Archive 12.11.2024

After speaking with hundreds of AI teams across the globe, one thing is clear: the only certainty is uncertainty. 

From Boston to Dubai, I hear the same stories: AI teams feel stuck.

Leaders are forced into two bad options—build AI from scratch or buy a point solution that doesn’t quite fit. 

The trust in AI simply isn’t there yet; it’s not reliable enough for most businesses to fully depend on. 

And across the board, many teams lack the complete set of skills or expertise needed to truly deliver on AI’s promise.

At DataRobot, we understand the stakes.  Since our inception, we’ve focused on AI that drives meaningful impact and tackles tough  AI problems, which is why I am excited to announce the DataRobot Enterprise AI Suite. 

Introducing the DataRobot Enterprise AI Suite

Our commitment to helping organizations achieve meaningful outcomes with AI led us to create the enterprise AI suite—a comprehensive solution made up of customizable AI applications built on a flexible platform.

This suite is designed to give you everything you need to infuse AI into your business, secure your AI outcomes, and empower your AI teams. 

AI that works where and how your business teams want. 

We intimately understand the complex, multifaceted challenges AI teams wrestle with when trying to ensure AI fits seamlessly into stakeholder workflows, tools of choice, and exact business requirements.

That’s why we built a fully customizable suite of AI apps to address your unique business needs, making it easy to embed AI wherever your users work—whether in a standalone app, SAP applications, or tools like Slack and Microsoft Teams.

Multiple AI techniques at your fingertips.

We also appreciate that your organization’s needs aren’t one-size-fits-all. You need different AI techniques to create outcomes that make sense for your business.

Our suite gives you and your team the flexibility to apply the AI techniques that align with your business goals—whether it’s agentic flows, predictive insights, or retrieval-augmented generation (RAG)—enabling you to tackle multiple use cases with confidence.

Streamline collaboration. 

One thing we hear often is how challenging it is for teams to collaborate effectively when developing, deploying, and governing AI outcomes. Pipelines are often brittle and complex, and handing off projects from one team to another can feel like an uphill battle, making it difficult to get AI solutions into production efficiently.

To help ease this burden, we designed our AI app development platform with bespoke tools that equip each team member with what they need—without the hassle of stitching together multiple tools. 

You have the flexibility and control to integrate and work with the tools you prefer, accessing the most popular LLMs and models, or bringing your own.

And to accelerate development and delivery even further, our platform automates the most labor-intensive steps, from data prep and testing to CI/CD pipelines and governance.

Think of the efficiency, collaboration, reliability, transparency, and quality assurance DevOps has provided for software developers. 

That’s what we’re doing for AI. 

By giving everyone on your AI team the exact tooling and techniques to work how and where they want, while ensuring seamless collaboration between project hand-offs, we’re ensuring your AI model integrity is maintained from end-to-end. 

The combination of our AI app-building platform, diverse AI techniques, and fully customizable apps that integrate seamlessly into business workflows is why leading Fortune 500 companies turn to DataRobot. We’re able to help them augment their hyperscaler investments and scale AI, delivering 10x the number of use cases.

As part of our enterprise AI suite, we’ve also launched over 30 new, industry-leading features. 
You’ll gain deeper insights into what these are in future blog posts but, because there are ones I believe will be massively impactful I want to highlight a few that I’m most excited about.

Infuse AI into your business 

Despite billions invested in AI, only a third of AI teams feel equipped with the tools needed to meet their business objectives. Generative AI app experience and implementing AI into the business were two of the primary unmet needs facing teams. 

Two new features are designed to address these specific challenges.

Customizable AI apps and agents 

We’ve codified the most common technology patterns in AI use cases into fully customizable templates, drawing from a decade of applied AI experience and thousands of diverse deployments. 

This gives you and your team complete control over business logic, app logic, security, governance, and UX, allowing you to focus on outcomes instead of piecing together pipelines. 

Once built, apps can be shared as templates or added to our AI App Gallery for others to use and build upon.

One-click embedding of AI into popular business applications

Because AI isn’t always used in standalone apps, we’ve added one-click embedding for Slack, Microsoft Teams, and SAP. 

As SAP’s only AI partner of choice, we’ve made it effortless to embed your applications and monitoring agents into the SAP ecosystem. With direct access to SAP Datasphere and AI Core, your models are closer to your data, which means faster, more efficient scoring. 

Secure your AI outcomes 

For over a decade, we’ve worked with AI leaders in highly regulated industries like finance, healthcare, and government, making AI security and governance a central focus. 

This experience has shaped our approach to managing regulatory, security, and operational risks, and sharpened our understanding that as you scale, your risk exponentially grows with your AI surface area.

Now, as generative AI and agentic workflows add new layers of complexity, we’re applying our experiences to help you keep your AI solutions secure and compliant in production.

Our new industry-first tools help you ensure consistent, reliable oversight as you expand:

Regulation-ready AI and one-click generative AI compliance reports

With regulations like the EU AI Act, NYC Law No. 144, and California Law AB-2013 on the rise, compliance can be complex and time-consuming.

Our one-click generative AI compliance reports simplify this process by mapping key requirements directly to your documentation, streamlining model risk management (MRM) processes and regulatory adherence. 

And because we know that achieving compliance is only the first step, we’ve introduced governance testing and alerts to help you maintain compliance, adapt to evolving standards, and reduce manual review time.

We’ve provided compliance documentation for predictive AI for years and have seen firsthand how it speeds up MRM processes, scales operations, and increases AI solutions in production tenfold. Extending this capability to generative AI was a natural next step.

AI red-team testing and add-on observability shield 

Brand reputation and risk management are top of mind for AI leaders implementing generative AI, and manual security checks can’t always keep pace. 

To address this, our new red-team testing flags vulnerabilities like PII leaks, prompt injections, toxicity, and bias, so you can maintain safety and compliance with less effort.

In addition, our new add-on observability offers real-time monitoring for any generative AI application built off the DataRobot platform. With just a single line of code, you can:

• Create an AI firewall that delivers observability, intervention, and moderation across all your agentic and generative AI solutions.
  
  
• Apply enterprise governance, real-time oversight, and moderation to components like LLMs, vector databases, and RAG flows.
  

Empower your AI teams

Unlocking AI’s full potential requires overcoming technical hurdles that can stretch model development to an average of seven months. And with most AI projects passing through five different teams, using an average of seven different tools and twelve coding languages, the process is often fragmented and complex.

This fractured approach leaves teams tangled in tooling rather than driving results and expanding their skills. 

We’ve always prioritized giving teams the flexibility to work in the environments they prefer, with smooth transitions between code and GUI, predictive and generative. 

That’s why we’ve prioritized giving teams flexibility to work in their preferred environments, ensuring smooth transitions between code and GUI, predictive and generative models. 

Our latest release goes even further, with new building blocks that simplify the development process from start to finish.

• Coding blocks: These highly scalable coding blocks eliminate code complexity, streamline collaboration, accelerate production, and simplify maintenance over time—creating a seamless path for building end-to-end AI applications.
  
  
• Automated data preparation: We’ve minimized the manual and complex work needed to make data AI-ready for both predictive and generative applications. Enhanced functionality, including automated data healing, insights visualization, and feature detection help you move from raw data to feature engineering and modeling in minutes instead of hours.
  
  
• Multimodal incremental learning and unstructured data processing: With our new incremental learning across different data types, your teams can finally unlock the full potential of your organization’s data. This unique approach empowers teams to tackle a wide range of AI use cases without limitations on data size or type, process data efficiently, prevent overfitting, improve accuracy, and control compute costs.
  

In addition, built-in optical character recognition (OCR) transforms unstructured documents into generative AI-ready data, accelerating model training and enhancing outcomes.

AI that works for your business

The future of AI is about more than just technology — it’s about solving real business challenges in a way that makes sense for your organization. 

Imagine AI seamlessly integrated into your workflows, evolving with your needs, and empowering your teams to make smarter decisions faster.

This is just the beginning. With the right tools and techniques, you can unlock new potential, streamline processes, and drive real value. 

The path forward is full of possibilities, and the next step is yours to take.

To learn more, check out DataRobot’s full suite of products.

The post The DataRobot Enterprise AI Suite: driving the next evolution of AI for business appeared first on DataRobot.

Today is an exciting and historic day at DataRobot. As Debanjan shared, it’s time for a new approach to AI. One that is focused on business outcomes. 

With the rapid advances in machine learning and generative AI, you’re likely seeing AI become essential to your strategy and bottom line. It’s more critical than ever to integrate AI into your business to stay competitive. 

This requires an evolution in what we deliver with our enterprise AI suite and how we tell our story at DataRobot — carrying our history of innovation and impact forward in a new way that meets the AI needs of businesses today.

Our mission

At DataRobot, our mission is to deliver AI that maximizes impact and minimizes risk for your business.  

And the way we do that is by focusing on what matters most to you — by making AI work for your business, not making your business work for AI.

We infuse AI into your business. We do this by helping organizations seamlessly integrate AI into their existing processes with an open platform, user-friendly tooling, and high-impact applications. 

We secure your AI outcomes. We do this by enabling organizations to scale AI with confidence using built-in interoperability, governance, and observability capabilities.

We empower your AI teams. We do this by maximizing team impact with AI workflows, built-in collaboration, orchestration, and a rich array of capabilities that unite models, teams, and individual needs. 

Our new look

While our mission is one component of the DataRobot story, how we show up every day is also key to who we are and our brand.  

Today, we unveiled a new visual identity:

Our new logo is made up of building blocks that illustrate many meanings: simplifying AI complexity into actionable insights; weaving and infusing AI into your business; and the coming together of teams, processes, and technology that businesses want and need. 

We’ve also introduced a new palette of bright, fresh colors that represent clarity and match the energy of market momentum and innovation at DataRobot. 

And a new typography system that balances technical innovation and approachability.

Our vision

DataRobot was founded to make AI useful and accessible to businesses in every industry.

For 12 years, we have spent each day working and learning from our customers and industry experts.

As we look ahead, our vision is to make business better with AI.

To our customers and partners, thank you for your partnership. We wouldn’t be here without you, and we’re thrilled to continue innovating together.

To our team, past and present, thank you for the contributions that continue to make DataRobot what it is today.

To learn more about our new approach to AI: 

• Register for our upcoming webinars, “Infuse AI into your business: How to deliver the AI applications your end users need” and  “Secure your AI outcomes: eliminate AI risks and scale with confidence”
• Read our blog, “The DataRobot Enterprise AI Suite: Driving the next evolution of AI for business”
• Tour the product

The post The next evolution of AI for business: our brand story appeared first on DataRobot.

This is the first time worldwide that the technological solution, based on robotics and advanced computing, developed by Comau, has been tested in real installation conditions in an EDP solar park.

We introduce Anthology, a method for conditioning LLMs to representative, consistent, and diverse virtual personas by generating and utilizing naturalistic backstories with rich details of individual values and experience.

What does it mean for large language models (LLMs) to be trained on massive text corpora, collectively produced by millions and billions of distinctive human authors?

In “Language Models as Agent Models”, compelling evidence suggests that recent language models could be considered models of agents: provided with a textual context, LLMs are capable of generating conditional text that represents the characteristics of an agent likely to have produced that context. This suggests that, with appropriate conditioning, LLMs could be guided to approximate the responses of a particular human voice, rather than the mixture of voices that otherwise emerges. If realized, this capability of LLMs would have significant implications for user research and social sciences—conditioned language models as virtual personas of human subjects could serve as cost-effective pilot studies and supporting best practices in human studies, e.g. the Belmont principles of justice and beneficence.

In this work, we introduce Anthology, an approach for steering LLMs to representative, consistent, and diverse virtual personas by providing richly detailed life narratives of individuals as conditioning context to models. Read More

Study findings could serve as a foundation for broader neuro-monitoring applications across intensive care units globally.

A research team has recently published a study titled "Topology, dynamics, and control of a muscle-architected soft arm," in Proceedings of the National Academy of Sciences. The paper, which made the cover, describes an unprecedented computational model that captures the intricate muscular architecture of an octopus arm.

Research describes a computational model that captures the intricate muscular architecture of an octopus arm.

A robot, trained for the first time by watching videos of seasoned surgeons, executed the same surgical procedures as skillfully as the human doctors, say researchers.

A robot, trained for the first time by watching videos of seasoned surgeons, executed the same surgical procedures as skillfully as the human doctors.

The new TMS320F28P55x series of C2000™ MCUs with an integrated edge artificial intelligence (AI) hardware accelerator enables smarter real-time control, with up to 99% fault detection accuracy.

Too Many ‘Major AI Product Releases’ Not Ready for Prime Time

Back in my garage-band playing days, I remember turning to the group’s rhythm guitarist during a rehearsal and letting him know that his top strings were all flat.

To which he replied — with a toothy grin — “Close enough for Rock ‘n Roll!”

Unfortunately, that completely juvenile, “I”m-to-cool-to-give-a-damn?” swagger has been cropping-up all over the AI marketplace lately.

Last week, for example, ChatGPT-maker OpenAI released a new search engine to the world that some are heralding as a ‘Google-killer.’

But many people who actually used the search engine quickly discovered that the ‘fairy-dust-from-the-future’ was confidently bringing back text summaries of searches that were simply wrong.

Moreover, medical users of OpenAI’s Whisper transcription app are finding out that the tool — in some cases — is inserting ‘invented facts’ into the transcriptions.

Meaning that if a doctor has diagnosed someone with cancer, the resulting Whisper transcription may ‘invent’ a fact that contradicts the doctor’s diagnosis — or ‘invent’ a treatment that is not recommended for that form of cancer.

Oops.

Sadly, other — normally highly respected names in Big Tech — are also playing the same game.

Google’s recently popular NotebookLM, for example, has been hailed by some as ‘insanely magical’ for its ability to scrutinize a text document and then quickly auto-generate an audio discussion about that document by two, extremely human-sounding robotic voices.

The only problem: Turns-out, those cheery robotic voices also gleefully make-up facts not found in the source text.

And let’s not get started on Google Gemini’s initially bungled release of Gemini’s imaging capability back in February, which depicted America’s founding fathers — and Nazis — as racial minorities.

Meanwhile, even Apple is getting into the act.

In late October, the company breathlessly unveiled its supposedly ‘game-changing,’ much anticipated AI software update, dubbed ‘Apple Intelligence,’ which according to some, was destined to remake the world as we know it.

Instead, users quickly learned the ‘wunderkind’ AI writing and editing tools on board Apple Intelligence were actually much weaker versions of what you can get with the latest paid version of ChatGPT at 20-bucks-a-month.

Bottom-line: While many who follow tech closely are well aware of the Silicon Valley ethic, ‘Move Fast, Break Things and Apologize Afterwards’ we’ve reached a point where that bravado is endangering lives — and seriously eroding the public’s confidence in AI.

For example: Should we really be forced to put-up with a product used in a medical setting that could write down the wrong diagnosis and recommend the wrong treatment?

Should we really allow a product to stay on the market, even in experimental form, that auto-generates fictional interpretations of text documents — without an accompanying warning label?

Should we really be in awe of one of the top five most valuable companies on the planet, which pretends to release a ‘bleeding-edge,’ AI editing and writing tool — only to learn the app is actually generations behind the state-of-the-art?

No.

We shouldn’t.

Don’t get me wrong: I am in awe of many AI products that are truthfully marketed and advertised.

For example: I think OpenAI’s flagship product, ChatGPT, is an amazing tool for auto-writing and myriad other uses.

And I admire the fact that ChatGPT’s maker, OpenAI, has — from the very beginning — included a highly prominent warning label on the ChatGPT Web site that unequivocally declares the tool is prone to making-up facts.

But when the reverse is true, and we come across AI companies that are repeatedly releasing AI tools on the market that they fully realize are deeply flawed — and in some cases, even life-threatening — we have no choice but to brand them as who they really are:

Charlatans.

In other news and analysis on AI writing:

*The Waiting is the Hardest Part: No GPT-5 for 2024: Avid fans of ChatGPT — present company included — learned with some remorse that the tool will not be upgraded for a while.

That’s a blow to writers, given that the current version — ChatGPT-4 — seems to be best overall version of OpenAI’s software options for creative and nonfiction writing.

A major update would have most likely made it even better by far.

Still, we can hope for an update in 2025.

*Sweet Nothings: When ‘Whisper’ Medical Transcriptions Become Creative Writing: In a disturbing finding, many researchers are finding that Whisper — a transcription tool from ChatGPT-maker OpenAI — is making-up facts.

Observes lead writer Garance Burke: “Experts said that such fabrications are problematic because Whisper is being used in a slew of industries worldwide to translate and transcribe interviews, generate text in popular consumer technologies and create subtitles for videos.

“More concerning, they said, is a rush by medical centers to utilize Whisper-based tools to transcribe patients’ consultations with doctors.”

*Whisper Alternative Otter.ai Apparently Sticks to the Script: Writer Radhika Rajkumar advises that users of transcription tool Whisper — which has been found to make-up facts in the transcriptions it renders — should use Otter.ai instead.

Observes Rajkumar: “While you’re waiting for OpenAI to resolve the issue, we recommend trying Otter.ai, a journalist-trusted AI transcription tool.”

*Notion: Promising an AI Email Inbox That Thinks Like You: Notion is promising to deliver a new AI-powered app in early 2025 that will highly automate and customize every facet of your email experience.

Observes writer Emma Roth: “Much like Notion’s other tools, the company says Mail will distill email down to its building blocks, allowing you to create an inbox with views, layouts and actions tailored to your preferences.

“You can also use Notion AI to automatically organize, archive, or draft emails based on a prompt.”

*Google’s Gemini Comes to Gmail-on-the-Web: Leaving no stone unturned, Google has decided to offer AI help when you’re writing emails with Gmail on the Web.

Observes writer Emma Roth: “In addition to generating an email draft, ‘Help me write’ can also provide suggestions on how to formalize, elaborate, or shorten a message.

“Google’s ‘Help me write’ feature is only available to users who subscribe to Google One AI Premium or have the Gemini add-on for Workspace.”

*Microsoft Notepad Gets the AI Treatment: Maybe Even Your Grocery List Will Read Like Poetry: Like many other tech titans, Microsoft continues to make good on its intention to embed AI everywhere.

This time, AI is coming to its Notepad app.

Dubbed ‘Rewrite,’ the new feature “promises to spruce-up your text with the help of AI.

“Using an AI model called GPT, Rewrite can revise sentences, modify the tone, or alter the length of your text,” according to writer Lance Whitney.

*Claude Comes to Your Desktop: Because Browser AI is So 2024: Users of Claude — a top alternative to ChatGPT — can now work with the ‘auto-writer and more’ directly from Windows and Mac desktops.

Observes writer Lance Whitney: “The new apps work similarly to the Web site and are available for free users and paid subscribers.

“For now, the apps are tagged with a beta label, which may indicate that Anthropic is still tweaking them.”

*Living the AI Dream: Reducing Email Reading Time By 97%: Users of AI-powered data-analysis tool Snowflake report that the platform is saving companies significant time by auto-reading emails.

Case in point: Thomas Bodenski, CEO, TS Imagine, who reports that he’s using Snowflake’s AI to scan incoming emails for ‘crucial, actionable events.’

The result: Bodenski has reduced the time needed to process, understand and act on those emails by 97%.

*AI Big Picture: AI Now ‘Pitch Perfect’ for Most Marketers: A new study from The University of Pennsylvania finds that 62% of workers in marketing and sales are now using AI as a core tool.

Observes Stefano Puntoni, a marketing professor at the university: “Generative AI has rapidly evolved from a tool of experimentation to a core driver of business transformation.

“Companies are no longer just exploring AI’s potential.

“They are embedding it into their strategies to scale growth, streamline operations and enhance decision-making.”

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 Close Enough for Rock ‘n Roll! appeared first on Robot Writers AI.

Robots are supposed to do boring or unpleasant jobs for us. However, tedious tasks such as cleaning the bathroom are challenging to automate. How is it possible to calculate the movement of a robot arm so that it can reach every part of a washbasin? What if the basin has unusually curved edges? How much force should be applied at which point?

A research team, led by Professor Hoon Eui Jeong from the Department of Mechanical Engineering at UNIST has introduced an innovative magnetic composite artificial muscle, showcasing an impressive ability to withstand loads comparable to those of automobiles. This material achieves a stiffness enhancement of more than 2,700 times compared to conventional systems. The study is published in Nature Communications.

New Project GR00T workflows and AI world model development technologies to accelerate robot dexterity, control, manipulation and mobility.

Claire chatted to Pratap Tokekar from the University of Maryland about how teams of robots with different capabilities can work together.

Pratap Tokekar is an Associate Professor in the Department of Computer Science and the Institute for Advanced Computer Studies at the University of Maryland, and an Amazon Scholar. Previously, he was a Postdoctoral Researcher at the GRASP lab of University of Pennsylvania and later, an Assistant Professor at Virginia Tech. He has a degree in Electronics and Telecommunication from the College of Engineering Pune in India and a Ph.D. in Computer Science from the University of Minnesota. He received the Amazon Research Award in 2022, and the NSF CAREER award in 2020.