optimizers, huge datasets, careful initialization, sits entirely outside what the theorem promises. It’s not an algorithm a network runs during training, nor is it a recipe for building one. Instead, it’s the mathematical justification for why introducing non-linearity fundamentally changes what a neural network is capable of representing in the first place. AI Fundamentals: Understanding Activation Functions (Part 1) was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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