Category robots

A robot, designed to mimic the motion of a snail, has been developed by researchers at the University of Bristol.

Sat in a circle on the nursery floor, a group of Swiss three-year-olds ask a robot called Nao questions about giraffes and broccoli.

As directed by the facility’s warehouse management software (WMS), a fleet of 142 AMRs continuously delivers mobile shelves to associates at workstations equipped with light directed picking modules on both floors.

Parents spend roughly 300 hours each year cleaning up after their kids. That's nearly two weeks that could be otherwise spent doing anything else. Researchers in Carnegie Mellon University's Department of Mechanical Engineering, in collaboration with Google DeepMind and University of Washington, are hopeful that parents will regain tidying time and then some with LocoMan, a four-legged version of the Jetsons' beloved maid "Rosie the Robot," that could lend a hand in the not-so-distant future.

Would you trust a robot to look after your cat? New research suggests it takes more than a carefully designed robot to care for your cat, the environment in which they operate is also vital, as well as human interaction.

A new article highlights how artificial intelligence stands on the threshold of making monumental contributions to the field of sleep medicine. Through a strategic analysis, researchers examined advancements in AI within sleep medicine and spotlighted its potential in revolutionizing care in three critical areas: clinical applications, lifestyle management, and population health. The committee also reviewed barriers and challenges associated with using AI-enabled technologies.

Would you trust a robot to look after your cat? New research suggests it takes more than a carefully designed robot to care for your cat, the environment in which they operate is also vital, as well as human interaction.

Setting the Stage: The Shift from Consumer to Enterprise AI

In recent years, the surge of generative AI breakthroughs has not only generated global buzz but also significantly influenced consumer behaviors, prompting millions to embrace these technologies daily. Initially, the market saw a wave of startups racing to introduce innovative, buzzworthy generative AI products targeting individual users. However, a distinct shift is now observable as the focus pivots from broad consumer applications to more specialized enterprise solutions.

This strategic shift brings multiple advantages. Firstly, it allows companies to target a specific clientele, adapting and refining their products based on direct feedback, ensuring a better fit for specific business needs. Secondly, this approach opens avenues for more stable, recurring revenue streams – a critical factor in business sustainability. Thirdly, such targeted solutions are more appealing to venture capitalists, who see the clear path to profitability through focused application and scaling in enterprise environments.

This trend is not newly minted but is instead borrowed from the playbooks of Big Tech giants like Microsoft, Google, and Amazon. These companies have successfully leveraged the software-as-a-service (SaaS) model for years and are now embedding sophisticated AI capabilities into their product suites. 

As these leading firms infuse their products with heavy doses of AI, critical questions arise. Is there a clear leader among these solutions? How do they differentiate themselves in the marketplace? What factors influence their adoption within enterprises? n this article, we’ll explore in depth how enterprise AI solutions from Microsoft, Google, Amazon, and OpenAI are competing to enhance productivity among their enterprise customers.

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Generative AI Solutions for Enterprises by Big Tech

As leaders in the tech industry, Google, Microsoft, and Amazon boast unparalleled technical expertise and have long been pioneers in software and cloud services. Yet, the realm of generative AI is a frontier where even these giants find themselves in somewhat unfamiliar territory. The rapid development and deployment of generative AI features often mirror the dynamics of startup products, characterized by fluctuating performance stability and evolving feature sets. In their race to outpace competitors, these companies sometimes launch AI-driven functionalities that are still in their nascent stages, focusing on getting the technology into users’ hands quickly, even if it means initial limitations and instabilities.

However, it appears that the adoption of these AI solutions is less about being first to market and more about who already has a foothold in corporate environments. Due to the logistical and technical challenges associated with switching large-scale enterprise tools, companies are more likely to adopt new technologies that integrate seamlessly with the systems they already use. Therefore, existing customer bases play a pivotal role. For example, organizations deeply embedded in the Google Workspace ecosystem are inclined to adopt Gemini for Google Workspace, whereas those accustomed to Microsoft 365 might lean towards exploring Microsoft Copilot. Similarly, businesses that rely on AWS cloud services are prime candidates for Amazon Q.

Though early adoption patterns are influenced heavily by existing affiliations, other factors also shape how these solutions are received and integrated. Let’s dive deeper into each solution to understand how they are tailored to fit their respective ecosystems and what sets them apart from one another.

Gemini for Google Workspace

Gemini for Google Workspace emerges as a cutting-edge AI assistant deeply integrated within Google’s popular suite of Workspace applications, including Gmail, Docs, Sheets, Slides, and Meet. Gemini also functions as a standalone tool that allows users to interact directly with the AI to research specific topics. 

AI Models. While Google claims that the most capable Gemini models power their AI integrations in Workspace, user experiences suggest a disparity in capabilities between the standalone Gemini chatbot and its counterparts embedded within the apps. The standalone version often outshines the integrated features in terms of intelligence and responsiveness, pointing to possible variations in the implementation of the AI models across different applications.

Integrations. Officially, Gemini’s generative AI features are integrated across several core applications such as Gmail, Docs, Sheets, Slides, and Meet. However, in practice, substantial AI enhancements are only evident in Gmail and Docs. 

Functionality. In Gmail, Gemini aids in drafting, refining, and customizing emails by adjusting tone, length, and generating contextually appropriate email replies. Docs benefit similarly, with features that allow users to draft and refine documents, modify tone, summarize content, and transform selected bulks of text based on specific prompts. Conversely, Sheets currently only supports the creation of custom templates driven by user prompts, and in Slides, the generative AI features are restricted to generating images from text in selected styles – excluding depictions of people. In Meet, AI enhances the user experience by improving lighting, audio quality, and offering virtual background generation.

Overall Impression. While Gemini’s AI capabilities bring significant improvements to individual applications like Gmail and Docs, the integration across different applications remains limited. This lack of interconnected functionality means users cannot seamlessly transfer AI-generated content or tasks between different apps, such as creating a presentation in Slides directly from a Docs outline or syncing data from Sheets into a comprehensive email via Gmail. Despite these limitations, the available features operate with a commendable level of stability and reliability.

Pricing. Gemini for Google Workspace is available in two primary pricing tiers aimed at business users: the Gemini Business plan at $20 per user per month and the Gemini Enterprise plan at $30 per user per month, both requiring an annual commitment.

Microsoft Copilot

Microsoft Copilot stands as a dynamic digital assistant engineered to enhance productivity across the Microsoft 365 ecosystem, which includes applications like Word, Excel, PowerPoint, Outlook, and Teams. Available also as a standalone tool for research purposes, Copilot’s primary function is to automate routine tasks and support data analysis and decision-making processes. This assistant is capable of accessing and analyzing all types of company data, from emails and meeting notes to chats and documents, streamlining workflows across the board.

AI Models. Microsoft Copilot primarily leverages the powerful capabilities of GPT-4 for its text generation tasks and DALL-E 3 for creating visually compelling images. Simpler tasks might be handled by other, smaller AI models, optimizing resource usage and efficiency. Looking ahead, Microsoft’s ongoing development of its own large-scale language models suggests that Copilot could soon be powered by Microsoft’s own AI models.

Integrations. Copilot boasts deep integration across the Microsoft 365 suite, including Teams, Word, Outlook, PowerPoint, and Excel. 

Functionality. Microsoft Copilot offers a comprehensive set of functionalities that surpass those found in many of its competitors. In applications like Outlook and Word, its capabilities are similar to those of Google’s Gemini, such as drafting, summarizing, and querying documents. However, Copilot extends significantly beyond these features, especially in handling presentations and spreadsheets. In PowerPoint, users can generate presentations from textual prompts or existing files, with slides including high-quality images generated by DALL-E. Excel functionalities are robust, including adding formula columns, data sorting and filtering, and generating insightful visualizations. Copilot in Teams enhances collaboration through features like live meeting recordings and transcriptions, with the possibility to summarize meetings and list action items in real time, while meeting is still in progress.

Overall Impression. Microsoft Copilot is at a notably advanced stage of integrating generative AI within its suite, offering a broad spectrum of tools that significantly enhance enterprise productivity. Although there are opportunities for improving the interconnections among different applications and occasional issues with performance reliability, Copilot already represents a formidable productivity tool that can substantially benefit teams.

Pricing. Microsoft 365 Copilot is available at a cost of $30 per user per month, with an annual commitment. 

Amazon Q Business

Amazon Q Business is a sophisticated generative AI-powered assistant designed to enhance enterprise operations by answering questions, providing summaries, generating content, and completing tasks securely utilizing data from enterprise systems. Its capabilities are designed to streamline workflows and enhance decision-making processes across various departments.

AI Models. Amazon Q Business is powered by a suite of foundational models from Amazon Bedrock, ensuring robust performance and versatility in handling diverse data-intensive tasks across an organization’s digital landscape.

Integrations. Amazon Q Business boasts integration capabilities with over 40 applications, including popular tools like Gmail, Slack, Google Drive, Microsoft OneDrive, Amazon WorkDocs, Amazon S3, Microsoft Teams, Oracle Database, and Salesforce. This extensive array of integrations allows enterprises to leverage generative AI across a wide range of software tools, enhancing productivity and operational efficiency.

Functionality. The broad integrations enable Amazon Q Business to support a variety of use cases. For instance, its conversational interface can be used to create tickets in Jira, send notifications in Slack, and update various dashboards. Within Amazon QuickSight, the AI features enable users to analyze data, create visualizations, and generate custom reports. Importantly, the system respects the principle of least privilege, limiting access to information based on an employee’s specific role within the organization. This ensures that the security and access controls established in applications like Slack are maintained even when integrated with Amazon Q.

Overall Impression. As Amazon Q Business is a recent addition to the market, comprehensive user reviews are sparse. However, the information available suggests that Amazon has effectively utilized generative AI to serve as a conduit connecting various data sources, applications, and tools across an enterprise. This capability has the potential to substantially enhance productivity across different organizational functions.

Pricing. Amazon Q Business offers two pricing plans: Lite at $3 per user per month and Pro at $20 per user per month. 

ChatGPT Enterprise

ChatGPT Enterprise by OpenAI represents an enhanced version of the widely-used ChatGPT conversational model, tailored specifically for business applications. It offers exclusive access to the most advanced version of ChatGPT, delivering high-speed performance, extended context windows for processing longer inputs, and superior analytical capabilities. Additionally, it provides customization options and enhanced data privacy and security protections, making it ideal for corporate use.

AI Models. ChatGPT Enterprise operates on the latest and most powerful models from OpenAI. At the moment, GPT-4o is being integrated to become the default LLM for new conversations. However, users have the flexibility to select other GPT models, accommodating different needs and preferences. Furthermore, ChatGPT Enterprise incorporates DALL-E 3 for advanced image generation and Whisper for accurate voice transcription.

Integrations. Unlike solutions from other big tech companies, ChatGPT Enterprise does not integrate directly into existing tools and product suites. Instead, it maintains a standalone setup where users engage with the AI through the same conversational interface available to all ChatGPT users. However, this setup still allows for significant versatility, enabling users to work with various data types, including code and tables, either by uploading files directly to ChatGPT or developing custom applications via API access.

Functionality. ChatGPT Enterprise excels in its ability to assist with a broad spectrum of tasks through its conversational interface. Users can engage in research, draft various types of texts and documents, utilize the model for coding and debugging, analyze and visualize data from uploaded spreadsheets, and generate images from text prompts using the DALL-E 3 model. Additionally, companies can leverage API access to the ChatGPT model to develop specialized applications tailored to the specific needs of different departments such as HR, marketing, sales, customer support, finance, and legal.

Overall Impression. While ChatGPT Enterprise does not natively integrate with other work tools, its robust performance and flexibility make it a preferred choice among many Fortune 500 companies. These organizations benefit from the powerful models driving ChatGPT, which consistently deliver top-tier results. Additionally, they often have teams that can build specialized applications using API access to GPT models, effectively integrating powerful OpenAI models into the internal workflows.

Pricing. The pricing for ChatGPT Enterprise is not standardized and is typically customized based on usage volume and specific enterprise needs. While exact pricing details are not publicly disclosed, it is reported to be around $60 per user per month with a minimum of 150 users and a 12-month contract.

Final Thoughts: How Big Tech Competition is Redefining Productivity in Enterprise

As competition intensifies in the tech industry, Big Tech giants are rapidly integrating generative AI into their enterprise solutions, aiming not only to retain their current customer bases but also to expand them. This integration is driven by the need to stay competitive and relevant in an increasingly AI-centric world.

Microsoft has been at the forefront of this integration, pioneering the inclusion of AI within its Microsoft 365 suite. While it has made significant strides in embedding AI functionality natively into its applications, there is still room for improvement, particularly in enhancing the interconnectedness of these applications and stabilizing performance.

Google, known for its early work in large language models, is somewhat behind in the race, with only limited generative AI capabilities currently integrated into the Google Workspace. However, its established tech stack and infrastructure position it well to potentially catch up quickly as it continues to develop and deploy AI functionalities.

Amazon has taken a slightly different approach with Amazon Q, focusing on creating a robust AI conversational tool that integrates with a wide range of applications. This approach not only leverages AI to pull information from diverse sources but also enables it to initiate actions across various platforms, paving the way for a more interconnected and productive enterprise environment.

These developments herald an exciting era for AI in enterprise applications. As each company continues to evolve and refine its offerings, the landscape of enterprise AI is set to be transformed, promising enhanced efficiencies and new capabilities. We are indeed in exciting times for AI in the business world, and staying tuned to these advancements will be key to understanding how AI will reshape the enterprise landscape in the years to come.

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The post The AI Arms Race in Big Tech: An Overview of Emerging Enterprise Solutions appeared first on TOPBOTS.

Advanced 3D vision systems are a stark contrast to the vision software of manufacturing’s past. Many existing systems still require professional CAD programming to ensure the robot can recognise shapes.

Actually, sometimes consumers don't want to talk to a real person when they're shopping online, a new study suggests. In fact, what they really want is a chatbot that makes it clear that it is not human at all. In a new study, researchers found that people preferred interacting with chatbots when they felt embarrassed about what they were buying online -- items like antidiarrheal medicine or, for some people, skin care products.

Ukraine’s New Foreign Ministry Spokeswoman is a ‘Digital Person’

While a number of news media outlets have been using digital news avatars for a number of years, Ukraine became the first country to designate a ‘digital personality’ as an official spokesperson.

Dubbed ‘Victoria,’ the cyber persona has been entrusted to make official government statements for Ukraine’s foreign ministry.

Interestingly, while AI is used to render and animate Victoria’s image, her statements will be written — and pre-verified — by humans employed by Ukraine’s foreign ministry press service.

Victoria’s credibility will also be enhanced by an anti-fraud QR code, which will lead to the text version of what Victoria is saying on the foreign ministry’s Web site.

Bottom line: This is a big deal, given that a major country
on the world’s political stage is entrusting a synthetic personality to often make life-and-death statements impacting humans.

In a phrase, world leaders, Ukrainian soldiers ducking mortar shells on the battlefield — and screaming school children huddling in subways as bombs drop from above — will need to trust their lives to what Victoria says.

*In-Depth Guide: Unleashing Your Inner Ventriloquist: Best AI Voice Tools Ranked: Writers looking to parlay their work into podcasts, videos, slideshow voice-overs and the like will want to check-out this guide to the best in text-to-voice tools.

The verdict from writer Alice Martin: The best AI voice generators use advanced AI that produce voices that sound incredibly human.

They can also use “a variety of presets — or even clone your own voice — making AI voices popular for projects such as virtual assistants, video games, professional voice-overs and social media content,” Martin adds.

*AI Now Crafts Fictional Characters While You Nap: AI pioneer Sudowrite is promising a new module writers can use to auto-build personality traits, background, physical appearances and mannerisms for fictional characters.

Also promised is a new world-building tool that will enable writers to auto-design fictional worlds ranging from dystopian cities to magical realms.

The AI tool — which uses AI engines like GPT-4 and Claude 3 to work its magic — will also be enhanced system-wide to enable writers to auto-generate fiction more efficiently.

*Elon Musk Serves-Up AI Cliff Notes for X Users: Social network X — formerly Twitter — now features news summaries generated by AI, dubbed ‘Stories on X.’

Observes writer Karissa Bell: “X is using Grok (a ChatGPT competitor) to publish AI-generated summaries of news and other topics that trend on the platform.

“According to X, Grok relies on users’ posts to generate the text snippets.

“Some seem to be more news-focused — while others are summaries of conversations happening on the platform itself.”

Both X and Grok are owned by Elon Musk.

*University of Texas to Grammarly: Be Our Guest: Add UT to the list of universities that have decided to give AI a full bear hug.

Specifically, the institution is now working with AI writing assistant Grammarly to find ways to integrate AI into higher education.

Observes Art Markman, vice provost, academic affairs: “We are in an era with a lot of uncertainty surrounding AI and education.

“This is a chance to demonstrate how to use generative AI as a positive source for education, teach responsibility to our students and engage an industry leader to improve our understanding of classroom AI.”

*Say Goodbye to Snoozeworthy: Facebook Parent Promises New AI Ad Tool: Meta is promising a new AI tool that will enable advertisers to auto-generate images and copy for their products and services.

Observes writer Kimberley Kao: “The new features will eventually allow its (Meta’s) 10 million advertisers to upload images of their products to generate new versions of the images and accompanying text for marketing purposes.”

Digital properties owned by Meta featuring advertising include Facebook, Instagram, WhatsApp, Reels and Threads.

*A Look Into the Belly of the Beast: AI and the Smearing of Sports Illustrated: Futurism takes a long, hard look at Advon in this piece — the AI content writing firm that tried to pass-off fake descriptions of journalists as human writers for Sports Illustrated.

Observes writer Maggie Harrison Dupre: “The response was explosive: The magazine’s union wrote that it was ‘horrified,’ while its publisher cut ties with AdVon and subsequently fired its CEO before losing the rights to Sports Illustrated entirely.”

Turns-out that even after the Sports Illustrated debacle, Advon is still at it, striking “deals with publishers in which it provides huge numbers of low-quality product reviews — often for surprisingly prominent publications,” Dupre adds.

*Second Fiddle: Microsoft Building AI Engine ‘Nearly as Good’ as ChatGPT: Tech titan Microsoft is promising to roll-out an AI engine soon that will be nearly as good as ChatGPT, Google Gemini and similar competitors.

Observes Reuters: “The exact purpose of the model has not been determined yet and will depend on how well it performs.

“Microsoft could preview the new model as soon as its Build developer conference later this month.”

*New Study: The Promise and Perils of AI and Journalism: A new report from Northwestern University finds that AI-generated content could spell the end of many writing jobs.

Observes writer Mark Caro: “Many people who practice or care about journalism fear that generative AI — with its ability to create content with little human involvement — could be the final nail in the local news coffin.

“Given how some chain owners have prioritized cost-cutting and profit-making over sustained journalistic quality, what is to stop them from replacing more reporters and editors with robots?

“Can news consumers be relied upon to discern between human-reported journalism and machine-generated content—and does it matter?”

Unfortunately, for many writers and journalists, that question has already been answered.

*AI Big Picture: Randy Travis Gets His Voice Back — With a Little Help from AI: World-famous country singer Randy Travis — who lost his voice to a stroke in 2013 — has a new single out.

Entitled, “Where That Came From,” the new song was put together by AI, which sampled recordings of Travis’ songs to create the all-AI production.

Observes writer Wes Davis: “Travis’ song is a good, edge-case example of AI being used to make music that actually feels legitimate.

“But on the other hand, it also may open a new path for Warner, which owns the rights to vast catalogs of music from famous, dead artists that are ripe for digital resurrection and — if they want to go there — potential profit.

“As heartwarming as this story is, it makes me wonder what lessons Warner Music Nashville — and the record industry as a whole — will take away from this song.”

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 Virtual Spokespeople Get Real appeared first on Robot Writers AI.

AI and robotics make the factories of the future smarter and more efficient. Manufacturers need to use these exponential technologies to gain a competitive advantage.

The 2024 IEEE International Conference on Robotics and Automation (ICRA) will take place from 13-17 May, in Yokohama, Japan. The event will feature plenary and keynote talks, technical sessions, posters, workshops and tutorials.

Plenary speakers

There are three plenary talks at the conference this year:

• Yoky Matsuoka – How to Turn a Roboticist into a Corporate Explorer
• Sami Haddadin – The Great Robot Accelerator: Collective Learning of Optimal Embodied AI
• Sunil K Agrawal – Rehabilitation Robotics: How to Improve Daily Functions in People with Impairments?

Keynote talks

There will be 15 keynote talks, given by:

• Lianqing Liu – Biosyncretic sensing, actuation and intelligence for robotics
• Dawn M. Tilbury – Digital Twins for Manufacturing Automation
• Claudio Pacchierotti – Beyond Force Feedback: Cutaneous Haptics in Human-centered Robotics
• Yu Sun – Medical Robotics for Cell Surgery – Science and Applications
• Yasuhisa Hirata – Adaptable AI-enabled Robots to Create a Vibrant Society – Moonshot R&D Projects in Japan
• Calin Belta – Formal Methods for Safety-Critical Control
• Manuel Catalano – Robots in the Wild: From Research Labs to the Real World
• Harold Soh – Building Guidance Bridges with Generative Models for Robot Learning and Control
• Lorenzo Sabattini – Unleashing the power of many: decentralized control of multi-robot systems
• Myunghee Kim – Human-wearable robot co-adaptation
• Yoko Yamanishi – Emergent Functions of Electrically-induced Bubbles and Intra-cellular-Cybernetic Avatar
• Kensuke Harada – Robotic manipulation aiming for industrial applications
• Iolanda Leite – The Quest for Social Robot Autonomy
• Rong Xiong – Integration of Robotics and AI: Changes and Challenges
• Mariana Medina-Sánchez – Tiny Robots, Big Impact: Transforming Gynecological Care

Tutorials

The tutorials will be held on Monday 13 May and Friday 17 May.

• Tutorial on Ergodic Control
• Connected Robotics Platform for ROS Deployment in Real-world Network Settings
• How to manage fleets of robots with open source software
• Riemann and Gauss meet Asimov: 2nd Tutorial on Geometric Methods in Robot Learning, Optimization and Control
• Cloud and Fog Robotics: A Hands-on Tutorial with ROS2 and FogROS2
• Choreographic swarms: From scripting to emergent expressive behaviors to CONNECT humans and robots

Workshops

The workshops will also be held on Monday 13 May and Friday 17 May. There are 73 to choose from this year.

• Debates on the Future of Robotics Research
• Workshop on Field Robotics
• Continuum and Soft robotics for medical applications with rising stars on the stage
• Agile Robotics: From Perception to Dynamic Action
• 6th Workshop on Long-Term Human Motion Prediction
• 2nd HERMES Workshop: Multi-Robot Sensing & Perception in Extreme Environments
• Towards Collaborative Partners: Design, Shared Control, and Robot Learning for Physical Human-Robot Interaction
• Society of Avatar-Symbiosis through Social Field Experiments
• 3rd Workshop on Future of Construction: Lifelong Learning Robots in Changing Construction Sites
• What does Responsible Robotics mean?: Stretching roboticists’ horizons from an academic, government and philosophical perspective
• Workshop on Ontologies and Standards for Robotics and Automation
• Exploring Role Allocation in Human-Robot Co-Manipulation
• C4SR+: Continuum, Compliant, Cooperative, Cognitive Surgical Robotic Systems in the Embodied AI Era
• Dynamic Duos: Human-Robot Co-Worker Adaptation in Manufacturing
• Autonomy in Robotics Surgery: State of the art, technical and regulatory challenges for clinical application
• 2nd Robot-Assisted Medical Imaging ICRA-RAMI
• Back to the Future: Robot Learning Going Probabilistic
• Bioinspired, soft and other novel design paradigms for aerial robotics
• Impulsive motion in soft robotic and microrobotic systems
• Agile Movements II: Animal Behavior, Biomechanics, and Robot Devices
• Bimanual manipulation: On kitchen challenges
• The robotics, psychology and neuroscience of body augmentation
• Sustainable Soft Robots: Working with the Environment
• Robotics and Sustainability: A Bidirectional Relationship
• 2nd Workshop on Mobile Manipulation and Embodied Intelligence (MOMA) Integrating Perception, Learning and Control for Full Autonomy
• (Re)designing the tree of robotic life: a game of alternative timelines
• Advancing Sustainable Food Systems through Agri-Robotics Innovations
• Co-design in Robotics: Theory, Practice, and Challenges
• Robot Trust for Symbiotic Societies
• Soft Continuum Manipulators: Bottlenecks and Applications
• Advanced human-robot interfaces based on physiological signals and sensory stimulations
• Assistive Systems: Lab to Patient Care
• MAD-Games: Workshop on Multi-Agent Dynamic Games
• Workshop on Robot Ethics – Ethical, Legal and User Perspectives in Robotics and Automation (WOROBET)
• Applications and Future Directions of Affective Technologies
• ProxyTouch: Whole-body Proxy-Tactile Architectures for Industrial and Service Applications
• Bio-inspired robotics and robotics for biology
• A Future Roadmap for Sensorimotor Skill Learning for Robot Manipulation
• Robotics and Automation in Nuclear Environments
• 3D Visual Representations for Robot Manipulation
• Advancements in Trajectory Optimization and Model Predictive Control for Legged Systems – 2nd Edition
• Robots for Understanding Natural Ecosystems
• Cooking Robotics: Perception and motion planning
• Robot Software Architectures (RSA24)
• Multi-Object Grasping: Progress and Prospects
• First Workshop on Vision-Language Models for Navigation and Manipulation
• Radar in Robotics: Resilience from Signal to Navigation
• Anthropomorphic and zoomorphic end-effectors: asset or useless bias?
• Workshop on Resilient Off-road Autonomy
• Breaking Swarm Stereotypes
• Translational research in medical robotics: From Lab Bench to Clinical Use – How to?
• 4th Workshop on Representing and Manipulating Deformable Objects
• Loco-Manipulation: Algorithms, Challenges & Applications
• Cognition across species: from nature to robotic application
• Unconventional Robots: Universal Lessons for Designing Unique Systems
• Accelerating Discovery in Natural Science Laboratories with AI and Robotics
• Human-Robot Companionship for Healthcare and Wellness: Which Form of Companionship for What Type of Care?
• Humanoid Whole-body Control: From human motion understanding to humanoid locomotion
• RoboNerF: Neural Fields in Robotics
• ViTac 2024: Robot Embodiment through Visuo-Tactile Perception
• Supervised autonomy: how to shape human-robot interaction from the body to the brain
• Robots and roboticists in the age of climate change
• Human-Centric Multilateral Teleoperation: Perception, Telecommunication, and Coordination
• Innovations and Applications of Human Modeling in Physical Human-Robot Interaction
• How to Ensure Correct Robot Behaviors? Software Challenges in Formal Methods for Robotics
• Advancing Wearable Devices and Applications through Novel Design, Sensing, Actuation, and AI
• Wearable Intelligence for Healthcare Robotics (WIHR): from Brain Activity to Body Movements
• Emerging Technologies in Smart Exoskeleton Systems
• Expanding Frontiers of Sim2Real: Robotics, biomechanics, plasma physics, chip design, and beyond
• Nursing Robotics: a new field emerging from the integration between robotics and nursing science
• Speed-dating to long-term relationships: Art-robot Residencies Enabled by Common Language
• Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots
• 2nd Workshop on NeuroDesign in Human-Robot Interaction: The making of engaging HRI technology your brain can’t resist

You can see the programme overview here, with a detailed programme available here.

Imagine a slime-like robot that can seamlessly change its shape to squeeze through narrow spaces, which could be deployed inside the human body to remove an unwanted item.

Our mission at DataRobot has been to help customers use AI to drive business value. 

Business value is built into our DNA, and nothing is better than hearing the success stories directly from our customers.

We’re thrilled to share that our customers have recognized DataRobot in the TrustRadius Top Rated Award for the third consecutive year in the following categories:

• Data science
• Machine learning
• Predictive analytics

We are incredibly proud of this award — based solely on customer reviews.

About TrustRadius

TrustRadius is a buyer intelligence platform for business technology and its annual Top Rated Awards are based entirely on customer feedback – they aren’t influenced by outside opinion. TrustRadius looks at the recency of reviews, relevancy of products compared to others in the same category, and overall ratings. 

With a trScore of 8.8 out of 10 and nearly 60 verified reviews from our customers, we’re proven as one of the most valuable platforms in our industry, with demonstrated impact and results.

Why our customers trust DataRobot

In their own words, our customers share the wins they’ve experienced by using the DataRobot AI Platform:

• “[DataRobot’s AI Platform] is frictionless…you don’t have to worry about sticking a bunch of services together as with the hyperscalers…it just works.” – Director of Information Technology, Real Estate 
• “A powerful tool…greatly reducing the time required to build powerful and accurate models.” – Data and Insights Lead, Healthcare
• “An excellent interface – fast enough to use for development and easy to share insights with business users.” – Information Technology, Financial Services
• “A great return on investment.” – Analyst, Insurance

When I spoke with our Chief Customer Officer, Jay Schuren, he shared his sincere appreciation for our brilliant customers and thanked them for this recognition. He said:

We continually strive to wow our customers. The Top Rated Award is only made possible by our customers’ success. When our customers win, we join them in celebrating the business transformations made possible with AI.

Learn more

Hear how customers deliver AI value at FordDirect, Freddie Mac, 84.51°, and many more.  

The post DataRobot Recognized by Customers with TrustRadius Top Rated Award for Third Consecutive Year  appeared first on DataRobot AI Platform.