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Digitaltrends
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Watch Boston Dynamics’ Atlas robot do a backflip in a Santa suit
- Boston Dynamics' Atlas robot, dressed in a Santa suit, performs a flawless back somersault in a holiday video message.
- The Atlas robot has been capable of backflips since 2017 and has undergone significant improvements over the years.
- It has transitioned from a hydraulic-powered version to a fully electric version, becoming stronger, more dexterous, and more agile.
- Boston Dynamics aims to commercialize the Atlas robot, similar to its Spot robot, which has been tested in industrial settings.
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The Robot Report
23h
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Draganfly completes hospital drone delivery proof-of-concept flights
- Draganfly Inc. has completed proof-of-concept flights for a drone delivery project with Mass General Brigham.
- The project aims to minimize traditional logistical delays in healthcare delivery by using drones for timely access to medical supplies.
- Draganfly's drone technology will address healthcare logistics challenges, including traffic congestion and outdated delivery methods.
- The medical drone delivery market is expected to reach $1.9 billion by 2032, presenting significant growth opportunities.
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The Robot Report
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Matternet adds ANRA’s UTM tech to expand drone delivery
- Matternet has partnered with ANRA Technologies to integrate unmanned aircraft system traffic management (UTM) capabilities into its drone delivery software.
- The collaboration will help Matternet scale its drone networks and expand its home delivery service in Silicon Valley.
- The integration of UTM solutions will enable Matternet to manage flight paths and ensure safe operations in shared airspace.
- Matternet aims to deploy its technology across major urban and suburban areas in the United States and Europe.
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The Robot Report
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New research analyzes safety of Waymo robotaxis
- Waymo shared new research with Swiss Re which shows Waymo’s autonomous vehicle technology is superior to human-driven vehicles. It found an 88% reduction in property damage claims and a 92% reduction in bodily injury claims over human-driven vehicles. Across 25.3 million miles, the Waymo Driver was involved in just nine property damage claims and two bodily injury claims. Waymo’s safety advantages hold true when compared to newer vehicles equipped with modern safety technology. Waymo data indicates that over 25 million fully autonomous miles, the Waymo Driver had fewer serious collisions than human drivers.
- Auto liability claims aggregated from 25.3 million fully autonomous miles driven by Waymo suggest its autonomous vehicle technology has fewer accidents than any human-driven vehicles. It’s a world leader in driverless automotive technology.
- Swiss Re analysed Waymo’s liability claims and found a reduction of 88% in property damage claims and a 92% reduction in bodily injury claims, and even when compared to newer vehicles using ADAS (advanced driver assistance systems) features.
- Waymo has demonstrated groundbreaking safety performance when compared to human-driven vehicles, with fewer serious collisions and no responsibility in the vast majority of collisions involving its vehicles.
- This research offers evidence that auto insurance claims can be used as a powerful tool in evaluating the safety performance of autonomous vehicles in the real world.
- Waymo’s leading technology is already conducting trialling services in limited areas across Phoenix, San Francisco, Los Angeles, and Austin, delivering more than 150,000 trips per week.
- Waymo's research offers a scalable framework for ongoing assessment of the safety impact autonomous vehicles have on the road. It’s proof that autonomous vehicles can greatly benefit society by offering a safer driving experience.
- While Waymo is the clear leader in the field, it’s worth noting that it isn’t the only company attempting to deploy robotaxis in the US, with other competitors including Nuro and Zoox.
- It’s important to consider the limitations of this research, such as the fact that Waymo’s robotaxis are currently only available in urban areas, and Waymo has yet to deploy its robotaxis in regions with snowy weather, which could pose significant challenges for the technology.
- Waymo has plans to start services in Miami and Tokyo in 2025, cementing its position as the leader of fully autonomous vehicle technology.
- Swiss Re's research shows that auto liability claims can be used as a powerful tool in measuring the safety performance of autonomous vehicles, making it more scalable for ongoing safety analysis.
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Unite
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Understanding Shadow AI and Its Impact on Your Business
- The rapid adoption of AI by businesses presents a hidden challenge, the emergence of Shadow AI.
- Shadow AI refers to the use of AI technologies and platforms that haven't been approved or vetted by the organization's IT or security teams.
- Over 60% of employees admit using unauthorized AI tools for work-related tasks.
- There are several risks associated with Shadow AI, such as data privacy violations, regulatory noncompliance, and operational and reputational risks.
- Shadow AI is becoming more common due to limited organizational resources, misaligned incentives, and the use of free tools.
- Shadow AI appears in multiple forms, such as chatbots, data analysis models, marketing automation tools, and generative AI applications.
- Managing the risks of Shadow AI requires a focused strategy emphasizing visibility, risk management, and informed decision-making.
- Organizations should establish clear policies, classify data and use cases, acknowledge benefits, educate and train employees, monitor and control AI usage, and collaborate with IT and business units.
- The future of AI will rely on strategies that align organizational goals with ethical and transparent technology use.
- To learn more about managing AI ethically, stay tuned to Unite.ai for the latest insights and tips.
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TechCrunch
21h
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Nvidia’s CES 2025 keynote: How to watch
- Nvidia's CES 2025 keynote will be livestreamed on January 6.
- Nvidia is expected to make big announcements, with the RTX 5000 series GPU being a highlight.
- Founder and CEO Jensen Huang will discuss topics such as AI, robots, and automotive.
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Unite
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How Microsoft’s AI Ecosystem Outperforms Salesforce and AWS
- Microsoft's AI ecosystem leverages advanced algorithms to automate tasks that require human involvement. The company's AI capabilities integrate effectively with other Microsoft products that support various organizational needs. The introduction of Copilot Studio allows businesses to create customized AI agents with ease without technical expertise. Real-world use cases for Microsoft's AI agents include customer service, sales automation, and supply chain management. The Microsoft AI ecosystem offers a comprehensive solution with robust security features, scalable system, pre-built AI agents, and easy integration with existing systems. Microsoft is outperforming its competitors, Salesforce and AWS, in delivering a broad, enterprise-grade ecosystem.
- AI agents are autonomous systems designed to perform tasks that would typically require human involvement. By using advanced algorithms, these agents can handle a wide range of functions, from answering customer inquiries to predicting business trends. This automation not only streamlines repetitive processes but also allows human workers to focus on more strategic and creative activities. Advancements in generative AI and predictive AI have further enhanced the capabilities of these agents.
- The adoption of AI agents has increased, with over 100,000 organizations now utilizing Microsoft’s AI solutions to automate their processes. For every dollar spent on generative AI, companies are realizing an average of $3.70 in return. Microsoft’s AI solutions are a key player in the rapidly evolving AI field.
- Microsoft's AI solutions are built on its strong foundation in cloud computing and are designed to address the needs of large organizations. A key development in Microsoft’s AI efforts is the introduction of Copilot Studio. This platform enables businesses to create and deploy customized AI agents with ease, using a no-code interface which makes it accessible even for those without technical expertise.
- Microsoft's AI agents' flexibility and adaptability make them highly effective across various industries. These agents help automate tasks such as customer service and supply chain management. They can handle large volumes of customer inquiries, predict inventory needs, and improve workflows, ultimately increasing operational efficiency and providing real-time solutions.
- While Salesforce and AWS have valuable AI capabilities, they offer different levels of integrated, enterprise-grade solutions than Microsoft. For businesses looking for a broad, scalable AI ecosystem, Microsoft's offering emerges as the more comprehensive and accessible choice.
- Microsoft has designed its AI solutions to integrate with over 1,400 enterprise systems. The company strongly emphasizes compliance with global regulations ensuring that AI systems are used responsibly and ethically. With the widespread use of AI in enterprise operations, Microsoft stays ahead by providing a reliable and efficient solution for businesses looking to embrace AI and drive digital transformation.
- Microsoft’s AI agent ecosystem offers a comprehensive, scalable, and integrated solution for businesses looking to enhance their operations through automation and data-driven insights. The wide array of pre-built AI agents for tasks like customer service, sales, and supply chain management ensures that businesses can quickly adopt AI with minimal disruption.
- With its strong focus on enterprise needs, robust security features, and easy integration with existing systems, Microsoft’s AI solutions are helping organizations streamline processes, improve customer experience, and drive growth.
- Microsoft’s AI ecosystem offers a comprehensive solution with a scalable system, pre-built AI agents, and easy integration with existing systems. The Microsoft AI ecosystem offers a broad, enterprise-grade ecosystem that is flexible and adaptable to a wide range of industries.
- The Microsoft's AI ecosystem is a comprehensive and reliable choice for companies looking to integrate AI at scale.
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Unite
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How AI is Making Sign Language Recognition More Precise Than Ever
- A team at Florida Atlantic University has used artificial intelligence and a new approach to improve Sign Language recognition's accuracy. Their technology can now recognize American Sign Language (ASL) alphabet gestures with precision. The researchers have carefully integrated transfer learning, meticulous data creation, and precise tuning for the system's remarkable performance. MediaPipe and YOLOv8, when put together, provide a 98% accuracy rate and 99% F1 score. The next step is to teach this system wider-ranging hand shapes and gestures and ensure it works in any environment while being fast for practical and real-time uses.
- Previous sign language recognition technology has struggled with Accuracy because of the complex languages and unique grammar and syntax of each sign language worldwide. Understanding each element of sign language, which includes facial expressions and body language, is more challenging than just hand movements. The Florida Atlantic University's College of Engineerging and Computer Science focused on recognizing ASL alphabet gestures with clarity using 21 keys precisely marked on the hands in the dataset of 29,820 static images.
- MediaPipe is an expert hand watcher capable of tracking finger movements and hand positions accurately. This ability to provide accurate hand landmark tracking makes it an exceptional option for sign language interpreters to detect hand gestures' precise coordinates. The YOLOv8 is an advanced object detection technology that understands what these gestures represent after interpreting the tracked points of each sign and predicting the probability of a hand gesture being present within each image.
- The system understands each person's hand gestures, detecting and classifying American Sign Language gestures with 98% precision. This technology works flawlessly in different and elaborate lighting, different hand positions, and different people's signs, making it quite versatile and futuristic. The researchers are working to cover all the aspects previously missed and teach the system even more hand shapes and gestures, making it smoother, quicker, and reliable for all types of people just like their real hands.
- The success of this project will open up exciting possibilities to enhance communication and understanding among people, regardless of any language barriers. This technology can be used to create tools that would improve communication for the deaf and hard-of-hearing community. The researchers are hoping to work on the speed and reliability of communication between hearing-impaired and normal people and how to integrate this technology seamlessly into everyday life.
- The translation of sign languages can be highly complex because each sign represents a unique concept. Different grammars and syntaxes unique to each language worldwide make building accurate sign language translations difficult. Being able to break these communication barriers through this technology is essential for people to connect on different aspects of life.
- Such progress in communication technology represents a step closer to real-world applications in either healthcare or education, where instant language translation could make life easier for both deaf and hearing communities. The cumulative efforts of engineers and Computer Scientists to improve communication demonstrate the versatility of programming and AI in technology's continued growth and development.
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The Robot Report
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How Waste Robotics, Greyparrot are enhancing sorting robots
- Waste Robotics uses FANUC robot arms, paired with its proprietary AI and gripper technology, to sort a variety of waste materials. It is also using Greyparrot’s AI to ensure its robots are being used at the most important parts of the waste sortation process. The combined product called the Robot Validator, allows Waste Robotics’ customers to know that they will be getting the most out of their robots from day one.
- Waste Robotics specializes in the last mile of waste sorting. The added AI layer from Greyparrot uses cameras to track all the materials passing on conveyor belts and create real-time insights on a live dashboard.
- The Robot Validator is a product that will help MRFs understand the impact robotics can have on their facilities. These facility managers working with us and Waste Robotics are not locked into a specific robot or robot provider before they see the data they need to inform their decision.
- The partnership is helping Waste Robotics and Greyparrot grow from each other as the companies offer complementary technology. The ambition is to impact the waste management ecosystem.
- In one Canadian materials recovery facility, the system identified significant recovery opportunities, detecting over 1,260 valuable objects per hour being lost to the residue line. Of these items, more than half were classified as highly valuable, and 90% were suitable for robotic recovery.
- The Robot Validator allows Waste Robotics' customers to ensure that their robots are deployed in the right places. Greyparrot's AI helps robots identify and pick up a variety of items. This includes municipal solid waste, recyclables, metals, and construction and demolition.
- Waste sorting is an ideal use case for robotics. It's repetitive, back-breaking work that involves handling a variety of materials. Waste Robotics said it specializes in this last mile of waste sorting. It wanted to ensure its customers were getting as much value from its robots as possible.
- The Robot Validator is a product that will help MRFs understand the impact robotics can have on their facilities. Without gathering the data and analyzing it with AI ahead of robot implementation, MRFs lack visibility into waste flow composition.
- Initial client feedback has been highly positive. In one Canadian materials recovery facility, the system identified significant recovery opportunities, detecting over 1,260 valuable objects per hour being lost to the residue line.
- Waste Robotics and Greyparrot see many opportunities to grow from each other as the companies offer complementary technology. The combined AI from both companies becomes a real force in material recovery and waste sorting.
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Livescience
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Future robots could one day tell how you're feeling by measuring your sweat, scientists say
- Scientists have discovered that future robots might be able to detect human emotions by measuring skin conductance, which measures how well skin conducts electricity.
- In a study, skin conductance was found to correlate with emotions such as fear, surprise, and 'family bonding emotions,' making it an accurate method for real-time emotion detection.
- When combined with other physiological signals like heart rate and brain activity, skin conductance could contribute to the development of emotionally intelligent devices and services.
- Integrating skin conductance technology with robotics could lead to applications like smart devices that respond to users' emotions or streaming platforms that recommend content based on mood.
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Unite
4d
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Monetizing Research for AI Training: The Risks and Best Practices
- Scholarly publishers have started to monetize their research content to provide training data for large language models (LLMs).
- Major academic publishers, including Wiley, Taylor & Francis, and others, have reported substantial revenues from licensing their content to tech companies developing generative AI models.
- Risks arise when questionable research infiltrates these AI training datasets, as the scholarly community is no stranger to issues of fraudulent research.
- The implications are profound when LLMs train on databases containing fraudulent or low-quality research. Such models could perpetuate inaccuracies, posing harmful consequences for fields like medicine.
- Publishers must improve their peer-review process to catch unreliable studies before they make it into training datasets.
- Choosing publishers and journals with a strong reputation for high-quality, well-reviewed research is key in reducing the risks of flawed research disrupting AI training.
- AI tools themselves can also be designed to identify suspicious data and reduce the risks of questionable research spreading further.
- Transparency is an essential factor, and publishers and AI companies should openly share details about how research is used and where royalties go.
- Open access to high-quality research should be encouraged to ensure inclusivity and fairness in AI development.
- By focusing on reliable, well-reviewed research, we can build better AI tools, protect scientific integrity, and maintain the public’s trust in science and technology.
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Unite
4d
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Hunyuan-Large and the MoE Revolution: How AI Models Are Growing Smarter and Faster
- Hunyuan-Large, Tencent’s cutting-edge open-source AI model, is a significant advancement in AI technology, built using the Transformer architecture.
- Hunyuan-Large is notable due to its use of the MoE model that activates only the relevant experts for each task, enabling the model to handle complex challenges while optimizing resource usage.
- With 389 billion parameters, Hunyuan-Large is one of the largest AI models available today, far exceeding earlier models like GPT-3, which has 175 billion parameters.
- Hunyuan-Large can generate precise code from natural language descriptions, which earlier models struggled with, and is ideal for tasks that involve understanding and generating detailed information.
- MoE represents a transformation in how AI models function, offering a more efficient and scalable approach, even in healthcare and finance with large-scale data analysis.
- MoE enables AI systems to process large datasets without excessive computational resources, which is essential as cloud-based AI services become more common.
- Hunyuan-Large is highly effective for applications that require quick, accurate, and context-aware responses, including healthcare, NLP, and computer vision.
- Looking forward, MoE models, such as Hunyuan-Large, will play a central role in the future of AI, particularly in the emerging fields of edge AI and personalized AI.
- The need for ethical concerns around AI is still crucial as these models become more powerful, requiring significant computational resources and transparency.
- Hunyuan-Large is a game-changer for AI efficiency and scalability, making AI more accessible, sustainable and ready to drive change across industries.
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