New AI Forges Video Stories in Minutes

AI startup Fable Studio is out with a new tool — dubbed ‘Showrunner,’ that long-term, promises to forge entire TV episodes and movies in minutes.

Simply enter a text prompt describing what you want, click enter, and in about 22 minutes, you’ll have your 22-minute TV episode.

Even better: You can use the tool to drop yourself into the action as a character.

Currently demoing with short clips in cartoon form, Fable Studio is promising film-like TV shows and movies in about two years, according to CEO Edward Saatchi.

In other news and analysis on AI:

*ChatGPT Rated Number One Consumer App: Venture capital firm Andreessen Horowitz has released its latest analysis of the AI market, pegging ChatGPT as the top AI app, based on unique monthly visits.

On its heels is Google’s Gemini chatbot, followed by DeepSeek – a chatbot made in China.

Also high-up on the list:

–Grok
–Character.ai
–Perplexity
–Claude

*Google’s Text-to-Talking-Heads Podcast Tool Gets an Upgrade: NotebookLM, a research and production tool that has wowed users by being able to transform an article or other text into an audio podcast featuring two people discussing the substance of that text has a new spring in its step.

Now, you can tweak the tool so that your text-to-podcast emerges in any of four formats:

–Brief, which offers-up a two-minute summary of the text
–Deep Dive, which offers an in-depth discussion of your
material
–Critique, which features talking heads critically discussing
your text
–Debate, which creates two hosts who take different points-
of-view on your article or similar

*Google’s New ‘Nano Banana’ Image Editor: Cool Use Cases: The fervor over Google’s new image editor continues to rage across the Web, as increasingly numbers of users are entranced by its power and surgical precision.

One of the new tool’s most impressive features: The ability to stay true to the identity of a human face – no matter how many times it remakes that image.

For a quick study, check-out these videos on YouTube, that show you scores of ways to use the new editor – officially known as Gemini 2.5 Flash Image:

–Google Gemini 2.5 Flash Image (Nano Banana) – 20 Creative Use Cases

–15 New Use Cases with Nano Banana

–The Ultimate Guide to Gemini 2.5- Flash (Nano Banana)

–New Gemini 2.5 Flash Image is Insane & Free

–Nano Banana Just Crushed Image Editing

*One Educator’s Take: Human-Generated Writing Still Essential in the Age of AI: College writing teacher Liz Stillwaggon Swan insists that without formal writing instruction, college students will be unleashed on the world sans the ability to think clearly and deeply.

Observes Swan: “I explain to my students that writing is a process of making the subconscious conscious—of bringing hazy, half-baked assumptions, biases, intuitions, ideas, anxieties, and hopes to the surface.

”Often, we don’t know what we believe until we start writing. We put our feelings and experiences into words and stories, even arguments, and through that arduous process, we begin to feel utterly human.”

*Another Educator’s Take: AI Has Rendered Traditional Writing Instruction Obsolete: It’s time to trash the teaching of writing at the college level as we know it, according to John Villasenor, a writing instructor at University of California Los Angeles.

Instead, today’s college students – who already know that AI will be handling most of the writing needed in years to come – should be taught how to get the most from AI when using it for writing.

Observes Villasenor: “It means helping students become proficient at using AI as a force multiplier to improve the depth, versatility, and speed of their writing.

“Today’s young people know that when it comes to writing, the technology landscape has undergone a tectonic shift, and they have already found their new footing. Those of us involved in teaching them need to do the same.”

*AI Agents and Marketing: A Primer: AI startup Smartcat.ai is offering a free eBook detailing how marketers can use multiple agents to automate much of their work.

Observes Nicole Di Nicola, VP of marketing, Smartcat.ai: “It’s like every marketer can now become a content creator. A product marketer can take a messaging doc and have AI turn it into a campaign plan with emails and sequences—fewer handoffs, less lag, more ownership. ”

One caveat: Given that AI agents are brand new technology that sometimes gets ahead of its skis, ‘pilot trial’ is the operative phrase here.

*Anthropic to Cough-Up $1.5 Billion to Book Authors: AI startup Anthropic – maker of the popular Claude chatbot – has agreed to pay $1.5 billion to authors and publishers as compensation for using their intellectual property to train its AI.

Observes writer Cade Metz: “The settlement is the largest payout in the history of U.S. copyright cases.

”Anthropic will pay $3,000 per work to 500,000 authors.”

*Google Promising AI Writing for More Android Phones: Google’s Gboard’s AI Writing Tools will be rolled-out to more phones in coming weeks, according to the tech goliath.

The tool enables users to proofread and rephrase text on their Android smartphones – without being forced to go to the cloud.

But so far, only devices featuring Gemini Nano v2 or higher are being promised the tools.

*AI BIG PICTURE: AI’s Next Killer App: Emotional Manipulation?: Geoffrey Hinton, the Godfather of AI, warns that the tech will soon be better at manipulating people than the most accomplished con man.

Observes writer Eric Hal Schwartz: Hinton “believes AI will be smarter than humans in ways that let them push our buttons, make us feel things, change our behavior, and do it better than even the most persuasive human being.

“The nightmare is an AI that understands us so well that it can change us, not by force, but by suggestion and influence.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Hollywood Killer appeared first on Robot Writers AI.

Artificial intelligence is reshaping law, ethics, and society at a speed that threatens fundamental human dignity. Dr. Maria Randazzo of Charles Darwin University warns that current regulation fails to protect rights such as privacy, autonomy, and anti-discrimination. The “black box problem” leaves people unable to trace or challenge AI decisions that may harm them.

A spinal cord injury in most vertebrates likely inhibits locomotion and induces paralysis—not so in eels. They not only possess the ability to move through water, and surprisingly, across land when intact, but can also continue to swim even if their spinal cord is severed.

Although regulating the safe use of fully autonomous vehicles across jurisdictions and international routes still has a fair bit of ironing out to do, AT providers and OEMs are making stark improvements to the technology inside our trucks.

Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers better understand the dynamics and formation of the universe.

Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers better understand the dynamics and formation of the universe.

Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of robotic arms to work together faster and smarter in busy industrial settings, potentially saving manufacturers hundreds of hours of planning time and unlocking new levels of flexibility and efficiency.

Anyone at the show who visits our booth will be able to find something relevant to their own operations that can solve a key problem and offer improved performance, lower costs, and greater sustainability.

General Motors Co. worker Annie Ignaczak spent years walking in circles on concrete factory floors, assembling the same parts and counting down hundreds of pieces she and her coworkers needed to finish before lunch.

Image provided by the authors – generated using Gemini.

For many of us, artificial intelligence (AI) has become part of everyday life, and the rate at which we assign previously human roles to AI systems shows no signs of slowing down. AI systems are the crucial ingredients of many technologies — e.g., self-driving cars, smart urban planning, digital assistants — across a growing number of domains. At the core of many of these technologies are autonomous agents — systems designed to act on behalf of humans and make decisions without direct supervision. In order to act effectively in the real world, these agents must be capable of carrying out a wide range of tasks despite possibly unpredictable environmental conditions, which often requires some form of machine learning (ML) for achieving adaptive behaviour.

Reinforcement learning (RL) [6] stands out as a powerful ML technique for training agents to achieve optimal behaviour in stochastic environments. RL agents learn by interacting with their environment: for every action they take, they receive context-specific rewards or penalties. Over time, they learn behaviour that maximizes the expected rewards throughout their runtime.

Image provided by the authors – generated using Gemini.

RL agents can master a wide variety of complex tasks, from winning video games to controlling cyber-physical systems such as self-driving cars, often surpassing what expert humans are capable of. This optimal, efficient behaviour, however, if left entirely unconstrained, may turn out to be off-putting or even dangerous to the humans it impacts. This motivates the substantial research effort in safe RL, where specialized techniques are developed to ensure that RL agents meet specific safety requirements. These requirements are often expressed in formal languages like linear temporal logic (LTL), which extends classical (true/false) logic with temporal operators, allowing us to specify conditions like “something that must always hold”, or “something that must eventually occur”. By combining the adaptability of ML with the precision of logic, researchers have developed powerful methods for training agents to act both effectively and safely.

However, safety isn’t everything. Indeed, as RL-based agents are increasingly given roles that either replace or closely interact with humans, a new challenge arises: ensuring their behavior is also compliant with the social, legal and ethical norms that structure human society, which often go beyond simple constraints guaranteeing safety. For example, a self-driving car might perfectly follow safety constraints (e.g. avoiding collisions), yet still adopt behaviors that, while technically safe, violate social norms, appearing bizarre or rude on the road, which might cause other (human) drivers to react in unsafe ways.

Norms are typically expressed as obligations (“you must do it”), permissions (“you are permitted to do it”) and prohibitions (“you are forbidden from doing it”), which are not statements that can be true or false, like classical logic formulas. Instead, they are deontic concepts: they describe what is right, wrong, or permissible — ideal or acceptable behaviour, instead of what is actually the case. This nuance introduces several difficult dynamics to reasoning about norms, which many logics (such as LTL) struggle to handle. Even every-day normative systems like driving regulations can feature such complications; while some norms can be very simple (e.g., never exceed 50 kph within city limits), others can be more complex, as in:

1. Always maintain 10 meters between your vehicle and the vehicles in front of and behind you.
2. If there are less than 10 meters between you and the vehicle behind you, you should slow down to put more space between yourself and the vehicle in front of you.

(2) is an example of a contrary-to-duty obligation (CTD), an obligation you must follow specifically in a situation where another primary obligation (1) has already been violated to, e.g., compensate or reduce damage. Although studied extensively in the fields of normative reasoning and deontic logic, such norms can be problematic for many basic safe RL methods based on enforcing LTL constraints, as was discussed in [4].

However, there are approaches for safe RL that show more potential. One notable example is the Restraining Bolt technique, introduced by De Giacomo et al. [2]. Named after a device used in the Star Wars universe to curb the behavior of droids, this method influences an agent’s actions to align with specified rules while still allowing it to pursue its goals. That is, the restraining bolt modifies the behavior an RL agent learns so that it also respects a set of specifications. These specifications, expressed in a variant of LTL (LTLf [3]), are each paired with its own reward. The central idea is simple but powerful: along with the rewards the agent receives while exploring the environment, we add an additional reward whenever its actions satisfy the corresponding specification, nudging it to behave in ways that align with individual safety requirements. The assignment of specific rewards to individual specifications allows us to model more complicated dynamics like, e.g., CTD obligations, by assigning one reward for obeying the primary obligation, and a different reward for obeying the CTD obligation.

Still, issues with modeling norms persist; for example, many (if not most) norms are conditional. Consider the obligation stating “if pedestrians are present at a pedestrian crossing, THEN the nearby vehicles must stop”. If an agent were rewarded every time this rule was satisfied, it would also receive rewards in situations where the norm is not actually in force. This is because, in logic, an implication holds also when the antecedent (“pedestrians are present”) is false. As a result, the agent is rewarded whenever pedestrians are not around, and might learn to prolong its runtime in order to accumulate these rewards for effectively doing nothing, instead of efficiently pursuing its intended task (e.g., reaching a destination). In [5] we showed that there are scenarios where an agent will either ignore the norms, or learn this “procrastination” behavior, no matter which rewards we choose. As a result, we introduced Normative Restraining Bolts (NRBs), a step forward toward enforcing norms in RL agents. Unlike the original Restraining Bolt, which encouraged compliance by providing additional rewards, the normative version instead punishes norm violations. This design is inspired by the Andersonian view of deontic logic [1], which treats obligations as rules whose violation necessarily triggers a sanction. Thus, the framework no longer relies on reinforcing acceptable behavior, but instead enforces norms by guaranteeing that violations carry tangible penalties. While effective for managing intricate normative dynamics like conditional obligations, contrary-to-duties, and exceptions to norms, NRBs rely on trial-and-error reward tuning to implement norm adherence, and therefore can be unwieldy, especially when trying to resolve conflicts between norms. Moreover, they require retraining to accommodate norm updates, and do not lend themselves to guarantees that optimal policies minimize norm violations.

Our contribution

Building on NRBs, we introduce Ordered Normative Restraining Bolts (ONRBs), a framework for guiding reinforcement learning agents to comply with social, legal, and ethical norms while addressing the limitations of NRBs. In this approach, each norm is treated as an objective in a multi-objective reinforcement learning (MORL) problem. Reformulating the problem in this way allows us to:

• Prove that when norms do not conflict, an agent who learns optimal behaviour will minimize norm violations over time.
• Express relationships between norms in terms of a ranking system describing which norm should be prioritized when a conflict occurs.
• Use MORL techniques to algorithmically determine the necessary magnitude of the punishments we assign such that it is guarantied that so long as an agent learns optimal behaviour, norms will be violated as little as possible, prioritizing the norms with the highest rank.
• Accommodate changes in our normative systems by “deactivating” or “reactivating” specific norms.

We tested our framework in a grid-world environment inspired by strategy games, where an agent learns to collect resources and deliver them to designated areas. This setup allows us to demonstrate the framework’s ability to handle the complex normative scenarios we noted above, along with direct prioritization of conflicting norms and norm updates. For instance, the figure below

displays how the agent handles norm conflicts, when it is both obligated to (1) avoid the dangerous (pink) areas, and (2) reach the market (blue) area by a certain deadline, supposing that the second norm takes priority. We can see that it chooses to violate (1) once, because otherwise it will be stuck at the beginning of the map, unable to fulfill (2). Nevertheless, when given the possibility to violate (1) once more, it chooses the compliant path, even though the violating path would allow it to collect more resources, and therefore more rewards from the environment.

In summary, by combining RL with logic, we can build AI agents that do not just work, they work right.

This work won a distinguished paper award at IJCAI 2025. Read the paper in full: Combining MORL with restraining bolts to learn normative behaviour, Emery A. Neufeld, Agata Ciabattoni and Radu Florin Tulcan.

Acknowledgements

This research was funded by the Vienna Science and Technology Fund (WWTF) project ICT22-023 and the Austrian Science Fund (FWF) 10.55776/COE12 Cluster of Excellence Bilateral AI.

References

[1] Alan Ross Anderson. A reduction of deontic logic to alethic modal logic. Mind, 67(265):100–103, 1958.

[2] Giuseppe De Giacomo, Luca Iocchi, Marco Favorito, and Fabio Patrizi. Foundations for restraining bolts: Reinforcement learning with LTLf/LDLf restraining specifications. In Proceedings of the international conference on automated planning and scheduling, volume 29, pages 128–136, 2019.

[3] Giuseppe De Giacomo and Moshe Y Vardi. Linear temporal logic and linear dynamic logic on finite traces. In IJCAI, volume 13, pages 854–860, 2013.

[4] Emery Neufeld, Ezio Bartocci, and Agata Ciabattoni. On normative reinforcement learning via safe reinforcement learning. In PRIMA 2022, 2022.

[5] Emery A Neufeld, Agata Ciabattoni, and Radu Florin Tulcan. Norm compliance in reinforcement learning agents via restraining bolts. In Legal Knowledge and Information Systems JURIX 2024, pages 119–130. IOS Press, 2024.

[6] Richard S. Sutton and Andrew G. Barto. Reinforcement learning – an introduction. Adaptive computation and machine learning. MIT Press, 1998.

For all their technological brilliance, from navigating distant planets to performing complex surgery, robots still struggle with a few basic human tasks. One of the most significant challenges is dexterity, which refers to the ability to grasp, hold and manipulate objects. Until now, that is. Scientists from the Toyota Research Institute in Massachusetts have trained a robot to use its entire body to handle large objects, much like humans do.

What follows is a grounded look at why growth will accelerate, where the value concentrates, and how organizations can capture returns while managing risk.

In everyday life, it's a no-brainer to be able to grab a cup of coffee from the table. Multiple sensory inputs such as sight (seeing how far away the cup is) and touch are combined in real-time. However, recreating this in artificial intelligence (AI) is not quite as easy.

Agentic AI is here, and the pace is picking up. Like elite cycling teams, the enterprises pulling ahead are the ones that move fast together, without losing balance, visibility, or control.

That kind of coordinated speed doesn’t happen by accident. 

In our last post, we introduced the concept of an AI gateway: a lightweight, centralized system that sits between your agentic AI applications and the ecosystem of tools they rely on — APIs, infrastructure, policies, and platforms. It keeps those components decoupled and easier to secure, manage, and evolve as complexity grows. 

In this post, we’ll show you how to spot the difference between a true AI gateway and just another connector — and how to evaluate whether your architecture can scale agentic AI without introducing risk.

Self-assess your AI maturity

In elite cycling, like the Tour de France, no one wins alone. Success depends on coordination: specialized riders, support staff, strategy teams, and more, all working together with precision and speed.

The same applies to agentic AI.

The enterprises pulling ahead are the ones that move fast together. Not just experimenting, but scaling with control.  

So where do you stand?

Think of this as a quick checkup. A way to assess your current AI maturity and spot the gaps that could slow you down:

• Solo riders: You’re experimenting with generative AI tools, but efforts are isolated and disconnected.
  
• Race teams: You’ve started coordinating tools and workflows, but orchestration is still patchy.
  
• Tour-level teams: You’re building scalable, adaptive systems that operate in sync across the organization.
  

If you are aiming for that top tier – not just running proofs of concept, but deploying agentic AI at scale — your AI gateway becomes mission-critical.

Because at that level, chaos doesn’t scale. Coordination does.

And that coordination depends on three core capabilities: abstraction, control and agility.

Let’s take a closer look at each.

Abstraction: coordination without constraint

In elite cycling, every rider has a specialized role. There are sprinters, climbers, and support riders, each with a distinct job. But they all train and race within a shared system that synchronizes nutrition plans, coaching strategies, recovery protocols, and race-day tactics.

The system doesn’t constrain performance. It amplifies it. It allows each athlete to adapt to the race without losing cohesion across the team.

That’s the role abstraction plays in an AI gateway.

It creates a shared structure for your agents to operate in without tethering them to specific tools, vendors, or workflows. The abstraction layer decouples brittle dependencies, allowing agents to coordinate dynamically as conditions change.

What abstraction looks like in an AI gateway

LLMs, vector databases, orchestrators, APIs, and legacy tools are unified under a shared interface, without forcing premature standardization. Your system stays tool-agnostic — not locked into any one vendor, version, or deployment model.

Agents adapt task flow based on real-time inputs like cost, policy, or performance, instead of brittle routes hard-coded to a specific tool. This flexibility enables smarter routing and more responsive decisions, without bloating your architecture.

The result is architectural flexibility without operational fragility. You can test new tools, upgrade components, or replace systems entirely without rewriting everything from scratch. And because coordination happens within a shared abstraction layer, experimentation at the edge doesn’t compromise core system stability.

Why it matters for AI leaders

Tool-agnostic design reduces vendor lock-in and unnecessary duplication. Workflows stay resilient even as teams test new agents, infrastructure evolves, or business priorities shift.

Abstraction lowers the cost of change — enabling faster experimentation and innovation without rework.

It’s what lets your AI footprint grow without your architecture becoming rigid or fragile.

Abstraction gives you flexibility without chaos; cohesion without constraint.

Control: manage agentic AI without touching every tool

In the Tour de France, the team director isn’t on the bike, but they’re calling the shots. From the car, they monitor rider stats, weather updates, mechanical issues, and competitor moves in real time.

They adjust strategy, issue commands, and keep the entire team moving as one.

That’s the role of the control layer in an AI gateway.

It gives you centralized oversight across your agentic AI system — letting you respond fast, enforce policies consistently, and keep risk in check without managing every agent or integration directly.

What control looks like in an AI gateway

Governance without the gaps

From one place, you define and enforce policies across tools, teams, and environments.

Role-based access controls (RBAC) are consistent, and approvals follow structured workflows that support scale.

Compliance with standards like GDPR, HIPAA, NIST, and the EU AI Act is built in.

Audit trails and explainability are embedded from the start, versus being bolted on later.

Observability that does more than watch

With observability built into your agentic system, you’re not guessing. You’re seeing agent behavior, task execution, and system performance in real time. Drift, failure, or misuse is detected immediately, not days later.

Alerts and automated diagnostics reduce downtime and eliminate the need for manual root-cause hunts. Patterns across tools and agents become visible, enabling faster decisions and continuous improvement.

Security that scales with complexity

As agentic systems grow, so do the attack surfaces. A robust control layer lets you secure the system at every level, not just at the edge, applying layered defenses like red teaming, prompt injection protection, and content moderation. Access is tightly governed, with controls enforced at both the model and tool level.

These safeguards are proactive, built to detect and contain risky or unreliable agent behavior before it spreads.

Because the more agents you run, the more important it is to know they’re operating safely without slowing you down.

Cost control that scales with you

With full visibility into compute, API usage, and LLM consumption across your stack, you can catch inefficiencies early and act before costs spiral.

Usage thresholds and metering help prevent runaway spend before it starts. You can set limits, monitor consumption in real time, and track how usage maps to specific teams, tools, and workflows.

Built-in optimization tools help manage cost-to-serve without compromising on performance. It’s not just about cutting costs — it’s about making sure every dollar spent delivers value.

Why it matters for AI leaders

Centralized governance reduces the risk of policy gaps and inconsistent enforcement.

Built-in metering and usage tracking prevent overspending before it starts, turning control into measurable savings.

Visibility across all agentic tools supports enterprise-grade observability and accountability.

Shadow AI, fragmented oversight, and misconfigured agents are surfaced and addressed before they become liabilities.

Audit readiness is strengthened, and stakeholder trust is easier to earn and maintain.

And when governance, observability, security, and cost control are unified, scale becomes sustainable. You can extend agentic AI across teams, geographies, and clouds — fast, without losing control.

Agility:  adapt without losing momentum

When the unexpected happens in the Tour de France – a crash in the peloton, a sudden downpour, a mechanical failure — teams don’t pause to replan. They adjust in motion. Bikes are swapped. Strategies shift. Riders surge or fall back in seconds.

That kind of responsiveness is what agility looks like. And it’s just as critical in agentic AI systems.

What agility looks like in an AI gateway

Agile agentic systems aren’t brittle. You can swap an LLM, upgrade an orchestrator, or re-route a workflow without causing downtime or requiring a full rebuild.

Policies update across tools instantly. Components can be added or removed with zero disruption to the agents still operating. Workflows continue executing smoothly, because they’re not hardwired to any one tool or vendor.

And when something breaks or shifts unexpectedly, your system doesn’t stall. It adjusts, just like the best teams do.

Why it matters for AI leaders

Rigid systems come at a high price. They delay time-to-value, inflate rework, and force teams to pause when they should be shipping.

Agility changes the equation. It gives your teams the freedom to adjust course — whether that means pivoting to a new LLM, responding to policy changes, or swapping tools midstream — without rewriting pipelines or breaking stability.

It’s not just about keeping pace. Agility future-proofs your AI infrastructure, helping you respond to the moment and prepare for what’s next.

Because the moment the environment shifts — and it will — your ability to adapt becomes your competitive edge.

The AI gateway benchmark

A true AI gateway isn’t just a pass-through or a connector. It’s a critical layer that lets enterprises build, operate, and govern agentic systems with clarity and control.

Use this checklist to evaluate whether a platform meets the standard of a true AI gateway.

Abstraction
Can it decouple workflows from tooling? Can your system stay modular and adaptable as tools evolve?

Control
Does it provide centralized visibility and governance across all agentic components?

Agility
Can you adjust quickly — swapping tools, applying policies, or scaling — without triggering risk or rework?

This isn’t about checking boxes. It’s about whether your AI foundation is built to last.

Without all three, your stack becomes brittle, risky, and unsustainable at scale. And that puts speed, safety, and strategy in jeopardy.

(CTA)Want to build scalable agentic AI systems without spiraling cost or risk? Download the Enterprise guide to agentic AI.

The post Not all AI gateways are built for agentic AI. Here’s how to tell. appeared first on DataRobot.

Researchers at the University of Minnesota Twin Cities have developed aerial robots equipped with artificial intelligence (AI) to detect, track and analyze wildfire smoke plumes. This innovation could lead to more accurate computer models that will improve air quality predictions for a wide range of pollutants.