Category robots

Researchers have demonstrated miniature soft hydraulic actuators that can be used to control the deformation and motion of soft robots that are less than a millimeter thick. The researchers have also demonstrated that this technique works with shape memory materials, allowing users to repeatedly lock the soft robots into a desired shape and return to the original shape as needed.

Today, researchers are developing ways to accelerate discovery by combining automated experiments, artificial intelligence and high-performance computing. A novel tool that leverages those technologies has demonstrated that AI can influence materials synthesis and conduct associated experiments without human supervision.

To help probe the mystery of how brains control movement, scientists have created a virtual rat with an artificial brain that can move around just like a real rodent. The researchers found that activations in the virtual control network accurately predicted neural activity measured from the brains of real rats producing the same behaviors.

Researchers are breaking through the difficulties of robotic recognition of various common, yet complex, items. Their layered sensor is equipped with material detection at the surface and pressure sensitivity at the bottom, with a porous middle layer sensitive to thermal changes. An efficient cascade classification algorithm rules out object types in order, from easy to hard, starting with simple categories like empty cartons before moving on to orange peels or scraps of cloth.

Researchers from North Carolina State University have demonstrated miniature soft hydraulic actuators that can be used to control the deformation and motion of soft robots that are less than a millimeter thick. The researchers have also demonstrated that this technique works with shape memory materials, allowing users to repeatedly lock the soft robots into a desired shape and return to the original shape as needed.

In the rapidly evolving landscape of Generative AI (GenAI), data scientists and AI builders are constantly seeking powerful tools to create innovative applications using Large Language Models (LLMs). DataRobot has introduced a suite of advanced LLM evaluation, testing, and assessment metrics in their Playground, offering unique capabilities that set it apart from other platforms. 

These metrics, including faithfulness, correctness, citations, Rouge-1, cost, and latency, provide a comprehensive and standardized approach to validating the quality and performance of GenAI applications. By leveraging these metrics, customers and AI builders can develop reliable, efficient, and high-value GenAI solutions with increased confidence, accelerating their time-to-market and gaining a competitive edge. In this blog post, we will take a deep dive into these metrics and explore how they can help you unlock the full potential of LLMs within the DataRobot platform.

Exploring Comprehensive Evaluation Metrics 

DataRobot’s Playground offers a comprehensive set of evaluation metrics that allow users to benchmark, compare performance, and rank their Retrieval-Augmented Generation (RAG) experiments. These metrics include:

• Faithfulness: This metric evaluates how accurately the responses generated by the LLM reflect the data sourced from the vector databases, ensuring the reliability of the information. 
• Correctness: By comparing the generated responses with the ground truth, the correctness metric assesses the accuracy of the LLM’s outputs. This is particularly valuable for applications where precision is critical, such as in healthcare, finance, or legal domains, enabling customers to trust the information provided by the GenAI application. 
• Citations: This metric tracks the documents retrieved by the LLM when prompting the vector database, providing insights into the sources used to generate the responses. It helps users ensure that their application is leveraging the most appropriate sources, enhancing the relevance and credibility of the generated content.The Playground’s guard models can assist in verifying the quality and relevance of the citations used by the LLMs.
• Rouge-1: The Rouge-1 metric calculates the overlap of unigram (each word) between the generated response and the documents retrieved from the vector databases, allowing users to evaluate the relevance of the generated content. 
• Cost and Latency: We also provide metrics to track the cost and latency associated with running the LLM, enabling users to optimize their experiments for efficiency and cost-effectiveness. These metrics help organizations find the right balance between performance and budget constraints, ensuring the feasibility of deploying GenAI applications at scale.
• Guard models: Our platform allows users to apply guard models from the DataRobot Registry or custom models to assess LLM responses. Models like toxicity and PII detectors can be added to the playground to evaluate each LLM output. This enables easy testing of guard models on LLM responses before deploying to production.

Efficient Experimentation 

DataRobot’s Playground empowers customers and AI builders to experiment freely with different LLMs, chunking strategies, embedding methods, and prompting methods. The assessment metrics play a crucial role in helping users efficiently navigate this experimentation process. By providing a standardized set of evaluation metrics, DataRobot enables users to easily compare the performance of different LLM configurations and experiments. This allows customers and AI builders to make data-driven decisions when selecting the best approach for their specific use case, saving time and resources in the process.

For example, by experimenting with different chunking strategies or embedding methods, users have been able to significantly improve the accuracy and relevance of their GenAI applications in real-world scenarios. This level of experimentation is crucial for developing high-performing GenAI solutions tailored to specific industry requirements.

Optimization and User Feedback

The assessment metrics in Playground act as a valuable tool for evaluating the performance of GenAI applications. By analyzing metrics such as Rouge-1 or citations, customers and AI builders can identify areas where their models can be improved, such as enhancing the relevance of generated responses or ensuring that the application is leveraging the most appropriate sources from the vector databases. These metrics provide a quantitative approach to assessing the quality of the generated responses.

In addition to the assessment metrics, DataRobot’s Playground allows users to provide direct feedback on the generated responses through thumbs up/down ratings. This user feedback is the primary method for creating a fine-tuning dataset. Users can review the responses generated by the LLM and vote on their quality and relevance. The up-voted responses are then used to create a dataset for fine-tuning the GenAI application, enabling it to learn from the user’s preferences and generate more accurate and relevant responses in the future. This means that users can collect as much feedback as needed to create a comprehensive fine-tuning dataset that reflects real-world user preferences and requirements.

By combining the assessment metrics and user feedback, customers and AI builders can make data-driven decisions to optimize their GenAI applications. They can use the metrics to identify high-performing responses and include them in the fine-tuning dataset, ensuring that the model learns from the best examples. This iterative process of evaluation, feedback, and fine-tuning enables organizations to continuously improve their GenAI applications and deliver high-quality, user-centric experiences.

Synthetic Data Generation for Rapid Evaluation

One of the standout features of DataRobot’s Playground is the synthetic data generation for prompt-and-answer evaluation. This feature allows users to quickly and effortlessly create question-and-answer pairs based on the user’s vector database, enabling them to thoroughly evaluate the performance of their RAG experiments without the need for manual data creation.

Synthetic data generation offers several key benefits:

• Time-saving: Creating large datasets manually can be time-consuming. DataRobot’s synthetic data generation automates this process, saving valuable time and resources, and allowing customers and AI builders to rapidly prototype and test their GenAI applications.
• Scalability: With the ability to generate thousands of question-and-answer pairs, users can thoroughly test their RAG experiments and ensure robustness across a wide range of scenarios. This comprehensive testing approach helps customers and AI builders deliver high-quality applications that meet the needs and expectations of their end-users.
• Quality assessment: By comparing the generated responses with the synthetic data, users can easily evaluate the quality and accuracy of their GenAI application. This accelerates the time-to-value for their GenAI applications, enabling organizations to bring their innovative solutions to market more quickly and gain a competitive edge in their respective industries.

It’s important to consider that while synthetic data provides a quick and efficient way to evaluate GenAI applications, it may not always capture the full complexity and nuances of real-world data. Therefore, it’s crucial to use synthetic data in conjunction with real user feedback and other evaluation methods to ensure the robustness and effectiveness of the GenAI application.

Conclusion

DataRobot’s advanced LLM evaluation, testing, and assessment metrics in Playground provide customers and AI builders with a powerful toolset to create high-quality, reliable, and efficient GenAI applications. By offering comprehensive evaluation metrics, efficient experimentation and optimization capabilities, user feedback integration, and synthetic data generation for rapid evaluation, DataRobot empowers users to unlock the full potential of LLMs and drive meaningful results.

With increased confidence in model performance, accelerated time-to-value, and the ability to fine-tune their applications, customers and AI builders can focus on delivering innovative solutions that solve real-world problems and create value for their end-users. DataRobot’s Playground, with its advanced assessment metrics and unique features, is a game-changer in the GenAI landscape, enabling organizations to push the boundaries of what is possible with Large Language Models.

Don’t miss out on the opportunity to optimize your projects with the most advanced LLM testing and evaluation platform available. Visit DataRobot’s Playground now and begin your journey towards building superior GenAI applications that truly stand out in the competitive AI landscape.

The post Empowering AI Builders with DataRobot’s Advanced LLM Evaluation and Assessment Metrics appeared first on DataRobot AI Platform.

Today's intelligent robots can accurately recognize many objects through vision and touch. Tactile information, obtained through sensors, along with machine learning algorithms, enables robots to identify objects previously handled.

Today's intelligent robots can accurately recognize many objects through vision and touch. Tactile information, obtained through sensors, along with machine learning algorithms, enables robots to identify objects previously handled.

A "compact automation" is a mechatronic system that performs a whole series of consecutive production steps autonomously. "Compact" refers to the small dimensions, in the millimeter and centimeter range, of the products to be processed.

Nightmare material or truly man's best friend? A team of researchers equipped a dog-like quadruped robot with a mechanized arm that takes air samples from potentially treacherous situations, such as an abandoned building or fire. The robot dog walks samples to a person who screens them for potentially hazardous compounds.

Nightmare material or truly man's best friend? A team of researchers equipped a dog-like quadruped robot with a mechanized arm that takes air samples from potentially treacherous situations, such as an abandoned building or fire. The robot dog walks samples to a person who screens them for potentially hazardous compounds, says the team that published its study in Analytical Chemistry. While the system needs further refinement, demonstrations show its potential value in dangerous conditions.

Scientists have created innovative soft robots equipped with electronic skins and artificial muscles, allowing them to sense their surroundings and adapt their movements in real-time.

The public release of ChatGPT and other large language models (LLMs) has allowed developers worldwide to start experimenting with these models to enhance the interactive capabilities of their own systems. Similar generalizable models for robotic manipulation, however, remain scarce.

The most pressing dilemma for the logistics industry is perhaps maximizing output levels that match consumer needs while not having enough employees taking up physically demanding tasks like parcel picking and sorting.

ChatGPT Offers More Features for Free Users

We live in a world where much of the AI we use seems downright magical — and very often, absolutely free.

ChatGPT-maker OpenAI has upped-the-ante on that trend, rolling-out a spate of even more new features that can be used for a song.

For writers, that means free access to ‘custom-GPTs’ — or custom versions of ChatGPT designed for specific writing tasks like stylized auto-writing, editing and proofing, SEO-optimization and the like.

Writers will also be able to take advantage of new ChatGPT tools for data analytics and image manipulation.

And they’ll also have access to Memory, a powerful new feature that is especially handy for writers looking to train ChatGPT to closely mimic their personal writing style — or remember all of their personal preferences as users of ChatGPT.

In other news and analysis on AI writing:

*AI for the Camera Shy: Instantly Create an Avatar Spokesperson: AI video toolmaker Captions has released a new tool that enables video-makers to create instant avatars to serve as spokespeople in short clips.

Dubbed ‘AI Creator,’ the tool also offers users the ability to customize production elements of their videos, such as camera angles, lighting, clothing for the spokesperson and a background.

Observes Gaurav Misra, CEO, Captions: “Not everyone who wants to create content also wants to be on camera. “Since our mission has always been to empower anyone to effectively communicate their stories through video, launching AI Creator feels like the natural next step.

“Now, not only can users record and edit their talking videos with Captions. (They can now) generate a talking video entirely on Captions as well.”

*Need Workshops, Seminars, Polls and More?: Just Add Text and Stir: Mentimeter has released a new AI tool that auto-creates workshops, quizzes, seminars, polls and similar content from a text prompt.

The tool works by analyzing an input prompt and crafting a ‘purpose-built presentation — following Mentimeter’s knowledge base of best practices for facilitating meetings and classes.’

Observes Niklas Ingvar, co-founder, Mentimeter: “Our customers highlight that they often lack sufficient time to level-up traditional one-way presentations into sessions that encourage active participation.

“This new capability not only saves time, but also assists users to focus on delivering impactful content and engaging their audience effectively.”

*Extra! Extra!: AI Coming to 100+ More Newsrooms: Add another 100+ news publishers to the list of news outlets that are going all in on AI.

ChatGPT-maker OpenAI has announced that it’s working with WAN-IFRA — the World Association of News Publishers — to further promulgate AI in news.

Observes Tom Ruben, a media exec at OpenAI: “This program is designed to turbo-charge the capabilities of 128 newsrooms across Europe, Asia and Latin America.”

*Canva Looking to Eat Microsoft’s and Google’s Lunch: Consumers now have an alternative to Microsoft Office and Google Workspace.

Dubbed Canva Enterprise, the productivity platform is shot-through with AI and designed to simplify work.

Observes Melanie Perkins, CEO, Canva: “In this next chapter, we’ll take the three fragmented ecosystems that organizations face—the design needs of each professional industry, the AI creation and editing tools and all the workflow products—bringing it all into one single platform.”

*James Bond, Meet AI: Spy Reports Now Shaken, Not Stirred — and Algorithmically Generated: Impressed by early gains in their use of AI to find patterns in spy-collected data, U.S. intelligence agencies are “scrambling to embrace the AI revolution,” according to writer Frank Bajak.

One example, according to Bajak: “Thousands of analysts across the 18 U.S. intelligence agencies now use a CIA-developed GenAI called Osiris.

“It runs on unclassified and publicly or commercially available data — what’s known as open-source. It writes annotated summaries and its chatbot function lets analysts go deeper with queries.”

*Flesh-Bags One, AI-Automated News Site, Zero: In a victory for mere humans, an AI news aggregator that apparently regurgitated stories from legitimate journalists — after quick, AI re-writes — has gone dormant.

The reason: Lack of oversight by human beings resulted in error-ridden content that in at least one case, severely damaged the reputation of a respected Irish talk-show host.

Observes lead writer Kashmir Hill: “Even though AI-generated stories are often poorly constructed, they can still outrank their source material on search engines and social platforms, which often use AI to help position content.

“The artificially elevated stories can then divert advertising spending — which is increasingly assigned by automated auctions without human oversight.”

*AI Inside: Google’s Chromebook Gets a Makeover: Fans of Chromebook and AI may cotton to a slew of AI features Google is integrating into the latest Chromebook.

Observes writer Nathan Ingraham: “Chromebook Plus models are getting a host of features that Google first teased last year as well as some new ones we haven’t heard about before.”

One of the handiest features for scribes is an AI-automated writer.

Observes Ingraham: “The ‘help me write’ feature Google soft-launched earlier this year is now available on all Chromebook Plus laptops.

“This should work across any text entry field you find on a Web site — whether that’s a Google product like Gmail or a site like Facebook.

“You can use it to get a prompt, or have it analyze what you’ve already written to make it more formal, or more funny.

“Basically it’s a generative text tool that you can use across the Web.”

*AI-Powered Legal Tools: Now All in One Place: Lawyers looking for the lowdown on the full spectrum of AI tools available to them now have a directory to call their own.

Offered by Artificial Lawyer and Theorem, the directory enables buyers to evaluate “a wide range of leading solutions, leverage an RFP Builder to help you match the best legal tech products with your projects, and you can take part in Theorem’s Legal Tech Stack Community to learn more about what tools the market is using.

“The goal is to improve the procurement process for finding the right legal tech tools for you.”

*AI Big Picture: Bringing New Meaning To, ‘A License to Print Money:’ AI Company Joins the $3 Trillion Club: You know you’re doing well as an AI company when you become one of only three businesses on the planet valued at $3 trillion.

AI chipmaker Nvidia did just that earlier this month — a company in the right place at the right time that arguably manufacturers the world’s most coveted and extremely powerful chips for AI applications.

For the record, Nvidia is the number two most valuable company on Earth — just behind Microsoft and a step ahead of Apple.

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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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