Category robots in business

AI-powered algorithm can analyze video recordings of clinical sleep tests and more accurately diagnose REM sleep behavior disorder.

Interpolation is a mathematical technique used to estimate unknown values that lie between known data points. Interpolation helps transform raw sensor data into stunning, full-color images in embedded vision systems.

Researchers at Stanford University, Princeton University, and Dexterity recently developed TidyBot++, a holonomic mobile robot that can perform various household chores and could help to train or test new algorithms for robotics applications.

Facing high employee turnover and an aging population, nursing homes have increasingly turned to robots to complete a variety of care tasks, but few researchers have explored how these technologies impact workers and the quality of care. A new study on the future of work finds that robot use is associated with increased employment and employee retention, improved productivity and a higher quality of care.

A new artificial intelligence tool combines data from medical images with text to predict cancer prognoses and treatment responses.

Scientists have invented compact wearable devices that deliver rich, expressive, and pleasant tactile sensations that go far beyond the buzzing vibrations of today's consumer devices.

Developing and managing AI is like trying to assemble a high-tech machine from a global array of parts. 

Every component—model, vector database, or agent—comes from a different toolkit, with its own specifications. Just when everything is aligned, new safety standards and compliance rules require rewiring.

For data scientists and AI developers, this setup often feels chaotic. It demands constant vigilance to track issues, ensure security, and adhere to regulatory standards across every generative and predictive AI asset.

In this post, we’ll outline a practical AI governance framework, showcasing three strategies to keep your projects secure, compliant, and scalable, no matter how complex they grow.

Centralize oversight of your AI governance and observability

Many AI teams have voiced their challenges with managing unique tools, languages, and workflows while also ensuring security across predictive and generative models. 

With AI assets spread across open-source models, proprietary services, and custom frameworks, maintaining control over observability and governance often feels overwhelming and unmanageable. 

To help you unify oversight, centralize the management of your AI, and build dependable operations at scale, we’re giving you three new customizable features:

1. Bolt-on observability

As part of the observability platform, this feature activates comprehensive observability, intervention, and moderation with just two lines of code, helping you prevent unwanted behaviors across generative AI use cases, including those built on Google Vertex, Databricks, Microsoft Azure, and open-sourced tools.

It provides real-time monitoring, intervention and moderation, and guards for LLMs, vector databases, retrieval-augmented generation (RAG) flows, and agentic workflows, ensuring alignment with project goals and uninterrupted performance without extra tools or troubleshooting.

2. Advanced vector database management

With new functionality, you can maintain full visibility and control over your vector databases, whether built in DataRobot or from other providers, ensuring smooth RAG workflows.

Update vector database versions without disrupting deployments, while automatically tracking history and activity logs for complete oversight.

In addition, key metadata like benchmarks and validation results are monitored to reveal performance trends, identify gaps, and support efficient, reliable RAG flows.

3. Code-first custom retraining

To make retraining simple, we’ve embedded customizable retraining strategies directly into your code, regardless of the language or environment used for your predictive AI models.

Design tailored retraining scenarios, including as feature engineering re-tuning and challenger testing, to meet your specific use case goals.

You can also configure triggers to automate retraining jobs, helping you to discover optimal strategies more quickly, deploy faster, and maintain model accuracy over time. 

Embed compliance into every layer of your generative AI 

Compliance in generative AI is complex, with each layer requiring rigorous testing that few tools can effectively address.

Without robust, automated safeguards, you and your teams risk unreliable outcomes, wasted work, legal exposure, and potential harm to your organization. 

To help you navigate this complicated, shifting landscape, we’ve developed the industry’s first automated compliance testing and one-click documentation solution, designed specifically for generative AI. 

It ensures compliance with evolving laws like the EU AI Act, NYC Law No. 144, and California AB-2013 through three key features:

1. Automated red-team testing for vulnerabilities

To help you identify the most secure deployment option, we’ve developed rigorous tests for PII, prompt injection, toxicity, bias, and fairness, enabling side-by-side model comparisons.

2. Customizable, one-click generative AI compliance documentation

Navigating the maze of new global AI regulations is anything but simple or quick. This is why we created one-click, out-of-the-box reports to do the heavy lifting.

By mapping key requirements directly to your documentation, these reports keep you compliant, adaptable to evolving standards, and freedom from tedious manual reviews.

3. Production guard models and compliance monitoring

Our customers rely on our comprehensive system of guards to protect their AI systems. Now, we’ve expanded it to provide real-time compliance monitoring, alerts, and guardrails to keep your LLMs and generative AI applications compliant and safeguard your brand.

One new addition to our moderation library is a PII masking technique to protect sensitive data.

With automated intervention and continuous monitoring, you can detect and mitigate unwanted behaviors instantly, minimizing risks and safeguarding deployments.

By automating use case-specific compliance checks, enforcing guardrails, and generating custom reports, you can develop with confidence, knowing your models stay compliant and secure.

Tailor AI monitoring for real-time diagnostics and resilience

Monitoring isn’t one-size-fits-all; each project needs custom boundaries and scenarios to maintain control over different tools, environments, and workflows. Delayed detection can lead to critical failures like inaccurate LLM outputs or lost customers, while manual log tracing is slow and prone to missed alerts or false alarms.

Other tools make detection and remediation a tangled, inefficient process. Our approach is different.

Known for our comprehensive, centralized monitoring suite, we enable full customization to meet your specific needs, ensuring operational resilience across all generative and predictive AI use cases. Now, we’ve enhanced this with deeper traceability through several new features.

1. Vector database monitoring and generative AI action tracing

Gain full oversight of performance and issue resolution across all your vector databases, whether built in DataRobot or from other providers.

Monitor prompts, vector database usage, and performance metrics in production to spot undesirable outcomes, low-reference documents, and gaps in document sets.

Trace actions across prompts, responses, metrics, and evaluation scores to quickly analyze and resolve issues, streamline databases, optimize RAG performance, and improve response quality.

2. Custom drift and geospatial monitoring

This enables you to customize predictive AI monitoring with targeted drift detection and geospatial tracking, tailored to your project’s needs. Define specific drift criteria, monitor drift for any feature—including geospatial—and set alerts or retraining policies to cut down on manual intervention.

For geospatial applications, you can monitor location-based metrics like drift, accuracy, and predictions by region, drill down into underperforming geographic areas, and isolate them for targeted retraining.

Whether you’re analyzing housing prices or detecting anomalies like fraud, this feature shortens time to insights, and ensures your models stay accurate across locations by visually drilling down and exploring any geographic segment.

Peak performance starts with AI that you can trust 

As AI becomes more complex and powerful, maintaining both control and agility is vital. With centralized oversight, regulation-readiness, and real-time intervention and moderation, you and your team can develop and deliver AI that inspires confidence. 

Adopting these strategies will provide a clear pathway to achieving resilient, comprehensive AI governance, empowering you to innovate boldly and tackle complex challenges head-on.

To learn more about our solutions for secure AI, check out our AI Governance page.

The post New AI governance solutions for trust, security, and compliance appeared first on DataRobot.

Amazon, which pioneered same-day delivery, uses an AI system that requires digesting a huge amount of data to determine the best routes to avoid traffic and get the driver to your home in the fastest possible time.

In research published in Advanced Intelligence Systems, scientists have developed an innovative, soft, wearable robot to help workers avoid job-related injuries while lifting, lowering, and carrying objects.

The ANYmal robot addresses key challenges in industrial operations by offering autonomous, highly mobile inspection capabilities in complex and hazardous environments. Its four-legged design enables navigation through uneven terrains, stairs, and confined spaces.

Humans and animals are the key inspiration for many robotic systems developed to date, as they possess body structures that innately support efficient locomotion. While many bipedal (i.e., two-legged) robots are humanoids, meaning that their body resembles that of humans, others draw inspiration from other animals that walk on two legs, such as ostriches and some other birds.

Researchers have harnessed artificial intelligence to take a key step toward slashing the time and cost of designing new wireless chips and discovering new functionalities to meet expanding demands for better wireless speed and performance.

Scientists have developed an advanced swarm navigation algorithm for cyborg insects that prevents them from becoming stuck while navigating challenging terrain.

Elephant Robotics launches academic research collection on Cobots, AGVs, and Mobile Robots from prestigious global universities.

ChatGPT Can Approximate — But Not Completely Mimic — Your Writing Style

Here’s the truth: While ChatGPT can mimic your writing style to some extent, the AI is not yet able to offer you an exact, 100% match of the way you choose your words — at least for now.

That said, unless you’re a professional writer — or someone who simply loves words with abandon — you may be perfectly satisfied with a quick, down-and-dirty prompt that mimics the broad strokes of your writing style in a ‘good enough’ way.

For example, if you’re not overly picky, you can use a down-and-dirty, write-like-me prompt using these words: “You are a world-class writer — with an irreverent sense of humor — known for clear, concise, colorful prose. Please rewrite the text following the colon using no less than 300 words and no more than 315 words:”

Such a prompt should be more than adequate for you –unless you’ve found yourself unable to sleep some nights because you’re tortured by a phrase you know should have been written just a bit differently.

Essentially: If you are among the easy-going-ilk, you can use the above prompt — or something similarly brief that better reflects your writing style– skip the rest of this post and saunter happily away, snickering at the rest of us.

However, if you’re like me and you often derive a deep, dark, twisted — and some might say concerningly disturbed — pleasure in agonizing over the wording, feel, cadence or some other highly esoteric feature associated with a single sentence, a single phrase — or even a single word — I’m afraid a down-and-dirty prompt won’t work for you.

Put another way: If you’ve suffered the fate of being a ‘born writer’ or a ‘born word-lover,’ you’ll need a much more sophisticated prompt to get you within shouting distance of what you consider to be your highly personal writing style.

For the record, the reason why ChatGPT is not yet able to offer you an exact, 100% match of your writing style is rooted in the method the AI uses to learn your writing style.

Specifically: ChatGPT learns to mimic your writing style by:

*Analyzing one or more samples of your writing

*Assembling of a list of generalized descriptors that it believes characterizes your work

*Referring to that list of generalized descriptors when you ask it to auto-write an email — or other text — in your writing style

The problem with ChatGPT’s approach: While resorting to assembling a list of general descriptors to characterize your writing style takes a decent stab at defining a highly personal writing style, its methodology unfortunately falls short — by its inherent design — of being able to fully mimic a highly personal writing style.

For example, ChatGPT may analyze a number of examples of your writing and conclude that one of its key features is that it’s ‘witty.’

But the problem with that descriptor is that witty is a generic term that applies to any number of variations of wit.

Robin Williams is witty.

But so is Mae West, George Carlin, Maria Bamford, Dave Chapelle, Ali Wong, Ricky Gervais, Amy Schumer and Eddie Izzard.

But as we all know, no one would ever mistake the wit of Robin Williams for the wit of Maria Bamford, confuse Dave Chappelle with Ali Wong, or listen to Amy Schumer and think, “Hmm, she sounds just like Eddie Izzard.”

Each of these world-famous comedians have etched their unique, comic perspectives on the world.

And that is the reason, in great part, why these masters of wit are so famous: They are witty like no one else on earth.

Unfortunately, this problem of using the generalized descriptor of ‘witty’ is compounded exponentially by the fact that ChatGPT also uses other, equally general and equally generic descriptors of your writing after reading a few samples of what you consider to be your best stuff.

For example: After analyzing your writing, ChatGPT may also conclude that your highly personal writing style is gripping, evocative, persuasive, authoritative — as well as any number of other adjectives that can be used to characterize what you’ve written.

And again, those descriptors do get ChatGPT closer to describing your singular, highly personal writing style.

But in the real world, as we know — and as we’ve seen with the characterization of ‘witty’ — there are countless shades of meaning that these descriptors are attempting to capture.

That said, with the right prompt that you personally fabricate, ChatGPT can still offer you a decent approximation of your writing style — which you can use as a strong draft of text that you subsequently polish.

Plus, given that ChatGPT has become increasingly more powerful and more refined with each new revision, there’s a chance that someday, ChatGPT may become so powerful and so perceptive, it may in fact be able to analyze your writing style with an unmatched, piercing, nano-focused insight — and then mimic your highly personalized writing style with breathtaking precision.

In the meantime, the good news is that getting ChatGPT to auto-write in a style that is a reasonable approximation of your writing style is fairly straightforward.

Here’s a quick summary of the steps:

Step One: Ask ChatGPT to analyze one or more samples of your writing style and generate a prompt to be used to mimic your writing style (which you’ll refine further).

Step Two: Take note of the descriptors ChatGPT uses in its report to you that serve as the basis of the prompt it created.

Step Three: Ask ChatGPT to run a second analysis of the descriptor categories it missed that you’d like included in its characterization of your writing style.

Step Four: Edit ChatGPT’s prompt to your liking.

Step Five: Test the prompt.

Step Six: Revise the prompt as needed.

Give it a shot — and then when ChatGPT comes out with its promised upgrade early this year, give it another shot.

You may get much better results the second time around.

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–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.

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