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.
DataRobot Playground
Begin Your Journey Towards Building Superior GenAI Applications
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-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.
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.”
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.”
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.”
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.”
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.”
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.”
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 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.
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.
AI Technology Is Redefining the Manufacturing Sector
Best Ways AI Is Improving Manufacturing In 2024
Artificial Intelligence technology has the power to automate and revolutionizing the entire manufacturing ecosystem. From automating routine tasks, prototype designs and development, robot deployment and maintenance, and supply chain automation to facility remote monitoring, everything can be automated seamlessly by implementing AI in manufacturing.
To be competitive with other manufacturing companies, organizations need to accelerate with AI transformation and speed up smart manufacturing operations. While it may sound intimidating, Artificial Intelligence in manufacturing is also exciting, and it doesn’t go away.
The below figure depicts the market growth of AI technology in manufacturing industry for a period of 2015 to 2025.
Source: Markets and Markets
Top AI Applications In Manufacturing Industry
How AI transforms manufacturing?
Let’s continue reading to know where AI is being applied in the manufacturing sector and how AI works in manufacturing. All the below AI use cases in manufacturing will ensure the best business outcomes to the companies. Let’s take a look at How AI technology redefining the manufacturing industry?
1.AI Virtual Assistants Bots
It is one of the best use cases of AI in manufacturing industry. AI virtual assistants better communicates with workforce, solve queries, perform assigned tasks, and offer personalized recommendations, etc. Using Natural Language Processing capabilities, AI manufacturing applications deliver human-like responses and executes every task faster, efficient with 100% accuracy.
2. Predictive Maintenance and Analytics
The best answer is predictive maintenance. Yes. Adoption of AI technology in manufacturing industry will assist manufacturers in monitor and analyze the device performance and predict the need of overhaul or maintenance services. Such automated process will prevent the equipment breakdowns and ensure efficient productivity. It is one of the top benefits of AI in manufacturing industry.
Using Artificial intelligence in manufacturing industry, Manufacturers can tracks operational efficacy and derives accurate predictions into maintenance.
Hence, Artificial Intelligence solutions processes device data, learns from experiences, identifies patterns, and finds out the possibilities of damages before the product hauls. This kind of AI applications in manufacturing industry will improve productivity and ensures lower maintenance costs.
3.Process Automation
Many routine tasks in a manufacturing plant will be automated with the trending AI. Intelligent AI applications in manufacturing industry will automate approximately 75% of routine tasks done by admins, managers, and operational department workforce.
From data entry, reports generation, employee monitoring, sales entries, order management, warehouse management, and inventory management to logistics and distribution operations data can be automated and virtually accessed through a centralized cloud platform. Hence, the importance of AI technology in manufacturing industry is paramount.
4. Facial Recognition Apps
Facial recognition is one of the core technology of AI. The use of AI in manufacturing industry for ensuring high-level security standards inside the production premises is on demand. In the manufacturing industry, AI applications with facial recognition capabilities can be used for quality and security purposes.
Facial Recognition System concept.
5. Fraud Detection
AI in manufacturing industry will be used for detecting fraudulent actions in the production, warehouse, and point of sales operations. Manufacturers can monitor identical anomalies to flag customer orders outside of normal patterns. The use of AI technology in manufacturing industry will also greatly helpful for organizations in monitoring the facilities and find irregular activities remotely.
These are the most popular and well-defined applications of AI in manufacturing. The future of artificial intelligence in manufacturing industries has bright scope and lets manufacturers grab the digital opportunities.
Many industry scholars agree that AI has a big impact on all businesses, especially the manufacturing sector. IT service management company, Gartner said that by 2020, the number of users of advanced analytics platforms would be differentiated by increased data discovery capabilities, which will double the rate and double the business value”.
AI is embedded in several technologies, from networked supply chains using predictive analytics to ERP solutions with built-in-BI capabilities.
In a thought to give you a clear idea, here we are taking a reputed manufacturing companyas an example, which has introduced AI to redefine manufacturing analytics.
Sight Machine Launched AI to Redefine Manufacturing Analytics
Sight Machine, a manufacturing Analytics Company, located in San Francisco, aims to disrupt the manufacturing industry by offering an Artificial intelligence-based manufacturing analytics platform that collects, improves and inspects information to acquire actionable insights and real-time visibility.
The company’s Factory TX platform provides manufacturers with the adaptability to obtain the data of machine from the cloud and production facilities. Their tool allows for quick deployment and centralized management of multiple industrial facility IoT data intake. Insights are generated in real-time and integrate device data, process quality data, and product data with installed Enterprise Resource Planning data and handled to produce important insights using Machine Learning and AI.
Benefits of Using AI in Sight Machine
The main advantage of AI in manufacturing is that it makes digital twin resources as well as procedures. Using AI, the plant management will track man, machine, material, and also process that drives ceaseless process development.
In addition, this platform helps in combining data from several manufacturers, historians, Enterprise Resource Planning, and other machines. Moreover, this platform helps improve quality, reduce time, increase the utilization of capacity, reduce scrap, measures and improves OEE and productivity, etc. Artificial Intelligence will also play an important role in helping humans do their jobs. AI is just a tool, but not a replacement, for improving business insight and decision-making.
Impact Of AI In Manufacturing Industry
The term AI enables workers to visualize the future, where machines run themselves and robots are gradually welded and bolted to parts without human attention. As a large number of smart manufacturing companies grows, humans are still playing an important role in operations, and this is likely to continue for decades.
These false assumptions can lead to improvements and advanced applications. Employees may assume that AI technology coverts to reductions in the count of human jobs. Engineers, data scientists, and other technicians may mistakenly assume that the profession in manufacturing is short-lived and will soon be replaced by technology. Such fears could prevent the next generation of skilled professionals from even considering employment in the manufacturing industry.
The lack of skilled professionals in manufacturing is one of the main concerns of industry; About 426,000 Manufacturing jobs in the USA are not filled because there are not enough skilled applicants to obtain those. Yet, 80% of companies lack the workforce needed to implement AI projects.
Therefore, a mystery is created. As skilled workers are in a dilemma that AI technology will eliminate jobs, they might not applied for vacant positions. But the lack of skilled employees makes it tough to implement those sophisticated technologies.
There is a high demand for employees who are skilled in AI technology, but hiring candidates is not so simple. Hence, A few manufacturers are giving training to them to develop internal AI expertise. One of the practical ways to gain experience is turning to software providers and third-party resources.
Final Words
Leveraging new and trending technologies and updating processes like AI technology and Machine Learning are keys to remaining competitive and relevant in the manufacturing industry. Artificial Intelligence is one of the most effective and powerful platform that manufacturers can adopt. As Manufacturing firms need to focus on the future with utmost confidence, they will make active decisions.
A team of researchers has developed a wirelessly activated device that mimics the wavelike muscular function in the esophagus and small intestine responsible for transporting food and viscous fluids for digestion.
Every industry has the same problem: figuring out what customers will want in a three-, five-, or 10-year timeframe. The reason we need to know what customers will want is that it takes from three to 10 years to create […]
The integration of professional machine vision applications is usually very complex. Since no programming skills are required for such machine vision software, industrial image processing can thus also provide a valuable contribution to digitization.
