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A new leap in lab automation is shaking up how scientists discover materials. By switching from slow, traditional methods to real-time, dynamic chemical experiments, researchers have created a self-driving lab that collects 10 times more data, drastically accelerating progress. This new system not only saves time and resources but also paves the way for faster breakthroughs in clean energy, electronics, and sustainability—bringing us closer to a future where lab discoveries happen in days, not years.

Move over OpenAI, Elon Musk’s new AI — dubbed Grok 4 — is now top dog.

Released last week, Grok 4 has passed all competitors in an average of key benchmark tests, as documented by ArtificialAnalysis.ai.

X (formerly Twitter) subscribers can get access to Grok 4 via chatbot at the X Premium level ($8/month) or Premium+ level ($40/month).

There’s also a seriously enhanced version of Grok 4 that goes for a cool $300/month.

In other news and analysis on AI writing:

*Grammarly Beefs-Up With AI Powered Email: AI pioneer Grammarly, which is evolving from an AI writer/proofreader into a full-fledged AI productivity suite, is adding AI-powered email to the mix.

Specifically, the AI goliath has inked a deal to acquire AI email provider Superhuman.

Superhuman “claims its users send and respond to 72% more emails per hour,” according to writer Krystal Hu.

*Research Powerhouse Perplexity Launches ‘Comet’ AI Browser: Attempting to go one better on Google’s new ‘AI Mode,’ Perplexity is out with a new browser that delivers AI summaries in response to queries.

Observes writer Maxwell Zeff: “Users can also access Comet Assistant, a new AI agent from Perplexity that lives in the web browser and aims to automate routine tasks.

“Perplexity says the assistant can summarize emails and calendar events, manage tabs and navigate web pages on behalf of users.”

*Ready or Not, Here Come The AI Browser Wars: Writer Grant Harvey offers an excellent look at the latest wrinkle in AI research: AI-powered browsers.

Besides Perplexity’s Comet AI browser, writers can now also try out the beta version of the DIA AI – and should expect an AI browser from OpenAI soon, according to Harvey.

Observes Harvey: “It’s already a three-way cage match.”

*One Researcher’s Take: Dump Perplexity for Consensus AI: Academic researcher Andy Stapleton – who is rabidly fascinated in all things AI research – advises that Perplexity users should instead opt for Consensus AI.

Consensus AI is not only faster, according to this 11-minute video from Stapleton.

Consensus AI has also come up with a way to deliver AI research results completely devoid of AI hallucinations, according to Stapleton.

*AI Agents: Still Not Ready for Prime Time?: Add Futurism magazine to the growing list of naysayers who believe AI agents are being over-hyped.

Ideally, AI agents are designed to work independently on a number of tasks for you – such as researching, writing and continually updating an article, all on its own.

But writer Joe Wilkins finds that “the failure rate is absolutely painful,” with OpenAI’s AI agent failing 91% of the time, Meta’s AI agent failing 93% of the time and Google’s AI agent failing 70% of the time.

*Google Gemini Now Transforms An Image Into Video: A new feature added to the Gemini AI chatbot now allows you to transform any image – say a headshot of yourself – into a video.

Observes writer Jess Weatherbed: “The new photo-to-video capability is powered by Google’s Veo 3 video model.

“It can transform reference images into eight-second videos complete with AI-generated audio, including background noises, environmental sounds, and speech.”

*American Federation of Teachers: We’re All-In on AI: Looks like the debate over the wisdom of using of AI in education – at least at the K-12 level in the U.S. – is over.

The American Federation of Teachers – the U.S.’ second largest teachers union – has been gifted $23 million from some of the biggest players in AI to start a National Academy for AI Instruction, based in New York City.

Observes writer Natasha Singer: “The industry funding is part of a drive by U.S. tech companies to reshape education with generative AI chatbots.”

And that.

As they say.

Is that.

*Google Releases ‘Gemini for Education:’ Google is out with a unique version of its Gemini chatbot – designed especially for students and teachers.

Observes Akshay Kirtikar, a senior product manager at Google: “Gemini for Education provides default access to our premium AI models, soon with significantly higher limits than what consumers get at no cost, plus enterprise-grade data protection and an admin-managed experience as a core Workspace service.”

*AI BIG PICTURE: Ford CEO: 50% of Jobs Will Be Wiped Away by AI: Stick a fork in it: The days of AI as a cheery collaborator are officially but a wistful memory.

Ask Ford CEO Jim Farely — the latest of industry titans of who are talking tough on AI and jobs.

Farely’s version of the unvarnished truth: As many as half of all jobs will be lost to AI.

Observes writer Craig Hale: “Dario Amodei, CEO of AI giant Anthropic, also predicted that around half of entry-level, white-collar jobs could be at risk — leading to unemployment rates 10-20% higher within five years.”

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 Elon Musk’s New AI: Number One appeared first on Robot Writers AI.

Researchers have developed an artificial intelligence (AI) system that enables a four-legged robot to adapt its gait to different, unfamiliar terrain, just like a real animal, in what is believed to be a world first. The work has been published in Nature Machine Intelligence.

Everyone’s talking about AI agents and natural language interfaces. The hype is loud, and the pressure to keep up is real.

For supply chain leaders, the promise of AI isn’t just about innovation. It’s about navigating a relentless storm of disruption and avoiding costly missteps. 

Volatile demand, unreliable lead times, aging systems — these aren’t abstract challenges. They’re daily operational risks.

When the foundation isn’t ready, chasing the next big thing in AI can do more harm than good. Real transformation in supply chain decision-making starts with something far less flashy: structure.

That’s why a practical, three-layer AI strategy deserves more attention. It’s a smarter path that meets supply chains where they are, not where the hype cycle wants them to be.

1. The data layer: build the foundation

Let’s be honest: if your data is chaotic, incomplete, or scattered across a dozen spreadsheets, no algorithm in the world can fix it. 

This first layer is about getting your data house in order. Structured or unstructured, it has to be clean, consistent, and accessible.

That means resolving legacy-system headaches, cleaning up duplicative data, and standardizing formats so downstream AI tools don’t fail due to bad inputs. 

It’s the least glamorous step, but it’s the one that determines whether your AI will produce anything useful down the line.

2. The contextual layer: teach your data to think

Once you’ve locked down trustworthy data, it’s time to add context. Think of this layer as applying machine learning and predictive models to uncover patterns, trends, and probabilities.

This is where demand forecasting, lead-time estimation, and predictive maintenance start to flourish.

Instead of raw numbers, you now have data enriched with insights, the kind of context that helps planners, buyers, and analysts make smarter decisions.

It’s the muscle of your stack, turning that data foundation into something more than an archive of what happened yesterday.

3. The interactive layer: connect humans with artificial intelligence

Finally, you get to the piece everyone wants to talk about: agents, copilots, and conversational interfaces that feel futuristic. 

But these tools can only deliver value if they stand on solid layers one and two.

If you rush to launch a chatbot on top of bad data and missing context, it’ll be like hiring an eager intern with no training. It might sound impressive, but it won’t help your team make better calls.

When you build an interactive layer on a trustworthy, well-contextualized data foundation, you enable planners and operators to work hand in hand with AI.

That’s when the magic happens. 

Humans stay in control while offloading the repetitive grunt work to their AI helpers.

Why a layered approach beats chasing shiny things

It’s tempting to jump straight to agentic AI, especially with the hype swirling around these tools. But if you ignore the layers underneath, you risk rolling out AI that fails spectacularly — or worse, quietly undermines confidence in your systems.

A three-layer approach helps supply chain teams scale responsibly, build trust, and prioritize business impact. 

It’s not about slowing down; it’s about setting yourself up to move faster, with fewer costly mistakes.

Curious how this framework looks in action?

Watch our on-demand webinar with Norfolk Iron & Metal for a deeper dive into layered AI strategies for supply chains.

The post The three-layer AI strategy for supply chains appeared first on DataRobot.

The method you choose for delivering empty cartons can significantly impact productivity and operational efficiency. Here, we explore three common methods for empty carton induction to Pick Modules, highlighting the benefits of using an overhead conveyor system.

Since early January 2025, residents of Birmingham in the UK have been caught in the dispute between the city council and the Unite union over pay, terms and conditions for waste and recycling collectors. The latest attempt at talks broke down in acrimony.

When successful artist Ai-Da unveiled a new portrait of King Charles this week, the humanoid robot described what inspired the layered and complex piece, and insisted it had no plans to "replace" humans.

Imagine a physician attempting to reach a cancerous nodule deep within a patient's lung—a target the size of a pea, hidden behind a maze of critical blood vessels and airways that shift with every breath. Straying one millimeter off course could puncture a major artery, and falling short could mean missing the cancer entirely, allowing it to spread untreated.

An interactive and powerful autonomous mobile robot for order fulfillment. Lumabot AMRs boost picking productivity and their human counterparts, intelligently moving material to the right person at the right time

A robot trained on videos of surgeries performed a lengthy phase of a gallbladder removal without human help. The robot operated for the first time on a lifelike patient, and during the operation, responded to and learned from voice commands from the team—like a novice surgeon working with a mentor.

As waiting rooms fill up, doctors get increasingly burned out, and surgeries take longer to schedule and more get canceled, humanoid surgical robots offer a solution. That's the argument that UC San Diego robotics expert Michael Yip makes in a perspective piece in Science Robotics.

The “Filics Unit” is the first product and consists of two omnidirectional robots that have been specially developed for handling Euro pallets. The system moves autonomously through the warehouse, traveling under pallets and transporting them.

Conventional robots, like those used in industry and hazardous environments, are easy to model and control, but are too rigid to operate in confined spaces and uneven terrain. Soft, bio-inspired robots are far better at adapting to their environments and maneuvering in otherwise inaccessible places.

Collaborative robotics like person-to-goods or “pick assist” AMRs are intelligently designed to work with people, not replace them. Adoption of AMRs for automated order fulfillment is turning this challenge into a competitive advantage.

Neural networks first treat sentences like puzzles solved by word order, but once they read enough, a tipping point sends them diving into word meaning instead—an abrupt “phase transition” reminiscent of water flashing into steam. By revealing this hidden switch, researchers open a window into how transformer models such as ChatGPT grow smarter and hint at new ways to make them leaner, safer, and more predictable.