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Robotics News
The Robot Report
4h
317
Image Credit: The Robot Report
EPC optimizes new GaN-based motor drive for humanoid robots
- Efficient Power Conversion Corp. (EPC) has launched EPC91104, a high-performance, three-phase brushless DC motor drive inverter reference design.
- The design is suitable for powering compact, precision motors in humanoid robots, such as those used for wrist, finger, and toe movements.
- EPC's GaN-based technology offers advantages over traditional silicon-based devices, including higher efficiency, faster switching speeds, and smaller size.
- The EPC91104 reference design board is priced at $780.00 and is compatible with controller boards from leading manufacturers.
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Unite
4h
181
Image Credit: Unite
Pipeshift Secures $2.5M to Streamline Open-Source AI Deployment
- Pipeshift has secured $2.5 million in seed funding to help build, deploy and manage open-source AI models for enterprises.
- Pipeshift's modular MLOps stack accommodates a broad range of AI workloads, including LLMs, vision models and audio models.
- Enterprises that use Pipeshift are given direct control over their infrastructure and can scale from day one with features such as in-built autoscalers, load balancers and schedulers.
- The platform aims to change the way open-source GenAI is revolutionised as it drastically reduces time-to-production and simplifies complex deployment workflows.
- Pipeshift's orchestration platform also eliminates the need for extensive engineering investments by streamlining production throughput and deployment.
- Investors in the seed funding round include Y Combinator, Arka Ventures, GradCapital, MyAsiaVC, Good News Ventures, Nivesha Ventures and Astir VC.
- Pipeshift offers multi-cloud orchestration, Kubernetes cluster management, 360° observability and model fine-tuning and deployment.
- Benefits of the platform include up to 60% GPU infrastructure cost saving, 30X faster time-to-production and 55% reduction in engineering resources.
- Pipeshift has already collaborated with more than 30 companies, including NetApp.
- The platform's hybrid cloud compatibility and modular flexibility across various enterprises makes it ideal for enterprise AI projects.
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TechCrunch
6h
41
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Meta’s Yann LeCun predicts a ‘new AI architectures paradigm’ within 5 years and ‘decade of robotics’
- Meta's chief AI scientist, Yann LeCun, predicts a 'new paradigm of AI architectures' within the next three to five years.
- LeCun believes that the current flavor of AI, such as generative AI and large language models, will be replaced by a new paradigm that overcomes the limitations of existing AI systems.
- These limitations include a lack of understanding of the physical world, lack of persistent memory, lack of reasoning, and lack of complex planning capabilities.
- LeCun also suggests that the next decade could be the 'decade of robotics,' where AI and robotics combine to create intelligent applications with a better understanding of the real world.
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Medium
10h
60
Image Credit: Medium
You Only Live Once: How does Single-Life Reinforcement Learning work?
- Single-life Reinforcement Learning (SLRL) aims to train robots with sub-optimal prior knowledge so that the training process more closely mirrors real-life conditions. This means if the robot is only given a single chance to complete a task, SLRL must enable it to backtrack when it gets stuck. SLRL rewards the robot for particular actions taken during the task, such as moving toward areas where there are cries for help or by opening doors. The Q-weighted adversarial learning algorithm (QWALE) which guides the robot to consistently move closer to the pathways that lead to task completion, performed better in 4 RL challenges than previous distribution matching methods.
- SLRL is based on concepts used in Reinforcement Learning (RL), where a robot makes decisions to achieve specific goals. Usually, RL resets the environment so that the robot can repeatedly learn, but SLRL needs to adapt to real-life scenarios where there is only one chance to complete a task. If the robot gets stuck, it must be able to backtrack and navigate unknown terrain. SLRL employs the Q-function, policy, state-action, reward, and entropy concepts to enable the robot to find its way to complete tasks with minimal backtracking.
- The researchers equipped the robot with sub-optimal prior knowledge to train it in disaster relief scenarios so it can rescue people from burning buildings. The robot also has a discriminator for evaluating the distance between the prior knowledge and the current state when navigating new environments. QWALE incentivizes the robot to move closer to the pathways that lead to task completion, which assists in recovery when mistakes or trapped situations occur.
- QWALE needs to be agnostic to the quality of prior experiences to help robots tackle more challenging and unknown terrains. The more similar the task or state, the quicker robots can complete a task. The researchers found that QWALE outperformed other algorithms, achieving the best results in four different RL challenges - such as arranging items on a table and using tools to achieve complex tasks in a kitchen environment.
- The study found that standard fine-tuning using RL methods doesn't account for mistakes or trapped situations, and prior data significantly improves backtracking and recovery. QWALE required 20 to 40% fewer steps on average compared to other methods, demonstrating how efficient and effective SLRL can be in evaluating prior knowledge. Future models using SLRL could enable sophisticated robots to handle new environments more effectively.
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TechCrunch
21h
204
Image Credit: TechCrunch
Ati Motors raises $20M as India’s robotics industry grows
- Ati Motors has raised $20 million in funding as the Indian-based autonomous mobile robots (AMR) startup aims for global expansion.
- The firm plans to tap into the increased demand for domestic manufacturing in the U.S., India, and Southeast Asian nations, as countries look to reduce their dependence on China.
- Ati Motors has developed seven robots that can move trolleys, bins, and pallets in factories or warehouses. Their robots are equipped with 3D lidar sensors and have spatial awareness, enabling them to operate in various tough environments.
- The startup offers a robots-as-a-service (RaaS) model and has deployed its Sherpa robots across 40 manufacturers, including Airbus, Hyundai, and Samsung.
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Unite
4h
91
Image Credit: Unite
Rebecca Carr, CEO of SmartRecruiters – Interview Series
- Rebecca Carr is the CEO at SmartRecruiters, bringing over 15 years of experience in recruiting and HR technology to the space.
- SmartRecruiters offers an all-in-one solution for interviews, offers, and onboarding, with the aim of simplifying recruitment.
- As CEO, Carr's vision is to ensure SmartRecruiters remains a strategic partner that not only innovates but also transforms how organizations think about and execute hiring strategies.
- Balancing technological advancement with strategy is about aligning innovation with purpose and ensuring new capabilities we develop directly support our mission to create hiring success for each and every customer.
- AI is a game-changer that could transform hiring into a faster, fairer and more effective process.
- SmartRecruiters integrates AI and automation to simplify hiring, delivering proactive insights like candidate prioritization, and action reminders.
- To build trust in AI, Carr highlights the importance of fairness and transparency, designing AI systems to mitigate bias and providing tools for customers to assess and fine-tune their processes.
- SmartRecruiters' integrations with platforms like Slack and Teams unify workflows and eliminate silos, meeting users where they already work or search for work.
- Over the next several years, SmartRecruiters aims to expand its platform's adaptability and predictive capabilities.
- SmartRecruiters' focus is to create solutions that are intuitive, adaptable, and impactful—solutions that not only solve today’s problems but evolve to meet the demands of tomorrow’s workforce.
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Unite
4h
37
Image Credit: Unite
AI-Powered Personalization: Balancing Automation and Human Creativity in the Digital Age
- Personalization has become the cornerstone of all online experiences. Ironically, to drive deeper human connection and personalization, brands and marketers are turning to machines, specifically AI.
- AI-powered tools can leverage natural language processing and generation to create compelling copy, support content optimization and SEO, and even produce visuals and video creation.
- While AI can generate content, it lacks the genuine creative and emotional depth humans bring to storytelling. Marketers understanding how to balance AI-driven efficiency and human oversight to maintain brand integrity while ensuring high-quality, personalized experiences for their audiences.
- The potential of AI to create digital experiences is vast and still evolving AI tools are only as strong as the data on which they are trained. Strategies for True AI-Powered Personalization require a strategic approach and a willingness to shift how things have always been done.
- Integrate AI into existing content workflows, A/B test to optimize AI content.
- Use AI to enhance content creation instead of replacing it.
- The north star of any AI personalization strategy is to maintain brand quality, consistency and authenticity to your company’s values. The final puzzle piece for AI-personalized content that resonates with real people is human oversight.
- To strike the right balance, implement a robust review process. AI personalization offers tremendous opportunities for brands to more easily and consistently scale meaningful and engaging experiences for their audiences.
- By balancing AI automation with human expertise, marketers can become much more efficient at delivering game-changing, personalized experiences that feel authentic, enhance brand loyalty, and drive measurable results.
- In doing so, they’ll not only meet but exceed the expectations of today’s audiences.
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Unite
4h
310
Image Credit: Unite
Western Bias in AI: Why Global Perspectives Are Missing
- AI systems struggle to understand cultural nuances or language patterns outside their training due to the dominance of Western languages, cultures and perspectives. Consequently, the Western-centric datasets have created significant cultural and geographic biases limiting their effectiveness for diverse populations. This imbalance demands the urgent need for AI systems to adopt more inclusive approaches that represent the diverse perspectives and realities of the global population.
- AI bias is not simply an error or oversight - it arises from how AI systems are designed and developed. Most AI research and innovation have been mainly concentrated in Western countries, resulting in systemic concentration, and dominance of English in the field of academic publications, datasets, and technological frameworks. Consequently, the foundational design of AI systems often fails to include the diversity of global cultures and languages, leaving vast regions underrepresented.
- Bias in AI can reinforce harmful assumptions and deepen systemic inequalities. It goes beyond technical limitations, reinforcing societal inequalities and deepening divides, dictating the urgent need for AI systems to adopt more inclusive approaches that represent the diverse perspectives and realities of the global population.
- Facial recognition technology has faced criticism for higher error rates among ethnic minorities, leading to serious real-world consequences. Neglecting global diversity in AI development can limit innovation and reduce market opportunities. Companies that fail to account for diverse perspectives risk alienating large segments of potential users.
- Most AI tools, including virtual assistants and chatbots, perform well in a few widely spoken languages and overlook the less-represented ones. AI systems reinforce Western dominance in technology by prioritizing only a tiny fraction of the world's linguistic diversity. This exclusion means that millions of AI-powered tools remain inaccessible or ineffective, widening the digital divide.
- Creating more diverse datasets can help fix Western bias in AI. Projects like Masakhane, which supports African languages, and AI4Bharat, which focuses on Indian languages, are great examples of how inclusive AI development can succeed. Federated learning allows data collection and training from underrepresented regions without risking privacy. Collaboration between developers and researchers from underserved regions must also be part of the AI creation process.
- Governments must enforce rules that require diverse data in AI training and hold companies accountable for biased outcomes. Tech companies have a responsibility to invest in these regions. They should fund local research, hire diverse teams, and create partnerships that focus on inclusion. Advocacy groups can raise awareness and push for change.
- Addressing Western bias in AI requires significantly changing how AI systems are designed and trained. However, technology alone is not enough - laws and policies also play a key role. Fixing Western bias in AI demands urgent attention and prioritizing inclusivity in design, data, and development.
- AI can become a tool that uplifts all communities, not just a privileged few. The issue of Western bias in AI is not just a technical flaw, but an issue that demands urgent attention. By prioritizing inclusivity in design, data, and development, AI can transform lives, bridge gaps, and create opportunities.
- AI needs multilingual, multicultural, and regionally representative data to serve people worldwide. Laws and policies, advocacy groups, and collaboration must come together to fill the gaps, ensuring that AI systems represent the world’s diversity and serve everyone fairly.
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Unite
4h
331
Image Credit: Unite
Aligning AI’s Potential With Practical Reality
- AI tools have seen widespread business adoption since ChatGPT's 2022 launch, with 98% of small businesses surveyed by the US Chamber of Commerce using them.
- A recent survey found that 3 out of 4 workers report AI has actually increased their workloads.
- Bridging the gap between AI's vast promise and its practical impact on enterprise operations is a critical challenge for organizations to fully realize the value of their AI investments and grow adoption among their workers and stakeholders.
- To maximize impact, the design of AI tools must evolve to go beyond isolated, text-based interfaces into integrated, workflow-enhancing applications that better meet the operational needs of large organizations.
- The next phase of AI evolution will increasingly be agentic, blending seamlessly into the background of enterprise operations and allowing teams to focus on high-level ideation and strategy leading to automated operations, bypassing manual execution but still retaining human-in-the-loop control that relies on non-automatable human judgment.
- A productized approach to AI development, deployment, and operations, akin to how Apple revolutionized the tech industry with the launch of the iPhone, is required to transition from 'experimental' to 'essential' AI solutions.
- To move towards this more sophisticated productized version of AI, tackling the gaps within the enterprise data estate is vital.
- Focusing on ensuring the integrity, cleanliness, and quality of data in the first instance can help keep costs down in the long run.
- Companies that embed responsible AI principles on a robust, well-governed data foundation will be better positioned to scale their applications efficiently and ethically.
- Closing data gaps and making responsible AI a centerpiece of strategy will be key to maintaining trust with stakeholders, continuing to meet strategic compliance imperatives, and ensuring AI systems are not only scalable but also reliable and effective.
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TechDigest
4h
283
Image Credit: TechDigest
RSPCA launches “Big Conversation” on Animal Welfare in the Age of AI
- The RSPCA has launched the initiative “Animal Futures: The Big Conversation” to explore the future of animal welfare in a technology-driven society.
- A recent survey reveals that 26% of Britons would consider owning a robot pet, with concerns among younger generations about the potential replacement of the human-animal bond by AI.
- The initiative focuses on critical issues such as the impact of technology and AI on animal welfare, alternative meat sources, animals in farming systems, and wildlife conservation.
- The RSPCA aims to engage 10,000 people in this online discussion platform to shape the future of animal welfare policies and government discussions.
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Hackernoon
5h
192
Image Credit: Hackernoon
Rahul Gangolli’s Leadership in Robotics and Manufacturing Programs
- Rahul Gangolli is a Senior Technical Program Manager at Amazon, known for his leadership in robotics and manufacturing operations.
- With a Master’s in Industrial Engineering and a MicroMasters in Business Management, Rahul brings expertise in Lean Manufacturing and cross-functional collaboration.
- His journey into robotics and manufacturing was inspired by witnessing his grandfather's struggle with Parkinson’s disease.
- Rahul's leadership is characterized by data-driven decision-making, problem-solving, and driving cross-functional collaboration.
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The Robot Report
9h
291
Image Credit: The Robot Report
Outrider uses reinforcement learning to speed path planning by tenfold
- Outrider Technologies Inc. has deployed reinforcement learning (RL) techniques to maximize freight throughput at customer sites.
- With RL, Outrider's path-planning speed has increased by 10x, allowing for more efficient and safer movement of freight through busy distribution yards.
- The AI-driven capabilities of Outrider complemented by redundant safety mechanisms, enabling automation of manual tasks in logistics yards for improved efficiency and safety.
- Using RL, Outrider has created models that reinforce desired behaviors and discourage undesirable behaviors, ensuring compliance with traffic rules and safe distances from other vehicles.
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Medium
19h
233
Image Credit: Medium
Robotic Insects: Pioneering the Future of Precision Pollination
- The new generation of robotic pollinators draws inspiration from nature’s most efficient pollinators — bees.
- Advancements in designing robotic insects include increased agility, precision, and endurance.
- These robots have the potential to revolutionize agriculture by enabling artificial pollination in controlled environments.
- While robotic insects still lag behind natural pollinators, ongoing research is bringing us closer to deploying swarms of robotic pollinators in real-world scenarios.
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BGR
21h
78
Image Credit: BGR
Watch this wild Chinese robot dance, skate across the snow, and more
- Chinese robotics company DEEP Robotics has showcased its latest quadruped robot, Lynx, in a video.
- The video demonstrates the robot's ability to dance, skate across ice, navigate through deep snow, and perform flips.
- The versatility of the Chinese robot raises concerns, considering the existence of weaponized robot dogs and flame-throwing robot dogs.
- It is likely that the Chinese military may explore potential applications for the quadruped robot, such as reconnaissance and scouting.
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